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Byline: Neeta. Singh, Ankur. Mandelia, Neha. Nigam, Sangeeta. Yadav, Mandakini. Pradhan Context: Klippel-Trenaunay-Weber syndrome (KTWS) is a rare disease characterized by a triad of venous malformations, vascular skin nevus and [...]

India's renewable energy opportunities include solar, wind, small hydropower and biopower, among other technologies. The Indian government recognizes harnessing these resources are vital for economic growth and improving the climate. [...]

AbstractMillettia pinnatais an important medicinal plant that has been used as a treatment of various diseases due to presence of wide range of pharmacological properties. The plant contains quercetin, kaempferol, karanjin, pongaglabrone, kanjone, kanugin, gammatin, pongaglabol, and other bioflavonoids. Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, and antidiabetic activities etc. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols, i.e. biosynthesis of kaempferol from dihydrokaempferol. The current work examined the binding affinity-based approach to improve the enzyme catalytic activity using computational methods. Sequential site-directed mutagenesis was used to create four mutants with the goal to increase hydrogen bonds and further improving the ligand (dihydrokaempferol) binding efficiency. Simulations were done to monitor the stability of the mutants followed by molecular docking to confirm interactions with ligand. For structure validation, various dynamic analysis like RMSD, RMSF, ROG, SASA, H-bond, PCA, DCCM, and FEL were performed, which predicts the stability of wild-type (WT) proteins and mutants. The Mutant_2 and Mutant_3 showed maximum H-bonding and better stability than other mutants and WT that proved higher affinity suggesting improved catalysis. Mutant_2 and Mutant_3 exhibited binding affinities of −7.6 and −8.2 kcal/mol, respectively for the ligand. The outcome of present study will provide significant improvement in synthesis of kaempferol and other plant-based flavonoids.Communicated by Ramaswamy H. Sarma

Investigations on the anticancer potential of dietary phytoconstituents have made significant progress over the past 10 years. Eugenol, a phenolic compound found in various plants, has gained attention for its diverse medicinal properties. Prominent eugenol-producing plants include clove, cinnamon, tulsi, and pepper. The substance has been shown to possess features that include anesthetic, neuroprotective, anti-inflammatory, anti-diabetic, and anti-cancer effects. Eugenol is employed as a marker in pharmaceutical analysis for individual medications and drug packages. It has demonstrated the potential applications in domestic, personal, and dental care as well as in other industries. Eugenol is a flavoring component found in baked products, chewing gum, and non-alcoholic beverages. Eugenol exhibits numerous health benefits, including antioxidant, anti-inflammatory, antimicrobial, and anticancer activities. Its antioxidant properties stem from its ability to neutralize reactive oxygen species (ROS) through free radical scavenging and hydrogen donation. Eugenol’s anti-inflammatory and analgesic effects have been linked to its role in reducing oxidative stress and modulating inflammatory processes. Like other phytoconstituents, eugenol is vulnerable to oxygen sensitivity and volatility, and it suffers poor bioavailability. To resolve these problems nanotechnology intervention continues to be a worthwhile option. Recently, few studies have reported about the novel eugenol nanoformulation approaches for the enhancement of therapeutic efficiency. In this review, an effort has been made to compile developed formulations, extraction methods, mechanisms of action, and clinical trials.

The prediction of compressive strength in concrete is an important part of civil engineering because it influences structural design and durability. Recent improvements have brought nano-silica as an addition, improving concrete’s mechanical qualities. This study investigates the use of the Random Forest (RF) method to estimate the compressive strength of nano-silica concrete, taking advantage of its ability to manage complicated, non-linear interactions among factors. We used a large dataset with varying concentrations of nano-silica and other concrete mix components. Normalisation and feature selection were used as preprocessing techniques to improve the model’s performance. The RF model, which is known for its resilience and accuracy, was trained and verified, displaying better predictive performance than typical linear regression models. The results showed that the RF method could efficiently capture the subtle correlations between nano-silica content and compressive strength, resulting in accurate predictions over a wide range of concrete compositions. This prediction model is a vital tool for civil engineers, allowing for more precise and efficient design of nano-silica concrete mixtures, resulting in stronger and more durable projects.

This work depicts modeling and analysis of two-staged power electronic interface used for grid-connected solar photovoltaic generator. The power circuit of power electronic interface comprises of a quadratic boost converter with voltage multiplier cell and 1-ϕvoltage source inverter. The said converter provides a higher voltage conversion ratio and lower voltage stress than other converters. The control circuit of the power electronic interface comprises of perturb and observe-based maximum power point tracking scheme with adaptive perturbation. For the dual-loop control for the grid-connected inverter, fractional-order PI controller and variable band hysteresis current controller are used. Small-signal modeling and analysis of converter and inverter configuration are provided in this work. The simulation results and experimental validation of the grid-connected system are provided in this work.

In the clinical development of farnesyltransferase inhibitors (FTIs) for HRAS-mutant tumors, responses varied by cancer type. Co-occurring mutations may affect responses. We aimed to uncover cooperative genetic events specific to HRAS-mutant tumors and to study their effect on sensitivity to FTIs. Using targeted sequencing data from the MSK-IMPACT and Dana-Farber Cancer Institute Genomic Evidence Neoplasia Information Exchange databases, we identified comutations that were observed predominantly in HRAS-mutant versus KRAS-mutant or NRAS-mutant cancers. HRAS-mutant cancers had a higher frequency of coaltered mutations (48.8%) in the MAPK, PI3K, or RTK pathway genes, compared with KRAS-mutant (41.4%) and NRAS-mutant (38.4%) cancers (p< 0.05). Class 3 BRAF, NF1, PTEN, and PIK3CAmutations were more prevalent in HRAS-mutant lineages. To study the effects of comutations on sensitivity to FTIs, HrasG13Rwas transfected into “RASless” (Kraslox/lox/Hras−/−/Nras−/−/RERTert/ert) mouse embryonic fibroblasts (MEFs), which sensitized nontransfected MEFs to tipifarnib. Comutation in the form of Ptenor Nf1deletion and Pik3caH1047Rtransduction led to resistance to tipifarnib in HrasG13R-transfected MEFs in the presence or absence of KrasWT, whereas BrafG466Etransduction led to resistance to tipifarnib only in the presence of KrasWT. Combined treatment with tipifarnib and MEK inhibition sensitized cells to tipifarnib in all settings, including in MEFs with PI3K pathway comutations. HRAS-mutant tumors demonstrate lineage-dependent MAPK or PI3K pathway alterations, which confer resistance to tipifarnib. The combined use of FTIs and MEK inhibition is a promising strategy for HRAS-mutant tumors.

Diffraction gratings, with their periodically ordered structures, have been critical components in acoustics, optics, and spectroscopy for over a century. The classical grating equation describes the emergence of diffraction phenomena by gratings, considering the groove periodicity and the characteristics of the incident wave. These gratings find extensive applications in communication, spectroscopy, architectural acoustics, and underwater research, and they are foundational to pioneering investigations in phononic crystals and meta-materials. While much attention has been given to understanding the diffraction behavior of linear acoustics concerning gratings, the literature lacks research regarding the influence of high-amplitude ultrasonic waves, which introduce observable nonlinear effects. This experimental enquiry presents a pioneering methodology for isolating higher harmonics from these nonlinear phenomena. We have developed a spatial filtering apparatus with a single-frequency transducer and a specially designed grating profile, enabling precise frequency selection or rejection.

•‘Alphonso’ is variety of mango that has suffered from spongy tissue malady.•Potential of X-ray imaging was tested for identifying internal spongy tissue defects.•CNN, AlexNet, ResNet, VGG16 and VGG19 models were used for automatic classification.•Models were optimized by hyperparameter tuning and cross-validation technique.•Deep transfer learning models achieved >90% mango binary classification accuracy.

Byline: Priyanka. Lekhwani, Anant. Nigam, Nikhil. Marwah, Shradha. Jain Background: Dentistry has made a remarkable progress in techniques, materials and technologies, however the anxiety of the child related to dental [...]

Text recognition systems typically work well for printed documents but struggle with handwritten documents due to different writing styles, background complexities, added noise of image acquisition methods, and deformed text images such as strike-offs and underlines. These deformities change the structural information, making it difficult to restore the deformed images while maintaining the structural information and preserving the semantic dependencies of the local pixels. Current adversarial networks are unable to preserve the structural and semantic dependencies as they focus on individual pixel-to-pixel variation and encourage non-meaningful aspects of the images. To address this, we propose a Variable Cycle Generative Adversarial Network (VCGAN) that considers the perceptual quality of the images. By using a variable Content Loss (Top-kVariable Loss (TVk) ), VCGANpreserves the inter-dependence of spatially close pixels while removing the strike-off strokes. The similarity of the images is computed with TVkconsidering the intensity variations that do not interfere with the semantic structures of the image. Our results show that VCGANcan remove most deformities with an elevated F1 score of 97.40%and outperforms current state-of-the-art algorithms with a character error rate of 7.64%and word accuracy of 81.53%when tested on the handwritten text recognition system

This paper adopts a highly effective numerical approach for approximating non-linear stochastic Volterra integral equations (NLSVIEs) based on the operational matrices of the Walsh function and the collocation method. The method transforms the integral equation into a system of algebraic equations, which allows for the derivation of an approximate solution. Error analysis is performed, confirming the effectiveness of the proposed method, which results in a linear order of convergence. Numerical examples are provided to illustrate the precision and effectiveness of the proposed method.

In the present study, the multiple rectangular and a plus shaped slots microstrip patch antenna (MPA) is designed and its performance has been analysed on the basis of various dielectric constants material such as silicon, silicon dioxide, polyethylene, Teflon, etc. Introduction of slots in MPA is important to find the optimized geometry. In this article, MPA has been designed with nine rectangular slots and one plus shaped slot for multiband application and investigate its accomplishment. The miniaturization has been achieved by inserting the rectangular and plus shaped slots that has been further created on FR4 substrate. The dimensions of this proposed MPA are 26 mm × 18.61 mm and the antenna is uniform from both the sides. The proposed antenna can operate on different frequencies with operating bands of 3.62 GHz for global system for mobile, 5.27 GHz for wireless fidelity (Wi-Fi), and 6.74 GHz for worldwide interoperability for microwave access (WiMAX). The proposed MPA might also be used for certain other broadband applications in the above respective frequencies. Computer simulation technology programme is utilized to characterize the parameters like Voltage Standing Wave Ratio, return loss (S11), surface current, and radiation pattern.

Byline: Anita. Singh, Ankur. Mandelia, Apurva. Kawdiya, Kirti. Naranje, Neha. Nigam Bronchogenic cyst is one of the rare developmental lung conditions. Depending on the location, it can cause significant compression [...]

To reduce the fabrication complexity and cost of nanoscale biosensors, a novel electrically doped concept is proposed for the first time, aiming to implement a dielectric-modulated junctionless tunnel field-effect transistor (DM-ED-JLTFET) for label-free biomolecule detection. The n+ drain and p+ source regions in the proposed device are induced by applying a bias of polarity gate-1 (PG-1) = +1.2 V and PG-2 = -1.2 V, respectively, over the ultrathin silicon body. A nanogap cavity beneath the PG-2 terminal is formed by etching a part of the dielectric oxide layer toward the tunneling interface to capture the biomolecule test sample. The existence of neutral and charged molecules in the cavities has been investigated using changes in the electrical characteristics of the proposed biosensor, such as drain current, energy band, and electric field. The sensing performance of the proposed biosensor is evaluated in terms of drain current ( ${I} {_{\text {DS}}}$ ), subthreshold swing (SS), threshold voltage ( ${V} {_{\text {Th}}}$ ), switching ratio ( ${I} {_{\text {ON} }}/{I} {_{\text {OFF} }}$ ), and transconductance-to-current ratio ( ${g} {_{\text {m}}}/{I} {_{\text {DS}}}$ ). The proposed DM-ED-JLTFET biosensor achieves a maximum sensitivity of ${5.58} \times {10}^{{10}}$ with a fully filled nanogap for a neutral biomolecule with a dielectric constant of 12. The effects of non-ideal issues on sensitivity, such as different fill factors (FFs) and steric hindrances, are also studied of the proposed biosensor to understand the practical challenges.

Objective:Practitioners and scientists are re-examining marginalised wound care therapies to find strategies that combat the growing problem of antimicrobial resistance (AMR) without compromising patient outcomes. Maggot therapy (MT) makes up just an estimated 0.02% of UK's National Health Service spending on wound care. This study aims to uncover why MT is not used more often, despite its affordability and high level of efficacy for both debridement and disinfection, particularly in the context of AMR infections, and to determine what can be done to ensure MT is more effectively used in the future to improve patient outcomes and manage the growing problem of AMR.Method:For this investigation, a qualitative review of case studies using MT against AMR infections and a quantitative analysis of randomised control trials (RCTs) were performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework.Results:Analysis showed that MT is highly effective against a range of infections and wound types, and compares well against conventional therapies. The low use of MT may be due in part to the documented ‘yuck factor’, often associated with maggots as well as misconceptions around the cost, efficacy and accessibility of MT. To overcome these factors, more RCTs on the spectrum and efficacy of MT across various clinical manifestations are needed, as well as professional and public engagement campaigns.Conclusion:MT is an underused therapy, particularly regarding AMR infections, and expanding its use in these circumstances appears warranted. MT could play a vital role in conserving the efficacy of the existing pool of antimicrobials available and should be considered in the development of antimicrobial stewardship programmes.Declaration of interest:This work was supported by the Swansea Employability Academy, Swansea University (internal funding). The authors have no conflicts of interest to declare.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.

Byline: Nazia. Walvir, Safia. Rana, Zeeba. Jairajpuri, Sujata. Jetley, Aruna. Nigam Objectives: We aimed to histopathologically evaluate the morphological spectrum, apoptotic index (AI), and mitotic index (MI) of endometrial lesions [...]

Byline: Rohit. Kapoor, Ankur. Mandelia, Nayab. Farzana, Neha. Nigam, Preeti. Dabadghao, Shyamendra. Sharma, Pujana. Kanneganti, Shantanu. Pande Bronchial carcinoid is the most common primary malignant lung tumor in children; however, [...]

Atherosclerosis may induce coronary heart diseases (CHD) that progress to myocardial infarction (MCI), if untreated. The pulse plethysmogram (PPG) plot demonstrated the change in peripheral blood volume influence induced by the sympathetic branch of the autonomic nervous system that causes vasoconstriction. Thus, the present work has been aimed at proposing a noninvasive, low-cost, and wearable technology for the forecasting of CHD and MCI subjects using PPG-derived features with a support vector machine (SVM). A total of 70 volunteers (50-55 years) including MCI (n = 10), CHD (n = 30), and Control (n = 30) has been participated. Digital PPG was recorded for 10 min from the index finger of the left hand in the supine position. Ten samples from each subject were selected from the PPG signal for deriving nonlinear pulse rate variability (PRV) features using Kubios 2.0. The sensitivity, specificity, and accuracy were calculated from the obtained confusion matrix in the classification of control, CHD, and MCI classes. The findings suggested that the reduced value of PRV features in the Poincare plot, detrended fluctuation analysis recurrence plot, and correlation dimension while higher entropy measures in MCI than the CHD subjects. The best accuracy of 98.2% and 98.3% were observed with quadratic functions in the prediction of CHD and MCI subjects from the control group, respectively. The obtained results suggested the applicability of nonlinear PRV features in designing the low-cost real-time wearable technique in the prediction of CHD and MCI subjects.

ABSTRACTThe Arabian Sea (AS), a basin in the North Indian Ocean (NIO), possesses strong coastal upwelling in its eastern and western margins. The spatio-temporal variability of upwelling over Somalia, Oman and the southwest coast of India is analysed using observed datasets. The spectral analysis of upwelling indices highlights that the semi-annual and annual frequencies were dominant by two orders of magnitude compared with the inter-annual variability over these upwelling zones. The inter-annual variability of coastal upwelling was demarcated with variations in the causative forcing of upwelling associated with the positive modes of Indian Ocean Dipole (pIOD), El-Niño Southern Oscillation (El-Niño) and combined pIOD and El-Niño events over the southwest coast of India (SWCI) and Somalia. Along SWCI, the sea surface temperature and meridional wind stress weakened leading to warming associated with the suppression of upwelling during El-Niño, pIOD and combined events. Along Somalia, the upwelling favourable wind stress showed slight weakening due to pIOD and strong decline due to El-Niño and combined modes. Along Oman, the upwelling favourable wind stress indicated negligible changes due to pIOD, an increase due to El-Niño and a slight decline due to combined modes.

•A single-chain CD38 T-cell engager targets CD38-negative LSCs activated by IFN-γ and induces CD38 expression on AML blasts.•CD38 T-cell engager activates T cells against autologous leukemia cells and in AML mouse models.

Alzheimer's Disease (AD), the most common type of dementia, is characterized by memory issues that worsen with time. When evaluating single modalities, there are specific efficient learning techniques that produce a poor identification rate. The clinical significance of Mild Cognitive Impairment (MCI) has clinical importance, which is crucial for identifying and categorizing AD patients. This study includes the utilization of 3D T1 weighted /Magnetic Resonance Imaging (3D T1WI MRI) images where the structural changes in the brain are identified for predicting early AD. The classification accuracy for AD, MCI, and Normal Control (NC) has also increased due to cognitive evaluations when comparing three one vs one classifications, i.e.AD vs. NC, MCI vs. NC, AD vs. MCI, and multi-class classification. The proposed approach’s novelty is a binary and multi-class classification of structural MRI images using a Siamese 3D Convolutional Neural Network (Siamese 3D-CNN). The Stochastic Gradient Descent (SGD) has been employed to modify the proposed network's weights, and 5-Fold Cross-Validation (CV) has been used for each binary classification task. Our study used the neuropsychological data of 417 people from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the suggested methodology. Experimental results demonstrate that accuracy, sensitivity, and specificity for three binary classifications produce the maximum percentage, in contrast to other relevant approaches found in the literature. In addition, the multi-class classification accuracy of 99.8% was attained.

Pseudopeptide-based bioinspired materials are emerging for selective recognition of biologically significant analytes and are applicable in the modern nanoscience field. Therefore, we have developed novel multifunctional C2-symmetric soft pseudopeptides by amino acid l-valine and salicylaldehyde fragments using a series of aliphatic linkers. They are highly selective and sensitive to Zn (II) ions under physiological conditions and reveal significant fluorescence enhancement with the PET mechanism. The molecular self-assembly shows zinc-induced morphological transformation of the rod-shaped assembly into a chain-like morphology. Such a metal-induced hierarchical nano-assembly may have relevance for specific nanobiotechnology applications.

We report on a newly born female with fibular hemimelia, tibial campomelia, and lower limb oligodactyly with an acyanotic small apical muscular ventricular septal defect (VSD) and a tiny patent ductus arteriosus (PDA) with left to right shunt, as well as an absent uvula. This is the first-ever report of FATCO syndrome associated with these anomalies in an Indian female neonate. It is a rarer finding in the medical literature, to the best of authors’ knowledge. Does this rare combination suggest an emerging genetic syndrome? Given the paucity of this rare syndromic with the lack of a standardized management approach, each FATCO syndrome is reported to understand its treatment approach as well as to create awareness.

Sometimes I look down at the forest floor below and I wonder what is out there. All my life, I have been sitting on the same branch, on the same [...]

Byline: Gaurav. Kohli, Pratik. Shenoy, Danish. Halim, Saurabh. Nigam, Sachin. Shetty, Dinesh. Talwar, Alok. Sen Purpose: To study the safety and efficacy of pre-operative suprachoroidal triamcinolone acetonide (SCTA) for achieving [...]

Byline: Jyoti. Nigam, Anant. Tripathi, Manoj. Yadav Chondroid syringoma is a benign mixed tumor characterized by sweat gland elements in a cartilaginous stroma. A 34-year-old male patient presented with a [...]

Ever since the isolation of Amycolatopsis mediterraneiin 1957, this strain has been the focus of research worldwide. In the last 60 years or more, our understanding of the taxonomy, development of cloning vectors and conjugation system, physiology, genetics, genomics, and biosynthetic pathway of rifamycin B production in A. mediterraneihas substantially increased. In particular, the development of cloning vectors, transformation system, characterization of the rifamycin biosynthetic gene cluster, and the regulation of rifamycin B production by the pioneering work of Heinz Floss have made the rifamycin polyketide biosynthetic gene cluster (PKS) an attractive target for extensive genetic manipulations to produce rifamycin B analogues which could be effective against multi-drug-resistant tuberculosis. Additionally, a better understanding of the regulation of rifamycin B production and the application of newer genomics tools, including CRISPR-assisted genome editing systems, might prove useful to overcome the limitations associated with low production of rifamycin analogues.

Climate change and human activity have increased fires in India. Fine particulate matter (PM2.5) is released into the atmosphere by stubble burning in Punjab and Haryana and forest fires in the north-eastern and central areas of the country. Accurate short-term PM2.5estimates are essential to protect human health and reduce acute air pollution. However, global air quality forecasting methods grapple with a persistent assumption of fire emissions. They use near-real-time fire emissions throughout the prediction cycle. Air quality forecasts are prone to inaccuracies and biases due to fire emissions’ dynamic nature. We employ spatiotemporal deep learning techniques, specifically ConvLSTM and ConvGRU, to forecast fire emission locations up to three days in advance. Through our evaluation, we find that ConvLSTM outperforms ConvGRU in terms of prediction accuracy and performance. The chosen model provides a very good correlation coefficient (≈0.8) for the 1st day forecast and a moderate value (0.5-0.55) for subsequent 2nd and 3rd days forecasts. The predictors NDVI, temperature, wind, surface pressure, and total cloud cover are included to our model training to improve these correlations. In Punjab-Haryana, wind input improves results. This fire burning location prediction method could improve air quality forecasting. Our deep learning model can improve forecasts by revealing the complex interactions of components and reflecting fire emissions’ dynamic nature. This research may help improve air quality forecasts in the face of rising fire events, protecting communities across the Indian subcontinent.

This paper uses dynamic capabilities theory to explore the role of readiness to change as mediator in the intricate relationship between workforce agility and digital transformation. A total of 161 usable responses were gathered in two-waves of surveys from Indian organizations. Structural equation modelling was utilized to rigorously test the research hypotheses. The results unearthed a noteworthy positive link between workforce agility and the readiness to change, and this readiness, in turn, significantly influenced digital transformation. Intriguingly, the study also unveiled a direct relationship between workforce agility and digital transformation. The findings contribute a valuable thread to the literature, as it seamlessly weaves the concept of workforce agility into the fabric of digital transformation with Industry 4.0 context. This study would empower HR managers with a deeper understanding of the workforce's mindset and their preparedness to navigate technological changes. Such insights are indispensable, as effective change implementation hinges on insightful leadership.

Despite the noticeable strengths of massive open online courses (MOOCs), the high attrition rate of learners is posing challenges in its way to successful diffusion. This created the need to know the reasons behind the high attrition rate among learners. The present work identifies nine MOOC attrition specific reasons from the literature on MOOC and resistance to innovation. The study also classifies and prioritises the attrition reasons in terms of criticality. The reasons for attrition are grouped according to their nature into three categories, referred to as general reasons. The prominence of reasons is established by using the best worst method (BWM), a multi-criteria decision-making (MCDM) technique. The optimised weights determined by BWM arranged them from most critical to least critical in terms of learners attrition. The results revealed technical reasons as the most critical general reason. The most and least critical specific reasons are identified as perceived risk and no penalty or reward. The study also suggested effective measures to address the challenges posed by various attrition reasons.

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This work evaluates two widely validated CNN models VGG and ResNet in the biometric domain based applications. These models have shown exceptional performance on the large ImageNet dataset but the predictive capability of these models for domain specific-tasks where limited data samples are present need to be checked. Here in this paper efforts have been made to analyze two interesting biometric problems (1) multi-class oculus classification (2) fingerprint sensor classification. The distinguish experimental results are evaluated on the benchmark datasets like IIIT-D, ND, IITK contact-lens datasets and FVC 2002, FVC 2004, FVC 2006, IIITD-MOLF and IITK fingerprint datasets. We have raised many interesting questions regarding the reliability and applicability of deep-learning models specifically for biometric based applications. Experimental results along with in-depth feature analysis have shown that indeed residual connections with pre-trained network provides good prior for model weights and thus helps in better generalization.

The design and development of Terahertz (THz) and sub-Terahertz (sub-THz) communication systems entail the need for new channel models that can precisely predict channel attributes at such frequencies ( $\geq 100$ GHz) in outdoor and dynamic environments. This work proposes a novel hybrid-stochastic ultra-wideband channel model for sub-THz bands, developed within the framework of a class of spatio-temporal stochastic processes called the ambit-process. The proposed model is capable of supporting bandwidths of upto 1GHz. The spatio-temporal evolution of the ambit framework allows for a spatially consistent, reasonably accurate and tractable characterization of the fading statistics and multipath propagation of the cellular channels. We leverage a recently proposed convolution-based low-complexity algorithm with necessary modifications to study key features of the microcellular sub-THz channel like associated diffused reflection and scattering, molecular absorption, spatio-temporal correlations, and consistency between the time-evolving delay and Doppler of the multipaths. Simulation results on path loss, shadowing, delay spread, and channel correlations indicate that the ambit model accurately captures the typicalities of an urban microcellular sub-THz channel and agrees well with the measurement results reported in the literature.

Molecular subtyping has been a major focus of bladder cancer research over the past decade. Despite many promising associations with clinical outcomes and treatment response, its clinical impact has yet to be defined. As part of the 2022 International Society of Urological Pathology Conference on Bladder Cancer, we reviewed the current state of the science for bladder cancer molecular subtyping. Our review included several different subtyping systems. We derived the following 7 principles, which summarize progress and challenges of molecular subtyping: (1) bladder cancer has 3 major molecular subtypes: luminal, basal-squamous, and neuroendocrine; (2) signatures of the tumor microenvironment differ greatly among bladder cancers, particularly among luminal tumors; (3) luminal bladder cancers are biologically diverse, and much of this diversity results from differences in features unrelated to the tumor microenvironment, such as FGFR3 signaling and RB1 inactivation; (4) molecular subtype of bladder cancer associates with tumor stage and histomorphology; (5) many subtyping systems include idiosyncrasies, such as subtypes recognized by no other system; (6) there are broad fuzzy borders between molecular subtypes, and cases that fall on these fuzzy borders are often classified differently by different subtyping systems; and (7) when there are histomorphologically distinct regions within a single tumor, the molecular subtypes of these regions are often discordant. We reviewed several use cases for molecular subtyping, highlighting their promise as clinical biomarkers. Finally, we conclude that data are currently insufficient to support the routine use of molecular subtyping to guide bladder cancer management, an opinion shared with the majority of conference attendees. We also conclude that molecular subtype should not be considered an “intrinsic” property of a tumor but should instead be considered the result of a specific laboratory test, performed using a specific testing platform and classification algorithm, validated for a specific clinical application.

The 2022 International Society of Urological Pathology (ISUP) Consensus Conference on Urinary Bladder Cancer Working Group 2 was tasked to provide evidence-based proposals on the applications of grading in noninvasive urothelial carcinoma with mixed grades, invasive urothelial carcinoma including subtypes (variants) and divergent differentiations, and in pure non-urothelial carcinomas. Studies suggested that predominantly low-grade noninvasive papillary urothelial carcinoma with focal high-grade component has intermediate outcome between low- and high-grade tumors. However, no consensus was reached on how to define a focal high-grade component. By 2004 WHO grading, the vast majority of lamina propria-invasive (T1) urothelial carcinomas are high-grade, and the rare invasive low-grade tumors show only limited superficial invasion. While by 1973 WHO grading, the vast majority of T1 urothelial carcinomas are G2 and G3 and show significant differences in outcome based on tumor grade. No consensus was reached if T1 tumors should be graded either by the 2004 WHO system or by the 1973 WHO system. Because of the concern for underdiagnosis and underreporting with potential undertreatment, participants unanimously recommended that the presence of urothelial carcinoma subtypes and divergent differentiations should be reported. There was consensus that the extent of these subtypes and divergent differentiations should also be documented in biopsy, transurethral resection, and cystectomy specimens. Any distinct subtype and divergent differentiation should be diagnosed without a threshold cutoff, and each type should be enumerated in tumors with combined morphologies. The participants agreed that all subtypes and divergent differentiations should be considered high-grade according to the 2004 WHO grading system. However, participants strongly acknowledged that subtypes and divergent differentiations should not be considered as a homogenous group in terms of behavior. Thus, future studies should focus on individual subtypes and divergent differentiations rather than lumping these different entities into a single clinicopathological group. Likewise, clinical recommendations should pay attention to the potential heterogeneity of subtypes and divergent differentiations in terms of behavior and response to therapy. There was consensus that invasive pure squamous cell carcinoma and pure adenocarcinoma of the bladder should be graded according to the degree of differentiation. In conclusion, this summary of the International Society of Urological Pathology Working Group 2 proceedings addresses some of the issues on grading beyond its traditional application, including for papillary urothelial carcinomas with mixed grades and with invasive components. Reporting of subtypes and divergent differentiation is also addressed in detail, acknowledging their role in risk stratification. This report could serve as a guide for best practices and may advise future research and proposals on the prognostication of these tumors.

The design and development of sub-Terahertz (sub-THz) cellular systems entail the need for new channel models that can precisely predict channel characteristics beyond 100GHz in outdoor and dynamic environments. This work proposes a novel multiple-input and multiple-output (MIMO) channel model for cellular communication, developed within the framework of a class of spatio-temporal stochastic processes called ambit-process. The modeling methodology effectively captures the typicalities of sub-THz propagation like molecular absorption and scattering of the evolving multipaths while accounting for the propagation delay of electromagnetic waves across large array apertures deployed at the transmitter and the receiver. This allows for an accurate characterization of the spatial-wideband effect along with other relevant spatio-temporal attributes of the channel. Numerical simulations indicate a good level of agreement between the spectral efficiency and spatio-temporal correlation of the proposed model against a state-of-the-art stochastic Terahertz (THz) channel model and measurements reported in the literature.

Urorectal septum malformation sequence (URSMS) is an uncommon disease characterized by a failure of the anorectal septum to divide the cloaca and fuse with the cloacal membrane. Complete URSMS is usually lethal in newborn due to severe renal dysfunction and pulmonary hypoplasia. Partial URSMS is compatible with life with a single perineal opening draining a common cloaca with an imperforate anus which amenable to surgical management. Antenatal diagnosis of URSMS is challenging because of multisystem, complex abnormalities involving gastrointestinal, urogenital tract, cardiovascular, and musculoskeletal systems. In this case report, we describe a 15-week male fetus with partial URSMS having a spectrum of multisystem structural anomalies associated with fetal neuroblastoma in retroperitoneal location and adrenal neuroblastoma in situ.

Identification of groundwater potential is important for the preparation of management plan of groundwater resource in watershed scale. Groundwater potential zone in watershed scale is identified using GIS, remote sensing satellite data, and Multi-Criteria Decision Analysis technique. Different thematic layers, i.e. geological formation, lineament, soil, etc. were used for the identification of groundwater potential zones, while groundwater quality parameters, i.e. hardness, pH, etc. were used for the preparation of groundwater quality index map. The present study focused on identification of possible groundwater potential zones and groundwater quality index both in terms of quantity and quality. The Analytic Hierarchy Approach is utilized to identify the groundwater potential zones. Conclusively, 27.1% area falls under low–medium groundwater potential zone and 9.3% area falls under above the permissible limit of water quality. In total, 240 suitable water harvesting structures, namely storage tank (120 nos), percolation tank (70 nos), stop dam (34 nos) and check dam (16 nos) with their sites, were identified and suggested for water harvesting to enhance the groundwater conditions to meet the scarcity of water resources in agriculture and domestic use.

Glioblastoma (GBM) is a grade-IV astrocytoma, which is the most common and aggressive type of brain tumor, spreads rapidly and has a life-threatening catastrophic effect. GBM mostly occurs in adults with an average survival time of 15 to 18 months, and the overall mortality rate is 5%. Significant invasion and drug resistance activity cause the poor diagnosis of GBM. Naringenin (NRG) is a plant secondary metabolite byproduct of the flavanone subgroup. NRG can cross the blood-brain barrier and deliver drugs into the central nervous system when conjugated with appropriate nanocarriers to overcome the challenges associated with gliomas through naringenin-loaded nanoformulations. Here, we discuss several nanocarriers employed that are as delivery systems, such as polymeric nanoparticles, micelles, liposomes, solid lipid nanoparticles (SLNs), nanosuspensions, and nanoemulsions. These naringenin-loaded nanoformulations have been tested in various in vitroand in vivomodels as a potential treatment for brain disorders. This review nanoformulations of NRG can a possible therapeutic alternative for the treatment of neurological diseases are discussed.