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18,486 results on '"Ghaderi, A"'

201. Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Author

Iman Peivaste, Saba Ramezani, Ghasem Alahyarizadeh, Reza Ghaderi, Ahmed Makradi, and Salim Belouettar

Subjects
Medicine, Science
Abstract

Abstract This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.

Published
2024
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202. A Study on a Fractional q-Integro-Differential Inclusion by Quantum Calculus with Numerical and Graphical Simulations

Author

Mehran Ghaderi and Shahram Rezapour

Subjects
boundary value problem, fixed point, fractional calculus, integro-differential inclusion, quantum calculus, Mathematics, QA1-939
Abstract

In this paper, we investigate the existence of a solution for the fractional q-integro-differential inclusion with new double sum and product boundary conditions. One of the most recent techniques of fixed point theory, i.e. endpoints property, and inequalities, plays a central role in proving the main results. For a better understanding of the issue and validation of the results, we presented numerical algorithms, tables and some figures. The paper ends with an example.

Published
2024
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203. Comparison of three doses of amikacin on alternate days with a daily dose of meropenem during the same period for the treatment of urinary tract infection with E. coli: a double-blind clinical trial

Author

Behzad Mohsenpour, Amjad Ahmadi, Hero Azizzadeh, Ebrahim Ghaderi, Katayon Hajibagheri, Shahla Afrasiabian, Gohar Lotfi, and Zhila Farzinpoor

Subjects
Amikacin, Meropenem, E. Coli, ESBL, UTIs, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Objectives Urinary tract infections (UTIs) are very common infections in humans, and Escherichia coli (E. coli) is the commonest pathogen leading to UTIs. The generation of beta-lactamase enzymes in this bacterium results in its resistance against many antibiotics. This study compares three doses of amikacin on alternate days with a daily dose of meropenem in the same period for the treatment of UTIs with E. coli in a double-blind clinical trial. Methods The current double-blind clinical trial compares three doses of amikacin on alternate days with a daily dose of meropenem in the same period for the treatment of UTIs with E. coli. The patients were assigned to two groups: Intervention (receiving a single dose of amikacin once a day at 48-h intervals for a week, three doses) and control (receiving meropenem for 1/TDS for a week). Results The E. coli infection frequency was 61 (21 cases of non-ESBL and 40 cases of ESBL-positive infections) and the frequency of the other infections was 52 (46%). In the patients with ESBL E. coli infection, ciprofloxacin (21; 70%) showed the highest antibiotic resistance, and nitrofurantoin (33; 91.7%) showed the highest sensitivity. The baseline variables between the control and intervention groups indicated no significant difference (p > 0.05). The frequency of signs and symptoms showed no significant difference between the amikacin and meropenem groups in the first 24 h and the first week. In the second week of follow-up, no clinical signs or symptoms were observed in the two groups. Conclusion The results of this study showed that treatment with amikacin, 1 g q48h, for one week (three doses) has the same result as meropenem, 1 g q8h, for one week (21 doses). The results are the same for the treatment of UTIs with ESBL positive and ESBL negative. Amikacin can be used once every 48 h to treat UTIs, is less expensive and can be administered on an outpatient basis. Trial registration This study was registered in the Iranian Registry of Clinical Trials (IRCT) with ID number: IRCT20170417033483N2 on the date 2018-02-13.

Published
2024
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204. Negative prognostic behaviour of PD-L1 expression in tongue and larynx squamous cell carcinoma and its significant predictive power in combination with PD-1 expression on TILs

Author

Simin Ahmadvand, Lotf-Ali Norouzi, Yousef Mohammadi, Akbar Safaei, Bijan Khademi, Maziar Motiee-Langroudi, and Abbas Ghaderi

Subjects
Head and Neck Squamous Cell Carcinoma, Checkpoint Inhibitor Immunotherapy, PD-1, PD-L1, Tumor Infiltrating Lymphocytes, Tumor Microenvironment, Immunologic diseases. Allergy, RC581-607
Abstract

Abstract Background Biomarkers that can predict outcome will improve the efficacy of treatment for HNSCC patients. In this regard, we retrospectively evaluated the prognostic effect of PD1, PD-L1, and CD45RO in tongue and larynx squamous cell carcinomas. Methods FFPE tissue blocks of 63 larynx and 40 tongue squamous cell carcinoma samples were selected, cut into 3 µm sections, and immunohistochemically stained for PD1, PD-L1, and CD45RO. The slides were evaluated by an expert pathologist, and results were analysed using Chi-square, univariate, and multivariable Cox regression methods. Results TC-PD-L1 expression (P = 0.001) and its expression intensity (P = 0.002) were significantly correlated with a higher percentage of PD-1 + tumor infiltrating lymphocytes. In univariate survival analysis, TC-PD-L1 and its expression intensity had a significant impact on both DFS (HR: 0.203; P = 0.003 and HR: 0.320; P = 0.005) and OS (HR: 0.147; P = 0.002 and HR: 0.322; P = 0.005). Based on the multivariate analysis, PD1 (DFS: HR: 3.202; P = 0.011, OS: HR: 2.671; P = 0.027) and TC-PD-L1 (DFS: HR: 0.174; P = 0.006, OS: HR: 0.189; P = 0.009) were found to be independent prognostic markers. In the second part, scoring systems were defined based on the expression status of PD1 and PD-L1. Patients with higher scores were expected to have longer DFS and OS. In multivariate analysis, the PD1/TC-PD-L1 (DFS: P = 0.001, OS: P = 0.003) scoring systems showed superior prognostic effects. Interestingly, at the highest levels of this score, none of the patients experienced recurrence or cancer-caused death. Conclusion Collectively, this study suggests negative prognostic behaviour for TC-PD-L1 protein and introduces the PD-1/TC-PD-L1 scoring system as a strong prognostic marker in OS and DFS prediction of tongue and larynx HNSCC patients.

Published
2024
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205. Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Author

Mahdi Ghaderi, Seyed Ahmad Mireei, Aminollah Masoumi, Mohammad Sedghi, and Majid Nazeri

Subjects
Medicine, Science
Abstract

Abstract Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Published
2024
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206. Biochar: a high performance and renewable basic carbocatalyst for facilitating room temperature synthesis of 4H-benzo[h]chromene and pyranopyrazoles in water

Author

Dariush Khalili, Ali Asghar Ramjerdi, Hamid Reza Boostani, and Arash Ghaderi

Subjects
Biomass, Biochar, Basic carbocatalyst, Heterogeneous catalysis, Multicomponent synthesis, Environment sustainability, Environmental sciences, GE1-350, Agriculture
Abstract

Abstract This study has introduced a pioneering methodology by employing biochars as a basic carbocatalyst in the context of multicomponent reactions. Biochars were produced from different manures and organic wastes using the pyrolysis-carbonization process under limited oxygen conditions. The prepared biochars were well characterized using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Brunauer–Emmett–Teller (BET) analysis, and powder X-ray diffraction (XRD). The chemical characteristics and potentiometric titration analysis provide compelling evidence of the intriguing basicity properties exhibited by the prepared biochars. The pH values, ash content, and potentiometric titration results confirmed the exceptional basicity characteristics of cow manure biochar formed at 600 oC (CB600), establishing it as the most basic carbocatalyst in this study. Encouraged by these initial results, the activity of the biochars as basic carbocatalysts was evaluated in multicomponent synthesis of 4H-benzo[h]chromene and pyranopyrazoles and 600 °C exhibited the most pronounced catalytic performance owing to its superior total basicity. By these findings, it can be asserted that this work introduces the groundbreaking application of biochars as potent basic carbocatalysts for the multicomponent synthesis of structurally diverse heterocycles. Unveiling the vital basic role of biochars will definitely open up new opportunities in organic chemistry and provide salient features for environmentally-friendly chemistry, including easy retrieval, non-toxicity, and widespread accessibility. Graphical Abstract

Published
2024
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207. Comparing P53 expression and genome-wide transcriptome profiling to Comet assay in lymphocytes from melanoma patients and healthy controls

Author

Mojgan Najafzadeh, Parisa Naeem, Nader Ghaderi, Shohreh Jafarinejad, Zahra Karimi, Mehran Ghaderi, Pouria Akhbari, Rojan Ghaderi, Pedram Farsi, Andrew Wright, and Diana Anderson

Subjects
Medicine, Science
Abstract

Abstract This study compared the expression of TP53 in lymphocytes from malignant melanoma (MM) patients with positive sentinel nodes to healthy controls (HCs) following exposure to various doses of UVA radiation. The Lymphocyte Genome Sensitivity (LGS) assay indicated significant differences in DNA damage in lymphocytes between MM patients and HCs. qPCR data demonstrated an overall 3.4-fold increase in TP53 expression in lymphocytes from MM patients compared to healthy controls, following treatment with 0.5 mW/cm2 UVA radiation. Western blotting confirmed that p53 expression was increased in MM lymphocytes following UVA exposure compared to healthy individuals. Genome transcriptome profiling data displayed differences in gene expression between UVA-treated lymphocytes from MM patients and HCs. Peripheral lymphocytes from MM patients are more susceptible to the genotoxic effects of UVA compared to healthy individuals. Our previous studies showed that UVA exposure of various intensities caused significant differences in the levels of DNA damage between lymphocytes from cancer patients compared to HCs through the LGS assay. The present study’s results provide further credibility to the LGS assay as a screening test for cancer detection. Peripheral lymphocytes could be a promising blood biopsy biomarker for staging of carcinomas and prevention of carcinoma progression at early stages.

Published
2023
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208. Accelerating phase-field-based simulation via machine learning

Author

Peivaste, Iman, Siboni, Nima H., Alahyarizadeh, Ghasem, Ghaderi, Reza, Svendsen, Bob, Raabe, Dierk, and Mianroodi, Jaber R.

Subjects
Condensed Matter - Materials Science, Computer Science - Machine Learning, Physics - Computational Physics
Abstract

Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly when applied to large, complex systems. To reduce such computational costs, a Unet-based artificial neural network is developed as a surrogate model in the current work. Training input for this network is obtained from the results of the numerical solution of initial-boundary-value problems (IBVPs) based on the Fan-Chen model for grain microstructure evolution. In particular, about 250 different simulations with varying initial order parameters are carried out and 200 frames of the time evolution of the phase fields are stored for each simulation. The network is trained with 90% of this data, taking the $i$-th frame of a simulation, i.e. order parameter field, as input, and producing the $(i+1)$-th frame as the output. Evaluation of the network is carried out with a test dataset consisting of 2200 microstructures based on different configurations than originally used for training. The trained network is applied recursively on initial order parameters to calculate the time evolution of the phase fields. The results are compared to the ones obtained from the conventional numerical solution in terms of the errors in order parameters and the system's free energy. The resulting order parameter error averaged over all points and all simulation cases is 0.005 and the relative error in the total free energy in all simulation boxes does not exceed 1%.

Published
2022

209. Uncovering the protective potential of vanillic acid against traumatic brain injury-induced cognitive decline in male rats: Insights into underlying mechanisms

Author

Shahab Ghaderi, Parsa Gholipour, Samaneh Safari, Seyed Mahdi Sadati, Shahla Eyvari Brooshghalan, Rezvan Sohrabi, Khodabakhsh Rashidi, Alireza Komaki, Iraj Salehi, Abdolrahman Sarihi, Mohammad Zarei, Siamak Shahidi, and Masome Rashno

Subjects
Traumatic brain injury, Vanillic acid, Passive avoidance memory, Long-term potentiation, Oxidative stress, Neuronal loss, Therapeutics. Pharmacology, RM1-950
Abstract

Traumatic brain injury (TBI) is a significant contributor to global mortality and disability, and there is still no specific drug available to treat cognitive deficits in survivors. Vanillic acid (VA), a bioactive phenolic compound, has shown protective effects in various models of neurodegeneration; however, its impact on TBI outcomes remains elusive. Therefore, this study aimed to elucidate the possible role of VA in ameliorating TBI-induced cognitive decline and to reveal the mechanisms involved. TBI was induced using the Marmarou impact acceleration model to deliver an impact force of 300 g, and treatment with VA (50 mg/kg; P.O.) was initiated 30 minutes post-TBI. The cognitive performance, hippocampal long-term potentiation (LTP), oxidative stress markers, neurological function, cerebral edema, and morphological changes were assessed at scheduled points in time. TBI resulted in cognitive decline in the passive avoidance task, impaired LTP in the perforant path-dentate gyrus (PP-DG) pathway, increased hippocampal oxidative stress, cerebral edema, neurological deficits, and neuronal loss in the rat hippocampus. In contrast, acute VA administration mitigated all the aforementioned TBI outcomes. The data suggest that reducing synaptic plasticity impairment, regulating oxidative and antioxidant defense, alleviating cerebral edema, and preventing neuronal loss by VA can be at least partially attributed to its protection against TBI-induced cognitive decline.

Published
2024
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211. KIR-HLA gene diversities and susceptibility to lung cancer

Author

Hematian Larki, Marjan, Ashouri, Elham, Barani, Shaghik, Ghayumi, Seiyed Mohammad Ali, Ghaderi, Abbas, and Rajalingam, Raja

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Immunology, Oncology and Carcinogenesis, Lung Cancer, Lung, Cancer, Clinical Research, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Gene Frequency, Genotype, HLA Antigens, Histocompatibility Antigens Class I, Humans, Immunoglobulins, Ligands, Lung Neoplasms, Receptors, KIR
Abstract

Killer-cell immunoglobulin-like receptors (KIR) are essential for acquiring natural killer (NK) cell effector function, which is modulated by a balance between the net input of signals derived from inhibitory and activating receptors through engagement by human leukocyte antigen (HLA) class I ligands. KIR and HLA loci are polygenic and polymorphic and exhibit substantial variation between individuals and populations. We attempted to investigate the contribution of KIR complex and HLA class I ligands to the genetic predisposition to lung cancer in the native population of southern Iran. We genotyped 16 KIR genes for a total of 232 patients with lung cancer and 448 healthy controls (HC), among which 85 patients and 178 HCs were taken into account for evaluating combined KIR-HLA associations. KIR2DL2 and 2DS2 were increased significantly in patients than in controls, individually (OR 1.63, and OR 1.42, respectively) and in combination with HLA-C1 ligands (OR 1.99, and OR 1.93, respectively). KIR3DS1 (OR 0.67) and 2DS1 (OR 0.69) were more likely presented in controls in the absence of their relative ligands. The incidence of CxTx subset was increased in lung cancer patients (OR 1.83), and disease risk strikingly increased by more than fivefold among genotype ID19 carriers (a CxTx genotype that carries 2DL2 in the absence of 2DS2, OR 5.92). We found that genotypes with iKIRs > aKIRs (OR 1.67) were more frequently presented in lung cancer patients. Additionally, patients with lung cancer were more likely to carry the combination of CxTx/2DS2 compared to controls (OR 2.04), and iKIRs > aKIRs genotypes in the presence of 2DL2 (OR 2.05) increased the likelihood of lung cancer development. Here we report new susceptibility factors and the contribution of KIR and HLA-I encoding genes to lung cancer risk, highlighting an array of genetic effects and disease setting which regulates NK cell responsiveness. Our results suggest that inherited KIR genes and HLA-I ligands specifying the educational state of NK cells can modify lung cancer risk.

Published
2022

213. Scenario-Based Classroom Context Mode: Reshaping Non-Native Teachers' Decision-Making and Pedagogical Reasoning

Author

Saeedian, Sam and Ghaderi, Ata

Abstract

Teacher decision-making and pedagogical reasoning have been extensively investigated in the last two decades. However, there is a scarcity of research that examines the two constructs together in one single study. This study was an attempt to address this gap by implementing a teacher education course using the self-evaluation of teacher talk (SETT) framework that aimed to reshape the decision-making and pedagogical reasoning of ten non-native teachers. Data were collected over a 10-week period through Camtasia, which facilitated video-stimulated recall sessions. Conversation analysis was employed to analyze the interactions in classroom context between the learners and the novice teachers and the interactions between the novices and the first researcher-as-a-participant or experienced teacher. The findings showed that the novices relied on the SETT-oriented metalanguage to justify their decisions after the teacher education course (POST-TEC). In addition, although in the PRE-TEC phase, they lost their temper and codeswitched because of frustration when facing learners' reticence, they used a number of useful interactive decisions such as avoiding teacher echoes and on-the-spot corrections during discussions in the POST-TEC. It can be concluded that examining the SETT modes separately contributes to our richer understanding of classroom discourse, as each mode has its own specific pedagogic goals that teachers should pay attention to in their classroom decisions. Suggestions for further research and implications for making the decision-making and pedagogical reasoning of language teachers more explicit are provided.

Published
2023
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214. A 65‐year‐old woman with ALS and bilateral precentral motor band sign

Author

Sadegh Ghaderi, Seyed Amir Hossein Batouli, Sanjay Kalra, Sana Mohammadi, and Farzad Fatehi

Subjects
amyotrophic lateral sclerosis, biomarker, iron, motor band sign, quantitative susceptibility mapping, susceptibility‐weighted imaging, Medicine, Medicine (General), R5-920
Abstract

Key Clinical Message Advanced MRI techniques, including SWI, MinIP, and QSM, are instrumental in detecting the “motor band sign” in ALS, aiding in the early diagnosis and assessment of upper motor neuron involvement, which is critical for therapeutic interventions.

Published
2024
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215. The genomic landscape of the immune system in lung cancer: present insights and continuing investigations

Author

Mina Roshan-Zamir, Aida Khademolhosseini, Kavi Rajalingam, Abbas Ghaderi, and Raja Rajalingam

Subjects
lung neoplasms, immunogenetics, immunotherapy, genetic susceptibility, genetic variation, Genetics, QH426-470
Abstract

Lung cancer is one of the most prevalent malignancies worldwide, contributing to over a million cancer-related deaths annually. Despite extensive research investigating the genetic factors associated with lung cancer susceptibility and prognosis, few studies have explored genetic predispositions regarding the immune system. This review discusses the most recent genomic findings related to the susceptibility to or protection against lung cancer, patient survival, and therapeutic responses. The results demonstrated the effect of immunogenetic variations in immune system-related genes associated with innate and adaptive immune responses, cytokine, and chemokine secretions, and signaling pathways. These genetic diversities may affect the crosstalk between tumor and immune cells within the tumor microenvironment, influencing cancer progression, invasion, and prognosis. Given the considerable variability in the individual immunegenomics profiles, future studies should prioritize large-scale analyses to identify potential genetic variations associated with lung cancer using highthroughput technologies across different populations. This approach will provide further information for predicting response to targeted therapy and promotes the development of new measures for individualized cancer treatment.

Published
2024
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216. Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Author

Sana Mohammadi and Sadegh Ghaderi

Subjects
QSM, SWI, Putamen, Iron, Parkinson's disease, Parkinsonism, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

Published
2024
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220. Multi-Mode Spatial Signal Processor with Rainbow-like Fast Beam Training and Wideband Communications using True-Time-Delay Arrays

Author

Lin, Chung-Ching, Puglisi, Chase, Boljanovic, Veljko, Yan, Han, Ghaderi, Erfan, Gaddis, Jayce, Xu, Qiuyan, Poolakkal, Sreeni, Cabric, Danijela, and Gupta, Subhanshu

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Initial access in millimeter-wave (mmW) wireless is critical toward successful realization of the fifth-generation (5G) wireless networks and beyond. Limited bandwidth in existing standards and use of phase-shifters in analog/hybrid phased-antenna arrays (PAA) are not suited for these emerging standards demanding low-latency direction finding. This work proposes a reconfigurable true-time-delay (TTD) based spatial signal processor (SSP) with frequency-division beam training methodology and wideband beam-squint less data communications. Discrete-time delay compensated clocking technique is used to support 800~MHz bandwidth with a large unity-gain bandwidth ring-amplifier (RAMP)-based signal combiner. To extensively characterize the proposed SSP across different SSP modes and frequency-angle pairs, an automated testbed is developed using computer-vision techniques that significantly speeds up the testing progress and minimize possible human errors. Using seven levels of time-interleaving for each of the 4 antenna elements, the TTD SSP has a delay range of 3.8 ns over 800 MHz and achieves unique frequency-to-angle mapping in the beamtraining mode with nearly 12 dB frequency-independent gain in the beamforming mode. The SSP is prototyped in 65nm CMOS with an area of 1.98mm$^2$ consuming only 29 mW excluding buffers. Further, an error vector magnitude (EVM) of 9.8% is realized for 16-QAM modulation at a speed of 122.8 Mb/s.

Published
2022

234. General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Author

Zhou, Bin, Bennett, James E, Wickham, Aidan P, Singleton, Rosie K, Mishra, Anu, Carrillo-Larco, Rodrigo M, Ikeda, Nayu, Jain, Lakshya, Barradas-Pires, Ana, Heap, Rachel A, Lhoste, Victor PF, Sheffer, Kate E, Phelps, Nowell H, Rayner, Archie W, Gregg, Edward W, Woodward, Mark, Stevens, Gretchen A, Iurilli, Maria LC, Danaei, Goodarz, Di Cesare, Mariachiara, Aguilar-Salinas, Carlos A, Ahmad, Noor Ani, Bovet, Pascal, Chen, Zhengming, Damasceno, Albertino, Filippi, Sarah L, Janszky, Imre, Kengne, Andre P, Khang, Young-Ho, Khunti, Kamlesh, Laxmaiah, Avula, Lim, Lee-Ling, Lissner, Lauren, Margozzini, Paula, Mbanya, Jean Claude N, McGarvey, Stephen T, Shaw, Jonathan E, Söderberg, Stefan, Soto-Mota, Luis Adrián, Wang, Junyang, Zaccardi, Francesco, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Aekplakorn, Wichai, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkhatib, Buthaina M, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amougou, Norbert, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Bahijri, Suhad, Bajramovic, Izet, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Basit, Abdul, Bastos, Joao Luiz, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bettiol, Heloísa, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bhargava, Santosh K, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boggia, Jose G, Bogova, Elena, Bonaccio, Marialaura, Bonilla-Vargas, Alice, Borghs, Herman, Botomba, Steve, Bourne, Rupert, Boymatova, Khadichamo, Braeckman, Lutgart, Braithwaite, Tasanee, Brajkovich, Imperia, Branca, Francesco, Brenner, Hermann, Brewster, Lizzy M, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brug, Johannes, Bugge, Anna, Buntinx, Frank, Buoncristiano, Marta, Burns, Con, Cabrera de León, Antonio, Caixeta, Roberta B, Cama, Tilema, Can, Günay, Cândido, Ana Paula C, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardoso, Viviane C, Carlsson, Axel C, Casanueva, Felipe F, Casas, Maribel, Censi, Laura, Cervantes‐Loaiza, Marvin, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Queenie, Chaturvedi, Nish, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, 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Zainuddin, Ahmad A, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zapata, Maria Elisa, Zaw, Ko Ko, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zhang, Bing, Zhang, Zhen-Yu, Zhecheva, Yanitsa V, Zholdin, Bekbolat, Zimmet, Paul, Zins, Marie, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

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2024
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238. Global pattern, trend, and cross-country inequality of early musculoskeletal disorders from 1990 to 2019, with projection from 2020 to 2050

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Jin, Yingzhao, Guo, Cui, Abbasian, Mohammadreza, Abbasifard, Mitra, Abbott, J. Haxby, Abdullahi, Auwal, Abedi, Aidin, Abidi, Hassan, Abolhassani, Hassan, Abu-Gharbieh, Eman, Aburuz, Salahdein, Abu-Zaid, Ahmed, Addo, Isaac Yeboah, Adegboye, Oyelola A., Adepoju, Abiola Victor, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Aghamiri, Shahin, Ahmad, Danish, Ahmed, Ayman, Aithala, Janardhana P., Akhlaghi, Shiva, Akkala, Sreelatha, Alalwan, Tariq A., Albashtawy, Mohammed, Alemi, Hediyeh, Alhalaiqa, Fadwa Alhalaiqa Naji, Ali, Endale Alemayehu, Almustanyir, Sami, Al-Raddadi, Rajaa M., Alvis-Zakzuk, Nelson J., Al-Worafi, Yaser Mohammed, Alzahrani, Hosam, Alzoubi, Karem H., Amiri, Sohrab, Amu, Hubert, Amzat, Jimoh, Anderson, David B., Anil, Abhishek, Antony, Benny, Arabloo, Jalal, Areda, Damelash, Artaman, Al, Artamonov, Anton A., Aryal, Krishna K., Asghari-Jafarabadi, Mohammad, Ashraf, Tahira, Athari, Seyyed Shamsadin, Atinafu, Bantalem Tilaye, Atout, Maha Moh’d Wahbi, Azadnajafabad, Sina, Azhdari Tehrani, Hamed, Azzam, Ahmed Y., Badawi, Alaa, Baghcheghi, Nayereh, Bai, Ruhai, Baigi, Vali, Banach, Maciej, Banakar, Morteza, Banik, Biswajit, Bardhan, Mainak, Bärnighausen, Till Winfried, Barqawi, Hiba Jawdat, Barrow, Amadou, Bashiri, Azadeh, Batra, Kavita, Bayani, Mojtaba, Bayileyegn, Nebiyou Simegnew, Begde, Ahmet, Beyene, Kebede A., Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Rajbir, Bijani, Ali, Bitra, Veera R., Brazo-Sayavera, Javier, Buchbinder, Rachelle, Burkart, Katrin, Bustanji, Yasser, Butt, Muhammad Hammad, Cámera, Luis Alberto, Carvalho, Felix, Chattu, Vijay Kumar, Chaurasia, Akhilanand, Chen, Guangjin, Chen, Haowei, Chen, Lingxiao, Christensen, Steffan Wittrup McPhee, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Comachio, Josielli, Cruz-Martins, Natália, Cuschieri, Sarah, Dadana, Sriharsha, Dadras, Omid, Dai, Xiaochen, Dai, Zhaoli, Das, Saswati, Dashti, Mohsen, Delgado-Enciso, Ivan, Demisse, Biniyam, Denova-Gutiérrez, Edgar, Desye, Belay, Dewan, Syed Masudur Rahman, Dhingra, Sameer, Diress, Mengistie, Do, Thanh Chi, Do, Thao Huynh Phuong, Doan, Khanh Duy Khanh, Dutta, Sulagna, Dziedzic, Arkadiusz Marian, Edinur, Hisham Atan, Ekholuenetale, Michael, Elhadi, Muhammed, Eskandarieh, Sharareh, Esposito, Francesco, Fagbamigbe, Adeniyi Francis, Farokh, Parisa, Fatehizadeh, Ali, Feizkhah, Alireza, Fekadu, Ginenus, Ferreira, Nuno, Fetensa, Getahun, Fischer, Florian, Foroutan, Behzad, Foroutan Koudehi, Masoumeh, Franklin, Richard Charles, Fukumoto, Takeshi, Gandhi, Aravind P., Ganesan, Balasankar, Gau, Shuo-Yan, Gautam, Rupesh K., Gebre, Abadi Kahsu, Gebregergis, Miglas W.W., Ghaderi Yazdi, Bardiya, Gholami, Ali, Gill, Tiffany K., Goleij, Pouya, Gomes-Neto, Mansueto, Goyal, Anmol, Graham, Simon Matthew, Guan, Bin, Gupta, Bhawna, Gupta, Indarchand Ratanlal, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Habibzadeh, Farrokh, Hailu, Wase Benti, Hajibeygi, Ramtin, Halwani, Rabih, Haro, Josep Maria, Hartvigsen, Jan, Hasaballah, Ahmed I., Haubold, Johannes, Hebert, Jeffrey J., Hegazy, Mohamed I., Heidari, Golnaz, Heidari, Mohammad, Hezam, Kamal, Hiraike, Yuta, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hoveidaei, Amir Human, Hsu, Chi-Jen, Huda, Md Nazmul, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ikiroma, Adalia I., Ilic, Irena M., Ilic, Milena D., Iranmehr, Arad, Islam, Sheikh Mohammed Shariful, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Iwagami, Masao, Iyasu, Assefa N., Jacob, Louis, Jafarzadeh, Abdollah, Jahankhani, Kasra, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Janakiraman, Balamurugan, Jayarajah, Umesh, Jayaram, Shubha, Jeganathan, Jayakumar, Jokar, Mohammad, Jonas, Jost B., Joo, Tamas, Joseph, Nitin, Joshua, Charity Ehimwenma, Kabito, Gebisa Guyasa, Kamal, Vineet Kumar, Kandel, Himal, Kantar, Rami S., Karami, Jafar, Karaye, Ibraheem M., Karimi Behnagh, Arman, Kaur, Navjot, Kazemi, Foad, Kedir, Shemsu, Khadembashiri, Mohamad Mehdi, Khadembashiri, Mohammad Amin, Khader, Yousef Saleh, Khajuria, Himanshu, Khan, Mohammad Jobair, Khan, Moien AB, Khan Suheb, Mahammed Ziauddin, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khateri, Sorour, Khayat Kashani, Hamid Reza, Khonji, Mohammad Saeid, Khubchandani, Jagdish, Kian, Saeid, Kisa, Adnan, Kitila, Aiggan Tamene, Kolahi, Ali-Asghar, Koohestani, Hamid Reza, Korzh, Oleksii, Kostev, Karel, Kotnis, Ashwin Laxmikant, Koyanagi, Ai, Krishan, Kewal, Kuddus, Mohammed, Kumar, Narinder, Kurniasari, Maria Dyah, Ladan, Muhammad Awwal, Lahariya, Chandrakant, Laksono, Tri, Lallukka, Tea, Landires, Iván, Lasrado, Savita, Lawal, Basira Kankia, Le, Thao Thi Thu, Le, Trang Diep Thanh, Lee, Munjae, Lee, Wei-Chen, Lee, Yo Han, Lerango, Temesgen L., Lim, David, Lim, Stephen S., Lucchetti, Giancarlo, Ma, Zheng Feei, Maghazachi, Azzam A., Maghbouli, Nastaran, Malakan Rad, Elaheh, Malhotra, Armaan, Malik, Ahmad Azam, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Manu, Emmanuel, Mathangasinghe, Yasith, Mazzotti, Antonio, McPhail, Steven M., Mengist, Belayneh, Mesregah, Mohamed Kamal, Mestrovic, Tomislav, Miller, Ted R., Minh, Le Huu Nhat, Mirahmadi Eraghi, Mohammad, Mirrakhimov, Erkin M., Misganaw, Awoke, Mohamadian, Hashem, Mohamadkhani, Ashraf, Mohamed, Nouh Saad, Mohammadi, Esmaeil, Mohammadi, Soheil, Mohammed, Mesud, Mojiri-Forushani, Hoda, Mokdad, Ali H., Momenzadeh, Kaveh, Momtazmanesh, Sara, Monasta, Lorenzo, Montazeri, Fateme, Moradi, Yousef, Morrison, Shane Douglas, Mostafavi, Ebrahim, Mousavi, Parsa, Mousavi, Seyed Ehsan, Mulita, Admir, Murillo-Zamora, Efrén, Mustafa, Ghulam, Muthu, Sathish, Naik, Ganesh R., Naimzada, Mukhammad David, Nakhostin Ansari, Noureddin, Narasimha Swamy, Sreenivas, Nargus, Shumaila, Nascimento, Paulo R.C., Naseri, Amirreza, Natto, Zuhair S., Naveed, Muhammad, Nayak, Biswa Prakash, Nazri-Panjaki, Athare, Negaresh, Mohammad, Negash, Hadush, Nejadghaderi, Seyed Aria, Nguyen, Dang H., Nguyen, Hau Thi Hien, Nguyen, Hien Quang, Nguyen, Phat Tuan, Nguyen, Van Thanh, Niazi, Robina Khan, Ofakunrin, Akinyemi O.D., Okati-Aliabad, Hassan, Okonji, Osaretin Christabel, Olatubi, Matthew Idowu, Ommati, Mohammad Mehdi, Ordak, Michal, Owolabi, Mayowa O., P A, Mahesh, Padubidri, Jagadish Rao, Pan, Feng, Pantazopoulos, Ioannis, Park, Seoyeon, Patel, Jay, Patil, Shankargouda, Pawar, Shrikant, Pedersini, Paolo, Peprah, Prince, Perna, Simone, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Pigeolet, Manon, Prates, Elton Junio Sady, Rahim, Fakher, Rahimi, Zahra, Rahimi-Dehgolan, Shahram, Rahimi-Movaghar, Vafa, Rahman, Mohammad Hifz Ur, Rahmati, Masoud, Ramasamy, Shakthi Kumaran, Ramasubramani, Premkumar, Rapaka, Deepthi, Rashedi, Sina, Rashedi, Vahid, Rashidi, Mohammad-Mahdi, Rasouli-Saravani, Ashkan, Rawaf, Salman, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Rezaei, Nazila, Rezaei, Negar, Rezaei, Nima, Rezaei, Zahed, Riad, Abanoub, Roever, Leonardo, Roshanzamir, Sharareh, Roy, Priyanka, de Andrade Ruela, Guilherme, Saad, Aly M.A., Saddik, Basema, Sadeghian, Farideh, Saeed, Umar, Safary, Azam, Saghazadeh, Amene, Sagoe, Dominic, Sharif-Askari, Fatemeh Saheb, Sharif-Askari, Narjes Saheb, Sahebkar, Amirhossein, Sakshaug, Joseph W., Salami, Afeez Abolarinwa, Saleh, Mohamed A., Salehi, Sana, Samadzadeh, Sara, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Santos, Djanilson B., Santric-Milicevic, Milena M., Saqib, Muhammad Arif Nadeem, Saravanan, Aswini, Sawyer, Susan, Schaarschmidt, Benedikt Michael, Senapati, Sabyasachi, Sethi, Yashendra, Seylani, Allen, Shafaat, Amir, Shafie, Mahan, Shahabi, Saeed, Shahbandi, Ataollah, Shahrokhi, Shayan, Shaikh, Masood Ali, Shamim, Muhammad Aaqib, Shamshirgaran, Mohammad Ali, Sharfaei, Sadaf, Sharifan, Amin, Sharifi, Azam, Sharma, Rajendra, Sharma, Saurab, Shashamo, Bereket Beyene, Shi, Linhong, Shigematsu, Mika, Shiri, Rahman, Shivarov, Velizar, Siddig, Emmanuel Edwar, Sinaei, Ehsan, Singh, Ambrish, Singh, Jasvinder A., Singh, Paramdeep, Singh, Surjit, Singla, Shweta, Siraj, Md Shahjahan, Skryabina, Anna Aleksandrovna, Solanki, Ranjan, Solomon, Yonatan, Starodubova, Antonina V., Swain, Chandan Kumar, Talic, Stella, Tat, Nathan Y., Temsah, Mohamad-Hani, Terefa, Dufera Rikitu, Tesler, Riki, Thapar, Rekha, Tharwat, Samar, Thayakaran, Rasiah, Ticoalu, Jansje Henny Vera, Tovani-Palone, Marcos Roberto, Tusa, Biruk Shalmeno, Ty, Sree Sudha, Udoakang, Aniefiok John, Vahabi, Seyed Mohammad, Valizadeh, Rohollah, Van den Eynde, Jef, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Villafañe, Jorge Hugo, Vlassov, Vasily, Vo, Anh Truc, Vu, Linh Gia, Wang, Yuan-Pang, Wiangkham, Taweewat, Wickramasinghe, Nuwan Darshana, Winkler, Andrea Sylvia, Wu, Ai-Min, Yadollahpour, Ali, Yahya, Galal, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z., Zakham, Fathiah, Zangiabadian, Moein, Zarrintan, Armin, Zhong, Chenwen, Zhou, Hengxing, Zhu, Zhaochen, Zielińska, Magdalena, Zikarg, Yossef Teshome, Zitoun, Osama A., Zoladl, Mohammad, Tam, Lai-Shan, and Wu, Dongze

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2024
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