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The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming to accurately classify skin lesions. The use of transfer learning to boost skin cancer classification model precision is a promising strategy. In this work, we proposed a hybrid CNN with a transfer learning model and a random forest classifier for skin cancer disease detection. To evaluate the efficacy of the proposed model, it was verified over two datasets of benign skin moles and malignant skin moles. The proposed model is able to classify images with an accuracy of up to 90.11%. The empirical results and analysis assure the feasibility and effectiveness of the proposed model for skin cancer classification. [ABSTRACT FROM AUTHOR]

With the advancement of technology such as video monitoring, Internet-of-things, cloud, and machine learning, Industry 4.0 is working continuously to ensure the security of workers. The workers are equipped with sensors to analyze their activities. In general, the recognition of human activities in cloud-based industrial scenario is leveraged to monitor the safety of the workers. This paper introduced a new optimal clustering method for the activity recognition of workers in industry using cloud based IoT environment. The proposed method uses the temporal and spatial features of human workers in industry. The proposed method is tested on publicly available dataset of different activities maintained into three groups, namely movement, gestures, and object handling, in the context of the medium and small industrial environment. The experimental findings validate that the proposed method achieves 80.2 % , 81.05 % and 80.19 % of average accuracy for movement, gesture, and object handling activities, which clearly outperformed the fuzzy c-means, particle-swarm optimization, and HMM-based activity recognition methods. [ABSTRACT FROM AUTHOR]

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications. [ABSTRACT FROM AUTHOR]

Electricity theft not only results in higher electricity costs for regular paying customers but is also a safety threat to the public due to illegal power connections made for cheating. Many governments and private companies have suffered substantial losses from electricity theft or fraud. Several approaches have been presented in the literature for electricity fraud detection. The existing methods for electricity fraud detection show poor performance as the electricity datasets are generally unbalanced or skewed. To handle skewness issues, SMOTE and ADASYN-based oversampling techniques are employed in some electricity fraud detection methods. However, existing oversampling methods typically increase the overlapping of classes and introduce additional noise. Therefore, in this paper, a new oversampling method named ADASYN-SGWO has been presented. The proposed ADASYN-SGWO employs ADASYN for oversampling and SSO-GWO for intelligent undersampling, which mitigates class imbalance issues in electricity datasets. After reducing the issue of class imbalance, random forest pipeline has been used to detect electricity fraud. To assess the efficiency of the proposed approach, precision, recall, F1 score, accuracy, Matthews correlation coefficient, and balance classification rate of the proposed and other state-of-the-art, including XG-boost classifier, Logistic regression, Gaussian naive Bayes, Random Forest classifier, Ada Boost classifier, LGBM classifier, and Quadrant discriminative analysis, have been computed and compared. Further, the proposed approach is also statistically validated using Friedman and post hoc analysis. The statistical analysis and experimental outcomes witnessed that the proposed ADASYN-SGWO-PRF method has outperformed all the other considered methods with 98.2% accuracy, which is significant for real-time applications of electricity theft detection problems. [ABSTRACT FROM AUTHOR]

Multiple theories exist to explain cancer initiation, although a consensus on this is crucial for developing effective therapies. 'Somatic mutation theory' suggests that mutations in somatic cells during DNA repair initiates cancer but this concept has several attached paradoxes. Research efforts to identify quiescent cancer stem cells (CSCs) that survive therapy and result in metastasis and recurrence have remained futile. In solid cancers, CSCs are suggested to appear during epithelial-mesenchymal transition by the dedifferentiation and reprogramming of epithelial cells. Pluripotent and quiescent very small embryonic-like stem cells (VSELs) exist in multiple tissues but remain elusive owing to their small size and scarce nature. VSELs are developmentally connected to primordial germ cells, undergo rare, asymmetrical cell divisions and are responsible for the regular turnover of cells to maintain tissue homeostasis throughout life. VSELs are directly vulnerable to extrinsic endocrine insults because they express gonadal and gonadotropin hormone receptors. VSELs undergo epigenetic changes due to endocrine insults and transform into CSCs. CSCs exhibit genomic instability and develop mutations due to errors during DNA replication while undergoing excessive proliferation and clonal expansion to form spheroids. Thus tissue-resident VSELs offer a connection between extrinsic insults and variations in cancer incidence reported in various body tissues. To conclude, cancer is indeed a stem cell disease with mutations occurring as a consequence. In addition to immunotherapy, targeting mutations, and Lgr5 + organoids for developing new therapeutics, targeting CSCs (epigenetically altered VSELs) by improving their niche and epigenetic status could serve as a promising strategy to treat cancer. [ABSTRACT FROM AUTHOR]

Background: Patients with Inflammatory bowel disease (IBD) are susceptible to psychiatric co-morbidities. We aimed to ascertain the burden of anxiety, depression, and perceived stress in patients with IBD from north India. Methods: Consenting adult patients with an established diagnosis of IBD were enrolled. The enrolled patients filled the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS) questionnaires. The patient and disease characteristics were analyzed to determine the correlations and predictors of psychiatric comorbidities. Results: A total of 318 patients (255 UC, 63 CD; mean age 40.13 ± 12.06 years, 168 [52.8%] males; mean partial Mayo score 2.10 ± 2.35; and mean HBI 2.77 ± 2.13) were enrolled. The prevalence of anxiety, depression and moderate to high perceived stress was 14%, 12%, and 41%, respectively. Females had higher mean perceived stress, anxiety and depression scores compared to males. The partial Mayo score (PMS) correlated poorly with anxiety (ρ = 0.083, p = 0.187), depression (ρ = 0.123, p = 0.49) and perceived stress (ρ = 0.169; p = 0.007). The Harvey Bradshaw index (HBI) correlated fairly with anxiety (ρ = 0.336, p = 0.007) and poorly with depression (ρ = 0.287, p = 0.022) and perceived stress (ρ = 0.20; p = 0.117). Younger age (OR 0.93, 95% CI 0.90–0.97; p = 0.001) and hand-grip strength (OR 4.63, 95% CI 1.88–11.42; p = 0.001) predicted anxiety in patients with UC while rural area of residence (OR 4.75, 95% CI 1.03–21.98; p = 0.046) and HBI (OR 1.60, 95% CI 1.12–2.29; p = 0.009) were significant predictors of anxiety in patients with CD. Conclusion: Psychiatric comorbidities are common in patients with IBD, with higher prevalence in females. Young adults with UC and sarcopenia; and individuals with active CD living in rural areas are at an increased risk of anxiety. [ABSTRACT FROM AUTHOR]

Recently, precision weed management has emerged as a promising solution for reducing the use of herbicides which is hazardous to crops and human health. Thus, accurate identification of weed in the early stage is the urge of current agricultural practices. Despite recent progress, developing an efficient weed identification system for real field scenarios is a serious challenge. To overcome this, a number of deep learning-based methods have been introduced in the literature. However, these methods require large volume of annotated data images which is rarely available. To address this gap, in this paper, a novel WeedGan method has been introduced for generating realistic synthetic images. The proposed WeedGan adopted concept of federated learning to reduce the computational load by introducing two discriminators. Additionally, a new loss function is defined for the efficient training of the generator. Extensive experiments have been performed to validate the performance of the WeedGan. First, the quality of images generated by the WeedGan was validated on cotton weed dataset in terms of FID score, and discriminator accuracy. The results are compared against four other state-of-the-art GAN models namely, DC-GAN, W-GAN, Info-Gan, and VIT-GAN. Further, classifiers performance of the generated dataset was evaluated using seven state-of-the-art transfer learning-based methods on the original, basic augmented, and WeedGan augmented datasets. The experimental results demonstrate that the proposed WeedGan has outperformed all the considered methods by achieving FID score of 282.76. Moreover, the classification performance of WeedGan augmented dataset was recorded highest with 97.82% on testing and 99.87% training accuracy with DenseNet121. [ABSTRACT FROM AUTHOR]

Background: Fistulizing perianal Crohn's disease (CD) is a debilitating condition associated with significant morbidity and reduction in the quality of life. Magnetic resonance imaging (MRI) of the pelvis is the preferred imaging modality for the comprehensive assessment of the perianal fistula. There is a paucity of data from India on the MRI spectrum of complex perianal fistula in CD. Methods: A single-centre cross-sectional analysis of patients with fistulizing perianal CD, who underwent pelvic MRI between January 2020 and December 2021, was performed. The clinical (age, sex, disease duration, disease location and behavior, disease activity [Perianal Disease Activity Index, PDAI] and treatment received) and radiological (number and location of fistulae, extensions, number and location of internal and external openings, fistula activity, presence or absence of perianal abscess and associated proctitis) characteristics of complex perianal fistula (defined according to the American Gastroenterological Association classification) were recorded. Results: Of total 175 patients with CD who attended the gastroenterology clinic during the study period, 27 (15.42%) (mean age 42±15.5 years, 62.96% females and median disease duration four years) had complex perianal fistula and were included in the analysis. The mean PDAI was 5.48±2.53. The median Van Assche Index was 17 (interquartile range [IQR] 13–19). A majority (96.29%) of the fistulae were trans-sphincteric and four (14.81%) fistulae extended into the supralevator space. All fistulae were active on MRI. Concomitant perianal abscess and proctitis were seen in 59.26% (n=16) and 62.96% (n=17) of patients, respectively. Combination therapy with biologics and antibiotics/immune-suppressants were the most commonly prescribed medical therapy. Six (22.22%) patients underwent combined medical and surgical (non-cutting seton, fistulectomy, fecal diversion) treatment. Conclusion: The cumulative risk of the development of fistulizing perianal CD in a northern Indian cohort was similar to the western populations. Complex perianal fistulae were predominantly trans-sphincteric and commoner in females. MRI evaluation is pivotal for the delineation of fistula anatomy, assessment of disease extent and activity and the evaluation of concomitant perianal abscess and other complications. [ABSTRACT FROM AUTHOR]

Background/Aims: Primary sclerosing cholangitis (PSC) represents the most common hepatobiliary extraintestinal manifestation of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD). Limited data exist on PSC in patients with IBD from India. We aimed to assess the prevalence and disease spectrum of PSC in Indian patients with IBD. Methods: Database of IBD patients at 5 tertiary care IBD centers in India were analyzed retrospectively. Data were extracted and the prevalence of PSC-IBD was calculated. Results: Forty-eight patients out of 12,216 patients with IBD (9,231 UC, 2,939 CD, and 46 IBD unclassified) were identified to have PSC, resulting in a prevalence of 0.39%. The UC to CD ratio was 7:1. Male sex and pancolitis (UC) or colonic CD were more commonly associated with PSC-IBD. The diagnosis of IBD preceded the diagnosis of PSC in most of the patients. Majority of the patients were symptomatic for liver disease at diagnosis. Eight patients (16.66%) developed cirrhosis, 5 patients (10.41%), all UC, developed malignancies (3 colorectal cancer [6.25%] and 2 cholangiocarcinoma [4.16%]), and 3 patients died (2 decompensated liver disease [4.16%] and 1 cholangiocarcinoma [2.08%]) on follow-up. None of the patients mandated surgical therapy for IBD. Conclusions: Concomitant PSC in patients with IBD is uncommon in India and is associated with lower rates of development of malignancies. [ABSTRACT FROM AUTHOR]

Aim: To compare the healing following various maxillofacial incisions using 2-octyl cyanoacrylate (2-OCA) and prolene (4-0) sutures. Objective: To evaluate the complications and aesthetic outcome of the incision wounds after closure using 2-OCA and prolene (4-0) sutures. Materials and Methods: The patient undergoing an extraoral incision was evaluated for pain, inflammation, infection, dehiscence, scar, and surface texture. In Group I, 15 patient incisions were closed with 2-OCA and in Group II, 15 patient incisions were closed with prolene (4-0) suture. Result: A few patients experienced pain with Group I and then Group II, but infection, inflammation, and dehiscence were more in Group I than in Group II. Conclusion: A cosmetic outcome was higher in the cyanoacrylate group, and the surface texture was satisfying in the same group. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a type of contextual text mining that determines how people feel about emotional issues that are frequently discussed on social media. The sentiments of emotive data are analyzed using a variety of sentiment analysis approaches, including lexicon-based, machine learning-based, and hybrid methods. Unsupervised approaches, particularly clustering methods are preferred over other methods since they can be applied directly to unlabeled datasets. Therefore, a clustering method based on an improved exponential cuckoo search has been proposed in this study for sentiment analysis. The proposed clustering method finds the optimal cluster centers from emotive datasets, which are then utilized to determine the sentiment polarity of emotive contents. The proposed improved exponential cuckoo search is first tested on standard and CEC-2013 benchmark functions before being utilized to determine the best cluster centroids from sentimental datasets. To assess the efficiency of the proposed method, it has been compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size-based cuckoo search, and spiral cuckoo search on nine sentimental datasets. The Experimental results and statistical analysis have proven the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Cancer continues to remain a "Black Box," as there is no consensus on how it initiates, progresses, metastasizes, or recurs. Many imponderables exist about whether somatic mutations initiate cancer, do cancer stem cells (CSCs) exist, and if yes, are they a result of de-differentiation or originate from tissue-resident stem cells; why do cancer cells express embryonic markers, and what leads to metastasis and recurrence. Currently, the detection of multiple solid cancers through liquid biopsy is based on circulating tumor cells (CTCs) or clusters, or circulating tumor DNA (ctDNA). However, quantity of starting material is usually adequate only when the tumor has grown beyond a certain size. We posit that pluripotent, endogenous, tissue-resident, very small embryonic-like stem cells (VSELs) that exist in small numbers in all adult tissues, exit from their quiescent state due to epigenetic changes in response to various insults and transform into CSCs to initiate cancer. VSELs and CSCs share properties like quiescence, pluripotency, self-renewal, immortality, plasticity, enrichment in side-population, mobilization, and resistance to oncotherapy. HrC test, developed by Epigeneres, offers the potential for early detection of cancer using a common set of VSEL/CSC specific bio-markers in peripheral blood. In addition, NGS studies on VSELs/CSCs/tissue-specific progenitors using the All Organ Biopsy (AOB) test provide exomic and transcriptomic information regarding impacted organ(s), cancer type/subtype, germline/somatic mutations, altered gene expressions, and dysregulated pathways. To conclude, HrC and AOB tests can confirm the absence of cancer and categorize the rest of subjects into low/moderate/high risk of cancer, and also monitor response to therapy, remission, and recurrence. [ABSTRACT FROM AUTHOR]

Rapid urbanization has recently caused serious problems for cities all around the world. Smart cities have drawn much interest from researchers in the present research paradigm to manage the expanding urban population. Frameworks for smart cities are planned and implemented using platforms based on blockchain and the Internet of Things (BIOT). Smart cities may use the BIoT platform to provide improved transportation, food traceability, and healthcare services. Food safety is one of the sectors where less research has been done than the others. The importance of food safety is now more widely recognized, making it essential to improve the traceability and transparency of the food supply chain. In this paper, a novel BIOT-based layered framework using EOSIO has been proposed for effective food traceability. The proposed system first identifies the suitable traceability units to provide better transparency and traceability and then defines and implements a layered architecture using Ethereum and EOSIO blockchain platforms. The performance of the proposed EOSIO-based model is evaluated using the practicality of the consensus algorithm, block production rate, throughput, and block confirmation time. The proposed traceability system attains a block production rate of 0.5 s and a block confirmation time of 1 s, which is much lower than the Ethereum-based traceability system. Hence, from the experimental evidence, the superiority of the proposed EOSIO-based food traceability can be observed. [ABSTRACT FROM AUTHOR]

Obesity has emerged as a major public health problem because of its adverse impact on health, increased mortality risk, and associated social, psychological, and economic costs. The obesity pandemic and its health consequences have not spared the population with end-stage renal disease (ESRD). Obesity impacts many inter-related considerations for transplant practice including candidate selection, outcomes prediction pre and post-transplant, and waitlist management. Many ESRD patients, who are on maintenance dialysis and awaiting kidney transplantation, are obese. Most transplant centers consider obesity a relative contraindication for renal transplant due to associated risks of significant surgical complications and poor outcome. Bariatric surgery has emerged as an effective therapy for obesity, and the emerging experience although limited is promising. The initial reports in transplant patients, who were previously deemed unfit for transplantation due to clinically severe obesity, have demonstrated significant weight loss post bariatric surgery. The numbers of clinically severe obese ESRD patients, seeking bariatric surgery, are growing despite the invasive nature and the high costs of bariatric surgery. Laparoscopy has radically shortened the recovery time and decreased the complications associated with bariatric surgery. We report 4 patients with ESRD, who were on maintenance hemodialysis and were deemed unfit for renal transplant due to their clinically severe obesity. They underwent laparoscopic sleeve gastrectomy (LSG), a restrictive bariatric surgery, which induced effective weight loss. Subsequently, these patients underwent renal transplantation with satisfactory outcome and till date remains well. [ABSTRACT FROM AUTHOR]

Background: Fertility preservation and restoration in cancer patients/survivors is the need of present times when increased numbers of patients get cured of cancer but face infertility as a serious side effect. Resveratrol has beneficial effects on chemoablated ovaries and testes in mice but has failed to enter the clinics because of extremely poor bioavailability. The present study was undertaken to evaluate the protective and curative effects of Extremely active Resveratrol (XAR™)- a nano-formulation of resveratrol with significantly improved bioavailability- on mouse ovary and testis after chemotherapy. Effects of XAR™ and FSH were compared on stimulation of follicle growth in adult mice ovaries. XAR™ (25 mg/kg) was administered for two days prior to chemotherapy to study the protective effects on the mouse gonads. XAR™ was also administered for 14 days post chemoablation to study the regenerative effects. Besides effect on numbers of primordial and growing follicles and spermatogenesis, the effect of XAR™ was also evaluated on the transcripts specific for ovarian/testicular stem/progenitor/germ cells, their proliferation, differentiation, meiosis, and the antioxidant indices. Results: Similar to FSH, XAR™ increased the numbers of primordial follicles (PF) as well as growing follicles. It protected the gonads from the adverse effects of chemotherapy and showed the ability to regenerate non-functional, chemoablated gonads. Besides stimulating follicle growth in adult ovaries similar to FSH, XAR™ also protected the testes from the adverse effects of chemotherapy and improved spermatogenesis. This was accompanied by improved anti-oxidant indices. Conclusions: The results of the present study potentiate the use of XAR™ in pilot clinical studies to protect gonadal function during oncotherapy and also regenerate non-functional gonads in cancer survivors by improving antioxidant indices and stem cell-based tissue regeneration. [ABSTRACT FROM AUTHOR]

Background: Fertility preservation and restoration in cancer patients/survivors is the need of present times when increased numbers of patients get cured of cancer but face infertility as a serious side effect. Resveratrol has beneficial effects on chemoablated ovaries and testes in mice but has failed to enter the clinics because of extremely poor bioavailability. The present study was undertaken to evaluate the protective and curative effects of Extremely active Resveratrol (XAR™)- a nano-formulation of resveratrol with significantly improved bioavailability- on mouse ovary and testis after chemotherapy. Effects of XAR™ and FSH were compared on stimulation of follicle growth in adult mice ovaries. XAR™ (25 mg/kg) was administered for two days prior to chemotherapy to study the protective effects on the mouse gonads. XAR™ was also administered for 14 days post chemoablation to study the regenerative effects. Besides effect on numbers of primordial and growing follicles and spermatogenesis, the effect of XAR™ was also evaluated on the transcripts specific for ovarian/testicular stem/progenitor/germ cells, their proliferation, differentiation, meiosis, and the antioxidant indices. Results: Similar to FSH, XAR™ increased the numbers of primordial follicles (PF) as well as growing follicles. It protected the gonads from the adverse effects of chemotherapy and showed the ability to regenerate non-functional, chemoablated gonads. Besides stimulating follicle growth in adult ovaries similar to FSH, XAR™ also protected the testes from the adverse effects of chemotherapy and improved spermatogenesis. This was accompanied by improved anti-oxidant indices. Conclusions: The results of the present study potentiate the use of XAR™ in pilot clinical studies to protect gonadal function during oncotherapy and also regenerate non-functional gonads in cancer survivors by improving antioxidant indices and stem cell-based tissue regeneration. [ABSTRACT FROM AUTHOR]

Glucocorticoid induced osteoporosis (GIOP) is the second most leading cause of osteoporosis. We have identified a compound, a benzofuran pyran hybrid compound 4e that has osteogenic potential and we wanted to assess its efficacy in GIOP in male mice. We assessed the effect of dexamethasone and compound 4e on primary osteoblasts using various cell based and immunofluorescence assays. For in vivo studies we administered methylprednisolone and compound 4e as a prophylactic measure in male Balb/c mice for 28 days and then evaluated the effect on bone microarchitecture by microCT, bone formation by histology along with clinically relevant bone markers. Compound 4e preserved osteoblast differentiation as evident by higher ALP positive cells and mineralization in compound treated groups. Compound 4e also increased the expression of osteogenic genes. This compound guarded β-catenin expression both in vitro and in vivo as confirmed by western blot and immunofluorescence assays. This led to the preservation of bone microarchitecture and cortical thickness at 2.5 mg kg−1 and 5 mg kg−1 doses. Further compound 4e enhanced bone formation rate and regulated osteocyte death. The osteogenic potential of compound 4e was reflected by an increased level of serum marker osteocalcin and decreased levels of SOST and CTX-I. Overall, Compound 4e is able to overcome the catabolic effect of dexamethasone on bone by targeting the canonical WNT/β-catenin signaling as evidenced by both in vitro and in vivo studies. [ABSTRACT FROM AUTHOR]

Coherent surface scattering imaging is a powerful tool for imaging a surface/interface of a thin nanostructure deposited on an opaque substrate. A mathematical conversion of an object image from a scattering pattern is essential for coherent surface scattering imaging to visualize structures of specimens. It has been achieved by using phase retrieval algorithms requiring oversampling in scattering patterns and employing alternating projection approaches. It is a computationally challenging and time-consuming process. In this paper, we demonstrate CSSI-NN, which is a deep learning neural network model to predict images of objects from scattering intensities in coherent surface scattering imaging. This model allowing for the instant outcome from scattering patterns would be tremendously beneficial not only for effective experiments but also for data analysis of phase retrieval. [ABSTRACT FROM AUTHOR]

Cancer is a devastating disease whose incidence has increased in recent times and early detection can lead to effective treatment. Existing detection tools suffer from low sensitivity and specificity, and are high cost, invasive and painful procedures. Cancers affecting different tissues, ubiquitously express embryonic markers including Oct-4A, whose expression levels have also been correlated to staging different types of cancer. Cancer stem cells (CSCs) that initiate cancer are possibly the 'transformed' and pluripotent very small embryonic-like stem cells (VSELs) that also express OCT-4A. Excessive self-renewal of otherwise quiescent, pluripotent VSELs in normal tissues possibly initiates cancer. In an initial study on 120 known cancer patients, it was observed that Oct-4A expression in peripheral blood correlated well with the stage of cancer. Based on these results, we developed a proprietary HrC scale wherein fold change of OCT-4A was linked to patient status – it is a numerical scoring system ranging from non-cancer (0–2), inflammation (>2–6), high-risk (>6–10), stage I (>10–20), stage II (>20–30), stage III (>30–40), and stage IV (>40) cancers. Later the scale was validated on 1000 subjects including 500 non-cancer and 500 cancer patients. Ten case studies are described and show (i) HrC scale can detect cancer, predict and monitor treatment outcome (ii) is superior to evaluating circulating tumor cells and (iii) can also serve as an early biomarker. HrC method is a novel breakthrough, non-invasive, blood-based diagnostic tool that can detect as well as classify solid tumors, hematological malignancies and sarcomas, based on their stage. [ABSTRACT FROM AUTHOR]

With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score. [ABSTRACT FROM AUTHOR]

With the advancement of wireless communication, Internet of Things (IoT), and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big datasets produced from IoT devices, these algorithm fail to respond within the desired time due to high computation cost. This article presents a new metaheuristic-based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big datasets. The optimization efficacy the proposed method is validated against 17 benchmark functions, and the results are compared with five other recent algorithms, namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Furthermore, a parallel version of the proposed method is introduced using MapReduce [MapReduce-based MDBO (MR-MDBO)] for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on two benchmark UCI datasets and three real IoT-based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the six other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO-based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times. [ABSTRACT FROM AUTHOR]

Gravitational search algorithm is a nature-inspired algorithm based on the mathematical modelling of the Newton's law of gravity and motion. In a decade, researchers have presented many variants of gravitational search algorithm by modifying its parameters to efficiently solve complex optimization problems. This paper conducts a comparative analysis among ten variants of gravitational search algorithm which modify three parameters, namely Kbest, velocity, and position. Experiments are conducted on two sets of benchmark categories, namely standard functions and CEC2015 functions, including problems belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value and convergence graph. In experiments, IGSA has achieved better precision with balanced trade-off between exploration and exploitation. Moreover, triple negative breast cancer dataset has been considered to analysis the performance of GSA variants for the nuclei segmentation. The variants performance has been analysed in terms of both qualitative and quantitive with aggregated Jaccard index as performance measure. Experiments affirm that IGSA-based method has outperformed other methods. [ABSTRACT FROM AUTHOR]

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus, a hyperglycaemic condition that develops at the time of pregnancy, causes approximately 7% of complications during pregnancies world wide. Syzygium jambolanum has been proved clinically for the management of diabetes mellitus and is being prescribed for the management of diabetes in pregnant women. Objectives: This study has been designed to evaluate the safety of Syzygium jambolanum mother tincture on the development of zebrafish embryos. Materials and Methods: Zebrafish embryos were exposed to 1%, 5% and 10% of Syzygium jambolanum mother tincture with the same concentration of alcohol. Observations like mortality, hatching at 24, 48 and 72 hr post fertilization and yolk sac edema, length, eye width, pericardial edema and heart rate were recorded at 72 hr post fertilization. The locomotor activity was recorded at 7 day post fertilization. Results: Results showed that mortality in zebrafish embryos with the treatment of Syzygium jambolanum mother tincture at 10% could be due to alcohol as similar results were observed with 10% alcohol also. Moreover, changes observed in heart beat in embryos with the treatment of alcohol at different concentration were reduced by the Syzygium jambolanum indicating its possible protective role in cardiac parameters. Conclusion: Results suggest the safety of Syzygium jambolanum mother tincture at low concentrations (1% and 5%) on development of zebrafish embryos. The embryo toxicity induced by 10% concentration of Syzygium jambolanum may be attributed to the ethanol. Further developmental toxicity studies on animals need to be carried out to validate the results obtained followed by clinical validation. [ABSTRACT FROM AUTHOR]

Due to the rapidly spreading nature of coronavirus, a pandemic situation has emerged around the world. It is affecting society at large that includes the global economy and public health too. It was found in recent studies that the novel and unknown nature of this virus makes it more difficult to identify and treat the affected patient in the early stage. In this context, a time-consuming method named reverse transcription-polymerase chain reaction (RT-PCR) is being used to detect the positive cases of COVID-19, which requires blood samples of the suspects to diagnose the disease. This paper presents a new deep learningbased method to detect COVID-19 cases using chest X-ray images as the recent studies show that the radiology images have relevant features that can be used to predict the COVID-19. The proposed method is developed for binary classification to identify that a person is infected with COVID-19 or not. A total of 2400 X-ray images are taken for the experimental work. It includes 1000, COVID-19, and 1000, non-COVID-19 images, 200, COVID-19, and 200, non-COVID-19 testing images. The proposed method has been compared with the existing state-of-the-art methods on various statistical parameters which give better results with higher accuracy in diagnosing the COVID-19 cases. The proposed method has obtained 98.25% accuracy, 98.49% precision, 98% sensitivity, 98.50% specificity, and 98.25% F1 score. [ABSTRACT FROM AUTHOR]

The effect of substituents on the surface adsorption equilibria of thiophenols and isoquinolines on gold substrates was studied using surface-enhanced Raman spectroscopy (SERS) in order to determine the effects of the localized dipole moments and charge donating/withdrawing properties on the binding affinity. Two common classes of molecules used in SERS studies were examined, which included substituted aromatic thiols and nitrogen heterocyclic aromatic molecules (azaarenes), due to their strong affinity for gold surfaces. Unsubstituted thiophenol in aqueous solution binds strongly to gold surfaces. Therefore, it is difficult to measure an equilibrium constant, since even at concentrations of 10−8 M nearly a complete self-assembled monolayer (SAM) forms. In contrast, substituted thiophenols with electron-withdrawing groups, such as halogenated thiophenols, bind much less strongly, allowing equilibrium constants to be obtained. It is believed that the substituent withdrawing charge away from the sulfur atom affects the adsorption/binding between the analyte and surface. Thiophenols substituted with electron donating groups behaved similar to unsubstituted thiophenol, where a SAM was observed at concentrations as low as 10−8 M. These functional groups did not hinder the ability of the sulfur groups to bind with gold. In addition, a series of bromine-substituted isoquinolines, a group of azaarene compounds, were measured to determine the effects that the bromine substituent has when it is bound to the two different rings and if position on the rings has an effect. The azaarene class of molecules, including isoquinoline, adsorbs less strongly than thiophenols, and a dual Langmuir isotherm phenomenon is observed where protonated and neutral bromoisoquinoline molecules occupy two different types of sites on Klarite substrates, which consist of inverted micro-pyramids on Si wafers with rough/nanostructured Au coatings. Protonated isoquinolines bind to nucleophilic sites on the substrates which tend to occur on flatter regions of the substrate. By contrast, neutral isoquinolines bind to electrophilic sites which are predominant near microscopic edges on the substrate. The presence of the bromine substituent and its position in the fused ring structure changes the Gibbs free energies of adsorption, depending on which ring the substituent is in. These results can help to guide the development of SERS for analytical applications by demonstrating how changes in functional groups can affect the equilibrium constants, which are critical for determining the effectiveness of SERS as a tool for trace detection of analytes. [ABSTRACT FROM AUTHOR]

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In the past one decade there has been significant increase in the growth of digital data. Therefore, good data mining techniques are important for the better decision making. Clustering is one of the key element in the field of data mining. K-means is a very popular algorithm present in the literature which is widely used for the clustering purpose. However k-means algorithm suffers from the problem of stucking into local optimum solution because of it’s dependency on the random initialization of initial cluster center. In this paper a novel variant of Bat algorithm based on dynamic frequency is introduced. Further the proposed variant is hybridized with K-means to present a new approach for clustering in distributed environment. Since evolutionary computation is very computation intensive, traditional sequential algorithms are not able to provide satisfactory results within the reasonable amount of time for the large scale data problems. To mitigate this problem the proposed variant is parallelized using the MapReduce model in the Hadoop framework. The experimental results show that the proposed algorithm has outperformed K-means, PSO and Bat algorithm on eighty percent of the benchmark datasets in terms of intra-cluster distance. Further DBPKBA has also achieved significant speedup for dealing with massive datasets with increase in the number of nodes. [ABSTRACT FROM AUTHOR]

Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm’s COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models. [ABSTRACT FROM AUTHOR]

Resveratrol generated enormous interest as it improved functions of multiple organs and could delay aging in animal models. However, basic mechanism of action was not understood and due to poor bioavailability, it has failed to enter the market. A highly active nano-formulation of resveratrol (XAR™) with enhanced bioavailability is now available. Present study was undertaken to evaluate its effects on stem cells biology in the human peripheral blood. Twelve healthy participants were enrolled of which five received XAR™, five were age-matched placebo controls and two were 76 and 85 years old. Peripheral blood was processed to study serum profile to monitor cardiac and pancreatic functions and subjected to density gradient centrifugation to enrich pluripotent (VSELs) and adult stem cells that get enriched along with red blood cells and in the Buffy coat respectively on Day 2 and Day 15 after XAR™ treatment. The XAR™ treatment resulted in an increased expression of pluripotency transcripts specific for VSELs (Oct-4A, Nanog and Sox2) on D2; specific transcripts for differentiation in the progenitors including Oct-4, Ikaros, CD14, CD90 on D15, and anti-ageing and tumor suppressor transcripts NAD, SIRT1, SIRT6 and p53 in both stem cells and progenitors. An improvement of cardiac and pancreatic markers in serum profile was also observed on D15. The decline in VSELs numbers with age and beneficial effects of the XAR™ treatment were evident by up-regulation of specific transcripts and on serum profile. XAR™ is a promising molecule that has the potential to activate pluripotent VSELs and tissue committed adult stem cells ‘progenitors’ resulting in the rejuvenation of various body tissues and for improved, cancer-free health with advanced age. [ABSTRACT FROM AUTHOR]

Rail-based metro system is crucial for supporting a city's expansion and growth. However, urban metro projects being capital intensive with long gestation and pay back periods may not be financially viable but socially desirable. More and more cities in India are announcing metro projects committing huge budgetary resources to these projects at the cost of sectors such as health and education. Given the huge requirement of capital and possibility of attracting willing as well as capable private investors to undertake urban transport projects, promoting public-private partnerships (PPPs) could be a key priority. However, none of the metro projects launched during past 7 years in India have been planned on PPP framework. International experience in PPP metro and our own Delhi airport metro experience have demonstrated that many issues can influence the successful implementation of PPPs in such projects. In this context, outcome of Hyderabad metro is keenly watched as it is one of the largest metro rail projects built under a PPP framework anywhere in the world. The paper reviews the research conducted on critical success factors (CSFs) that are necessary for adopting PPP models in urban metros in Indian context. Eighteen CSFs identified through literature survey have been validated in a pilot study through structured questionnaire and grouped in seven macro-factors through hierarchical cluster analysis. A case study has been attempted using SAP-LAP framework to analyse the identified CSFs in real-life settings of the first two stages of Hyderabad metro project. Key players working on the project were interviewed during a field study to gather feedback on actions they took on factors critical to success. Observations and findings have been synthesised as learnings for future such projects. [ABSTRACT FROM AUTHOR]