Author: "Rajakumar A" / Publication Type: Electronic Resources - Searchworks@Jio Institute Digital Library Search Results

187 results on '"Rajakumar A"'

1. Childhood Cerebral Adrenoleukodystrophy: Case Report and Literature Review Advocating for Newborn Screening

Author: Rajakumar,Hamrish Kumar, Coimbatore Sathyabal,Varsha, Nachiappan,Revathi, Krishnaswamy Vijayaramanujam,Sivakumar, Rajakumar,Hamrish Kumar, Coimbatore Sathyabal,Varsha, Nachiappan,Revathi, and Krishnaswamy Vijayaramanujam,Sivakumar
Abstract: Hamrish Kumar Rajakumar,&ast; Varsha Coimbatore Sathyabal,&ast; Revathi Nachiappan, Sivakumar Krishnaswamy Vijayaramanujam Department of Pediatrics, Government Medical College, Omandurar, Government Estate, Chennai, Tamilnadu, India&ast;These authors contributed equally to this workCorrespondence: Hamrish Kumar Rajakumar, Tel +91-9150510826, Email hamrishkumar2003@gmail.comBackground: X-linked adrenoleukodystrophy (ALD) is a rare genetic disorder caused by a pathogenic variant of the ABCD1 gene, leading to impaired peroxisomal function and the accumulation of very long-chain fatty acids (VLCFAs). ALD presents a wide range of neurological and adrenal symptoms, ranging from childhood cerebral adrenoleukodystrophy to adrenomyeloneuropathy and adrenal insufficiency. Newborn screening (NBS) for ALD is available in some regions but remains lacking in others, such as India.Case Presentation: We present a case of a 10-year-old boy with ALD who presented with seizures, progressive weakness, visual impairment, and adrenal insufficiency. Despite symptomatic management and dietary adjustments, the disease progressed rapidly, leading to respiratory failure and eventual demise. The diagnosis was confirmed through molecular analysis and elevated VLCFA levels. Neuroimaging revealed characteristic white matter changes consistent with ALD.Conclusion: ALD is a devastating disease with no cure, emphasizing the importance of early detection through newborn screening and genetic testing. Management strategies include adrenal hormone therapy, gene therapy, and allogenic stem cell transplantation, as well as investigational treatments such as VLCFA normalization. Our case advocates the need for worldwide NBS and pediatric neurologic follow-up to enable early intervention and improve patient outcomes. Additionally, the association between ALD, recurrent febrile seizures, and unexplained developmental delay warrants further investigation to better understand disease progression a
Published: 2024

2. Smart Patient Consent Management Model for Health Information Exchange Based on Blockchain Technology

Author: K. R. Rohini, P. S. Rajakumar, S. Geetha, K. R. Rohini, P. S. Rajakumar, and S. Geetha
Abstract: Innovation for Electronic Medical Records (EMRs) has been hindered by years of excessive regulation and inefficient bureaucracy. As data science and personalization encourage individuals to take an active role in their healthcare and regain control of their own medical records, there is an urgent need for new approaches. The ability to exchange electronic health records is fundamental in contemporary healthcare systems for facilitating a wider range of health services and delivering high-quality treatment. Despite the requirement for utilizing medical information for various reasons, most patients still authorize paper forms with minimal participation. The present methods of managing patient consent and medical data exchange are laborious, expensive, and prone to failures, even with quality assurance measures in effect. Because of this, there may not be enough patient empowerment, which can lead to inefficiencies in the process and a lack of trust and transparency. A shortage of resources makes it harder to acquire individual consent, which is necessary for health data exchange. Healthcare organizations also grapple with patient consent. Blockchain-based platforms enable data exchange by developing a trusted user network. Users can share their data without relying on health service providers for time and resources. Blockchain-based systems necessitate data governance frameworks to specify and monitor data exchange and use. This research article aims to establish a system that healthcare organizations may use to easily gain patient consent for various objectives, while also giving patients more flexibility in managing their consent. In this study, a novel electronic consent model namely ‘Smart Consent Blockchain Based System (SCBCS)”, is built on the hyper ledger fabric Blockchain that employs a purpose-based access control method. Distributed ledger technology (blockchain) ensures that all metadata pertaining to patient records, permissions, and data
Published: 2024

3. An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network

Author: S, Rajakumar, P, William, A, Mabel Rose R., Rajaretnam, Subraja, A, Azhagu Jaisudhan Pazhani, A, Ahilan, S, Rajakumar, P, William, A, Mabel Rose R., Rajaretnam, Subraja, A, Azhagu Jaisudhan Pazhani, and A, Ahilan
Abstract: Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.
Published: 2024

4. Optimization of Distributed Generation in Radial Distribution Network for Active Power Loss Minimization using Jellyfish Search Optimizer Algorithm

Author: Ranga, Jarabala, Y, Thiagarajan, D, Kesavan, Deglus, Jovin, Reddy, Sibbala Bhargava, Palanisamy, Rajakumar, R. A., Priya, Ranga, Jarabala, Y, Thiagarajan, D, Kesavan, Deglus, Jovin, Reddy, Sibbala Bhargava, Palanisamy, Rajakumar, and R. A., Priya
Abstract: The inclusion of distributed generation (DG) units in the distribution network (DN) effectively cuts down the power losses (PL) and strengthens the voltage profile (VP). This paper examines the effect of allocating different distributed generation (DG) in radial distribution networks (RDN) through an implementation of an optimization technique using a recently introduced bio- inspired algorithm known as a jellyfish search optimizer (JSO). Unlike the other optimization algorithms, the JSO algorithm evades the local optimal trap and reaches the optimal solution in less time. The DG position(s) and size(s) are optimized for active power loss (APL) minimization with respect to several constraints. The effectiveness and robustness of the proposed optimization technique using JSO algorithm is investigated on a balanced IEEE RDNs with 33, 69 and 118-buses. The simulation outcomes are obtained for different types (type I, II and III) of DG placement. Additionally, a comprehensive comparative study has been performed for the JSO and other algorithms. The comparison exemplifies that the proposed JSO optimization approach produces a better optimal solution with steady convergence than other techniques reported in the literature. Also, the simulation findings show the potentiality of JSO optimization method for solving complex optimization problems.
Published: 2024

5. Polynomial-Time Classical Simulation of Noisy IQP Circuits with Constant Depth

Author: Rajakumar, Joel, Watson, James D., Liu, Yi-Kai, Rajakumar, Joel, Watson, James D., and Liu, Yi-Kai
Abstract: Sampling from the output distributions of quantum computations comprising only commuting gates, known as instantaneous quantum polynomial (IQP) computations, is believed to be intractable for classical computers, and hence this task has become a leading candidate for testing the capabilities of quantum devices. Here we demonstrate that for an arbitrary IQP circuit undergoing dephasing or depolarizing noise, whose depth is greater than a critical $O(1)$ threshold, the output distribution can be efficiently sampled by a classical computer. Unlike other simulation algorithms for quantum supremacy tasks, we do not require assumptions on the circuit's architecture, on anti-concentration properties, nor do we require $\Omega(\log(n))$ circuit depth. We take advantage of the fact that IQP circuits have deep sections of diagonal gates, which allows the noise to build up predictably and induce a large-scale breakdown of entanglement within the circuit. Our results suggest that quantum supremacy experiments based on IQP circuits may be more susceptible to classical simulation than previously thought., Comment: 17 pages, 5 figures
Published: 2024

6. Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction

Author: Dutta, Senjuti, Chen, Sherol, Mak, Sunny, Ahmad, Amnah, Collins, Katherine, Butryna, Alena, Ramachandran, Deepak, Dvijotham, Krishnamurthy, Pavlick, Ellie, Rajakumar, Ravi, Dutta, Senjuti, Chen, Sherol, Mak, Sunny, Ahmad, Amnah, Collins, Katherine, Butryna, Alena, Ramachandran, Deepak, Dvijotham, Krishnamurthy, Pavlick, Ellie, and Rajakumar, Ravi
Abstract: Image generation models are poised to become ubiquitous in a range of applications. These models are often fine-tuned and evaluated using human quality judgments that assume a universal standard, failing to consider the subjectivity of such tasks. To investigate how to quantify subjectivity, and the scale of its impact, we measure how assessments differ among human annotators across different use cases. Simulating the effects of ordinarily latent elements of annotators subjectivity, we contrive a set of motivations (t-shirt graphics, presentation visuals, and phone background images) to contextualize a set of crowdsourcing tasks. Our results show that human evaluations of images vary within individual contexts and across combinations of contexts. Three key factors affecting this subjectivity are image appearance, image alignment with text, and representation of objects mentioned in the text. Our study highlights the importance of taking individual users and contexts into account, both when building and evaluating generative models
Published: 2024

7. Trainability Barriers in Low-Depth QAOA Landscapes

Author: Rajakumar, Joel, Golden, John, Bärtschi, Andreas, Eidenbenz, Stephan, Rajakumar, Joel, Golden, John, Bärtschi, Andreas, and Eidenbenz, Stephan
Abstract: The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifying input parameters that yield high-quality solutions. However, understanding the complexity of training QAOA remains an under-explored area. Previous results have given analytical performance guarantees for a small, fixed number of parameters. At the opposite end of the spectrum, barren plateaus are likely to emerge at $\Omega(n)$ parameters for $n$ qubits. In this work, we study the difficulty of training in the intermediate regime, which is the focus of most current numerical studies and near-term hardware implementations. Through extensive numerical analysis of the quality and quantity of local minima, we argue that QAOA landscapes can exhibit a superpolynomial growth in the number of low-quality local minima even when the number of parameters scales logarithmically with $n$. This means that the common technique of gradient descent from randomly initialized parameters is doomed to fail beyond small $n$, and emphasizes the need for good initial guesses of the optimal parameters., Comment: Accepted at the 21st ACM International Conference on Computing Frontiers CF'24
Published: 2024

8. The chloroplast of Chlamydomonas reinhardtii as a platform for recombinant vaccine production

Author: Rajakumar, P. D.
Subjects: 570
Abstract: Infectious diseases represent an on-going problem adversely affecting the poultry industries and fish farming. Protein-base vaccines that are currently used for the prevention and spread of diseases have several drawbacks including high cost of recombinant production and delivery; and the needs to eliminate endotoxins when produced in bacterial platforms such E. coli. Therefore, there is an urgent need for effective, cheaper and safer vaccines. The microalga Chlamydomonas reinhardtii could be an ideal candidate for the production of recombinant protein such as vaccines. This is because: i) it has high growth rates; ii) it is generally regarded as safe (GRAS) to eat and therefore does not present a problem of endogenous toxin; iii) it has a genetically tractable chloroplast genome that allows a good expression of recombinant proteins that undergo correct folding to form an active product. Hence, in this research two C. reinhardtii transgenic chloroplast lines expressing the infectious bronchitis virus (IBV) muti-epitope genes (Spike Protein S540-564, Nulceocapsid Protein N67-83 and 120 residues of C terminus of the Nucleocapsid Protein) fused with the adjuvant, CTB and full length nervous necrosis virus (NNV) capsid gene were created. IBV causes respiratory disease in chickens. On the other hand, NNV causes cellular vacoulation and neuronal shape changes in fish. The IBV and NNV antigens were successfully detected anti-HA antibodies by western analysis. The lyophilised C. reinhardtii CTB-IBV that were fed to day 0 chicks, successfully triggered a mucosal and systemic immune response giving rise to antibodies against CTB, IBV and HA epitopes. The NNV fish vaccine trial is currently ongoing in Kasetart University, Thailand. Serum and mucus of NNV vaccinated fish will be analysed in the Purton Lab, UCL. The chloroplast of C. reinhardtii is an attractive organelle for the production of recombinant protein such as vaccines. However, the current level of recombinant protein production in the chloroplast of C. reinhardtii is normally less than 1% of total soluble protein. Therefore, in this study four C. reinhardtii mutant strains (UVM 2, 4, 10 and 11) were created through forward genetics. These mutants exhibit superior expression of recombinant genes in the chloroplast through forward genetics. These strains will be further analysed, so that they can be used to express recombinant genes such as peptide vaccine in the chloroplast of C. reinhardtii in the future.
Published: 2016

9. Constructing RNA based genetic circuits for the detection of endogenous microRNAs

Author: Rajakumar, Timothy and Fulga, Tudor
Subjects: 610
Abstract: Synthetic biology is a rapidly evolving field enabling the rational design of new biological functionality. RNA is particularly amenable to this rational design processes due to relatively straightforward structural modelling principles and its ability to perform diverse roles, from that of simple messenger RNAs to complex catalytic ribozymes. Consequently, the synthetic design and assembly of RNA building blocks into functional circuits and networks can far exceed the complexity of what could be achieved using protein components. MicroRNAs (miRNAs) are fascinating short non-coding RNA molecules (~22nt long) which have the capacity to bind and tune the expression of thousands of mRNAs, functioning as molecular rheostats during development, physiology and disease. The ability to detect miRNAs, via synthetic biosensor circuits would therefore offer a unique insight into cellular behavior in addition to the ability to link miRNA expression signatures to an output expression programme of choice. In this thesis, I have developed novel synthetic circuit architectures for the detection of miRNA activity. Firstly, protein and RNA modules were used in concert to develop a biosensor circuit consisting of two serially linked NOT gates that could activate output expression in response to a specific input miRNA. Secondly, methods were devised for the construction of a single molecule biosensor device, built entirely using RNA. At the heart of this device were novel miRNA responsive allosteric ribozymes which have been created through two independent in vitro selection methodologies devised for this study. Such engineered modules now constitute part of the synthetic biology toolbox, increasing the versatility with which miRNAs can serve as inputs to artificial genetic circuits.
Published: 2016

10. Dr Namrata TRUE TALES OF TEN T-PLL OVER A DECADE

Author: Rajakumar, Dr Namrata.N., Vijaya, Dr Raghavendra .H., Horatti, Dr laxmi B, Balikai, Dr Girish, Shamnur, Dr Murugesh, Rajakumar, Dr Namrata.N., Vijaya, Dr Raghavendra .H., Horatti, Dr laxmi B, Balikai, Dr Girish, and Shamnur, Dr Murugesh
Abstract: Background T- prolymphocytic leukemia(T-PLL) ,are aggressive lymphoid malignancies with unique morphologic, cytogenetic, immunophenotypic, and molecular features. T-PLL is a mature T-cell leukemia typically presenting at stages of exponentially rising lymphocyte counts in peripheral blood,accompanied by splenomegaly and bone marrow involvement. They are extremely rare ,comprising of <2% of mature T –leukemias and inherently aggressive and notoriously refractory to therapeutics.Extensive search for Indian literature on T-PLL and Immunophenotypic characterization of this entity did not yield any results. We analysed the characteristics, prognostic factors, outcomes and treatments of 10 patients with T-PLL . To our knowledge this is the largest series of T-PLL from India , this study reviews clinical, cytogentic and flow cytometric immunophenotyping findings, and differential diagnosis of patients with T-cell prolymphocytic leukemia from January 2012 to January 2022.Methods: Our laboratory received 4500 clinically suspected chronic leukemias, during a period of 10 years ,at kidwai memorial institute of oncology ,which is state cancer institute , where diagnostic flow cytometric analysis was done and leukemias were classified based on WHO 2008 criterias. Results: out of 4500 cases of Chronic lympho proliferative disorders sent for flow cytometric immunophenotyping, only 10 cases were diagnosed as T-PLL .Study period January 2012 to January 2022.Conclusions: Evaluation of immunophenotype of this rare entity by flow cytometry is a critical part of diagnosis and is a indespensible tool in distinguishing T-PLL from other mature T-cell lymphoid neoplasms.Awareness of this rare entity aids in work up .
Published: 2023

11. Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images

Author: Magesh, S., RajaKumar, P.S., Magesh, S., and RajaKumar, P.S.
Abstract: Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD. To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.
Published: 2023

12. 'Let's not Quote out of Context': Unified Vision-Language Pretraining for Context Assisted Image Captioning

Author: Kalarani, Abisek Rajakumar, Bhattacharyya, Pushpak, Chhaya, Niyati, Shekhar, Sumit, Kalarani, Abisek Rajakumar, Bhattacharyya, Pushpak, Chhaya, Niyati, and Shekhar, Sumit
Abstract: Well-formed context aware image captions and tags in enterprise content such as marketing material are critical to ensure their brand presence and content recall. Manual creation and updates to ensure the same is non trivial given the scale and the tedium towards this task. We propose a new unified Vision-Language (VL) model based on the One For All (OFA) model, with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. Our approach aims to overcome the context-independent (image and text are treated independently) nature of the existing approaches. We exploit context by pretraining our model with datasets of three tasks: news image captioning where the news article is the context, contextual visual entailment, and keyword extraction from the context. The second pretraining task is a new VL task, and we construct and release two datasets for the task with 1.1M and 2.2K data instances. Our system achieves state-of-the-art results with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. To the best of our knowledge, ours is the first effort at incorporating contextual information in pretraining the models for the VL tasks.
Published: 2023

13. A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection

Author: Badathala, Naveen, Kalarani, Abisek Rajakumar, Siledar, Tejpalsingh, Bhattacharyya, Pushpak, Badathala, Naveen, Kalarani, Abisek Rajakumar, Siledar, Tejpalsingh, and Bhattacharyya, Pushpak
Abstract: Hyperbole and metaphor are common in day-to-day communication (e.g., "I am in deep trouble": how does trouble have depth?), which makes their detection important, especially in a conversational AI setting. Existing approaches to automatically detect metaphor and hyperbole have studied these language phenomena independently, but their relationship has hardly, if ever, been explored computationally. In this paper, we propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously. We hypothesize that metaphors help in hyperbole detection, and vice-versa. To test this hypothesis, we annotate two hyperbole datasets- HYPO and HYPO-L- with metaphor labels. Simultaneously, we annotate two metaphor datasets- TroFi and LCC- with hyperbole labels. Experiments using these datasets give an improvement of the state of the art of hyperbole detection by 12%. Additionally, our multi-task learning (MTL) approach shows an improvement of up to 17% over single-task learning (STL) for both hyperbole and metaphor detection, supporting our hypothesis. To the best of our knowledge, ours is the first demonstration of computational leveraging of linguistic intimacy between metaphor and hyperbole, leading to showing the superiority of MTL over STL for hyperbole and metaphor detection.
Published: 2023

14. Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities

Author: Dutta, Senjuti, Mittal, Sid, Chen, Sherol, Ramachandran, Deepak, Rajakumar, Ravi, Kivlichan, Ian, Mak, Sunny, Butryna, Alena, Paritosh, Praveen, Dutta, Senjuti, Mittal, Sid, Chen, Sherol, Ramachandran, Deepak, Rajakumar, Ravi, Kivlichan, Ian, Mak, Sunny, Butryna, Alena, and Paritosh, Praveen
Abstract: The prevalence and impact of toxic discussions online have made content moderation crucial.Automated systems can play a vital role in identifying toxicity, and reducing the reliance on human moderation.Nevertheless, identifying toxic comments for diverse communities continues to present challenges that are addressed in this paper.The two-part goal of this study is to(1)identify intuitive variances from annotator disagreement using quantitative analysis and (2)model the subjectivity of these viewpoints.To achieve our goal, we published a new dataset\footnote{\url{https://github.com/XXX}} with expert annotators' annotations and used two other public datasets to identify the subjectivity of toxicity.Then leveraging the Large Language Model(LLM),we evaluate the model's ability to mimic diverse viewpoints on toxicity by varying size of the training data and utilizing same set of annotators as the test set used during model training and a separate set of annotators as the test set.We conclude that subjectivity is evident across all annotator groups, demonstrating the shortcomings of majority-rule voting. Moving forward, subjective annotations should serve as ground truth labels for training models for domains like toxicity in diverse communities.
Published: 2023

15. Nanotechnology- A Review on Application of Nanotechnology in Orthodontics

Author: Kavitha, R., Sarassri, N., Silambarasan, R. V., Karthikeyan, Kumar, Vinoth, Rajakumar, P., Kavitha, R., Sarassri, N., Silambarasan, R. V., Karthikeyan, Kumar, Vinoth, and Rajakumar, P.
Abstract: Nanotechnology in dentistry has evolved since last few years. Nanotechnology is manipulating matter at nanometer level. This concept is applied to the field of dentistry with the term Nano-dentistry. Nanotechnology has been boon to entire field of dentistry. Nano-dentistry is the science and technology of maintaining near-perfect oral health through the use of nanomaterials includes tissue engineering nano-robotics. Nano-sized materials have been used in various fields of dentistry; it is employed in orthodontics to enhance the quality of treatment. This review article provides the most recent and known innovation about the application of nanotechnology in orthodontics and its use in various treatment modalities.
Published: 2022

16. Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer.

Author: Joy, Ajin, Joy, Ajin, Nagarajan, Rajakumar, Saucedo, Andres, Iqbal, Zohaib, Sarma, Manoj K, Wilson, Neil, Felker, Ely, Reiter, Robert E, Raman, Steven S, Thomas, M Albert, Joy, Ajin, Joy, Ajin, Nagarajan, Rajakumar, Saucedo, Andres, Iqbal, Zohaib, Sarma, Manoj K, Wilson, Neil, Felker, Ely, Reiter, Robert E, Raman, Steven S, and Thomas, M Albert
Abstract: ObjectivesThis study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors.Materials and methodsProspectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods. A hybrid reconstruction technique, Dictionary Learning-Total Variation (DLTV), was also designed to further improve the quality of reconstructed spectra.ResultsThe CS reconstruction of prospectively undersampled (8x and 12x) 5D EP-JRESI data acquired in prostate cancer and healthy subjects were performed using DL, DLTV, TV and PM. It is evident that the hybrid DLTV method can unambiguously resolve 2D J-resolved peaks including myo-inositol, citrate, creatine, spermine and choline.ConclusionImproved reconstruction of the accelerated 5D EP-JRESI data was observed using the hybrid DLTV. Accelerated acquisition of in vivo 5D data with as low as 8.33% samples (12x) corresponds to a total scan time of 14 min as opposed to a fully sampled scan that needs a total duration of 2.4 h (TR = 1.2 s, 32 [Formula: see text]×16 [Formula: see text]×8 [Formula: see text], 512 [Formula: see text] and 64 [Formula: see text]).
Published: 2022

17. Individual participant data (IPD)-level meta-analysis of randomised controlled trials to estimate the vitamin D dietary requirements in dark-skinned individuals resident at high latitude

Author: Cashman, Kevin D., Kiely, Mairead E., Andersen, Rikke, Grønborg, Ida M., Tetens, Inge, Tripkovic, Laura, Lanham-New, Susan A., Lamberg-Allardt, Christel, Adebayo, Folasade A., Gallagher, J. Christopher, Smith, Lynette M., Sacheck, Jennifer M., Huang, Qiushi, Ng, Kimmie, Yuan, Chen, Giovannucci, Edward L., Rajakumar, Kumaravel, Patterson, Charity G., Öhlund, Inger, Lind, Torbjörn, Åkeson, Pia Karlsland, Ritz, Christian, Cashman, Kevin D., Kiely, Mairead E., Andersen, Rikke, Grønborg, Ida M., Tetens, Inge, Tripkovic, Laura, Lanham-New, Susan A., Lamberg-Allardt, Christel, Adebayo, Folasade A., Gallagher, J. Christopher, Smith, Lynette M., Sacheck, Jennifer M., Huang, Qiushi, Ng, Kimmie, Yuan, Chen, Giovannucci, Edward L., Rajakumar, Kumaravel, Patterson, Charity G., Öhlund, Inger, Lind, Torbjörn, Åkeson, Pia Karlsland, and Ritz, Christian
Abstract: Context and purpose: There is an urgent need to develop vitamin D dietary recommendations for dark-skinned populations resident at high latitude. Using data from randomised controlled trials (RCTs) with vitamin D3-supplements/fortified foods, we undertook an individual participant data-level meta-regression (IPD) analysis of the response of wintertime serum 25-hydroxyvitamin (25(OH)D) to total vitamin D intake among dark-skinned children and adults residing at ≥ 40° N and derived dietary requirement values for vitamin D. Methods: IPD analysis using data from 677 dark-skinned participants (of Black or South Asian descent; ages 5–86 years) in 10 RCTs with vitamin D supplements/fortified foods identified via a systematic review and predefined eligibility criteria. Outcome measures were vitamin D intake estimates across a range of 25(OH)D thresholds. Results: To maintain serum 25(OH)D concentrations ≥ 25 and 30 nmol/L in 97.5% of individuals, 23.9 and 27.3 µg/day of vitamin D, respectively, were required among South Asian and 24.1 and 33.2 µg/day, respectively, among Black participants. Overall, our age-stratified intake estimates did not exceed age-specific Tolerable Upper Intake Levels for vitamin D. The vitamin D intake required by dark-skinned individuals to maintain 97.5% of winter 25(OH)D concentrations ≥ 50 nmol/L was 66.8 µg/day. This intake predicted that the upper 2.5% of individuals could potentially achieve serum 25(OH)D concentrations ≥ 158 nmol/L, which has been linked to potential adverse effects in older adults in supplementation studies. Conclusions: Our IPD-derived vitamin D intakes required to maintain 97.5% of winter 25(OH)D concentrations ≥ 25, 30 and 50 nmol/L are substantially higher than the equivalent estimates for White individuals. These requirement estimates are also higher than those currently recommended internationally by several agencies, which are based predominantly on data from Whites and derived from standard meta-regression based on
Published: 2022
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18. The spatial landscape of clonal somatic mutations in benign and malignant prostate epithelia

Author: Erickson, A. M., Berglund, Emelie, He, Mengxiao, Marklund, Maja, Mirzazadeh, Reza, Schultz, N., Bergenstråhle, Ludvig, Kvastad, Linda, Andersson, Alma, Bergenstråhle, Joseph, Larsson, L., Rajakumar, T., Thrane, Kim, Ji, A. L., Tarish, F., Tanoglidi, A., Maaskola, Jonas, Colling, R., Mirtti, T., Hamdy, F. C., Woodcock, D. J., Helleday, T., Mills, I. G., Lamb, A., Lundenberg, J., Erickson, A. M., Berglund, Emelie, He, Mengxiao, Marklund, Maja, Mirzazadeh, Reza, Schultz, N., Bergenstråhle, Ludvig, Kvastad, Linda, Andersson, Alma, Bergenstråhle, Joseph, Larsson, L., Rajakumar, T., Thrane, Kim, Ji, A. L., Tarish, F., Tanoglidi, A., Maaskola, Jonas, Colling, R., Mirtti, T., Hamdy, F. C., Woodcock, D. J., Helleday, T., Mills, I. G., Lamb, A., and Lundenberg, J.
Abstract: QC 20220728
Published: 2022

19. Teachers' acceptance of technologies for 4IR adoption: implementation of the UTAUT model

Author: Ab Jalil, Habibah, Rajakumar, Manjula, Zaremohzzabieh, Zeinab, Ab Jalil, Habibah, Rajakumar, Manjula, and Zaremohzzabieh, Zeinab
Abstract: Education departments all around the globe are working to increase the extent to which teachers adopt innovative technology, in order to scale up pedagogical innovation that uses new technologies. However, only a few studies have been done on the adoption and use of these tools for teaching by instructors in non-Western contexts. Therefore, the objective of this study was to examine teachers’ behavior intention to adopt and use Industry 4.0 (IR4.0) technologies in Malaysia, in accordance with the unified theory of acceptance and the use of technology (UTAUT) model. A questionnaire was employed to acquire data from a randomly selected sample of 62 primary school teachers in Malaysia. The findings reveal that only two variables (namely, the facilitating conditions, and social influence variables) have a direct impact on the behavior intention of Malaysian primary school teachers to use IR4.0 technologies. Neither effort expectancy nor performance expectancy have an impact on the intention to use these technologies. The study concludes with a set of recommendations for improving policy and research on teachers’ use of IR4 for education. This work demonstrates how the findings may assist primary school teachers to improve their understanding of 4IR adoption, and provides valuable suggestions for 4IR scholars, producers, and users.
Published: 2022

20. The spatial landscape of clonal somatic mutations in benign and malignant tissue

Author: Erickson, Andrew, Berglund, Emelie, He, Mengxiao, Marklund, Maja, Mirzazadeh, Reza, Schultz, Niklas, Kvastad, Linda, Andersson, Alma, Bergenstråhle, Ludvig, Bergenstråhle, Joseph, Larsson, Ludvig, Shamikh, Alia, Basmaci, Elisa, De Stahl, Teresita Diaz, Rajakumar, Timothy, Thrane, Kim, Ji, Andrew L., Khavari, Paul A., Tarish, Firaz, Tanoglidi, Anna, Maaskola, Jonas, Colling, Richard, Mirtti, Tuomas, Hamdy, Freddie, Woodcock, Dan J., Helleday, Thomas, Mills, Ian G., Lamb, Alastair D., Lundeberg, Joakim, Erickson, Andrew, Berglund, Emelie, He, Mengxiao, Marklund, Maja, Mirzazadeh, Reza, Schultz, Niklas, Kvastad, Linda, Andersson, Alma, Bergenstråhle, Ludvig, Bergenstråhle, Joseph, Larsson, Ludvig, Shamikh, Alia, Basmaci, Elisa, De Stahl, Teresita Diaz, Rajakumar, Timothy, Thrane, Kim, Ji, Andrew L., Khavari, Paul A., Tarish, Firaz, Tanoglidi, Anna, Maaskola, Jonas, Colling, Richard, Mirtti, Tuomas, Hamdy, Freddie, Woodcock, Dan J., Helleday, Thomas, Mills, Ian G., Lamb, Alastair D., and Lundeberg, Joakim
Abstract: QC 20230406
Published: 2022

21. SARS-CoV-2 infection and venous thromboembolism after surgery: an international prospective cohort study

Author: Nepogodiev, D., Simoes, J. F. F., Li, E., Picciochi, M., Glasbey, J. C., Baiocchi, G., Blanco-Colino, R., Chaudhry, D., Alameer, E., El-Boghdadly, K., Wuraola, F., Ghosh, D., Gujjuri, R. R., Harrison, E. M., Lule, H., Kaafarani, H., Khosravi, M., Kronberger, I., Leventoglu, S., Mann, H., Mclean, K. A., Mengesha, M. G., Modolo, M. M., Ntirenganya, F., Norman, L., Outani, O., Pius, R., Pockney, P., Qureshi, A. U., Roslani, A. C., Satoi, S., Shaw, C., Bhangu, A., Omar, O. M., Ahmed, W. -U. -R., Argus, L., Ball, A., Bywater, E. P., Brar, A., Dawson, B. E., Duran, I., Elhadi, M., Jones, C. S., Kamarajah, S. K., Keatley, J. M., Lawday, S., Marson, E. J., Ots, R., Santos, I., Taylor, E. H., Trout, I. M., Varghese, C., Venn, M. L., Xu, W., Dajti, I., Gjata, A., Kacimi, S. E. O., Boccalatte, L., Cox, D., Townend, P., Aigner, F., Kronberger, I. E., Samadov, E., Alderazi, A., Hossain, K., Padmore, G., van Ramshorst, G., Lawani, I., Cerovac, A., Delibegovic, S., Gomes, G. M. A., Buarque, I. L., Gohar, M., Slavchev, M., Nwegbu, C., Agarwal, A., Martin, J., Ng-Kamstra, J., Olivos, M., Lou, W., Ren, D. -L., Calvache, J. A., Perez Rivera, C. J., Hadzibegovic, A. D., Kopjar, T., Mihanovic, J., Jimenez, P. M. A., Gouvas, N., Klat, J., Novysedlak, R., Amisi, N., Christensen, P., El-Hussuna, A., Batista, S., Lincango-Naranjo, E., Emile, S., Sandoval, D. A. A., Dhufera, H., Hailu, S., Kauppila, J. H., Arnaud, A. P., Demetrashvili, Z., Albertsmeier, M., Lederhuber, H., Loffler, M. W., Acquah, D. K., Ofori, B., Tabiri, S., Metallidis, S., Tsoulfas, G., Aguilera-Arevalo, M. -L., Recinos, G., Mersich, T., Wettstein, D., Kembuan, G., Milan, P. B., Khosravi, M. H., Mozafari, M., Hilmi, A., Mohan, H., Zmora, O., Gallo, G., Pata, F., Pellino, G., Fujimoto, Y., Kuroda, N., Abou Chaar, M. K., Ayasra, F., Fakhradiyev, I., Hamdun, I. H. S., Jin-Young, J., Jamal, M., Karout, L., Gulla, A., Rasoaherinomenjanahary, F., Samison, L. H., Sanchez, I. I. D., Gonzalez, D. S., Martinez, L., Martinez, M. J., Nayen, A., la Medina, A. R. -D., Nunez, J., Nashidengo, P. R., Shah, R., Shrestha, A. L., Jonker, P., Kruijff, S., Noltes, M., Steinkamp, P., Wright, D., Abdur-Rahman, L., Ademuyiwa, A., Adisa, A., Osinaike, B., Seyi-Olajide, J., Williams, O., Williams, E., Pejkova, S., Al Balushi, Z., Sayyed, R., Mohsen, M. A., Abukhalaf, S. A., Cukier, M., Gomez-Fernandez, H., Yip, S. S., Ojeda, X. P. V., Sacdalan, M. D., Major, P., Azevedo, J., Cunha, M. F., Zarour, A., Bonci, E. -A., Negoi, I., Efetov, S., Kochetkov, V., Litvin, A., Ingabire, J. A., Bucyibaruta, G., Faustin, N., Habumuremyi, S., Imanishimwe, A., de Dieu, H. J., Munyaneza, E., Ncogoza, I., Ndong, A., Radenkovic, D., Chew, M. H., Koh, F., Ngu, J., Panyko, A., Bele, U., Kosir, J. A., Daoud, H., Bravo, A. M. M., Jayarajah, U., Wickramasinghe, D., Adam, M. E. A. E., Rutegard, M., Sund, M., Adamina, M., Gialamas, E., Horisberger, K., Alshaar, M., Huang, A., Lohsiriwat, V., Charles, S., Jlassi, H., Isik, A., Lekuya, H. M., Kopetskyi, S., Alsaadi, H., Alshryda, S., Alser, O., Bankhead-Kendall, B., Breen, K., Mashbari, H., Cal, F. B., Al-Naggar, H., Maimbo, M., Mazingi, D., Abbott, T., Akhbari, M., Benson, R., Bhanderi, S., Biccard, B., Caruana, E., Chakrabortee, S., Chapatwala, R., Costas-Chavarri, A., Demetriades, A. K., Desai, A., Di Saverio, S., Drake, T., Edwards, J., Evans, J., Fiore, M., Ford, S., Fotopoulou, C., Fowler, A., Futaba, K., Ganly, I., James, H. G., Griffiths, E., Gronchi, A., Hutchinson, P., Hyman, G. Y., Incorvia, J., Jain, R., Jenkinson, M., Khan, T., Knight, S. R., Kolias, A., Kudsk-Iversen, S., Kwan, T. Y., Leung, E., Mayol, J., Mckay, S., Meara, J. G., Mills, E., Moug, S., Patel, A., Perinotti, R., Rice, H. E., Roberts, K., Schache, A., Shaw, R., Smart, N., Stephens, M., Stewart, G. D., Teasdale, E., Vaughan-Shaw, P., Vidya, R., Wright, N., Zimmelman, N., Agastra, E., Thereska, D., Lucchini, S. M., Laudani, V., Chwat, C., Salazar, I. I. P., Pachajoa, D. A. P., Duro, A., Arancibia, J. A. C., D'Aulerio, G., Dudi-Venkata, N., Egoroff, N., Farik, S., Lott, N., Moss, J. -L., Rennie, S., Tan, L., Vo, U. G., Watson, D., Watters, D., Bright, T., Hollington, P., Zhou, X., Kroon, H. M., Farfus, A., Barker, J., Watson, E., Stevens, S., Latif, H., Dawson, A. C., Chuan, A., Muralidharan, V., Wong, E., Ackermann, T., Pacilli, M., Hodgson, R., Heriot, A., Choong, P., Brown, W., Lidder, S., Yeung, J., Traeger, L., Regalo, G., Gourlay, R., Badiani, S., Koh, C., Putnis, S., Haider, F., Mitul, A. R., Komen, N., Dhondt, B., Cappeliez, S., Pigeolet, M., Schoneveld, M., Stijns, J., Oosterlinck, W., Flamey, N., Kpangon, C., Agbadebo, M., Tobome, S. R., Barros, A. V., Junior, S. A., do Amaral Campos, H. G., Gross, J., Coimbra, F. J. F., Kowalski, L. P., Makdissi, F., Nakagawa, S., Neto, J. P. D., Vartanian, J. G., Yazbek, G., Zequi, S. C., Flumignan, R., Jaworska, N., Dell, A., Shanthanna, H., Behzadi, A., Nessim, C., Mozel, M., St-germain, P., Russell, C., Groot, G., Safieddine, N., Wijeysundera, D., Eskander, A., Chadi, S., Mackenzie, S., Flexman, A., Heredia, F., Villanueva, J., Waissbluth, S., Macchiavello, R., Escudero, M. I., Fuentes, T., Mimica, X., Saenz, D. B., Caicedo, L., Alzate, J. P., Luna, J., Alonso, N. F. P., Silva, C. O., Rodriguez, J., Silva-Igua, L., Torres, M. L., Trujillo, L. M., Calvache, A. J. N., Balanta-Melo, J., Figueroa-Casanova, R., Garcia-Montoya, O. -J., Toro, C. A. M., Botero, M. V., Arango, M. C. M., Martinez, E. D., Perdomo, V. G., Montenegro, E., Rodriguez-Abreu, J., Mejia, D., Abouelnagah, G., Shehata, S., Rida, A. H. E. F., Hassan, R. A., Saad, M. M., Loaloa, M. R., Mostafa, B., Qassem, M., Fahmy, M., Abozied, H., Azzam, A. Y., Ghozy, S., Sallam, A., Shehta, A., Abdelkhalek, M., Samaka, R., Morsy, A., Sherif, A. E., Negussie, A., Fisseha, T., Shumbash, K., Abebe, M., Yasin, S. M., Akililu, Y. B., Megersa, A., Tefera, T., Assefa, M., Atnafu, B., Tsegaye, B., Bezabih, Y. S., Sisay, S., Bekele, K., Jira, M., Derilo, H., Degefa, E., Tadesse, A., Nidaw, M., Sarjanoja, E., Testelin, S., Boucher, S., Jouffret, L., Lakkis, Z., Le Bian, A. Z., Harper, L., des Deserts, M. D., Andre, B., Slim, K., Verhaeghe, R., Police, A., Girard, E., Chebaro, A., Nkembi, A. S., Arnalsteen, L., Ballouhey, Q., Mege, D., Jeandel, C., Duchalais, E., Bouche, P. -A., Manceau, G., Cretolle, C., Hervieux, E., Girard, N., Seguin-Givelet, A., Gaujoux, S., De Simone, B., Boisson, M., Bergeat, D., Fredon, F., Nappi, F., Kassir, R., Scalabre, A., Migliorelli, F., Ezanno, A. -C., Seeliger, B., Vaysse, C., Charbonneau, H., Misrai, V., Abbo, O., Angeles, M. A., Brunaud, L., Modabber, A., Wolf, S., Kamphues, C., Hohn, P., Glowka, T. R., Rokohl, A. C., Bork, U., Fluegen, G., Horch, R. E., Schmedding, A., Schnitzbauer, A., Eberbach, H., Schlager, D., Spelsberg, F., Keppler, L., Hecker, A., Wolfer, S., Ronellenfitsch, U., Nitschke, C., Peiper, C., Hakami, I., Welter, S., Nikolaieva, K., Roth, A., Lindert, J., Gousias, K., Rissmann, A., Linz, V. C., Rahbari, N., Rassweiler-Seyfried, M. -C., Gut, A. E., Gempt, J., Reim, D., Wagner, A., Keppler, A. M., Stoleriu, M. G., Saier, T., Stadler, J., Kaiser, J. C., Brunner, S. M., Pfister, K., Herzberg, J., Nowak, K., Reinhard, T., Stavrou, G. A., Konigsrainer, A., Konrads, C., Quante, M., Laban, S., von Pusch, S., Hirschburger, M., Doerner, J., Wiegering, A., Tampaki, E. C., Ruiz, A. G., Rodas, A., Portilla, A. L., Carrera, J., Duarte, A. B., Lowey, M., Barillas, S., Suroy, A., Vaishnav, D., Chowdappa, R. G., Madabhavi, I., Bhat, D., Venkatappa, S. K., Thakar, S., Jain, K., Kumar, A., Nagar, M., Mishra, T., Sekar, A., Gupta, A., Kaman, L., Karthigeyan, M., Tripathi, M., Rammohan, A., Vayoth, S. O., Rajanbabu, A., Subbian, A., Gupta, R., Raut, M., Evelyn, N., Kannaiyan, R. L., Matai, A., Misra, S., Bhende, V., Muthu, S., Ghosh, I., Sharma, A., Bajaj, A., Rajan, S., Agarwal, G., Pawar, P., Alexander, P., Vijayakumar, M. V., Hameed, Z., Badareesh, L., Chaudhry, N. K., Baliarsing, L., Dharap, S., Kulkarni, A., Thyavihally, Y., Sharma, R. D., Pramesh, C. S., Soni, R., Dube, S. K., Sharma, S., Singh, H., Bains, L., Ghodke, R., Sodhai, V., Maji, S., Basu, S., Mahakalkar, C., Kannan, R., Mehraj, A., Ranganath, N., Phadnis, A., Yadev, I., Kavalakat, A., Mittal, R., Vallam, K. C., Akhavizadegan, H., Maleki, E. R., Kandevani, N. Y., Ikele, H., Mcnestry, C., Fleming, C., O'Brien, S., Elwahab, S. A., Davis, N., Javadpour, M., Mcdonnell, B., Connor, C. O., Bolger, J., Clancy, C., Croghan, S. M., Donlon, N., Cullinane, C., Creavin, B., Muheilan, M., Earley, H., Kabir, S. M. U., Fahadullah, M., Ryan, E., Connelly, T., Hashimoto, D., Alqudah, M. A., Alajalen, A., Omari, R. Y., Qasem, A., Alawneh, Y., Ahmad, A., Aladawi, O., Alrayes, B., Haidar, H., Husain, S., Qassem, F., Sumadi, A., Salhiyeh, A. A., Al-Manaseer, B. M., Alsunna, Z., Ra'Ed, H., Hamad, F. R. B., Abuleil, A., Jimaale, E. A. M., Abu-Mehsen, M., Olaywah, N., Wafi, O., Ababneh, H., Abu-Ismail, L., Khamees, A., Alkhatib, A., Bolatbekova, R., Kulimbet, M., Nurgozhin, T., Saliev, T., Zhussupov, B., Almabayev, Y., Kaidarova, D., Tamoos, K., Aqeelah, A., Mohammed, A. A. K., Al Maadany, F., Alkadeeki, G., Gahwagi, M., Aldressi, W., Amnaina, M., Alansari, A. H. A., Alkaseek, A., Yagoub, G., Amer, A. B., Salem, M., Almugaddami, A., Burgan, D., Abdelkabir, M., Alshareef, K., al Islam Ben Jouira, R., Meelad, A., Bouhuwaish, A., Dwaga, S. E., Khalifa, H., Almiqlash, B., Suliaman, T., Alawami, M., Elhajdawe, F., Aboazamazem, H., Ellojli, I., Msherghi, A., Saleh, I. A., Alayan, M., Ndayishyigikiye, M. D., Munyika, A., Plarre, P., Borowski, D. W., Wells, C., Teague, R., Elliott, B., Kieser, D., Mohyieldin, O., Mcintosh, N., Haran, C., King, J., Ha, J., Mcguinness, M. J., Adesanya, O., Olaogun, J., Akinmade, A., Bwala, K., Agbonrofo, P., Afolabi, A., Usang, U., Ekenze, S., Olori, S., Lawal, T. A., Okunlola, A., Kache, S., Sale, D., Anyanwu, L. -J., Okereke, C., Tolani, M. A., Filipce, V., Todorovic, L., Stavridis, S., Massoud, J. G., Alsibai, S., Sultan, R., Altaf, H. N., Bhatti, A. B. H., Waqar, S. H., Aziz, A., Kerawala, A. A., Rai, L., Anwer, M., Tariq, A., Ayub, B., Niazi, S. U., Naseem, M. Y., Sarwar, M. Z., Khokhar, M. I., Zahid, I. A., Majid, H. J., Talat, N., Asif, M., Chaudhary, M. H., Farooq, U., Ahmad, S., Mabood, W., Bukhari, S. I., Tariq, M., Yaqoob, E., Javed, S., Malik, M. U., Yaqoob, H. N., Pacheco, G. M. F., Melendez, R. M., Urbina, A. D. P. P., Chiuyari, J. R., Alvarado, C. E. O., Lau, L. F. R., Borda-Luque, G., Niquen-Jimenez, M., Arias, C., Zegarra, S., Pallardel, J. B., Oscco, R. A. U., Mendiola, G., Colmenares, Y. T. C., Zapata, C. S., Ortiz, M. R., Borges, F. C., Viveiros, O., Serralheiro, P., Santos-Costa, P., Mendes, F., Melo, M. R., Cardoso, P., Soares, A., Pereira, R. G., Silva, N., Caiado, A., Sacras, M. L., Azevedo, P., Almeida-Reis, R., Oliveira, J., Nogueiro, J., Sampaio-Alves, M., Costa, L. C., Baia, C., Deus, A. C., Branquinho, R., Marcal, A., Tojal, A., Makkai-Popa, S. T., Mironescu, A., Grama, F., Toma, E. A., Filipescu, D., Bacalbasa, N., Motas, N., Ionescu, S., Ginghina, O., Costea, R., Zarnescu, N. O., Drasovean, R., Dimofte, M. -G., Porumb, V., Kirov, M., Molitvin, Y., Pykhteev, V., Raevskaya, M., Butyrskii, A., Alshahrani, M., Althumairi, A., Alzerwi, N., Al Ameer, A., Madkhali, T., Almulhim, A. S., Ghazwani, S., Ayoub, A., Iskander, O., Ghunaim, M., Alharthi, M., Alzaidi, T. M., Alyami, M., Al Amri, A., Alfakhri, A., Alhefdhi, A., Chowdhury, S., Nouh, T., Alshehri, A., Alzahrani, A., Alalawi, Y., Awad, S., Konate, I., Tendeng, J., Teo, N. Z., Aqil, S., Lopez, C. B., Mozo, A. S., Infante, A. R., Vivancos, P. C., Prieto, M., Roman, I. A. S., Fernandez, L. G., Vives, J. M. M., Carreras-Castaner, A., Diaz-Feijoo, B., Sieira-Gil, R., Turrado-Rodriguez, V., Lopez, A. S., Sanchez-Cabus, S., Toscano, M. J., Sin, M. C., Laura, S. G., Sole, O. M., Bellver, P. P., Perez-Bertolez, S., Prat-Ortells, J., Martinez, M. R., Rubio-Palau, J., Tarrado, X., Mendoza, V. A., Bescos, C., Espin-Basany, E., Espinosa-Bravo, M., Gil-Sala, D., Gonzalez-Suarez, S., Estruch, N. M., Marino, L. P., Rodriguez-Tesouro, A., Portilla, F. R., Perez, M. P. T., Vives, I., De Cortazar, U. G., Tudela, K., Landaluce-Olavarria, A., Gomez, M. E., Almoguera, J., Ugarte-Sierra, B., Jimenez, V., Bertrand, M., Puiggros, L. C., Delisau-Puig, O., Garcia-Adam ez, J., Bergkvist, D. J., Maldonado-Marcos, E., Garcia, L. D., Golet, M. R., Soto-Darias, I., Rahy-Martin, A. C., Enjuto, D., Ramos-Luengo, A., Fernandez, J. D., Duarte, C. L., Thies, C. O., Marquez, L., Vita, D. C., Dziakova, J., Velasco, J. C., Mateo-Sierra, O., Quintana-Villamandos, B., Valcarcel, C. R., Rio, J., de Leon, L. R. G., Di Martino, M., Prada, J., Gonzalez, J. S., Losada, M., Gomez, J. T. C., Corripio-Sanchez, R., Forero-Torres, A., Morales-Puebla, J. M., Perez-Chrzanowska, H., Gonzalez, S. V., Yebes, A., Zapardiel, I., Alonso, M. D., Palacios, N. M., Sanchez, A. C., Fernandez, F. S., Gurumeta, A. A., Abad-Motos, A., Corella, F., Ripolles-Melchor, J., Sanz-Gonzalez, R., Fuentes, M. A., Martin, M. T. F., Espino, P. C., Prats, M. C., Fernandez-Lopez, A. -J., Escudero, D. G., Soria, V. G., Alonso, J. A. M., Ruiz-Marin, M., Perez, B. G., Moya-Angeler, J., Martinez, D. F., Bayo, H. L., Colas-Ruiz, E., Romera, S. B., Pellice, M. T. G., Sole, M. J., Velasquez, E. J. R., Nunez, B., Jimenez, R., Zabaleta, J., Gonzalez-Gimeno, M. J., Vazquez, I. O., Ferrer, A. P., Martin-Laez, R., Suarez, M. M., Eiras, M. A. F., Ramallo-Solis, I., Gomez-Rosado, J. -C., Guillen, J. R. O., Boira, M. A., Bauset, J. C. C., Domenech, J., Badenes, R., Bernal-Sprekelsen, J. C., Sancho-Muriel, J., De Andres-Asenjo, B., Tejero-Pintor, F. J., Vallve-Bernal, M., Melero, A. V., Blasco, L. S., Escartin, J., Mallen, V. D., Srishankar, S., Sooriyabandara, C., Basnayake, O., Gunawansa, N., Weeraddana, P. D., Vimalakanthan, T., Gishanthan, S., Chandrasinghe, P., Sauvain, M. -O., Ghazal, A., Al-Sabbagh, Y., Alhassoun, T., Albared, S. M., Naem, A., Alnahr, H., Jisry, G., Hammed, A., Xaviour, O. F., Turinawe, G., Mubezi, I., Sikakulya, F. K., Kakeeto, A., Kabweru, W. M., Kiweewa, R., Matovu, P., Isaac, O., Mashhour, M., El Helw, A., Kherani, S., Mohamed, A., Abbas, F. M. A., Mohammed, D., Aldlyami, E., Kundra, R., Michael, A. L. R., Khalil, K. S., Dbeis, R., Shaikh, S., Ferry, J., Jamal, A., Siddique, H., Das, R., Ponugoti, N., Rattanasirivilai, P., Sultana, A., Mosley, F., Chan, M., Bateman, A., Davies-Jones, G., Georgiades, F., Ahmadi, N., Coonar, A., Baig, M., Khatri, C., Surendran, A., Sonksen, J., Sinnerton, R., Brennan, C., Faulkner, G., Greenhalgh, M., Emerson, H., Singisetti, K., Totty, J., Wilson, M., Lo, T., Corbett, H., Okonkwo, I., Arbane, G., Kerawala, C., Parmar, C., Bath, M., Odejinmi, F., Sagar, J., Talwar, R., Newman, S., Hammond, J., Moir, J., Duric, N., Szakmany, T., Talib, A. I., Youssef, M., Lewis-Lloyd, C., Lami, M., Ayub, K., Dean, B., Balasubramanya, S., Lakpriya, S., Rogers, L., Turner, P., Maher, M., Sigamoney, K., El Kafsi, J., Hardie, J., Johnson, D., Henein, C., Hollyman, M., Agarwal, K., Powell, S., Chauhan, G. S., Patel, R., Gagnier, J., Carmichael, H., Aurora, Olson, K. A., Etchill, E., Hirji, S., Naunheim, M., Drake, F., Reinke, C., Alecci, A., Vaysburg, D., Rodriquez, J., Shih, E., Ban, V. S., Coleman, J., Kaups-Fresno, K., Giorgakis, E., Dinome, M., Bhutiani, N., Alnachoukati, O., Aiken, T., Diehl, T., Gosain, A., Rattan, R., Ghani, M. O. A., Ruzgar, N. M., Liveris, A., Glass, N., Paranjape, C., Chin, T., Meola, A., Nicholson, K., Squiers, J., Lueckel, S., Reisenauer, J., Callcut, R., Mansour, A., Berndtson, A., Kornblith, L., Seegert, S., Martins, P., Al-Shehari, M., Al-Raimi, I., Ngulube, A., Siamuchembu, M., Mushiwokufa, W., Mlambo, B., Chinyowa, S., Bollano, E., Gjyli, K., Kerpaci, J., Vrapi, E., Zijaj, L., Djahida, B., Zohra, B. F., Khadidja, K., Bali, O., Benallel, N., Lalmi, I., Abdoun, M., Aouabed, N., Bouaoud, S., Bouchenak, K., Haif, A., Soualili, Z., Bastet, J., Bianco, A., Capitaine, D., Lozada, J. C., Esquivel, C., Figueroa, R., Garcia, M., Garcia, P. M., Gerchunoff, J. I., Iascano, F. M., Mondino, J., Muriel, M. E., Pablo, C., Passadore, M., Tornini, A., Traverso, R., Vargas, L., Zanoni, G., Berber, N., Cotta, E., Di Vincenzo, M., Gallino, E. 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A., Workneh, W., Genetu, S., Mebratu, E., Chanyalew, L., Deresse, T., Girma, A., Seid, M., Shimelash, D., Shitaw, A., Ayalew, T., Diress, T., Kassahun, B., Kassaye, B., Mulugeta, E., Tesfaw, G., Yimenu, B., Benecha, S., Aman, M., Mekete, A., Tesemma, A., Mohammed, A. A., Abdullahi, Y. Y., Adem, E., Ahmedin, M., Assefa, Y., Ayde, F., Berhe, A., Gebremeskel, Z., Siyoum, E. G., Teresa, S., Wude, Z., Yemer, F., Yohannes, D., Zeleke, E., Zerihun, G., Degefe, D. W., Derilo, H. T., Eliyas, N., Bayissa, F., Deressa, Y., Gemechu, L., Gurmu, T., Kasaye, A., Melese, L., Muleta, L., Mulugeta, G., Taye, S., Teshome, A., Tesso, B., Gebrekirstos, G., Haile, M., Kassa, H., Saddik, M., Mengist, A., Heikkinen, J., Helminen, O., Huhta, H., Kotkavaara, T., Mosorin, M. -A., Pitkanen, J., Qian, C., Sinikumpu, J., Sova, H., Tastula, M., Virta, V. 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M., Salle, H., Taibi, A., Tricard, J., Usseglio, J., Chopinet, S., Gregoire, E., Delpont, M., Blanchard, C., Crenn, V., de Vergie, S., Denis, W., Fragnaud, H., Regenet, N., Rigaud, J., Varenne, Y., Anract, P., Barat, M., Biau, D., Dautry, R., Dohan, A., Idier, L., Lang, E., Thouny, C., Tzedakis, S., Audenet, F., Cazelles, A., Chamouni, A., Karoui, M., Mejean, A., Clermidi, P., Langlais, T., Leonelli, L., Maisonneuve, E., Nicolas, B., Perrot, O., Prost, D., Thomin, A., Thouement, T., Athiel, Y., Berry, R., Boddaert, G., Bonnet, S., Cathala, N., Conso, C., Denet, C., Laforest, A., Levy-Zauberman, Y., Macek, P., Mombet, A., Ollat, D., Scamporlino, A., Zadegan, F., Amine, C. A., Baratte, B., Chartier-Kastler, E., Chereau, N., Colas, P. -A., Duquesne, I., Genser, L., Petit, G. G., Goumard, C., Hasani, A., Lim, C., Martellotto, S., Melot, C., Menegaux, F., Pinar, U., Pocard, M., Roupret, M., Scatton, O., Seisen, T., Severine, N., Turco, C., Chouillard, E., Beganton, P., Frasca, D., Kerforne, T., Corbiere, L., Gasmi, A., Ghemame, M., Guerin, S., Khene, Z. -E., Livin, M., Mahmoud, F., Maillot, B., Merdrignac, A., Mouriaux, F., Robin, F., Sulpice, L., Vazeux, C., Bertheuil, N., Bonneau, S., Cazemajou, C. T., Cousin, I., Defert, C., Fustec, E., Juricic, M., Lavoue, V., Mevel, G., Timoh, K. N., Renault, A., Violas, P., Schwarz, L., Tuech, J. J., Morichau-Beauchant, T., Sauvat, F., Haddad, E., Vermersch, S., Patrizi, A., Roux, A., Aime, A., Malgras, B., Cherkaoui, Z., D'Urso, A., Felli, E., Gonzalez, C. A., Ignat, M., Mutter, D., Pessaux, P., Vix, M., Gornes, H., Vergriete, K., Berthoumieu, P., Genre, L., Le Gall, H., Pierre, T., Chalhoub, M., Del, M., Martinez, A., Fuchs-Buder, T., Vancon, A., Faya, C. A., Asaph, E., Baruwa, E., Hofman, G., Machemedze, S., Idiata, M. M., Muhemi, R., Ndizeye, O., Nkunzimana, E., O'Connor, J., O'Connor, Z., Tchoba, S., Boumas, N., Devidze, G., Pisarevi, G., Baumgarten, S., Grusser, L., Holzle, F., Kowark, P., Kowark, A., Rossaint, R., Schafer, B., Wallqvist, J., Winnand, P., Ziemann, S., Anthuber, M., Broecheler, T., Edlinger, F., Gosslau, Y., Hyhlik-Duerr, A., Maksymiw, F., Shiban, E., Sommer, F., Sommer, B., Zerwes, S., Beyer, K., Lauscher, J. C., Lee, L. D., Loch, F. N., Schineis, C., Aghalarov, I., Belyaev, O., Braumann, C., Enste, A., Fahlbusch, T., Herzog, T., Horn, J., Jedanowski, J., Knipschild, J., Luu, A. M., Mohan, P. V., Siemen, L., Slobodkin, I., Strotmann, J. J., Uhl, W., Wolf, K., Coburn, M., Egger, E., Eichhorn, K., Enderes, J., Far, F., Franzen, A., Guresir, E., Hadjiathanasiou, A., Kalff, J. C., Kohistani, Z., Manekeller, S., Mustea, A., Probst, C., Randau, T., Recker, F., Schuss, P., Strassberger-Nerschbach, N., Strieth, S., Treede, H., van Beekum, C. J., Vatter, H., Velten, M., Vilz, T. O., Wirtz, D., Wittmann, M., Behr, M., Bulian, D. R., Marche, B., Moczko, T., Otchwemah, R., Schulz, S. -A., Thomaidis, P., Bruns, C., Cursiefen, C., Domrose, C., Fuchs, H., Heindl, L. M., Mallmann, M. R., Mallmann, C., Ratiu, D. A., Canzler, U., Distler, M., Korn, S., Meisel, C., Meusel, M., Petzold, A., Praetorius, C., von Renesse, J., Weitz, J., Wimberger, P., Alkhanji, N., Fung, S., Jaber, K., Knoefel, W. T., Vay, C., Clonda, O., Cottin, T., Juodiene, E., Juodis, D., Petrovic, B., Solodarenko, O., Binder, J., Grutzmann, R., Hackner, D., Junker, S., Arsalan, M., Banek, S., Chun, F., Faqar-Uz-Zaman, S. F., Keese, D., Kluth, L., Rolle, U., Van Linden, A., Walther, T., Bayer, J., Beck, J., Fung, C., Kraus, L. M., Leiber, C., Neidert, N., Sandkamp, R., Schnell, O., Schulte, A., Strahle, J., Jarmusch, S., Novotny, H. F. G., Becker, J., Fulghum, C., Gonschor, B., Herbolzheimer, M., Nicolaiciuc, S., Trulson, A., Vogelsang, H., Zimmermann, B., Akbari, A. A., Boening, A., Edinger, F., Hecker, M., Knitschke, M., Koch, C., Reichert, M., Schmidt, G., Schneck, E., Uhl, E., Nobrega, S. F., Kauffmann, P., Miller, C., Nemeth, M., Sievers, D., Kisser, U., Kleeff, J., Klose, J., Lorenz, K., Papendick, N., Plontke, S., Seiwerth, I., Steer, S., Thomssen, C., Ukkat, J., Dumpies, C. W., Gessner, M., Hanke, J., Klauke, F., Leuschner, S., Mendel, T., Muller, K., Schmidt, B., Schreiter, V., Stosberg, P., Vinz, F., Herbig, B., Sander, J., Schulte, T. M., Betz, C. S., Bewarder, J., Bier, J., Bottcher, A., Burg, S., Busch, C. -J., Bussmann, L., Gosau, M., Heuer, A., Izbicki, J., Klatte, T. O., Konig, D., Kopke, L. -G., Moeckelmann, N., Praetorius, M., Priemel, M., Stadlhofer, R., Stangenberg, M., Uzunoglu, F. G., Wittig, L., Zech, H., Zeller, N., Roux, F., Spaeth, T., Fricker, R., Muller, T., Schroder, L., Alasmari, M., Boeker, C., Hakami, I. A., Mall, J. W., Kyritsis, I., Graeb, C., Huber-Strossner, K., Branzan, D., Doss, M., Gockel, I., Jodicke, C., Osterhoff, G., Pempe, C., Sucher, R., Adler, T., Kelly, K., Merkle, J., Siebert, J., Hirche, C., Kneser, U., Tapking, C., Agrawal, R., Qureischie, H., Croner, R., Koslowski, L., Krause, H., Meyer, F., Turial, S., Al-Nawas, B., Battista, M. J., Goedeke, J., Hasenburg, A., Heider, J., Mueller, L. K., Zeller, S., Jentschura, S. L. P., Kowalewski, K. -F., Kriegmair, M., Mannle, D., Reissfelder, C., Rotter, N., Scherl, C., Seyfried, S., Kirschniak, A., Rolinger, J., Wilhelm, P., Bader, F. G., Ottl, S., Behr, A. V., Diamantis, A., Fichter, A., Grill, F., Heck, M., Jira, D., Kallmayer, M., Koll, F., Luhne, S., Meyer, B., Patachia, R., Pergolini, I., Safi, S., Schaffer, C., Schirren, M., Wegmann, H., Wirth, M., Zhu, Z., Aghamaliyev, U., Almaghrabi, M., Bocker, W., Bruder, J., Frank, K., Herterich, V., Huber, V., Ilmer, M., Kammerlander, C., Neuerburg, C., von Ehrlich-Treuenstatt, V. H., Werner, J., Borner, N., Fegg, F., Hartmann, D., Ladurner, R., Liokatis, P., Patzer, K., Schlager, J. G., Smolka, W., Zimmermann, P., Hatz, R., Steinhart, D., Glowalla, C., Schneidmueller, D., Henkel, K. W., Steiner, M., Holz, K., Knorr, C., Kuehlmann, B., Oikonomou, K., Prantl, L., Flurschutz, R., Honarpisheh, H., Kroger, M., Lepiorz, D., Luths, C., Niemeier, A., Sras, Y., Strate, T., Winter, T., Freiman, T., Gessler, F., Won, S. -Y., Widyaningsih, R., Hoffmann, S., Schafer, R. C., Thiel, J. T., Bertolani, E., Steidle, C., Uberruck, L., Yurttas, C., Ziegler, P., Zimmermann, A., Bolenz, C., Dayan, D., Greve, J., Hoffmann, T. K., Janni, W., Lobig, N., Schochter, F., Vahl, J. M., Wezel, F., Greif, V., Schmidbauer, S., Marsch, I., Schneider, R., Boenicke, L., Degener, S., Dreger, N. M., Horstmeier, F. C., Kruschwitz, J., Rombach, A., Schmidt, N., Seiberth, R., Singh, J., Smit, M., von Rundstedt, F. -C., Zirngibl, H., Diessner, J., Friedrich, S., Germer, C. -T., Helmer, P., Herrmann, J., Kranke, P., Kubler, H., Lock, J., Lotz, C., Meffert, R., Meybohm, P., Muller, S., Notz, Q., Popp, M., Schlegel, N., Schlesinger, T., Schmid, B., Sitter, M., Steinisch, A., Treutlein, A., van den Berg, A., Wurmb, T. E., Agyeman-Duah, N. K., Appiah, E., Armah, R., Asare, C., Awere-Kyere, L., Daary, D., Gakpetor, D., Allah, S. M., Obbeng, A., Osei-Poku, D., Puozaa, D., Tackie, E., Adu-Aryee, N. A., Agboadoh, N., Asare, O., Bowan, A. B., Brown, G. D., Clegg-Lamptey, J. -N., Dedey, F., Dery, C., Fenu, B. S., Harrison, M. A., Kumassah, P., Kuuzie, E., Nsaful, J., Olayiwola, D. O., Smith, C., Banka, C., Hussey, R., Azize, D. A., Aniakwo, L. A., Adofo-Asamoah, Y., Agbeno, E. K., Agyapong, M. M., Agyen, T., Mensah, K. A., Alhassan, B. A. B., Amoako-Boateng, M., Appiah-Thompson, P., Gyimah, N. A. -M., Asante-Bremang, M., Asante-Asamani, A., Atawurah, H., Appiah, A. B., Baidoo, R. O., Baidoo, E. I., Boakye, B., Boateng, A., Dadoe, D., Dayie, M. S. C. -J. K., Debrah, S., Doku, K., Enti, E., Ken-Amoah, S., Koggoh, P., Kpangkpari, R., Maison, P., Mensah, S., Mensah, P., Mensah, T. A., Morna, M. T., Nimako-Mensah, J., Nkrumah, J., Nortey, M., Ofori, E. O., Opandoh, I. N. M., Osei-Tutu, E., Adae, J. O., Quansah, K., Quartson, E., Rahman, G. A., Walawah, D., Yigah, M., Yussif, S., Jiagge, N., Nachelleh, E., Acquaye, J., Agbedinu, K., Agyei, F., Agyemang-Prempeh, A., Amo-Antwi, K., Amoah, M., Amoah, G., Amoako, Y. A., Aning, D. G., Ankobea-Kokroe, F., Mintah, D. A., Anyitey-Kokor, D., Appiah-kubi, A., Arthur, J., Ativor, V., Barnor, I., Braimah, Y., Darko-Asante, R., Davor, A., Issahalq, M. D., Dzogbefia, M., Emile, T., Fiifi-Yankson, P., Gaveh, V., Gudugbe, S., Gyabaah, S., Gyamfi, F. E., Gyedu, A., Gyimah, D., Hammond, B., Jonathan, B. -Y., Joseph, Y., Konney, T. O., Konney, A., Kusi, K., Kyei, I., Lovi, A., Naabo, N., Nimako, B., Nyadu, B., Nyarko, O. O., Ofosu-Barko, B. B., Oppong, P. P., Osabutey, A., Poku, M., Sagoe, R., Yifieyeh, A., Ansong, G., Abass, A., Abdul-Mumin, A., Adjeso, T., Alatiiga, J. A., Amadu, M., Annan, N., Leal, G. A., Bukari, M., Buunaaim, A., Cheyuo, E., Salifu, L. D., Damah, M., Dery, M., Dzantor, E. K., Garcia, O. G., Baba, Y. I., Issaka, A., Muntaka, A. -J. M., Murphy, J., Agyeman, Y. N., Amankwaa, W. O., Osman, I., Pie, S., Seidu, A. S., Sheriff, M., Charadan, A. M. S., Titigah, A., Yahaya, M., Yakubu, M., Yenli, E. M. T. -A., Grypiotis, I., Kiriakopoulos, N., Koliopoulos, G., Kyvelos, V., Micha, G., Papadopoulou, T., Balalis, D., Fradelos, E., Korkolis, D., Alexakis, N., Angelou, K., Haidopoulos, D., Prodromidou, A., Rodolakis, A., Thomakos, N., Psychogios, D., Antonakis, P., Bramis, K., Chardalias, L., Contis, I., Dafnios, N., Dellaportas, D., Dimitra, P., Fragkoulidis, G., Gkiokas, G., Gklavas, A., Hadjizacharias, T., Karageorgou, D., Konstadoulakis, M., Kontopoulou, C., Massaras, D., Memos, N., Papaconstantinou, I., Politis, D., Polydorou, A., Stamatis, K., Theodosopoulos, T., Vezakis, A., Avgerinos, K., Katunin, J., Kechagias, A., Kelgiorgi, D., Kritikos, N., Pengerma, P., Bisdas, T., Ioannidis, A., Konstantinidis, M., Konstantinidou, S., Patelis, N., Antonopoulou, M. I., Deskou, E., Kalles, V., Manatakis, D. K., Stamos, N., Tasis, N., Arkadopoulos, N., Danias, N., Economopoulou, P., Frountzas, M., Kokoropoulos, P., Michalopoulos, N., Selmani, J., Sidiropoulos, T., Vassiliu, P., Paraskevas, K. I., Bartziotas, D., Bouchagier, K., Galanis, I., Kalamatianos, T., Kapiris, S., Kolinioti, A., Mavrodimitraki, E., Metaxas, P., Nikova, A., Psarologos, M., Sotiropoulou, M., Stranjalis, G., Albanopoulos, K., Kondilis, P., Vrakopoulou, G. Z., Chrysovergis, A., Chrysovitsiotis, G., Giotakis, E., Kanellopoulou, V., Kyrodimos, E., Larentzakis, A., Pantos, P., Papanikolaou, V., Potamianos, S., Theodoropoulos, C., Triantafyllou, A., Triantafyllou, T., Georgousi, K., Panagiotou, P., Avramidis, E., Babis, G., Zafeiris, E., Antoniou, A., Bessias, N., Maras, D., Papas, T., Roditis, K., Tsagkos, I., Tsiantoula, P., Alexandrou, A., Baili, E., Charalabopoulos, A., Dimitroulis, D., Dorovinis, P., Garoufalia, Z., Kanavidis, P., Karavokyros, I., Karydakis, L., Kykalos, S., Kyros, E., Machairas, N., Mastoraki, A., Nikiteas, N., Papalampros, A., Schizas, D., Skotsimara, A., Stamopoulos, P., Syllaios, A., Terras, A., Tomara, N., Tsourouflis, G., Vagios, I., Zografos, C., Apostolou, K., Georgopoulos, N., Antoniou, T., Antzaka, C., Falara, A., Fragoulis, S., Ftikos, P., Matsouka, F., Perreas, K., Rellia, P., Samara, E., Smirli, A., Tasouli, A., Thanopoulos, A., Chalkias, P. L., Dedemadi, G., Mourmouris, P., Skolarikos, A., Theochari, N., Tzelves, L., Gaitanakis, S., Milas, T., Theodorakis, E., Karona, P., Kastanaki, P., Tzouganakis, A., Agalianos, C., Tsouknidas, I., Xenakis, A., Chrysos, E., Lasithiotakis, K., Nikolouzakis, T., Xenaki, S., Xynos, E., Lostoridis, E., Nagorni, E. -A., Pujante, A., Tourountzi, P., Al, A., Baxevanidou, K., Bouliaris, K., Efthimiou, M., Kalfountzos, C., Koukoulis, G., Lachanas, V., Petropoulos, K., Tsitiridis, I., Angelis, F., Arnaoutoglou, E., Baloyiannis, I., Bareka, M., Bouronikou, A., Christodoulidis, G., Daponte, A., Fergadi, M., Gkolias, N., Gkrinia, E., Hajiioannou, J., Kalogritsas, N., Karoni, E., Korais, C., Krestinidis, G., Ktisti, A., Magouliotis, D., Maiou, C., Malizos, K., Minasidou, M., Ntalouka, M., Ntziovara, A. M., Palla, E., Perivoliotis, K., Saini, F., Samara, A., Saratziotis, A., Skoulakis, C., Solomi, E., Stamoulis, K., Terzoudis, C. 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S., Shaheen, A., Mohammed, Y. S., Shehata, A., Shemeis, M., Shenit, K., Sheta, M., Soffar, A., Tanas, Y., Tarek, A., Yehia, N., Eid, O. K. M., Abdulfattah, A. Y., Elsaghir, A., Elganainy, M. F., Elsayed, O. I., Elmandouh, O., Hamam, O., Shehata, M. A., Abdelaziz, A. A., Abbas, A. M., El-Ghani, W. A., Abd Elazeem, H. A. S., Elsayed, M. A., Abd-Elkariem, A. Y., Abdalla, S., Abdel-Aleem, M., Abdelazeem, K., Abdelfattah, M., Abdelhafez, M., Abdelkarem, M. M., Abdelraouf, A., Abdullaha, L., Abubeih, H., Ahmed, M. E., Ahmed, N. E. A. E., Saad, S. A., Alaa, S., Alhussaini, A., Ali, A. A. A., Ali, I., Ali, W., Mohamed, M. A., Ashraf, M., Bassiouni, M., El-adawy, M. G., El-Sharkawi, M., Elkhayat, H., Elmaghraby, K., Elsdfy, S., Elsharkawy, M., Eltayeb, A., Mohamed, M. E., Essam, E., Fahd, M., Farouk, O., Gadelkareem, R., Ghoneim, A., Takrouney, M. H., Hamdan, A., Hamed, A. E. -R., Hasan, A., Hassanin, M. A., Hawal, I., Henes, K., Herdan, M. O., Hetta, H. F., Ibrahem, O., Ibrahim, I. H., Kamel, M., Khallaf, M., Elghazaly, S. M., Mahfouz, A., Mahmoud, O., Mahran, W., Mahrous, D., Marsis, A., Hussein, A. M., Sayed, A. M., Mohammed, M. M., Mokhtar, A., Monib, F. A., Nageeb, A., Nageh, M. A., Nageh, M., Omar, N. G., Ragab, A., Ramadan, A., Temerik, A. R., Safwat, H., Salah, O., Mahmoud, A. E. S., Saleh, A., Sallam, M., Samir, A., Sayad, R., Sayed, E., Sedik, A. S., Shahine, M., Soliman, A., Soliman, W., Taher, M. G., Wanees, R., Yousof, E. A., Youssef, A., Elabedeen, O. Z., Hassan, A. E. S., Fattah, S. A., Abostate, A., Ali, S., Salama, S. A. S., Allam, M., Alsadek, A., Arafa, E., Barakat, A., El Sherpiny, M., Elbehairy, G., Eleisawy, M., Elgendy, A., Elhawary, R., Eltregy, S., Hamdy, M., Hassan, G., Khalifa, A., Mahmoud, A., Altukhy, A. M., Noah, K., Noureldin, Y., Nowar, M., Saad, M., Elsaeidy, A. S., Elsaeidy, K. S., Sabry, H., Sameh, M., Said-elnaby, A. T., Zahed, M., Zahran, M., Zaid, A., Zoghary, A. Z., Atef, M., Abdelhamid, M. H. S., Alazab, E., Awad, A. K., Darwesh, A., El Fiky, L., Elgarhy, I., Nassef, M. A., Shaban, M., Shalaby, M. M. M., Wahba, A., Fouad, O. Y. O., Elsadek, M., Gad, D., Abdelsamed, A., Bayomy, A., Elsalhy, M., Fahim, A., Seleim, A., Afifi, E., Gamal, F. A., Gamal, M., Eldesouky, O., Eldwini, M. H. E. E., Fahmy, M. M. F., Hussein, A., Ouf, A., Sami, O., Shaat, S., Bahi, A. W. M. K., Atea, A., Gamal, I., El Kassas, M., Omar, W., Tawheed, A., Al-oribi, S., Aly, N. K., Elfiky, M., Elmaghraby, A. E., Elrahmany, A., Elsherbeeny, M., Helmy, Y., Nabil, A., Farahat, A. S., Soliman, M., Wassef, A., Abdelrahman, A., Elsaban, S., Ghaly, G., Hamdy, R., Mondy, H., Abdallah, L., Darwish, A. K. Z., Rabea, M., Azab, M. A., Abbas, A. S., El-Deeb, M., Fawzy, M., Ibraheem, M., Bakry, A., Elnems, S., Elsheikh, R., Gbreel, M. I., Hafez, M., Jammal, M., Hamam, K. M., Makram, A. M., Makram, O. M., Rabie, S., Askalany, S. Y., Abushanab, M. M. I., Eid, A., El Ghalid, M. R. I. A., Abdelaty, A. A., Fayad, E. A., Radwan, A., Shokry, A., Farouk, A. M., Elfallal, A., Elfeki, H., Elsaeed, M., Elsaid, M., Makroum, A., Elsaid, D. M., Mostafa, M., Omar, M. W., Rezk, M., Sakr, A., Sanad, A., Shalaby, M., Shawqy, M., Shetiwy, M., Shoma, A., Tawakl, N., Yunes, A., Abouzid, A., Atallah, K., Elmorsi, R., Elsherbini, A., Gaballa, K., Hamdy, O., Metwally, I. H., Refky, B., Zuhdy, M., Zahran, M. A., Abdellah, F., Abdelmawla, A., Abdrabou, A., Abubakr, A., Al Gohary, R., Al-Shazly, M. H., Allam, A. R., Aposaeeda, A., Asfour, M., Ayman, A., Omran, A. A., Daghash, I. T., Ebada, M. A., Eid, T., El Kelany, A., El Shemy, E., El-Hag-Aly, M., Elgamal, O., Elghoury, M., Elkased, A. F., Ellakwa, T. H., Ellakwa, H., Elmeanawy, A., Elnoamany, S., Elsabagh, A., Elsawey, A., Elshabrawy, E., Elshakhs, S., Elsoudy, N., Ezzat, E., Fawzy, A., Gamal, A., Gameel, A., Gharbia, K., Ghonaim, M., Ghonaim, A., Hafez, A., Ismail, Z., Khaled, M., Meselhy, M., Mikhail, P., Mougahed, M., Mustafa, A., Nada, A., Omran, J., Ebiad, M. S., Saleh, I., Salem, N., Salem, O., Selim, S., Shalaby, G., Sherif, H., Soltan, H., Wahbah, M., Abdelbary, E. Z., Zayan, A. H., Afify, A., Hadiya, H. A., Elshref, M. J., Ahmed, M. M., Nasser, N., Abdeldayem, H., Salama, I. A., Ammar, K., Ayoub, I., Badawy, M. T., Balabel, M., Fayed, Y., Gad, E. H., Gomaha, M., Hammad, E., Hegazy, O., Hegazy, E., Ibrahim, T., Kallaf, M., Macshut, M., Magdy, A., Oteem, A., Samaan, S., Sharshar, M., Shoreem, H., Soliman, E., Soliman, H. E., Yassein, T., Zakaria, H., Eldaly, A., Mashaly, S., Abd-Elsalam, S., Abdel-Elsalam, W., Shaalah, A. A., Elbahnasawy, M., Elhalaby, I., Hamada, M., Soliman, S. H., Hawila, A., Morsy, M. S., Naieem, M., Nasreddin, M., Salman, S., Sarsik, S., Shabana, A., Tolba, E., Zagho, M., Kams, L., Ratsep, T., Riips, K., Abdurehman, O., Admasu, A., Berhea, A., Eado, Y., Hailu, N., Kidane, M., Mohammed, A., Mohammed, K., Tibelt, A., Woldemariam, M. A., Yonael, L., Alemu, M., Aklilu, A. T., Asele, F., Assefa, S., Azmach, D., Bekele, P., Mamo, M., Maru, N., Mekuria, H., Menkir, A., Mikru, F., Siraw, E., Suga, H., Sumoro, N., Teka, A., Thomas, E., Woldemariam, D., Wubishet, E., Abebe, E., Abebe, K., Asfaw, F., Bergene, E., Eshete, M., Gebrehiwot, F. G., Gedefaw, Y., Mengesha, M. W., Mengiste, N., Menjeta, A., Muleta, M. B., Sefera, S., Tedla, Y., Teklu, D., Teshome, H., Tirfe, L., Tsegabrhan, S., Tsehaye, M., Worku, B., Molla, M., Supha, M., Taye, T., Abebe, N., Alemu, B., Chanie, A., Degefu, H., Desalegn, D., Gebre, H., Hussen, H., Kassa, D., Laeke, T., Mammo, T. N., Negash, S., Sahlu, A., Tiruneh, A. G., Wolde, A., Woldeselassie, H. G., Ahmed, M. Y., Zewdneh, B., Fentaw, J., Salia, S., Tabore, K., Gurara, A., Abreha, T., Abrha, T., Beyene, G., Weldu, M. A., Abdissa, D., Tesfaw, M., Wolle, M., Asmamaw, D., Assefa, B. Z., Bedane, D., Bekele, E., Getie, A., Melkamu, S., Mengesha, W., Shumye, G., Tagele, G., Tesfa, A. A., Workneh, W., Genetu, S., Mebratu, E., Chanyalew, L., Deresse, T., Girma, A., Seid, M., Shimelash, D., Shitaw, A., Ayalew, T., Diress, T., Kassahun, B., Kassaye, B., Mulugeta, E., Tesfaw, G., Yimenu, B., Benecha, S., Aman, M., Mekete, A., Tesemma, A., Mohammed, A. A., Abdullahi, Y. Y., Adem, E., Ahmedin, M., Assefa, Y., Ayde, F., Berhe, A., Gebremeskel, Z., Siyoum, E. G., Teresa, S., Wude, Z., Yemer, F., Yohannes, D., Zeleke, E., Zerihun, G., Degefe, D. W., Derilo, H. T., Eliyas, N., Bayissa, F., Deressa, Y., Gemechu, L., Gurmu, T., Kasaye, A., Melese, L., Muleta, L., Mulugeta, G., Taye, S., Teshome, A., Tesso, B., Gebrekirstos, G., Haile, M., Kassa, H., Saddik, M., Mengist, A., Heikkinen, J., Helminen, O., Huhta, H., Kotkavaara, T., Mosorin, M. -A., Pitkanen, J., Qian, C., Sinikumpu, J., Sova, H., Tastula, M., Virta, V. P., Bettoni, J., Dakpe, S., Devauchelle, B., Lavagen, N., Bastard, F., Bin, K., Breheret, R., Fouquet, O., Gueutier, A., Henric, N., Kahn, A., Kun-Darbois, J. -D., Moukoko, D., Pineau, A., Podevin, G., Schmitt, F., Alshawared, F., Beyrne, C. D., Lugans, L., Marie-Macron, L., Doussot, A., Ahmed, O., Alnajjar, L., Mis, T. C., Fliss, S., Giocanti-Auregan, A., Gregory, T., Guiraudet, P., Le Fouler, A., Martinod, E., Onorati, I., Peretti, M., Quilichini, J., Radu, D., Tresallet, T., Andro, C., Maffert, A., Chapman, T., Halden, M., Fayon, J., Mattevi, C., Marion, H., Bendjemar, L., Lionel, C., Mikhael, E., Macias, R. M., Villefranque, V., Volpin, E., Trilling, B., Boleslawski, E., Constance, H. -L., Drubay, V., El Amrani, M., Eveno, C., Lecolle, K., Martin, L., Noiret, B., Piessen, G., Truant, S., Zerbib, P., Aubry, E., Denimal, F., Lamblin, A., Barrat, B., Caire, F., Christou, N., Fourcade, L., Laloze, J., Bouv-Hez, M. M., Salle, H., Taibi, A., Tricard, J., Usseglio, J., Chopinet, S., Gregoire, E., Delpont, M., Blanchard, C., Crenn, V., de Vergie, S., Denis, W., Fragnaud, H., Regenet, N., Rigaud, J., Varenne, Y., Anract, P., Barat, M., Biau, D., Dautry, R., Dohan, A., Idier, L., Lang, E., Thouny, C., Tzedakis, S., Audenet, F., Cazelles, A., Chamouni, A., Karoui, M., Mejean, A., Clermidi, P., Langlais, T., Leonelli, L., Maisonneuve, E., Nicolas, B., Perrot, O., Prost, D., Thomin, A., Thouement, T., Athiel, Y., Berry, R., Boddaert, G., Bonnet, S., Cathala, N., Conso, C., Denet, C., Laforest, A., Levy-Zauberman, Y., Macek, P., Mombet, A., Ollat, D., Scamporlino, A., Zadegan, F., Amine, C. A., Baratte, B., Chartier-Kastler, E., Chereau, N., Colas, P. -A., Duquesne, I., Genser, L., Petit, G. G., Goumard, C., Hasani, A., Lim, C., Martellotto, S., Melot, C., Menegaux, F., Pinar, U., Pocard, M., Roupret, M., Scatton, O., Seisen, T., Severine, N., Turco, C., Chouillard, E., Beganton, P., Frasca, D., Kerforne, T., Corbiere, L., Gasmi, A., Ghemame, M., Guerin, S., Khene, Z. -E., Livin, M., Mahmoud, F., Maillot, B., Merdrignac, A., Mouriaux, F., Robin, F., Sulpice, L., Vazeux, C., Bertheuil, N., Bonneau, S., Cazemajou, C. T., Cousin, I., Defert, C., Fustec, E., Juricic, M., Lavoue, V., Mevel, G., Timoh, K. N., Renault, A., Violas, P., Schwarz, L., Tuech, J. J., Morichau-Beauchant, T., Sauvat, F., Haddad, E., Vermersch, S., Patrizi, A., Roux, A., Aime, A., Malgras, B., Cherkaoui, Z., D'Urso, A., Felli, E., Gonzalez, C. A., Ignat, M., Mutter, D., Pessaux, P., Vix, M., Gornes, H., Vergriete, K., Berthoumieu, P., Genre, L., Le Gall, H., Pierre, T., Chalhoub, M., Del, M., Martinez, A., Fuchs-Buder, T., Vancon, A., Faya, C. A., Asaph, E., Baruwa, E., Hofman, G., Machemedze, S., Idiata, M. M., Muhemi, R., Ndizeye, O., Nkunzimana, E., O'Connor, J., O'Connor, Z., Tchoba, S., Boumas, N., Devidze, G., Pisarevi, G., Baumgarten, S., Grusser, L., Holzle, F., Kowark, P., Kowark, A., Rossaint, R., Schafer, B., Wallqvist, J., Winnand, P., Ziemann, S., Anthuber, M., Broecheler, T., Edlinger, F., Gosslau, Y., Hyhlik-Duerr, A., Maksymiw, F., Shiban, E., Sommer, F., Sommer, B., Zerwes, S., Beyer, K., Lauscher, J. C., Lee, L. D., Loch, F. N., Schineis, C., Aghalarov, I., Belyaev, O., Braumann, C., Enste, A., Fahlbusch, T., Herzog, T., Horn, J., Jedanowski, J., Knipschild, J., Luu, A. M., Mohan, P. V., Siemen, L., Slobodkin, I., Strotmann, J. J., Uhl, W., Wolf, K., Coburn, M., Egger, E., Eichhorn, K., Enderes, J., Far, F., Franzen, A., Guresir, E., Hadjiathanasiou, A., Kalff, J. C., Kohistani, Z., Manekeller, S., Mustea, A., Probst, C., Randau, T., Recker, F., Schuss, P., Strassberger-Nerschbach, N., Strieth, S., Treede, H., van Beekum, C. J., Vatter, H., Velten, M., Vilz, T. O., Wirtz, D., Wittmann, M., Behr, M., Bulian, D. R., Marche, B., Moczko, T., Otchwemah, R., Schulz, S. -A., Thomaidis, P., Bruns, C., Cursiefen, C., Domrose, C., Fuchs, H., Heindl, L. M., Mallmann, M. R., Mallmann, C., Ratiu, D. A., Canzler, U., Distler, M., Korn, S., Meisel, C., Meusel, M., Petzold, A., Praetorius, C., von Renesse, J., Weitz, J., Wimberger, P., Alkhanji, N., Fung, S., Jaber, K., Knoefel, W. T., Vay, C., Clonda, O., Cottin, T., Juodiene, E., Juodis, D., Petrovic, B., Solodarenko, O., Binder, J., Grutzmann, R., Hackner, D., Junker, S., Arsalan, M., Banek, S., Chun, F., Faqar-Uz-Zaman, S. F., Keese, D., Kluth, L., Rolle, U., Van Linden, A., Walther, T., Bayer, J., Beck, J., Fung, C., Kraus, L. M., Leiber, C., Neidert, N., Sandkamp, R., Schnell, O., Schulte, A., Strahle, J., Jarmusch, S., Novotny, H. F. G., Becker, J., Fulghum, C., Gonschor, B., Herbolzheimer, M., Nicolaiciuc, S., Trulson, A., Vogelsang, H., Zimmermann, B., Akbari, A. A., Boening, A., Edinger, F., Hecker, M., Knitschke, M., Koch, C., Reichert, M., Schmidt, G., Schneck, E., Uhl, E., Nobrega, S. F., Kauffmann, P., Miller, C., Nemeth, M., Sievers, D., Kisser, U., Kleeff, J., Klose, J., Lorenz, K., Papendick, N., Plontke, S., Seiwerth, I., Steer, S., Thomssen, C., Ukkat, J., Dumpies, C. W., Gessner, M., Hanke, J., Klauke, F., Leuschner, S., Mendel, T., Muller, K., Schmidt, B., Schreiter, V., Stosberg, P., Vinz, F., Herbig, B., Sander, J., Schulte, T. M., Betz, C. S., Bewarder, J., Bier, J., Bottcher, A., Burg, S., Busch, C. -J., Bussmann, L., Gosau, M., Heuer, A., Izbicki, J., Klatte, T. O., Konig, D., Kopke, L. -G., Moeckelmann, N., Praetorius, M., Priemel, M., Stadlhofer, R., Stangenberg, M., Uzunoglu, F. 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M., Morales, S., Najera, R., Lorenzana, E. Q., Rivera, P., Rodas, L., Santos, V., Sosa, D., Ybarra, N., Cajti, J. D. P., Regil, C., Osorio, W. A., Arriola, R. R., Tale-Rosales, L. -F., Herrera, D., Rodriguez, S. T. T., Villeda, S. A., Anicetti, J., Medina, A. M. L., Ardebol, J., Barillas, K., De Leon, F., Recinos, S., Marroquin-Alpirez, M. -A., Yax, Y., Hon, S., Liu, Y. H., Liu, A. Q., Liu, S., Chiu, H. T. N., Chow, C. M. T., Hau, V., Ip, H. W., Mak, B., Mok, C. Y., Ng, D., Siu, Y. Y. E., Wong, K. F., Pu, J. J., Su, Y. -X., Au-Yeung, K. Y., Chan, D. Y. C., Chan, A., Chan, S. M., Chang, T. C., Chiu, T. W., Chiu, W. K., He, Z., Ho, M. F., Ho, K. K. W., Ho, J. Y. K., Kung, J. W. C., Lam, C. H., Lau, R. W. H., Ling, S. K. K., Lok, H. T., Mak, T. W. C., Ng, C. -F., Ng, S., Ng, K. K. -C., Ng, C. S. H., Ong, M. T. Y., Tan, T., Tang, S. F., Teoh, J. Y. -C., Teoh, A., Tsui, B. S. Y., Wong, G. K. C., Wong, R., Wong, K. C., Yee, C. H., Antal, Z., Kalman, A., Voros, P., Bahrehmand, K., Echim, T., Novak, Z., Sztipits, T., Bihari, L. A., Hidi, L., Piros, L., Rozsa, B., Sotonyi, P., Szatai, L., Herczeg, G., Pordany, B., Tornyi, F., Dede, K., Egyed, T., Saftics, G., Kulcsicka-Gut, J., Sipos, Z., Toth, D., Patel, B., Majmudar, H., Mankad, S., Pathan, S., Sharathkumarkl, L., Sasatti, L., Mahankali, S., Annayappa, S. K. S., Agrawal, T., Anandan, P., Baindur, A., Bd, M., Savitha, C., Anitha, G. S., Harigond, S., Tejeswini, K. K., Kesarla, V., Manangi, M., Hareesh, P. B., Ranjan, J. K. R., Shetty, A., Chikkanayakanahalli, S. S., Sunil, T., Syamala, S., Tharanath, K., Atal, A., Rajagopal, N., Srinath, B. S., Nk, V., Thirumanikandan, P. L., Mishra, N., Shrivastava, S., Wadhwani, R., Ahmad, R., Haq, Z. A., Behera, P., Dhurwe, R., Haq, R., Jain, V., Karna, S., Pushpalatha, K., Raj, S., Santoshi, J. A., Singh, P., Verma, V., Waindeskar, V., Yadav, M. S., Aziz, Z., Chakraborty, K., Chappity, P., Das, G., Datta, D., Jena, S. K., Kar, M., Khuntia, S., Kumar, P., Kumar, R., Kv, A., Mishra, A., Mishra, S., Mittal, Y., Muduly, D., Panda, R., Panigrahi, S., Parija, S., Patra, S., Rout, B., Sahu, R., Sarkar, S., Singh, S., Sudha, S., Sultania, M., Tripathy, S., Varghese, P., Kansay, R., Kaushik, R., Gupta, S. K., Khare, S., Kumar, V., Mohindra, S., Patil, N., Salunke, P., Savlania, A., Singh, K., Pradeep Krishna, R. V., Narasimhan, G., Rela, M., Appukuttan, A., Goudar, S., Mallick, S., Titus, C., Chidambarasamy, E. S., Madhavan, D., Murugesan, A., Narayanasamy, K., Barani Kumar, P. B., Rajan, F., Vijayaragavan, P., Borthakur, B., Chauhan, T. S., Govil, A., Hanjoora, V. M., Srivastava, A., Tiwari, S., Vishwani, V., Pendyala, K. S., Prasad, N., Bothra, J., Deb, M., Herle, K., Jayaram, H., Kannaiyan, L., Krithiga, A., Mukta, W., Nagpal, P., Pathak, P., Banerjee, S., Chaudhary, R., Choudhary, G. R., Chugh, A., Dixit, P., Elhence, A., Gahlot, N., Garg, M., Ghuman, N. K., Jha, D., Kala, P., Kaur, A., Madduri, V., Pandey, H., Pareek, P., Pathak, M., Rathod, K. J., Rodha, M. S., Saxena, R., Sharma, N., Shekhar, S., Singh, M., Suryanarayanan, B., Vishnoi, J. R., Amin, S., Sharma, T., Murugesan, K., Ramasamy, A. B. S., Chatterjee, D., Gerber, C. G., Naskar, U., Aggarwal, G., Agrawal, S. K., Jain, P., Jain, D., Kewlani, V., Pipara, A., Priya, N., Raja, R., Shakya, S., Thambudorai, R., Kharkongor, D., Akhtar, N., Anand, A., Asnani, M., Chaturvedi, A., Chaurasia, A., Enny, L. E., Garg, S., Jaiswal, A. K., Jaiswal, S., Jaware, Y., Kala, N., Karnatak, R., Kumar, U., Shrivastava, A. K., Mehrotra, S., Mishra, B., Namrata, Ojha, B. K., Ozair, A., Pal, U. S., Pandey, A., Raja, N., Ramakant, P., Roy, A., Sachan, R., Sankhwar, S., Sankhwar, P., Sarin, D., Shukla, A., Singh, K. R., Singh, R., Singh, U., Solanki, V., Sonkar, A. A., Srivastava, C., Suryawanshi, P., Tewarson, V., Verma, R., Verma, M. L., Yadav, A., Chand, G., Chanthar, K. M. M. V., Hoysal, D., Batra, N., Bhatti, A., Chopra, R., David, S., Dhar, T., Dutt, U. K., Dutta, R., Gandhi, S., Haque, P. D., Paul Sudhakar John, B., Veetil, S. K., Kaur, G., Kumar, N., Luther, A., Mahajan, A., Mandrelle, K., Mathews, S., Michael, V., Mukherjee, P., Pargal, P., Paul, R., Phillip, A., Phillips, R., Samuel, A., Singh, N., Singh, I., Singh, A., Grewal, S. S., Singhania, A., Thirumalai, S., Varghese, A., Wesley, J., Thomas, J., Zechariah, P., Ifthikar, M. A., Mathew, R. T., Shetty, R., Vijaykumar, M. V., Hameed, B. M. Z., Ibrahim, S., Jyothish, G., Krishna, S., Mathew, S., Kumar, A. S., Sinha, P. K., Sridhar, R., Agarwal, S., Balasubramaniam, S., Chhatrapati, S., Deshpande, C., Dharia, A., Fernandes, S., Gore, M., Jayakumar, A., Mhamane, R., Nirgude, A., Parab, S., Patil, M., Peswani, A., Sahu, A., Samel, S., Shah, F., Haridas, D., Shetty, V., Abraham, B., Agarwal, V., Ahmad, Q., Asari, A., Attar, M. I., Ayyalasomayajula, N. S., Bachawat, S., Bachhav, S., Badhe, V., Badhwar, S., Basa, V. I. K. A. S., Bothara, V., Chattopadhyay, S., Chaudhari, S., Chavan, R., Chawathe, P., Dadhich, S., Dalal, A., Date, A., Deshpande, M., Dev, P., Dongre, N., Doshi, A., Gade, M., Gajjar, S., Gawande, M., Ghalme, A., Ghugare, B., Gupta, I., Jain, M., Jenasamant, S. S., Joshi, V., Kalwadia, N., Kapadia, N., Kattana, H. B. R., Kaushik, T., Kayal, A., Kovale, S., Kulkarni, Y., Kumar, K., Kumawat, K., Lad, V., Maskara, N., Mistry, R., Mohanty, S. R., Motwani, K., Mulchandani, M., Nadkarni, M., Pandey, S., Parab, M., Pardiwala, D., Patkar, A., Pawar, N., Pawar, A., Pednekar, A., Peshattiwar, V., Pokharkar, H., Potdar, O., Pothare, A., Rahmani, F., Rajput, S., Narendra, N. R., Rao, A., Rao, S., Rohela, H., Sakhrekar, R., Salunkhe, D., Sandlas, G., Sarkar, H., Satpathy, A., Sekhar, R., Shelke, Y., Shetty, S. S., Shetye, S., Kulkarni, A. S., Singal, U., Solanki, F., Sonawane, R., Thete, R., Vhatkar, R., Vora, S., Waigankar, S., Wasnik, S., Yadav, M., Yedave, R., Acharya, H., Bangar, A., Bharucha, M., Dhumane, P., Karmarkar, S., Nathani, R., Nehe, A., Nene, A., Noronha, W. J., Palapalle, N., Patel, P., Poyyil, D., Redkar, R., Shah, M., Tewari, S., Bakshi, G., Chaudhari, V., Deshmukh, A., Desoouza, A., Nair, D., Nayak, P., Patkar, S., Puri, A., Qureshi, S., Shetty, P., Shylasree, T., Thakkar, P., Thiagarajan, S., Tiwari, V. K., Voppuru, S. R., Soni, A., Soni, G., Alam, J., Bagaria, D., Bajpai, M., Bisoi, A. K., Chauhan, S., Choudhary, N., Chouhan, R. S., Chumber, S., Farooque, K., Gudala, V., Joshi, M., Kabra, A., Kale, S. S., Kumar, S., Mahajan, D., Malhotra, R., Menon, P. R., Misra, B., Mittal, S., Parshad, R., Priyadarshini, P., Sagar, S., Sharma, P. B., Sharma, V., Trikha, V., Tyagi, M., Bansal, K., Varma, K., Bhardwaj, R., Mittal, A., Ponnusamy, S., Singh, G. R., Tuli, I., Bhor, P., Dhar, S., Kale, S., Aggarwal, M., Gupta, H., Deshpande, A., Gadekar, A., Jaysingani, T., Panjwani, T., Patil, R., Vaidya, N., Vishwakarma, S. K., Wayal, U., Dutta, D., Arora, R., Dhingra, M., Garg, P. K., Huda, F., Kandwal, P., Kant, R., Seenivasagam, R. K., Joshua, L. M., Poonia, D. R., Rajput, D., Sadhasivam, S., Singh, V., Chandanwale, K., Philip, M., Thorat, V., Choudhury, Y., Das, D., Das, M., Goala, S., Laskar, F. Y., Laskar, P., Malik, K., Sarkar, A., Singha, S. T., Singha, M. N., Singha, D., Srungavarapu, G., Tapkire, R., Tling, S., Andrabi, S. M. I., Bhat, G. A., Chowdri, N., Kour, R., Parray, F., Shah, Z. A. M. I. R. A. H. M. A. D., Wani, R., Adamala, S., Gowder, R., Hegde, S., Mashitha, M. S., Sreeram, S., Ankadavar, S., Bafna, D., Dhanani, B., Ingle, N., Jadhao, P., Joseph, S., Kapote, S., Karkada, S., Lad, P., Lohia, R., Madje, S., Narasiman, V., Chisthi, M., George, G., Jothi, H., Pareed, D., Mathew, G. D. C., Abel, L., Agrawal, M., Rabindranath, B., Baldia, M., Barla, R. K., Beck, M., Benjamin, S., Bliss, J., Cherian, L., Devarakonda, S., Ete, G., George, A. J. P., and Franceschini G. (ORCID:0000-0002-2950-3395)
Abstract: SARS-CoV-2 has been associated with an increased rate of venous thromboembolism in critically ill patients. Since surgical patients are already at higher risk of venous thromboembolism than general populations, this study aimed to determine if patients with peri-operative or prior SARS-CoV-2 were at further increased risk of venous thromboembolism. We conducted a planned sub-study and analysis from an international, multicentre, prospective cohort study of elective and emergency patients undergoing surgery during October 2020. Patients from all surgical specialties were included. The primary outcome measure was venous thromboembolism (pulmonary embolism or deep vein thrombosis) within 30 days of surgery. SARS-CoV-2 diagnosis was defined as peri-operative (7 days before to 30 days after surgery); recent (1–6 weeks before surgery); previous (≥7 weeks before surgery); or none. Information on prophylaxis regimens or pre-operative anti-coagulation for baseline comorbidities was not available. Postoperative venous thromboembolism rate was 0.5% (666/123,591) in patients without SARS-CoV-2; 2.2% (50/2317) in patients with peri-operative SARS-CoV-2; 1.6% (15/953) in patients with recent SARS-CoV-2; and 1.0% (11/1148) in patients with previous SARS-CoV-2. After adjustment for confounding factors, patients with peri-operative (adjusted odds ratio 1.5 (95%CI 1.1–2.0)) and recent SARS-CoV-2 (1.9 (95%CI 1.2–3.3)) remained at higher risk of venous thromboembolism, with a borderline finding in previous SARS-CoV-2 (1.7 (95%CI 0.9–3.0)). Overall, venous thromboembolism was independently associated with 30-day mortality (5.4 (95%CI 4.3–6.7)). In patients with SARS-CoV-2, mortality without venous thromboembolism was 7.4% (319/4342) and with venous thromboembolism was 40.8% (31/76). Patients undergoing surgery with peri-operative or recent SARS-CoV-2 appear to be at increased risk of postoperative venous thromboembolism compared with patients with no history of
Published: 2022

22. LaMDA: Language Models for Dialog Applications

Author: Thoppilan, Romal, De Freitas, Daniel, Hall, Jamie, Shazeer, Noam, Kulshreshtha, Apoorv, Cheng, Heng-Tze, Jin, Alicia, Bos, Taylor, Baker, Leslie, Du, Yu, Li, YaGuang, Lee, Hongrae, Zheng, Huaixiu Steven, Ghafouri, Amin, Menegali, Marcelo, Huang, Yanping, Krikun, Maxim, Lepikhin, Dmitry, Qin, James, Chen, Dehao, Xu, Yuanzhong, Chen, Zhifeng, Roberts, Adam, Bosma, Maarten, Zhao, Vincent, Zhou, Yanqi, Chang, Chung-Ching, Krivokon, Igor, Rusch, Will, Pickett, Marc, Srinivasan, Pranesh, Man, Laichee, Meier-Hellstern, Kathleen, Morris, Meredith Ringel, Doshi, Tulsee, Santos, Renelito Delos, Duke, Toju, Soraker, Johnny, Zevenbergen, Ben, Prabhakaran, Vinodkumar, Diaz, Mark, Hutchinson, Ben, Olson, Kristen, Molina, Alejandra, Hoffman-John, Erin, Lee, Josh, Aroyo, Lora, Rajakumar, Ravi, Butryna, Alena, Lamm, Matthew, Kuzmina, Viktoriya, Fenton, Joe, Cohen, Aaron, Bernstein, Rachel, Kurzweil, Ray, Aguera-Arcas, Blaise, Cui, Claire, Croak, Marian, Chi, Ed, Le, Quoc, Thoppilan, Romal, De Freitas, Daniel, Hall, Jamie, Shazeer, Noam, Kulshreshtha, Apoorv, Cheng, Heng-Tze, Jin, Alicia, Bos, Taylor, Baker, Leslie, Du, Yu, Li, YaGuang, Lee, Hongrae, Zheng, Huaixiu Steven, Ghafouri, Amin, Menegali, Marcelo, Huang, Yanping, Krikun, Maxim, Lepikhin, Dmitry, Qin, James, Chen, Dehao, Xu, Yuanzhong, Chen, Zhifeng, Roberts, Adam, Bosma, Maarten, Zhao, Vincent, Zhou, Yanqi, Chang, Chung-Ching, Krivokon, Igor, Rusch, Will, Pickett, Marc, Srinivasan, Pranesh, Man, Laichee, Meier-Hellstern, Kathleen, Morris, Meredith Ringel, Doshi, Tulsee, Santos, Renelito Delos, Duke, Toju, Soraker, Johnny, Zevenbergen, Ben, Prabhakaran, Vinodkumar, Diaz, Mark, Hutchinson, Ben, Olson, Kristen, Molina, Alejandra, Hoffman-John, Erin, Lee, Josh, Aroyo, Lora, Rajakumar, Ravi, Butryna, Alena, Lamm, Matthew, Kuzmina, Viktoriya, Fenton, Joe, Cohen, Aaron, Bernstein, Rachel, Kurzweil, Ray, Aguera-Arcas, Blaise, Cui, Claire, Croak, Marian, Chi, Ed, and Le, Quoc
Abstract: We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.
Published: 2022

23. Individual participant data (IPD)-level meta-analysis of randomised controlled trials to estimate the vitamin D dietary requirements in dark-skinned individuals resident at high latitude

Author: Cashman, Kevin D, Kiely, Mairead E, Andersen, Rikke, Grønborg, Ida M, Tetens, Inge, Tripkovic, Laura, Lanham-New, Susan A, Lamberg-Allardt, Christel, Adebayo, Folasade A, Gallagher, J Christopher, Smith, Lynette M, Sacheck, Jennifer M, Huang, Qiushi, Ng, Kimmie, Yuan, Chen, Giovannucci, Edward L, Rajakumar, Kumaravel, Patterson, Charity G, Öhlund, Inger, Lind, Torbjörn, Åkeson, Pia Karlsland, Ritz, Christian, Cashman, Kevin D, Kiely, Mairead E, Andersen, Rikke, Grønborg, Ida M, Tetens, Inge, Tripkovic, Laura, Lanham-New, Susan A, Lamberg-Allardt, Christel, Adebayo, Folasade A, Gallagher, J Christopher, Smith, Lynette M, Sacheck, Jennifer M, Huang, Qiushi, Ng, Kimmie, Yuan, Chen, Giovannucci, Edward L, Rajakumar, Kumaravel, Patterson, Charity G, Öhlund, Inger, Lind, Torbjörn, Åkeson, Pia Karlsland, and Ritz, Christian
Abstract: Context and purpose: There is an urgent need to develop vitamin D dietary recommendations for dark-skinned populations resident at high latitude. Using data from randomised controlled trials (RCTs) with vitamin D3-supplements/fortified foods, we undertook an individual participant data-level meta-regression (IPD) analysis of the response of wintertime serum 25-hydroxyvitamin (25(OH)D) to total vitamin D intake among dark-skinned children and adults residing at ≥ 40° N and derived dietary requirement values for vitamin D.Methods: IPD analysis using data from 677 dark-skinned participants (of Black or South Asian descent; ages 5-86 years) in 10 RCTs with vitamin D supplements/fortified foods identified via a systematic review and predefined eligibility criteria. Outcome measures were vitamin D intake estimates across a range of 25(OH)D thresholds.Results: To maintain serum 25(OH)D concentrations ≥ 25 and 30 nmol/L in 97.5% of individuals, 23.9 and 27.3 µg/day of vitamin D, respectively, were required among South Asian and 24.1 and 33.2 µg/day, respectively, among Black participants. Overall, our age-stratified intake estimates did not exceed age-specific Tolerable Upper Intake Levels for vitamin D. The vitamin D intake required by dark-skinned individuals to maintain 97.5% of winter 25(OH)D concentrations ≥ 50 nmol/L was 66.8 µg/day. This intake predicted that the upper 2.5% of individuals could potentially achieve serum 25(OH)D concentrations ≥ 158 nmol/L, which has been linked to potential adverse effects in older adults in supplementation studies.Conclusions: Our IPD-derived vitamin D intakes required to maintain 97.5% of winter 25(OH)D concentrations ≥ 25, 30 and 50 nmol/L are substantially higher than the equivalent estimates for White individuals. These requirement estimates are also higher than those currently recommended internationally by several agencies, which are based predominantly on data fro
Published: 2022

24. Building Students’ Personality for Employment Readiness through Soft-skills Training

Author: Saranya C, Saranya C, Guduru, Rajakumar, Saranya C, Saranya C, and Guduru, Rajakumar
Abstract: A winning personality is the physical attribute of a person and is considered as his or her success in personal, academic, and professional careers. However, in the ESL context, most engineering students seem to be unaware of the need for and importance of an appealing personality for achieving success in both personal and professional careers. Although students are given a short-term training in soft-skills by their respective college or institute, engineering students seem to lack aspects of a pleasing personality which helps them in job placements and later in the work environment. Therefore, the main aim of this study is to understand and build engineering students’ personality traits such as enthusiasm, dependability, and teamwork for a successful career. For this purpose, to understand the students’ personality types, 25 engineering students were administered a pre-test based on Carl Jung and Isabel Briggs Myers’ typological approach to personality. Students were helped in building personality through the soft-skills training. The data was analyzed and interpreted both qualitatively and quantitatively. The results indicated that having a pleasing personality and exhibiting soft-skills enables in building students’ individual personality for employment readiness. Implications were offered to students, placement trainers, and teachers. It is concluded that having a charming personality will support students in landing their desired jobs.
Published: 2022

25. Ductal adenocarcinoma of the prostate: A systematic review and meta-analysis of incidence, presentation, prognosis, and management

Author: Ranasinha, N, Omer, A, Philippou, Y, Harriss, E, Davies, L, Chow, K, Chetta, PM, Erickson, A, Rajakumar, T, Mills, IG, Bryant, RJ, Hamdy, FC, Murphy, DG, Loda, M, Hovens, CM, Corcoran, NM, Verrill, C, Lamb, AD, Ranasinha, N, Omer, A, Philippou, Y, Harriss, E, Davies, L, Chow, K, Chetta, PM, Erickson, A, Rajakumar, T, Mills, IG, Bryant, RJ, Hamdy, FC, Murphy, DG, Loda, M, Hovens, CM, Corcoran, NM, Verrill, C, and Lamb, AD
Abstract: CONTEXT: Ductal adenocarcinoma (DAC) is relatively rare, but is nonetheless the second most common subtype of prostate cancer. First described in 1967, opinion is still divided regarding its biology, prognosis, and outcome. OBJECTIVES: To systematically interrogate the literature to clarify the epidemiology, diagnosis, management, progression, and survival statistics of DAC. MATERIALS AND METHODS: We conducted a literature search of five medical databases from inception to May 04 2020 according to PRISMA criteria using search terms "prostate ductal adenocarcinoma" OR "endometriod adenocarcinoma of prostate" and variations of each. RESULTS: Some 114 studies were eligible for inclusion, presenting 2 907 170 prostate cancer cases, of which 5911 were DAC. [Correction added on 16 January 2021 after the first online publication: the preceding statement has been corrected in this current version.] DAC accounts for 0.17% of prostate cancer on meta-analysis (range 0.0837%-13.4%). The majority of DAC cases were admixed with predominant acinar adenocarcinoma (AAC). Median Prostate Specific Antigen at diagnosis ranged from 4.2 to 9.6 ng/mL in the case series.DAC was more likely to present as T3 (RR1.71; 95%CI 1.53-1.91) and T4 (RR7.56; 95%CI 5.19-11.01) stages, with far higher likelihood of metastatic disease (RR4.62; 95%CI 3.84-5.56; all P-values < .0001), compared to AAC. Common first treatments included surgery (radical prostatectomy (RP) or cystoprostatectomy for select cases) or radiotherapy (RT) for localized disease, and hormonal or chemo-therapy for metastatic disease. Few studies compared RP and RT modalities, and those that did present mixed findings, although cancer-specific survival rates seem worse after RP.Biochemical recurrence rates were increased with DAC compared to AAC. Additionally, DAC metastasized to unusual sites, including penile and peritoneal metastases. Where compared, all studies reported worse survival for DAC compared to AAC. CONCLUSION: When drawi
Published: 2021

26. Artificial Intelligence in Orthodontics: Prediction and Planning

Author: Vinoth Kumar. R, Vetriselvan, Abirami, A, Magdline, P, Rajakumar, M.K.Karthikeyan, Vinoth Kumar. R, Vetriselvan, Abirami, A, Magdline, P, Rajakumar, and M.K.Karthikeyan
Abstract: Artificial intelligence (AI) with advancement of technology has experienced remarkable growth and development in the field of dentistry. AI is an excellent tool that performs several tasks from diagnosis, treatment planning, predicting the outcome and prognosis particularly in the field of orthodontics based on individual preferences and constructed algorithm models. The present review was carried out to discuss briefly on the role and impact of AI in the field of orthodontics. It was observed that most of the AI models are based on artificial neural networks (ANNs) and convolutional neural networks (CNNs) systems widely used for Cephalometric landmarks identification, image recognition, decision making system to assist treatment planning, prediction of need for extraction and/or orthognathic surgeries, evaluating the cervical vertebrae growth pattern and maturation, predicting the facial attractiveness and post-orthognathic surgery facial profile. Further research on application of AI should be carried out focusing on formulating and establishing cloud-based platforms, integrating large data to improve learning algorithms and construct advanced automated decision making model systems with high specificity, precision, reliability to predict exact outcomes within a short span of time and improve the quality of life.
Published: 2021

27. Diagnosing Engineering Students’ Competence of English Language Skills at Entry Level Academic Programme

Author: Guduru, Rajakumar, Bommanaboina, Ramya Devi, Guduru, Rajakumar, and Bommanaboina, Ramya Devi
Abstract: In the Indian context, an academic study program at the Indian Institutes of Technology (IITs) comprises a mixed group of learners coming from various social-economic, education, and a wide range of linguistic backgrounds. These learners secure admission into various engineering programs at IITs, having qualified for the Joint Entrance Exam (JEE), a national-level competitive exam. To qualify for this exam, students prepare rigorously, even at the school level. The JEE exam tests students’ knowledge in the core subjects such as Physics, Chemistry, Mathematics, Aptitude and Reasoning; hence, students seem to focus more on these core subjects but not on the English language. These learners vary widely in their English language proficiency level in terms of oral and written English proficiency. It is believed that the level of language proficiency of learners has a direct influence on their academic performance in the program in which they are admitted. Therefore, the study aims at diagnosing 405 engineering students’ competence of English language skills at the entry-level undergraduate engineering study programs of Bachelor of Technology at the Indian Institute of Technology, Bhubaneswar. A diagnostic test that comprised the components of English language skills was designed and administered to 405 students. The results were analyzed and interpreted qualitatively and quantitatively. It was found that the majority of the students who performed well in the test had better education backgrounds. For the students who performed poorly, the study recommends possible implications besides some remedial measures.
Published: 2021

28. Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle

Author: Deshpande, Shreshta Rajakumar, Jung, Daniel, Bauer, Leo, Canova, Marcello, Deshpande, Shreshta Rajakumar, Jung, Daniel, Bauer, Leo, and Canova, Marcello
Abstract: Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios., Funding Agencies|United States Department of Energy, Advanced Research Projects Agency - Energy [2021]
Published: 2021
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29. Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles using Approximate Dynamic Programming

Author: Deshpande, Shreshta Rajakumar, Gupta, Shobhit, Gupta, Abhishek, Canova, Marcello, Deshpande, Shreshta Rajakumar, Gupta, Shobhit, Gupta, Abhishek, and Canova, Marcello
Abstract: Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was extensively tested virtually (using both deterministic and Monte Carlo simulations) across multiple real-world routes where energy savings of more than 20\% have been demonstrated. Further, the developed controller was deployed and tested at a proving ground in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in real-time applications., Comment: This work has been submitted to ASME (journal) for possible publication and is under review. Paper summary: 17 pages, 15 figures
Published: 2021

30. A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming

Author: Zhu, Zhaoxuan, Gupta, Shobhit, Pivaro, Nicola, Deshpande, Shreshta Rajakumar, Canova, Marcello, Zhu, Zhaoxuan, Gupta, Shobhit, Pivaro, Nicola, Deshpande, Shreshta Rajakumar, and Canova, Marcello
Abstract: Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions. In particular, the eco-driving problem seeks to design optimal speed and power usage profiles based upon available information from connectivity and advanced mapping features to minimize the fuel consumption between two designated locations. In this work, the eco-driving problem is formulated as a three-state receding horizon optimal control problem and solved via Dynamic Programming (DP). The optimal solution, in terms of vehicle speed and battery State of Charge (SoC) trajectories, allows a connected and automated hybrid electric vehicle to intelligently pass the signalized intersections and minimize fuel consumption over a prescribed route. To enable real-time implementation, a parallel architecture of DP is proposed for an NVIDIA GPU with CUDA programming. Simulation results indicate that the proposed optimal controller delivers more than 15% fuel economy benefits compared to a baseline control strategy and that the solver time can be reduced by more than 90% by the parallel implementation when compared to a serial implementation., Comment: This work has been accepted by the 2021 European Control Conference. Paper summary: 6 pages, 9 figures
Published: 2021

31. An Algorithm to Warm Start Perturbed (WASP) Constrained Dynamic Programs

Author: Gupta, Abhishek, Deshpande, Shreshta Rajakumar, Canova, Marcello, Gupta, Abhishek, Deshpande, Shreshta Rajakumar, and Canova, Marcello
Abstract: Receding horizon optimal control problems compute the solution at each time step to operate the system on a near-optimal path. However, in many practical cases, the boundary conditions, such as external inputs, constraint equations, or the objective function, vary only marginally from one time step to the next. In this case, recomputing the optimal solution at each time represents a significant burden for real-time applications. This paper proposes a novel algorithm to approximately solve a perturbed constrained dynamic program that significantly improves the computational burden when the objective function and the constraints are perturbed slightly. The method hinges on determining closed-form expressions for first-order perturbations in the optimal strategy and the Lagrange multipliers of the perturbed constrained dynamic programming problem are obtained. This information can be used to initialize any algorithm (such as the method of Lagrange multipliers, or the augmented Lagrangian method) to solve the perturbed dynamic programming problem with minimal computational resources., Comment: This work has been submitted to Automatica for possible publication and is under review. Paper summary: 14 pages, 3 figures
Published: 2021

32. The genome of the water strider Gerris buenoi reveals expansions of gene repertoires associated with adaptations to life on the water

Author: Armisen, David, Rajakumar, Rajendhran, Friedrich, Markus, Benoit, Joshua B., Robertson, Hugh M., Panfilio, Kristen A., Ahn, Seung-Joon, Poelchau, Monica F., Chao, Hsu, Dinh, Huyen, Doddapaneni, Harsha Vardhan, Dugan, Shannon, Gibbs, Richard A., Hughes, Daniel S. T., Han, Yi, Lee, Sandra L., Murali, Shwetha C., Muzny, Donna M., Qu, Jiaxin, Worley, Kim C., Munoz-Torres, Monica, Abouheif, Ehab, Bonneton, Francois, Chen, Travis, Chiang, Li-Mei, Childers, Christopher P., Cridge, Andrew G., Crumiere, Antonin J. J., Decaras, Amelie, Didion, Elise M., Duncan, Elizabeth J., Elpidina, Elena N., Fave, Marie-Julie, Finet, Cedric, Jacobs, Chris G. C., Jarvela, Alys M. Cheatle, Jennings, Emily C., Jones, Jeffery W., Lesoway, Maryna P., Lovegrove, Mackenzie R., Martynov, Alexander, Oppert, Brenda, Lillico-Ouachour, Angelica, Rajakumar, Arjuna, Refki, Peter Nagui, Rosendale, Andrew J., Santos, Maria Emilia, Toubiana, William, van der Zee, Maurijn, Jentzsch, Iris M. Vargas, Lowman, Aidamalia Vargas, Viala, Severine, Richards, Stephen, Khila, Abderrahman, Armisen, David, Rajakumar, Rajendhran, Friedrich, Markus, Benoit, Joshua B., Robertson, Hugh M., Panfilio, Kristen A., Ahn, Seung-Joon, Poelchau, Monica F., Chao, Hsu, Dinh, Huyen, Doddapaneni, Harsha Vardhan, Dugan, Shannon, Gibbs, Richard A., Hughes, Daniel S. T., Han, Yi, Lee, Sandra L., Murali, Shwetha C., Muzny, Donna M., Qu, Jiaxin, Worley, Kim C., Munoz-Torres, Monica, Abouheif, Ehab, Bonneton, Francois, Chen, Travis, Chiang, Li-Mei, Childers, Christopher P., Cridge, Andrew G., Crumiere, Antonin J. J., Decaras, Amelie, Didion, Elise M., Duncan, Elizabeth J., Elpidina, Elena N., Fave, Marie-Julie, Finet, Cedric, Jacobs, Chris G. C., Jarvela, Alys M. Cheatle, Jennings, Emily C., Jones, Jeffery W., Lesoway, Maryna P., Lovegrove, Mackenzie R., Martynov, Alexander, Oppert, Brenda, Lillico-Ouachour, Angelica, Rajakumar, Arjuna, Refki, Peter Nagui, Rosendale, Andrew J., Santos, Maria Emilia, Toubiana, William, van der Zee, Maurijn, Jentzsch, Iris M. Vargas, Lowman, Aidamalia Vargas, Viala, Severine, Richards, Stephen, and Khila, Abderrahman
Abstract: Background: Having conquered water surfaces worldwide, the semi-aquatic bugs occupy ponds, streams, lakes, mangroves, and even open oceans. The diversity of this group has inspired a range of scientific studies from ecology and evolution to developmental genetics and hydrodynamics of fluid locomotion. However, the lack of a representative water strider genome hinders our ability to more thoroughly investigate the molecular mechanisms underlying the processes of adaptation and diversification within this group. Results: Here we report the sequencing and manual annotation of the Gerris buenoi (G. buenoi) genome; the first water strider genome to be sequenced thus far. The size of the G. buenoi genome is approximately 1,000Mb, and this sequencing effort has recovered 20,949 predicted protein-coding genes. Manual annotation uncovered a number of local (tandem and proximal) gene duplications and expansions of gene families known for their importance in a variety of processes associated with morphological and physiological adaptations to a water surface lifestyle. These expansions may affect key processes associated with growth, vision, desiccation resistance, detoxification, olfaction and epigenetic regulation. Strikingly, the G. buenoi genome contains three insulin receptors, suggesting key changes in the rewiring and function of the insulin pathway. Other genomic changes affecting with opsin genes may be associated with wavelength sensitivity shifts in opsins, which is likely to be key in facilitating specific adaptations in vision for diverse water habitats. Conclusions: Our findings suggest that local gene duplications might have played an important role during the evolution of water striders. Along with these findings, the sequencing of the G. buenoi genome now provides us the opportunity to pursue exciting research opportunities to further understand the genomic underpinnings of traits associated with the extreme body plan and life history of water striders.
Published: 2018

33. Cellular and developmental basis of orofacial clefts.

Author: Ji, Yu, Ji, Yu, Garland, Michael A, Sun, Bo, Zhang, Shuwen, Reynolds, Kurt, McMahon, Moira, Rajakumar, Ratheya, Islam, Mohammad S, Liu, Yue, Chen, YiPing, Zhou, Chengji J, Ji, Yu, Ji, Yu, Garland, Michael A, Sun, Bo, Zhang, Shuwen, Reynolds, Kurt, McMahon, Moira, Rajakumar, Ratheya, Islam, Mohammad S, Liu, Yue, Chen, YiPing, and Zhou, Chengji J
Abstract: During craniofacial development, defective growth and fusion of the upper lip and/or palate can cause orofacial clefts (OFCs), which are among the most common structural birth defects in humans. The developmental basis of OFCs includes morphogenesis of the upper lip, primary palate, secondary palate, and other orofacial structures, each consisting of diverse cell types originating from all three germ layers: the ectoderm, mesoderm, and endoderm. Cranial neural crest cells and orofacial epithelial cells are two major cell types that interact with various cell lineages and play key roles in orofacial development. The cellular basis of OFCs involves defective execution in any one or several of the following processes: neural crest induction, epithelial-mesenchymal transition, migration, proliferation, differentiation, apoptosis, primary cilia formation and its signaling transduction, epithelial seam formation and disappearance, periderm formation and peeling, convergence and extrusion of palatal epithelial seam cells, cell adhesion, cytoskeleton dynamics, and extracellular matrix function. The latest cellular and developmental findings may provide a basis for better understanding of the underlying genetic, epigenetic, environmental, and molecular mechanisms of OFCs.
Published: 2020

34. Numerical Sequence Prediction using Bayesian Concept Learning

Author: Damarapati, Mohith, Enaganti, Inavamsi B., Rajakumar, Alfred Ajay Aureate, Damarapati, Mohith, Enaganti, Inavamsi B., and Rajakumar, Alfred Ajay Aureate
Abstract: When people learn mathematical patterns or sequences, they are able to identify the concepts (or rules) underlying those patterns. Having learned the underlying concepts, humans are also able to generalize those concepts to other numbers, so far as to even identify previously unseen combinations of those rules. Current state-of-the art RNN architectures like LSTMs perform well in predicting successive elements of sequential data, but require vast amounts of training examples. Even with extensive data, these models struggle to generalize concepts. From our behavioral study, we also found that humans are able to disregard noise and identify the underlying rules generating the corrupted sequences. We therefore propose a Bayesian model that captures these human-like learning capabilities to predict next number in a given sequence, better than traditional LSTMs., Comment: 7 pages, 6 figures. Was done as part of the course project at NYU Courant. To be extended for a conference proceeding
Published: 2020

35. Generating Target Graph Couplings for QAOA from Native Quantum Hardware Couplings

Author: Rajakumar, Joel, Moondra, Jai, Gard, Bryan, Gupta, Swati, Herold, Creston D., Rajakumar, Joel, Moondra, Jai, Gard, Bryan, Gupta, Swati, and Herold, Creston D.
Abstract: We present methods for constructing any target coupling graph using limited global controls in an Ising-like quantum spin system. Our approach is motivated by implementing the quantum approximate optimization algorithm (QAOA) on trapped ion quantum hardware to find approximate solutions to Max-Cut. We present a mathematical description of the problem and provide approximately optimal algorithmic constructions that generate arbitrary unweighted coupling graphs with $n$ nodes in $O(n)$ global entangling operations and weighted graphs with $m$ edges in $O(m)$ operations. These upper bounds are not tight in general, and we formulate a mixed-integer program to solve the graph coupling problem to optimality. We perform numeric experiments on small graphs with $n\le8$ and show that optimal sequences, which use fewer operations, can be found using mixed-integer programs. Noisy simulations of Max-Cut QAOA show that our implementation is less susceptible to noise than the standard gate-based compilation.
Published: 2020
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36. Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle

Author: Deshpande, Shreshta Rajakumar, Jung, Daniel, Canova, Marcello, Deshpande, Shreshta Rajakumar, Jung, Daniel, and Canova, Marcello
Abstract: This paper focuses on the velocity planning and energy management problems for Connected and Automated Vehicles (CAVs) with hybrid electric powertrains. The eco-driving problem is formulated in the spatial domain as a nonlinear dynamic optimization problem, in which information about the upcoming speed limits and road topography is assumed to be known a priori. To solve this problem, a novel Dynamic Programming (DP) based optimization method is proposed, in which a causal Equivalent Consumption Minimization Strategy (ECMS) is embedded. The underlying vehicle model to predict energy consumption over real-world routes is validated using experimental data. Further, a multi-layer hierarchical control architecture is proposed as a pathway to real-time implementation in a vehicle. The DP-ECMS algorithm is introduced for a long-horizon optimization problem, and then adapted for a receding horizon implementation using principles in Approximate Dynamic Programming (ADP). This computationally economical alternative to the traditional DP solution is then benchmarked and evaluated., Comment: This work has been submitted to IEEE for possible publication and is under review. Paper summary: 12 pages, 12 figures
Published: 2020

37. Assessment of Lipid and Metabolite Changes in Obese Calf Muscle Using Multi-Echo Echo-planar Correlated Spectroscopic Imaging.

Author: Nagarajan, Rajakumar, Nagarajan, Rajakumar, Carpenter, Catherine L, Lee, Cathy C, Michael, Navin, Sarma, Manoj K, Souza, Raissa, Xu, Edward, Velan, S Sendhil, Hahn, Theodore J, Go, Vay-Liang, Thomas, M Albert, Nagarajan, Rajakumar, Nagarajan, Rajakumar, Carpenter, Catherine L, Lee, Cathy C, Michael, Navin, Sarma, Manoj K, Souza, Raissa, Xu, Edward, Velan, S Sendhil, Hahn, Theodore J, Go, Vay-Liang, and Thomas, M Albert
Abstract: Obesity-related conditions including heart disease, stroke, and type 2 diabetes are leading causes of preventable death. Recent evidence suggests that altered myocellular lipid metabolism in obesity may lead to increased insulin resistance (IR) that predisposes to these disorders. To test the hypothesis that muscles rich in type I vs. type II muscle fibers would exhibit similar changes in intramyocellular lipid (IMCL) and extramyocellular lipid (EMCL) content in obesity, we utilized a new four-dimensional multi echo echo-planar correlated spectroscopic imaging technique that allows separate determination of IMCL and EMCL content in individual calf muscles in obese vs. normal healthy human subjects. Calf muscles were scanned in 32 obese and 11 healthy subjects using a 3T MRI/MRS scanner, and IR in the obese subjects was documented by glucose tolerance testing. In obese subjects, elevation of both IMCL and EMCL content was observed in the gastrocnemius and tibialis anterior muscles (with mixed type I and II fiber content), while a significant increase in only IMCL content (+48%, p < 0.001) was observed in the soleus muscle (predominantly type I fibers). These observations indicate unexpected differences in changes in myolipid metabolism in type I vs. type II rich muscle regions in obesity, perhaps related to IR, and warrant further investigation.
Published: 2017

38. The melting of metals on packed beds

Author: Rajakumar, Viruthiamparambath
Subjects: 661
Abstract: The steady state melting of Woods' metal spheres in packed beds under conditions of convective heat transfer from a counter-current stream of hot nitrogen has been studied. The gas and solid temperature profiles and the melting rates have been measured for two sphere sizes at various gas inlet temperatures and flow rates. Comparisons of the experimental axial temperature profiles with those predicted from a simplified mathematical model of the packed bed melting process have shown marked disagreements. Little improvement resulted from a modification of the model which has been derived to account for heat losses from the column walls. Various alternative models allowing for a heat transfer coefficient in the melting zone which is larger than that in the heating zone of the bed have been developed. Satisfactory agreement between the experimental and predicted profiles has been obtained when the model allowed for a heat transfer coefficient which increased linearly with the mass flow rate of liquid metal in the zone. The agreement between the theoretical and experimental melting rates and the depth of the melting zone was also satisfactory on the basis of this model. An estimate of the influence of liquid metal flow on the heat transfer coefficient has been obtained from experiments on heat transfer between mercury and a counter-current stream of hot nitrogen passing over stationary beds of steel spheres. It has been found that the heat transfer coefficient increased with the liquid flow rate through the bed, thus offering independent evidence of the enhancement. in the heat transfer rate caused by the liquid metal. The work has shown that the proposed model of the packed bed melting process could predict satisfactorily the performance of the melting bed except when the driving rates were large enough to cause uneven melting and bridging of the solids and severe channeling of the gas flow in the bed.
Published: 1971

39. A Quantum Safe Key Hierarchy and Dynamic Security Association for LTE/SAE in 5G Scenario

Author: Arul, Rajakumar, Raja, Gunasekaran, Almagrabi, Alaa Omran Almagrabi, Alkatheiri, Mohammed Saeed, Chauhdary, Sajjad Hussain, Bashir, Ali Kashif, Arul, Rajakumar, Raja, Gunasekaran, Almagrabi, Alaa Omran Almagrabi, Alkatheiri, Mohammed Saeed, Chauhdary, Sajjad Hussain, and Bashir, Ali Kashif
Abstract: Millions of devices are going to participate in 5G producing a huge space for security threats. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against Quantum computers. Therefore, in this paper, we propose a security protocol called Quantum Key GRID for Authentication and Key Agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in Long Term Evolution (LTE) architecture without any significant modifications in the underlying base system. The proposed QKGAKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes.
Published: 2019

40. An optimal multitier resource allocation of cloud RAN in 5G using machine learning

Author: Bashir, Ali Kashif, Arul, Rajakumar, Basheer, Shakila, Raja, Gunasekaran, Jayaraman, Ramkumar, Qureshi, Nawab Muhammad Faseeh, Bashir, Ali Kashif, Arul, Rajakumar, Basheer, Shakila, Raja, Gunasekaran, Jayaraman, Ramkumar, and Qureshi, Nawab Muhammad Faseeh
Abstract: The networks are evolving drastically since last few years in order to meetuser requirements. For example, the 5G is offering most of the available spec-trum under one umbrella. In this work, we will address the resource allocationproblem in fifth-generation (5G) networks, to be exact in the Cloud Radio AccessNetworks (C-RANs). The radio access network mechanisms involve multiplenetwork topologies that are isolated based on the spectrum bands and it shouldbe enhanced with numerous access technology in the deployment of 5G net-work. The C-RAN is one of the optimal technique to combine all the availablespectral bands. However, existing C-RAN mechanisms lacks the intelligence per-spective on choosing the spectral bands. Thus, C-RAN mechanism requires anadvanced tool to identify network topology to allocate the network resources forsubstantial traffic volumes. Therefore, there is a need to propose a frameworkthat handles spectral resources based on user requirements and network behav-ior. In this work, we introduced a new C-RAN architecture modified as multitierHeterogeneous Cloud Radio Access Networks in a 5G environment. This archi-tecture handles spectral resources efficiently. Based on the simulation analysis,the proposed multitier H-CRAN architecture with improved control unit innetwork management perspective enables augmented granularity, end-to-endoptimization, and guaranteed quality of service by 15 percentages over theexisting system.
Published: 2019

41. Real-Time Water Quality Modeling with Ensemble Kalman Filter for State and Parameter Estimation in Water Distribution Networks

Author: Rajakumar, Anjana G. (author), Mohan Kumar, M. S. (author), Amrutur, Bharadwaj (author), Kapelan, Z. (author), Rajakumar, Anjana G. (author), Mohan Kumar, M. S. (author), Amrutur, Bharadwaj (author), and Kapelan, Z. (author)
Abstract: This study presents a novel approach to real-time water quality state (chlorine concentration) and reaction parameter estimation in water distribution systems (WDSs) using ensemble Kalman filter (EnKF)-based data assimilation techniques. Two different types of EnKF-based methods are used in this study: noniterative restart-EnKF (NIR-EnKF) and iterative restart-EnKF (IR-EnKF). The use of these data assimilation frameworks for addressing key uncertainties in water quality models, such as uncertainty in the source or initial concentration of chlorine and uncertainty in the wall reaction parameter, is studied. The effect of ensemble size, number and location of measurement nodes, measurement error, and noise are also studied extensively in this work. The performance of the proposed methodology is tested on two different water networks: a brushy plains network and a large, citywide WDS, the Bangalore inflow network. The results of the simulation study show that both the NIR-EnKF and IR-EnKF methods are appropriate for dealing with uncertainty in source chlorine concentration, but the IR-EnKF method performs better than the NIR-EnKF method in the case of reaction parameter uncertainty., Accepted Author Manuscript, Sanitary Engineering
Published: 2019
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42. Automated Classification of Helium Ingress in Irradiated X-750

Author: Anderson, Chris, Klein, Jacob, Rajakumar, Heygaan, Judge, Colin, Beland, Laurent K, Anderson, Chris, Klein, Jacob, Rajakumar, Heygaan, Judge, Colin, and Beland, Laurent K
Abstract: Imaging nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and labour intensive manual process. It is a prime candidate for automation. Here, a region-based convolutional neural network is adapted to detect helium bubbles in micrographs of neutron-irradiated Inconel X-750 reactor spacer springs. We demonstrate that this neural network produces analyses of similar accuracy and reproducibility to that produced by humans. Further, we show this method as being four orders of magnitude faster than manual analysis allowing for generation of significant quantities of data. The proposed method can be used with micrographs of different Fresnel contrasts and magnification levels.
Published: 2019

43. A Console GRID LA Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

Author: Arul, Rajakumar, Raja, Gunasekaran, Bashir, Ali Kashif, Chaudry, Junaid, Ali, Amjad, Arul, Rajakumar, Raja, Gunasekaran, Bashir, Ali Kashif, Chaudry, Junaid, and Ali, Amjad
Abstract: The growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among the bulk of the human race that happens to be cellular technology users, has fueled the adaptation to Long Term Evolution (LTE)/ System Architecture Evolution (SAE). The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in the authentication mechanism used in LTE that without countermeasures might give superuser rights to unauthorized users. The LTE uses static LTE Key (LTE-K) to derive the entire key hierarchy such as LTE follows Evolved Packet System-Authentication and Key Agreement (EPS-AKA) based authentication which discloses user identity, location, and other Personally Identifiable Information (PII). To counter this, we propose a public key cryptosystem named International mobile subscriber identity Protected Console Grid-based Authentication and Key Agreement (IPG-AKA) protocol to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves the security of authentication procedures, it also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper proves that IPG-AKA improves security in authentication procedure and performance in the LTE., Comment: 12 pages, 11 figures
Published: 2019
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44. Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Author: Sprague, S. (Sheila), Schemitsch, E.H. (Emil H.), Swiontkowski, M.F. (Marc ), Della Rocca, G.J. (Gregory J.), Jeray, K.J. (Kyle J.), Liew, S. (Susan), Slobogean, G.P. (Gerard P.), Bzovsky, S. (Sofia), Heels-Ansdell, D. (Diane), Zhou, Q. (Qi), Bhandari, M. (Mohit), Sprag, S. (Sheila), Devereaux, P.J., Guyatt, G.H. (Gordon), Heetveld, M.J. (Martin J.), Richardson, M. (Martin), Thabane, L. (Lehana), Tornetta III, P. (Paul), Walter, S.D. (Stephen D.), McKay, P. (Paula), Scott, T. (Taryn), Garibaldi, A. (Alisha), Viveiros, H. (Helena), Swinton, M. (Marilyn), Gichuru, M. (Mark), Buckingham, L. (Lisa), Duraikannan, A. (Aravin), Maddock, D. (Deborah), Simunovic, N. (Nicole), Agel, J. (Julie), Lieshout, E.M.M. (Esther) van, Zielinski, S.M. (Stephanie), Rangan, A. (Amar), Hanusch, B.C. (Birgit C.), Kottam, L. (Lucksy), Clarkson, R. (Rachel), Haverlag, R. (Robert), McCormack, R. (Robert), Apostle, K. (Kelly), Boyer, D. (Dory), Moola, F. (Farhad), Perey, B. (Bertrand), Stone, T. (Trevor), Viskontas, D. (Darius), Lemke, H.M. (H. Michael), Zomar, M. (Mauri), Moon, K. (Karyn), Moon, R. (Raely), Oatt, A. (Amber), Buckley, R.E. (Richard E.), Duffy, P. (Paul), Korley, R. (Robert), Puloski, S. (Shannon), Powell, J. (James), Johnston, K. (Kelly), Carcary, K. (Kimberly), Lorenzo, M. (Melissa), McKercher, R. (Ross), Sanders, D. (David), MacLeod, M. (Mark), Lawendy, A.-R. (Abdel-Rahman), Tieszer, C. (Christina), Stephen, D. (David), Kreder, H. (Hans), Jenkinson, R. (Richard), Nousiainen, M. (Markku), Axelrod, T. (Terry), Murnaghan, J. (John), Nam, D. (Diane), Wadey, V. (Veronica), Yee, A. (Albert), Milner, K. (Katrine), Kunz, M. (Monica), Ghent, W. (Wesley), McKee, M.D. (Michael D.), Hall, J.A. (Jeremy A.), Nauth, A. (Aaron), Ahn, H. (Henry), Whelan, D.B. (Daniel B.), Vicente, M.R. (Milena R.), Wild, L.M. (Lisa M.), Khan, R.M. (Ryan M.), Hidy, J.T. (Jennifer T.), Coles, C. (Chad), Leighton, R. (Ross), Biddulph, M. (Michael), Johnston, D. (David), Glazebrook, M. (Mark), Alexander, D. (David), Coady, C. (Catherine), Dunbar, M. (Michael), Amirault, J.D. (J. David), Gross, M. (Michael), Oxner, W. (William), Reardon, G. (Gerald), Richardson, C.G. (C. Glen), Trenholm, J.A. (J. Andrew), Wong, I. (Ivan), Trask, K. (Kelly), MacDonald, S. (Shelley), Dobbin, G. (Gwendolyn), Bicknell, R. (Ryan), Yach, J. (Jeff), Bardana, D. (Davide), Wood, G. (Gavin), Harrison, M. (Mark), Yen, D. (David), Lambert, S. (Sue), Howells, F. (Fiona), Ward, A. (Angela), Zalzal, P. (Paul), Brien, H. (Heather), Naumetz, V. (V.), Weening, B. (Brad), Wai, E.K. (Eugene K.), Papp, S. (Steve), Gofton, W.T. (Wade T.), Liew, A. (Allen), Kingwell, S.P. (Stephen P.), Johnson, G. (Garth), O'Neil, J. (Joseph), Roffey, D.M. (Darren M.), Borsella, V. (Vivian), Avram, V. (Victoria), Oliver, T.M. (Todd M.), Jones, V. (Vicki), Vogt, M. (Michelle), Jones, C.B. (Clifford B.), Ringler, J.R. (James R.), Endres, T.J. (Terrence J.), Sietsema, D.L. (Debra L.), Walker, J.E. (Jane E.), Broderick, J.S. (J. Scott), Goetz, D.R. (David R.), Pace, T.B. (Thomas B.), Schaller, T.M. (Thomas M.), Porter, S.E. (Scott E.), Beckish, M.L. (Michael L.), Adams, J.D. (John D.), Barden, B.B. (Benjamin B.), Creek, A.T. (Aaron T.), Finley, S.H. (Stephen H.), Foret, J.L. (Jonathan L.), Gudger, G.K. (Garland K.), Gurich, R.W. (Richard W.), Hill, A.D. (Austin D.), Hollenbeck, S.M. (Steven M.), Jackson, L.T. (Lyle T.), Kruse, K.K. (Kevin K.), Lackey, W.G. (Wesley G.), Langan, J.W. (Justin W.), Lee, J. (Julia), Leffler, L.C. (Lauren C.), Miller, T.J. (Timothy J.), Murphy, R.L. (R. Lee), O'Malley, L.K. (Lawrence K.), Peters, M.E. (Melissa E.), Price, D.M. (Dustin M.), Tanksley, J.A. (John A.), Torres, E.T. (Erick T.), Watson, D.J. (Dylan J.), Watson, S.T. (Scott T.), Tanner, S.L. (Stephanie L.), Snider, R.G. (Rebecca G.), Nastoff, L.A. (Lauren A.), Bielby, S.A. (Shea A.), Teasdall, R.J. (Robert J.), Switzer, J.A. (Julie A.), Cole, P.A. (Peter A.), Anderson, S.A. (Sarah A.), Lafferty, P.M. (Paul M.), Li, M. (Mengnai), Ly, T.V. (Thuan V.), Marston, S.B. (Scott B.), Foley, A.L. (Amy L.), Vang, S. (Sandy), Wright, D.M. (David M.), Marcantonio, A.J. (Andrew J.), Kain, M.S.H. (Michael S.H.), Iorio, R. (Richard), Specht, L.M. (Lawrence M.), Tilzey, J.F. (John F.), Lobo, M.J. (Margaret J.), Garfi, J.S. (John S.), Vallier, H.A. (Heather A.), Dolenc, A. (Andrea), Breslin, M. (Mary), Prayson, M.J. (Michael J.), Laughlin, R. (Richard), Rubino, L.J. (L. Joseph), May, J. (Jedediah), Rieser, G.R. (Geoffrey Ryan), Dulaney-Cripe, L. (Liz), Gayton, C. (Chris), Shaer, J. (James), Schrickel, T. (Tyson), Hileman, B. (Barbara), Gorczyca, J.T. (John T.), Gross, J.M. (Jonathan M.), Humphrey, C.A. (Catherine A.), Kates, S. (Stephen), Ketz, J.P. (John P.), Noble, K. (Krista), McIntyre, A.W. (Allison W.), Pecorella, K. (Kaili), Davis, C.A. (Craig A.), Weinerman, S. (Stuart), Weingarten, P. (Peter), Stull, P. (Philip), Lindenbaum, S. (Stephen), Hewitt, M. (Michael), Schwappach, J. (John), Baker, J.K. (Janell K.), Rutherford, T. (Tori), Newman, H. (Heike), Lieberman, S. (Shane), Finn, E. (Erin), Robbins, K. (Kristin), Hurley, M. (Meghan), Lyle, L. (Lindsey), Mitchell, K. (Khalis), Browner, K. (Kieran), Whatley, E. (Erica), Payton, K. (Krystal), Reeves, C. (Christina), Cannada, L.K. (Lisa K.), Karges, D.E. (David E.), Dawson, S.A. (Sarah A.), Mehta, S. (Samir), Esterhai, J. (John), Ahn, J. (Jaimo), Donegan, D. (Derek), Horan, A.D. (Annamarie D.), Hesketh, P.J. (Patrick J.), Bannister, E.R. (Evan R.), Keeve, J.P. (Jonathan P.), Anderson, C.G. (Christopher G.), McDonald, M.D. (Michael D.), Hoffman, J.M. (Jodi M.), Tarkin, I. (Ivan), Siska, P. (Peter), Gruen, G. (Gary), Evans, A. (Andrew), Farrell, D.J. (Dana J.), Irrgang, J. (James), Luther, A. (Arlene), Cross, W.W. (William W.), Cass, J.R. (Joseph R.), Sems, S.A. (Stephen A.), Torchia, M.E. (Michael E.), Scrabeck, T. (Tyson), Jenkins, M. (Mark), Dumais, J. (Jules), Romero, A.W. (Amanda W.), Sagebien, C.A. (Carlos A.), Butler, M.S. (Mark S.), Monica, J.T. (James T.), Seuffert, P. (Patricia), Hsu, J.R. (Joseph R.), Stinner, D. (Daniel), Ficke, J. (James), Charlton, M. (Michael), Napierala, M. (Matthew), Fan, M. (Mary), Tannoury, C. (Chadi), Carlisle, H. (Hope), Silva, H. (Heather), Archdeacon, M. (Michael), Finnan, R. (Ryan), Le, T. (Toan), Wyrick, J. (John), Hess, S. (Shelley), Brennan, M.L. (Michael L.), Probe, R. (Robert), Kile, E. (Evelyn), Mills, K. (Kelli), Clipper, L. (Lydia), Yu, M. (Michelle), Erwin, K. (Katie), Horwitz, D. (Daniel), Strohecker, K. (Kent), Swenson, T.K. (Teresa K.), Schmidt, A.H. (Andrew H.), Westberg, J.R. (Jerald R.), Aurang, K. (Kamran), Zohman, G. (Gary), Peterson, B. (Brett), Huff, R.B. (Roger B.), Baele, J. (Joseph), Weber, T. (Timothy), Edison, M. (Matt), McBeth, J.C. (Jessica Cooper), Shively, K. (Karl), Ertl, J.P. (Janos P.), Mullis, B. (Brian), Parr, J.A. (J. Andrew), Worman, R. (Ripley), Frizzell, V. (Valda), Moore, M.M. (Molly M.), DePaolo, C.J. (Charles J.), Alosky, R. (Rachel), Shell, L.E. (Leslie E.), Hampton, L. (Lynne), Shepard, S. (Stephanie), Nanney, T. (Tracy), Cuento, C. (Claudine), Cantu, R.V. (Robert V.), Henderson, E.R. (Eric R.), Eickhoff, L.S. (Linda S.), Hammerberg, E.M. (E. Mark), Stahel, P. (Philip), Hak, D. (David), Mauffrey, C. (Cyril), Henderson, C. (Corey), Gissel, H. (Hannah), Gibula, D. (Douglas), Zamorano, D.P. (David P.), Tynan, M.C. (Martin C.), Pourmand, D. (Deeba), Lawson, D. (Deanna), Crist, B.D. (Brett D.), Murtha, Y.M. (Yvonne M.), Anderson, L.K. (Linda K.), Linehan, C. (Colleen), Pilling, L. (Lindsey), Lewis, C.G. (Courtland G.), Caminiti, S. (Stephanie), Sullivan, R.J. (Raymond J.), Roper, E. (Elizabeth), Obremskey, W. (William), Kregor, P. (Philip), Richards, J.E. (Justin E.), Stringfellow, K. (Kenya), Dohm, M.P. (Michael P.), Zellar, A. (Abby), Segers, M.J.M. (Michiel), Zijl, J.A.C. (Jacco A.C.), Verhoeven, B. (Bart), Smits, A.B. (Anke B.), De Vries, J.P.P.M. (Jean Paul P.M.), Fioole, B. (Bram), Van Der Hoeven, H. (Henk), Theunissen, E.B.M. (Evert B.M.), De Vries Reilingh, T.S. (Tammo S.), Govaert, L. (Lonneke), Wittich, P. (Philippe), De Brauw, M. (Maurits), Wille, J.C. (Jan), Go, P.M.N.Y.M. (Peter M.N.Y.M.), Ritchie, E.D. (Ewan D.), Wessel, R.N. (Ronald N.), Hammacher, E.R. (Eric), Visser, G.A. (Gijs A.), Stockmann, H. (Heyn), Silvis, R. (Rob), Snellen, J.P. (Jaap P.), Rijbroek, B. (Bram), Scheepers, J.J. (Joris J.), Vermeulen, E.G.J. (Erik G.J.), Siroen, M.P.C. (Michiel P.C.), Vuylsteke, R. (Ronald), Brom, H.L.F., Rijna, H., Rijcke, P.A.R. (Piet), Koppert, C.L. (Cees L.), Buijk, S.E. (Steven E.), Groenendijk, R.P.R. (Richard), Dawson, I. (Imro), Tetteroo, G.W.M. (Geert), Bruijninckx, M.M.M. (Milko), Doornebosch, P. (Pascal), Graaf, E.J.R. (Eelco) de, Elst, M. (Maarten) van der, Pol, C. (Carmen) van der, Riet, M. (Martijne) van 't, Karsten, T.M. (Thomas), Vries, M.R. (Mark) de, Stassen, L.P.S. (Laurents P.S.), Schep, N.W.L. (Niels), Ben Schmidt, G. (G.), Hoffman, W.H. (W. H.), Poolman, R.W. (Rudolf), Simons, M.P., Heijden, F.H.W.M. (Frank) van der, Willems, W.J. (Jaap), De Meulemeester, F.R.A.J. (Frank R.A.J.), Hart, C.P. (Cor P.) van der, Turckan, K. (Kahn), Festen, S. (Sebastiaan), Nies, F. (Frank) de, Out, N.J.M. (Nico J.M.), Bosma, J. (Jan), Kampen, A. (A.) van, Biert, J. (Jan), Van Vugt, A.B. (Arie B.), Edwards, M.J.R. (Michael J.R.), Blokhuis, T.J. (Taco J.), Frölke, J.P.M. (Jan Paul), Geeraedts, L.M.G. (Leo M.G.), Gardeniers, J.W.M. (Jean W.M.), Tan, E.C.T.H. (Edward C.T.H.), Poelhekke, L.M.S.J., De Waal Malefijt, M.C. (Maarten C.), Schreurs, B. (Bart), Roukema, G.R. (Gert), Josaputra, H.A. (Hong A.), Keller, P. (Paul), De Rooij, P.D. (Peter D.), Kuiken, H. (Hans), Boxma, H. (Han), Cleffken, B.I. (Berry), Liem, R. (Ronald), Rhemrev, S. (Steven), Bosman, C.H.R. (Coks H.R.), De Mol Van Otterloo, A. (Alexander), Hoogendoorn, J. (Jochem), Vries, A.C. (Alexander) de, Meylaerts, S.A.G. (Sven), Verhofstad, M.H.J. (Michiel), Meijer, J. (Joost), Van Egmond, T. (Teun), Van Der Brand, I. (Igor), Patka, P. (Peter), Eversdijk, M.G. (Martin), Peters, R. (Rolf), Hartog, D. (Dennis) den, Waes, O.J.F. (Oscar) van, Oprel, P.P. (Pim), Vis, H.M. (Harm) van der, Campo, M. (Martin), Verhagen, R. (Ronald), Albers, G.H.R. (G.H. Robert), Zurcher, A.W. (Arthur W.), Simmermacher, R.K.J., Van Mulken, J. (Jeroen), Wessem, K.J.P. van, Van Gaalen, S.M. (Steven M.), Leenen, L.P.H., Bronkhorst, M.W.G.A. (Maarten), Guicherit, O.R. (Onno R.), Goslings, J.C. (Carel), Ponsen, K.J. (Kees-jan), Bhatia, M. (Mahesh), Arora, V. (Vinod), Tyagi, V. (Vivek), Bedi, H. (Harvinder), Carr, A. (Ashley), Curry, H. (Hamish), Chia, A. (Andrew), Csongvay, S. (Steve), Donohue, C. (Craig), Doig, S. (Stephen), Edwards, E. (Elton), Etherington, G. (Greg), Esser, M. (Max), Gong, A. (Andrew), Jain, A. (Arvind), Li, D. (Doug), Miller, R. (Russell), Moaveni, A. (Ash), Russ, M. (Matthias), Ton, L. (Lu), Wang, O. (Otis), Dowrick, A. (Adam), Murdoch, Z. (Zoe), Sage, C. (Claire), Frihagen, F. (Frede), Clarke-Jenssen, J. (John), Hjorthaug, G. (Geir), Ianssen, T. (Torben), Amundsen, A. (Asgeir), Brattgjerd, J.E. (Jan Egil), Borch, T. (Tor), Bøe, B. (Berthe), Flatøy, B. (Bernhard), Hasselund, S. (Sondre), Haug, K.J. (Knut Jørgen), Hemlock, K. (Kim), Hoseth, T.M. (Tor Magne), Jomaas, G. (Geir), Kibsgård, T. (Thomas), Lona, T. (Tarjei), Moatshe, G. (Gilbert), Müller, O. (Oliver), Molund, M. (Marius), Nicolaisen, T. (Tor), Nilsen, F. (Fredrik), Rydinge, J. (Jonas), Smedsrud, M. (Morten), Stødle, A. (Are), Trommer, A. (Axel), Ugland, S. (Stein), Karlsten, A. (Anders), Ekås, G. (Guri), Vesterhus, E.B. (Elise Berg), Brekke, A.C. (Anne Christine), Gupta, A. (Ajay), Jain, N. (Neeraj), Khan, F. (Farah), Sharma, A. (Ateet), Sanghavi, A. (Amir), Trivedi, M. (Mittal), Rai, A. (Anil), Subash, (), Rai, K. (Kamal), Yadav, V. (Vineet), Singh, S. (Sanjay), Tetsworth, K. (Kevin), Donald, G. (Geoff), Weinrauch, P. (Patrick), Pincus, P. (Paul), Yang, S. (Steven), Halliday, B. (Brett), Gervais, T. (Trevor), Holt, M. (Michael), Flynn, A. (Annette), Prasad, A.S. (Amal Shankar), Mishra, V. (Vimlesh), Sundaresh, D.C. (D. C.), Khanna, A. (Angshuman), Cherian, J.J. (Joe Joseph), Olakkengil, D.J. (Davy J), Sharma, G. (Gaurav), Pirpiris, M. (Marinis), Love, D. (David), Bucknill, A. (Andrew), Farrugia, R.J. (Richard J), Pape, H.-C. (Hans-Christoph), Knobe, M. (Matthias), Pfeifer, R. (Roman), Hull, P. (Peter), Lewis, S. (Sophie), Evans, S. (Simone), Nanda, R. (Rajesh), Logishetty, R. (Rajanikanth), Anand, S. (Sanjeev), Bowler, C. (Carol), Dadi, A. (Akhil), Palla, N. (Naveen), Ganguly, U. (Utsav), Rai, B.S. (B. Sachidananda), Rajakumar, J. (Janakiraman), Jennings, A. (Andrew), Chuter, G. (Graham), Rose, G. (Glynis), Horner, G. (Gillian), Clark, C. (Callum), Eke, K. (Kate), Reed, M.R. (Mike), Inman, D. (Dominic), Herriott, C. (Chris), Dobb, C. (Christine), Sprague, S. (Sheila), Schemitsch, E.H. (Emil H.), Swiontkowski, M.F. (Marc ), Della Rocca, G.J. (Gregory J.), Jeray, K.J. (Kyle J.), Liew, S. (Susan), Slobogean, G.P. (Gerard P.), Bzovsky, S. (Sofia), Heels-Ansdell, D. (Diane), Zhou, Q. (Qi), Bhandari, M. (Mohit), Sprag, S. (Sheila), Devereaux, P.J., Guyatt, G.H. (Gordon), Heetveld, M.J. (Martin J.), Richardson, M. (Martin), Thabane, L. (Lehana), Tornetta III, P. (Paul), Walter, S.D. (Stephen D.), McKay, P. (Paula), Scott, T. (Taryn), Garibaldi, A. (Alisha), Viveiros, H. (Helena), Swinton, M. (Marilyn), Gichuru, M. (Mark), Buckingham, L. (Lisa), Duraikannan, A. (Aravin), Maddock, D. (Deborah), Simunovic, N. (Nicole), Agel, J. (Julie), Lieshout, E.M.M. (Esther) van, Zielinski, S.M. (Stephanie), Rangan, A. (Amar), Hanusch, B.C. (Birgit C.), Kottam, L. (Lucksy), Clarkson, R. (Rachel), Haverlag, R. (Robert), McCormack, R. (Robert), Apostle, K. (Kelly), Boyer, D. (Dory), Moola, F. (Farhad), Perey, B. (Bertrand), Stone, T. (Trevor), Viskontas, D. (Darius), Lemke, H.M. (H. Michael), Zomar, M. (Mauri), Moon, K. (Karyn), Moon, R. (Raely), Oatt, A. (Amber), Buckley, R.E. (Richard E.), Duffy, P. (Paul), Korley, R. (Robert), Puloski, S. (Shannon), Powell, J. (James), Johnston, K. (Kelly), Carcary, K. (Kimberly), Lorenzo, M. (Melissa), McKercher, R. (Ross), Sanders, D. (David), MacLeod, M. (Mark), Lawendy, A.-R. (Abdel-Rahman), Tieszer, C. (Christina), Stephen, D. (David), Kreder, H. (Hans), Jenkinson, R. (Richard), Nousiainen, M. (Markku), Axelrod, T. (Terry), Murnaghan, J. (John), Nam, D. (Diane), Wadey, V. (Veronica), Yee, A. (Albert), Milner, K. (Katrine), Kunz, M. (Monica), Ghent, W. (Wesley), McKee, M.D. (Michael D.), Hall, J.A. (Jeremy A.), Nauth, A. (Aaron), Ahn, H. (Henry), Whelan, D.B. (Daniel B.), Vicente, M.R. (Milena R.), Wild, L.M. (Lisa M.), Khan, R.M. (Ryan M.), Hidy, J.T. (Jennifer T.), Coles, C. (Chad), Leighton, R. (Ross), Biddulph, M. (Michael), Johnston, D. (David), Glazebrook, M. (Mark), Alexander, D. (David), Coady, C. (Catherine), Dunbar, M. (Michael), Amirault, J.D. (J. David), Gross, M. (Michael), Oxner, W. (William), Reardon, G. (Gerald), Richardson, C.G. (C. Glen), Trenholm, J.A. (J. Andrew), Wong, I. (Ivan), Trask, K. (Kelly), MacDonald, S. (Shelley), Dobbin, G. (Gwendolyn), Bicknell, R. (Ryan), Yach, J. (Jeff), Bardana, D. (Davide), Wood, G. (Gavin), Harrison, M. (Mark), Yen, D. (David), Lambert, S. (Sue), Howells, F. (Fiona), Ward, A. (Angela), Zalzal, P. (Paul), Brien, H. (Heather), Naumetz, V. (V.), Weening, B. (Brad), Wai, E.K. (Eugene K.), Papp, S. (Steve), Gofton, W.T. (Wade T.), Liew, A. (Allen), Kingwell, S.P. (Stephen P.), Johnson, G. (Garth), O'Neil, J. (Joseph), Roffey, D.M. (Darren M.), Borsella, V. (Vivian), Avram, V. (Victoria), Oliver, T.M. (Todd M.), Jones, V. (Vicki), Vogt, M. (Michelle), Jones, C.B. (Clifford B.), Ringler, J.R. (James R.), Endres, T.J. (Terrence J.), Sietsema, D.L. (Debra L.), Walker, J.E. (Jane E.), Broderick, J.S. (J. Scott), Goetz, D.R. (David R.), Pace, T.B. (Thomas B.), Schaller, T.M. (Thomas M.), Porter, S.E. (Scott E.), Beckish, M.L. (Michael L.), Adams, J.D. (John D.), Barden, B.B. (Benjamin B.), Creek, A.T. (Aaron T.), Finley, S.H. (Stephen H.), Foret, J.L. (Jonathan L.), Gudger, G.K. (Garland K.), Gurich, R.W. (Richard W.), Hill, A.D. (Austin D.), Hollenbeck, S.M. (Steven M.), Jackson, L.T. (Lyle T.), Kruse, K.K. (Kevin K.), Lackey, W.G. (Wesley G.), Langan, J.W. (Justin W.), Lee, J. (Julia), Leffler, L.C. (Lauren C.), Miller, T.J. (Timothy J.), Murphy, R.L. (R. Lee), O'Malley, L.K. (Lawrence K.), Peters, M.E. (Melissa E.), Price, D.M. (Dustin M.), Tanksley, J.A. (John A.), Torres, E.T. (Erick T.), Watson, D.J. (Dylan J.), Watson, S.T. (Scott T.), Tanner, S.L. (Stephanie L.), Snider, R.G. (Rebecca G.), Nastoff, L.A. (Lauren A.), Bielby, S.A. (Shea A.), Teasdall, R.J. (Robert J.), Switzer, J.A. (Julie A.), Cole, P.A. (Peter A.), Anderson, S.A. (Sarah A.), Lafferty, P.M. (Paul M.), Li, M. (Mengnai), Ly, T.V. (Thuan V.), Marston, S.B. (Scott B.), Foley, A.L. (Amy L.), Vang, S. (Sandy), Wright, D.M. (David M.), Marcantonio, A.J. (Andrew J.), Kain, M.S.H. (Michael S.H.), Iorio, R. (Richard), Specht, L.M. (Lawrence M.), Tilzey, J.F. (John F.), Lobo, M.J. (Margaret J.), Garfi, J.S. (John S.), Vallier, H.A. (Heather A.), Dolenc, A. (Andrea), Breslin, M. (Mary), Prayson, M.J. (Michael J.), Laughlin, R. (Richard), Rubino, L.J. (L. Joseph), May, J. (Jedediah), Rieser, G.R. (Geoffrey Ryan), Dulaney-Cripe, L. (Liz), Gayton, C. (Chris), Shaer, J. (James), Schrickel, T. (Tyson), Hileman, B. (Barbara), Gorczyca, J.T. (John T.), Gross, J.M. (Jonathan M.), Humphrey, C.A. (Catherine A.), Kates, S. (Stephen), Ketz, J.P. (John P.), Noble, K. (Krista), McIntyre, A.W. (Allison W.), Pecorella, K. (Kaili), Davis, C.A. (Craig A.), Weinerman, S. (Stuart), Weingarten, P. (Peter), Stull, P. (Philip), Lindenbaum, S. (Stephen), Hewitt, M. (Michael), Schwappach, J. (John), Baker, J.K. (Janell K.), Rutherford, T. (Tori), Newman, H. (Heike), Lieberman, S. (Shane), Finn, E. (Erin), Robbins, K. (Kristin), Hurley, M. (Meghan), Lyle, L. (Lindsey), Mitchell, K. (Khalis), Browner, K. (Kieran), Whatley, E. (Erica), Payton, K. (Krystal), Reeves, C. (Christina), Cannada, L.K. (Lisa K.), Karges, D.E. (David E.), Dawson, S.A. (Sarah A.), Mehta, S. (Samir), Esterhai, J. (John), Ahn, J. (Jaimo), Donegan, D. (Derek), Horan, A.D. (Annamarie D.), Hesketh, P.J. (Patrick J.), Bannister, E.R. (Evan R.), Keeve, J.P. (Jonathan P.), Anderson, C.G. (Christopher G.), McDonald, M.D. (Michael D.), Hoffman, J.M. (Jodi M.), Tarkin, I. (Ivan), Siska, P. (Peter), Gruen, G. (Gary), Evans, A. (Andrew), Farrell, D.J. (Dana J.), Irrgang, J. (James), Luther, A. (Arlene), Cross, W.W. (William W.), Cass, J.R. (Joseph R.), Sems, S.A. (Stephen A.), Torchia, M.E. (Michael E.), Scrabeck, T. (Tyson), Jenkins, M. (Mark), Dumais, J. (Jules), Romero, A.W. (Amanda W.), Sagebien, C.A. (Carlos A.), Butler, M.S. (Mark S.), Monica, J.T. (James T.), Seuffert, P. (Patricia), Hsu, J.R. (Joseph R.), Stinner, D. (Daniel), Ficke, J. (James), Charlton, M. (Michael), Napierala, M. (Matthew), Fan, M. (Mary), Tannoury, C. (Chadi), Carlisle, H. (Hope), Silva, H. (Heather), Archdeacon, M. (Michael), Finnan, R. (Ryan), Le, T. (Toan), Wyrick, J. (John), Hess, S. (Shelley), Brennan, M.L. (Michael L.), Probe, R. (Robert), Kile, E. (Evelyn), Mills, K. (Kelli), Clipper, L. (Lydia), Yu, M. (Michelle), Erwin, K. (Katie), Horwitz, D. (Daniel), Strohecker, K. (Kent), Swenson, T.K. (Teresa K.), Schmidt, A.H. (Andrew H.), Westberg, J.R. (Jerald R.), Aurang, K. (Kamran), Zohman, G. (Gary), Peterson, B. (Brett), Huff, R.B. (Roger B.), Baele, J. (Joseph), Weber, T. (Timothy), Edison, M. (Matt), McBeth, J.C. (Jessica Cooper), Shively, K. (Karl), Ertl, J.P. (Janos P.), Mullis, B. (Brian), Parr, J.A. (J. Andrew), Worman, R. (Ripley), Frizzell, V. (Valda), Moore, M.M. (Molly M.), DePaolo, C.J. (Charles J.), Alosky, R. (Rachel), Shell, L.E. (Leslie E.), Hampton, L. (Lynne), Shepard, S. (Stephanie), Nanney, T. (Tracy), Cuento, C. (Claudine), Cantu, R.V. (Robert V.), Henderson, E.R. (Eric R.), Eickhoff, L.S. (Linda S.), Hammerberg, E.M. (E. Mark), Stahel, P. (Philip), Hak, D. (David), Mauffrey, C. (Cyril), Henderson, C. (Corey), Gissel, H. (Hannah), Gibula, D. (Douglas), Zamorano, D.P. (David P.), Tynan, M.C. (Martin C.), Pourmand, D. (Deeba), Lawson, D. (Deanna), Crist, B.D. (Brett D.), Murtha, Y.M. (Yvonne M.), Anderson, L.K. (Linda K.), Linehan, C. (Colleen), Pilling, L. (Lindsey), Lewis, C.G. (Courtland G.), Caminiti, S. (Stephanie), Sullivan, R.J. (Raymond J.), Roper, E. (Elizabeth), Obremskey, W. (William), Kregor, P. (Philip), Richards, J.E. (Justin E.), Stringfellow, K. (Kenya), Dohm, M.P. (Michael P.), Zellar, A. (Abby), Segers, M.J.M. (Michiel), Zijl, J.A.C. (Jacco A.C.), Verhoeven, B. (Bart), Smits, A.B. (Anke B.), De Vries, J.P.P.M. (Jean Paul P.M.), Fioole, B. (Bram), Van Der Hoeven, H. (Henk), Theunissen, E.B.M. (Evert B.M.), De Vries Reilingh, T.S. (Tammo S.), Govaert, L. (Lonneke), Wittich, P. (Philippe), De Brauw, M. (Maurits), Wille, J.C. (Jan), Go, P.M.N.Y.M. (Peter M.N.Y.M.), Ritchie, E.D. (Ewan D.), Wessel, R.N. (Ronald N.), Hammacher, E.R. (Eric), Visser, G.A. (Gijs A.), Stockmann, H. (Heyn), Silvis, R. (Rob), Snellen, J.P. (Jaap P.), Rijbroek, B. (Bram), Scheepers, J.J. (Joris J.), Vermeulen, E.G.J. (Erik G.J.), Siroen, M.P.C. (Michiel P.C.), Vuylsteke, R. (Ronald), Brom, H.L.F., Rijna, H., Rijcke, P.A.R. (Piet), Koppert, C.L. (Cees L.), Buijk, S.E. (Steven E.), Groenendijk, R.P.R. (Richard), Dawson, I. (Imro), Tetteroo, G.W.M. (Geert), Bruijninckx, M.M.M. (Milko), Doornebosch, P. (Pascal), Graaf, E.J.R. (Eelco) de, Elst, M. (Maarten) van der, Pol, C. (Carmen) van der, Riet, M. (Martijne) van 't, Karsten, T.M. (Thomas), Vries, M.R. (Mark) de, Stassen, L.P.S. (Laurents P.S.), Schep, N.W.L. (Niels), Ben Schmidt, G. (G.), Hoffman, W.H. (W. H.), Poolman, R.W. (Rudolf), Simons, M.P., Heijden, F.H.W.M. (Frank) van der, Willems, W.J. (Jaap), De Meulemeester, F.R.A.J. (Frank R.A.J.), Hart, C.P. (Cor P.) van der, Turckan, K. (Kahn), Festen, S. (Sebastiaan), Nies, F. (Frank) de, Out, N.J.M. (Nico J.M.), Bosma, J. (Jan), Kampen, A. (A.) van, Biert, J. (Jan), Van Vugt, A.B. (Arie B.), Edwards, M.J.R. (Michael J.R.), Blokhuis, T.J. (Taco J.), Frölke, J.P.M. (Jan Paul), Geeraedts, L.M.G. (Leo M.G.), Gardeniers, J.W.M. (Jean W.M.), Tan, E.C.T.H. (Edward C.T.H.), Poelhekke, L.M.S.J., De Waal Malefijt, M.C. (Maarten C.), Schreurs, B. (Bart), Roukema, G.R. (Gert), Josaputra, H.A. (Hong A.), Keller, P. (Paul), De Rooij, P.D. (Peter D.), Kuiken, H. (Hans), Boxma, H. (Han), Cleffken, B.I. (Berry), Liem, R. (Ronald), Rhemrev, S. (Steven), Bosman, C.H.R. (Coks H.R.), De Mol Van Otterloo, A. (Alexander), Hoogendoorn, J. (Jochem), Vries, A.C. (Alexander) de, Meylaerts, S.A.G. (Sven), Verhofstad, M.H.J. (Michiel), Meijer, J. (Joost), Van Egmond, T. (Teun), Van Der Brand, I. (Igor), Patka, P. (Peter), Eversdijk, M.G. (Martin), Peters, R. (Rolf), Hartog, D. (Dennis) den, Waes, O.J.F. (Oscar) van, Oprel, P.P. (Pim), Vis, H.M. (Harm) van der, Campo, M. (Martin), Verhagen, R. (Ronald), Albers, G.H.R. (G.H. Robert), Zurcher, A.W. (Arthur W.), Simmermacher, R.K.J., Van Mulken, J. (Jeroen), Wessem, K.J.P. van, Van Gaalen, S.M. (Steven M.), Leenen, L.P.H., Bronkhorst, M.W.G.A. (Maarten), Guicherit, O.R. (Onno R.), Goslings, J.C. (Carel), Ponsen, K.J. (Kees-jan), Bhatia, M. (Mahesh), Arora, V. (Vinod), Tyagi, V. (Vivek), Bedi, H. (Harvinder), Carr, A. (Ashley), Curry, H. (Hamish), Chia, A. (Andrew), Csongvay, S. (Steve), Donohue, C. (Craig), Doig, S. (Stephen), Edwards, E. (Elton), Etherington, G. (Greg), Esser, M. (Max), Gong, A. (Andrew), Jain, A. (Arvind), Li, D. (Doug), Miller, R. (Russell), Moaveni, A. (Ash), Russ, M. (Matthias), Ton, L. (Lu), Wang, O. (Otis), Dowrick, A. (Adam), Murdoch, Z. (Zoe), Sage, C. (Claire), Frihagen, F. (Frede), Clarke-Jenssen, J. (John), Hjorthaug, G. (Geir), Ianssen, T. (Torben), Amundsen, A. (Asgeir), Brattgjerd, J.E. (Jan Egil), Borch, T. (Tor), Bøe, B. (Berthe), Flatøy, B. (Bernhard), Hasselund, S. (Sondre), Haug, K.J. (Knut Jørgen), Hemlock, K. (Kim), Hoseth, T.M. (Tor Magne), Jomaas, G. (Geir), Kibsgård, T. (Thomas), Lona, T. (Tarjei), Moatshe, G. (Gilbert), Müller, O. (Oliver), Molund, M. (Marius), Nicolaisen, T. (Tor), Nilsen, F. (Fredrik), Rydinge, J. (Jonas), Smedsrud, M. (Morten), Stødle, A. (Are), Trommer, A. (Axel), Ugland, S. (Stein), Karlsten, A. (Anders), Ekås, G. (Guri), Vesterhus, E.B. (Elise Berg), Brekke, A.C. (Anne Christine), Gupta, A. (Ajay), Jain, N. (Neeraj), Khan, F. (Farah), Sharma, A. (Ateet), Sanghavi, A. (Amir), Trivedi, M. (Mittal), Rai, A. (Anil), Subash, (), Rai, K. (Kamal), Yadav, V. (Vineet), Singh, S. (Sanjay), Tetsworth, K. (Kevin), Donald, G. (Geoff), Weinrauch, P. (Patrick), Pincus, P. (Paul), Yang, S. (Steven), Halliday, B. (Brett), Gervais, T. (Trevor), Holt, M. (Michael), Flynn, A. (Annette), Prasad, A.S. (Amal Shankar), Mishra, V. (Vimlesh), Sundaresh, D.C. (D. C.), Khanna, A. (Angshuman), Cherian, J.J. (Joe Joseph), Olakkengil, D.J. (Davy J), Sharma, G. (Gaurav), Pirpiris, M. (Marinis), Love, D. (David), Bucknill, A. (Andrew), Farrugia, R.J. (Richard J), Pape, H.-C. (Hans-Christoph), Knobe, M. (Matthias), Pfeifer, R. (Roman), Hull, P. (Peter), Lewis, S. (Sophie), Evans, S. (Simone), Nanda, R. (Rajesh), Logishetty, R. (Rajanikanth), Anand, S. (Sanjeev), Bowler, C. (Carol), Dadi, A. (Akhil), Palla, N. (Naveen), Ganguly, U. (Utsav), Rai, B.S. (B. Sachidananda), Rajakumar, J. (Janakiraman), Jennings, A. (Andrew), Chuter, G. (Graham), Rose, G. (Glynis), Horner, G. (Gillian), Clark, C. (Callum), Eke, K. (Kate), Reed, M.R. (Mike), Inman, D. (Dominic), Herriott, C. (Chris), and Dobb, C. (Christine)
Abstract: Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (for
Published: 2018
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45. A Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

Author: Arul, Rajakumar, Raja, Gunasekaran, Bashir, Ali Kashif, Chaudry, Junaid, Ali, Amjad, Arul, Rajakumar, Raja, Gunasekaran, Bashir, Ali Kashif, Chaudry, Junaid, and Ali, Amjad
Abstract: Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to long-term evolution (LTE)/system architecture evolution. The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE key to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System–Authentication and Key Agreement based authentication, which discloses user identity, location, and other personally identifiable information. To counter this, we propose a public key cryptosystem named “International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol” to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, but also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper prove that IPG-AKA improves security in authentication procedure and performance in the LTE.
Published: 2018

46. Marine algal secondary metabolites promising anti-angiogenesis factor against retinal neovascularization in CAM model

Author: Subramanian, Umadevi, ishorekumar, Meenakshi Sundaram, Muthuraman, Sundararaman, Munusamy, Ayyasamy Pudukadu, Rajakumar, Sundaram, Subramanian, Umadevi, ishorekumar, Meenakshi Sundaram, Muthuraman, Sundararaman, Munusamy, Ayyasamy Pudukadu, and Rajakumar, Sundaram
Abstract: Retinal angiogenesis is an angle of new blood vessels on retinal surface. This neovascularization condition within the eye contributes to visual loss. Commonest cause of this condition includes diabetes, retinopathy of prematurity, retinal vein occlusion, etc. A variety of endothelial cell growth factors have been identified as a responsible factor and previous studies report that marine metabolites are promising molecules against retinal angiogenesis. Based on the background information collected, the present study focused to insight the anti-angiogenesis effect of metabolites present in marine algae. Findings of CAM assay suggested that the extract obtained from the marine algae Dictayota dichotoma are effective against angiogenesis.
Published: 2018

47. Performance improvement of piezoelectric materials in energy harvesting in recent days – a review

Author: E, Varadha, S, Rajakumar, E, Varadha, and S, Rajakumar
Abstract: Piezoelectric elements are inevitable in modern day physics playing a vital role in many applications. Any piezoelectric element requires compression to produce energy in the form of a weak electrical ac signal. Mechanical vibrations are known to cause deflections which are enough to produce energy from the piezoelectric materials. In this paper, a review of the piezoelectric materials is made on their basic modes of excitation for producing energy. Also, various mechanisms and techniques used to harvest energy recently are presented and discussed extensively. Piezoelectric energy harvesting using MEMS is emphasized much as this is the era of micromechanical systems. Most of the piezoelectric energy harvesting systems relies on cantilever-oriented deflection to produce maximum vibration. In general cantilever beams fitted with piezoelectric materials produce electrical energy from mechanical vibration when deflected; hence detailed review on the different shapes of cantilever is also submitted. Significant parameters contributing to improved performance are dealt with special importance.
Published: 2018

48. Accelerated echo planar J-resolved spectroscopic imaging in prostate cancer: a pilot validation of non-linear reconstruction using total variation and maximum entropy.

Author: Nagarajan, Rajakumar, Nagarajan, Rajakumar, Iqbal, Zohaib, Burns, Brian, Wilson, Neil E, Sarma, Manoj K, Margolis, Daniel A, Reiter, Robert E, Raman, Steven S, Thomas, M Albert, Nagarajan, Rajakumar, Nagarajan, Rajakumar, Iqbal, Zohaib, Burns, Brian, Wilson, Neil E, Sarma, Manoj K, Margolis, Daniel A, Reiter, Robert E, Raman, Steven S, and Thomas, M Albert
Abstract: The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites.
Published: 2015

49. Synthesis and photophysical studies on triazole bridged dendrimers with phenothiazine as a surface unit

Author: Perumal Rajakumar, Perumal Rajakumar, Chinnadurai Satheeshkumar, Mahalingam Ravivarma, Perumal Rajakumar, Perumal Rajakumar, Chinnadurai Satheeshkumar, and Mahalingam Ravivarma
Abstract: ARKIVOC: vol. 2014, no. 4, (issn) 1551-7012, (dlps) 5550190.p008.375, This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 License. Please contact mpub-help@umich.edu to use this work in a way not covered by the license.
Published: 2014

50. Non-uniformly weighted sampling for faster localized two-dimensional correlated spectroscopy of the brain in vivo.

Author: Verma, Gaurav, Verma, Gaurav, Chawla, Sanjeev, Nagarajan, Rajakumar, Iqbal, Zohaib, Albert Thomas, M, Poptani, Harish, Verma, Gaurav, Verma, Gaurav, Chawla, Sanjeev, Nagarajan, Rajakumar, Iqbal, Zohaib, Albert Thomas, M, and Poptani, Harish
Abstract: Two-dimensional localized correlated spectroscopy (2D L-COSY) offers greater spectral dispersion than conventional one-dimensional (1D) MRS techniques, yet long acquisition times and limited post-processing support have slowed its clinical adoption. Improving acquisition efficiency and developing versatile post-processing techniques can bolster the clinical viability of 2D MRS. The purpose of this study was to implement a non-uniformly weighted sampling (NUWS) scheme for faster acquisition of 2D-MRS. A NUWS 2D L-COSY sequence was developed for 7T whole-body MRI. A phantom containing metabolites commonly observed in the brain at physiological concentrations was scanned ten times with both the NUWS scheme of 12:48 duration and a 17:04 constant eight-average sequence using a 32-channel head coil. 2D L-COSY spectra were also acquired from the occipital lobe of four healthy volunteers using both the proposed NUWS and the conventional uniformly-averaged L-COSY sequence. The NUWS 2D L-COSY sequence facilitated 25% shorter acquisition time while maintaining comparable SNR in humans (+0.3%) and phantom studies (+6.0%) compared to uniform averaging. NUWS schemes successfully demonstrated improved efficiency of L-COSY, by facilitating a reduction in scan time without affecting signal quality.
Published: 2017

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