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COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

Self-heating is a severe problem for high-power gallium nitride (GaN) electronic and optoelectronic devices. Various thermal management solutions, for example, flip-chip bonding or composite substrates, have been attempted. However, temperature rise due to dissipated heat still limits applications of the nitride-based technology. Here we show that thermal management of GaN transistors can be substantially improved via introduction of alternative heat-escaping channels implemented with few-layer graphene—an excellent heat conductor. The graphene–graphite quilts were formed on top of AlGaN/GaN transistors on SiC substrates. Using micro-Raman spectroscopy for in situ monitoring we demonstrated that temperature of the hotspots can be lowered by ∼20 °C in transistors operating at ∼13 W mm −1 , which corresponds to an order-of-magnitude increase in the device lifetime. The simulations indicate that graphene quilts perform even better in GaN devices on sapphire substrates. The proposed local heat spreading with materials that preserve their thermal properties at nanometre scale represents a transformative change in thermal management. Electronic and optoelectronic devices based on gallium nitride suffer from self-heating arising as a result of their operation. This study presents and demonstrates a strategy for managing this problem that relies on graphene quilts which dissipate the heat away.

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.

This novel study presents an in-depth mathematical analysis, investigation, and comparative peculiar assessment of mixing behaviors across different microchannel configurations: the Simple T-shape, Spiral T-shape, and Three-Dimensional Serpentine Passive Micromixer (TDSPM). Considering the pivotal role of micromixing in various applications, the research thoroughly employs the Navier-Stokes equations to analyze flow dynamics and measure the mixing performance of water and water-dye mixtures. The TDSPM, with its distinctive rectangular inlet duct and U-shaped repeating structures, optimizes fluid interaction by constricting flow pathways. The study highlights the superior performance of the TDSPM and thoroughly evaluates the mixing indices for all three micromixer types at Reynolds numbers ranging from 5 to 250. From the priority analysis, Reynolds number (38.49%) and velocity (38.69%) are the most influential factors in micromixer performance, followed by mixing path length (15.35%) and channel width (6.87%). Test 18 (Re = 200, Mixing Path = 25 mm, Velocity = 4.2 m/s, Channel Width = 5 mm) achieves 98% mixing efficiency with a 500 Pa pressure drop, optimizing performance with lower energy costs. Finally, this design leads to remarkable improvements in mixing efficiency over a broad spectrum of Reynolds numbers.

Epidemic of cardiovascular disease (CVD) is widely projected in South Asian population and estimated to get double in two decades. Ischemic heart disease (IHD) is one of the spectrums of CVD and acute myocardial infarction (AMI) being the common manifestations of IHD. National Cardiovascular Data Registry (NCDR) is a registry data that measure their practices and improve quality of care. In this project we aim to see our performance trends in the care of IHD including AMI patients over two year's period. A cross sectional study conducted at the Aga Khan University Hospital, Karachi, Pakistan. All patients aged 18 years and above admitted to adult Cardiology units with chest pain and acute coronary syndrome are eligible to be included in NCDR data set. Data on demographics and initial characteristics of patients were extracted from NCDR institutional dataset. The data was then compared between 2019 and 2020 on performance, quality, and efficiency metrics. In 2019 to 2020, 1542 patients with acute coronary syndrome and stable ischemic heart disease were admitted. Out of these, 1042 patients (67.8%) were males. According to our data, the 2020 mortality rate was about 5.25%. In 2019 and 2020, bleeding rates were 1.1% and 1.6%, respectively. Our data showed 100% PCI in 90 minutes in 2019 while 87% in 2020. According to the appropriateness criteria for PCI, 80% were appropriate, while 20% were possibly appropriate in both years. The median length of stay following a procedure was 2 days in 2019 and 1 day in 2020. This study described the common and unique characteristics of patients with myocardial infarction representing population from South Asian region. Overall, the procedural performance measure and outcome metrics are up to the international benchmarks. Cultural, financial, and pandemic effects identified certain challenges.

The presence of Eriophorum angustifolium in mine tailings of pyrite maintains a neutral pH, despite weathering, thus lowering the release of toxic elements into acid mine drainage water. We investigated if the presence of slightly elevated levels of free toxic elements triggers the plant rhizosphere to change the pH towards neutral by increasing organic acid contents. Plants were treated with a combination of As, Pb, Cu, Cd, and Zn at different concentrations in nutrient medium and in soil in a rhizobox-like system for 48–120 h. The pH and organic acids were detected in the mucilage dissolved from root surface, reflecting the rhizospheric solution. Also the pH of root–cell apoplasm was investigated. Both apoplasmic and mucilage pH increased and the concentrations of organic acids enhanced in the mucilage with slightly elevated levels of toxic elements. When organic acids concentration was high, also the pH was high. Thus, efflux of organic acids from the roots of E. angustifolium may induce rhizosphere basification.

The underutilization of digestate-derived polymers presents a pressing environmental concern as these valuable materials, derived from anaerobic digestion processes, remain largely unused, contributing to pollution and environmental degradation when left unutilized. This study explores the recovery and utilization of biodegradable polymers from biomass anaerobic digestate to enhance the performance of solar photovoltaic (PV) cells while promoting environmental sustainability. The anaerobic digestion process generates organic residues rich in biodegradable materials, often considered waste. However, this research investigates the potential of repurposing these materials by recovering and transforming them into high-quality coatings or encapsulants for PV cells. The recovered biodegradable polymers not only improve the efficiency and lifespan of PV cells but also align with sustainability objectives by reducing the carbon footprint associated with PV cell production and mitigating environmental harm. The study involves a comprehensive experimental design, varying coating thickness, direct normal irradiance (DNI) (A), dry bulb temperature (DBT) (B), and relative humidity (C) levels to analyze how different types of recovered biodegradable polymers interact with diverse environmental conditions. Optimization showed that better result was achieved at A = 8 W/m 2 , B = 40 °C and C = 70% for both the coated material studied. Comparative study showed that for enhanced cell efficiency and cost effectiveness, EcoPolyBlend coated material is more suited however for improving durability and reducing environmental impact NanoBioCelluSynth coated material is preferable choice. Results show that these materials offer promising improvements in PV cell performance and significantly lower environmental impact, providing a sustainable solution for renewable energy production. This research contributes to advancing both the utilization of biomass waste and the development of eco-friendly PV cell technologies, with implications for a more sustainable and greener energy future. This study underscores the pivotal role of exploring anaerobic digestate-derived polymers in advancing the sustainability and performance of solar photovoltaic cells, addressing critical environmental and energy challenges of our time.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 7 Given name: [Ashok] Last name [Kumar Yadav]. Also, kindly confirm the details in the metadata are correct.correct

Epigenetic aberrations are widespread in cancer, yet the underlying mechanisms and causality remain poorly understood 1 – 3 . A subset of gastrointestinal stromal tumours (GISTs) lack canonical kinase mutations but instead have succinate dehydrogenase (SDH) deficiency and global DNA hyper-methylation 4 , 5 . Here, we associate this hyper-methylation with changes in genome topology that activate oncogenic programs. To investigate epigenetic alterations systematically, we mapped DNA methylation, CTCF insulators, enhancers, and chromosome topology in KIT -mutant, PDGFRA -mutant and SDH-deficient GISTs. Although these respective subtypes shared similar enhancer landscapes, we identified hundreds of putative insulators where DNA methylation replaced CTCF binding in SDH-deficient GISTs. We focused on a disrupted insulator that normally partitions a core GIST super-enhancer from the FGF4 oncogene. Recurrent loss of this insulator alters locus topology in SDH-deficient GISTs, allowing aberrant physical interaction between enhancer and oncogene. CRISPR-mediated excision of the corresponding CTCF motifs in an SDH-intact GIST model disrupted the boundary between enhancer and oncogene, and strongly upregulated FGF4 expression. We also identified a second recurrent insulator loss event near the KIT oncogene, which is also highly expressed across SDH-deficient GISTs. Finally, we established a patient-derived xenograft (PDX) from an SDH-deficient GIST that faithfully maintains the epigenetics of the parental tumour, including hypermethylation and insulator defects. This PDX model is highly sensitive to FGF receptor (FGFR) inhibition, and more so to combined FGFR and KIT inhibition, validating the functional significance of the underlying epigenetic lesions. Our study reveals how epigenetic alterations can drive oncogenic programs in the absence of canonical kinase mutations, with implications for mechanistic targeting of aberrant pathways in cancers. Gastrointestinal stromal tumours can be initiated by gain-of-function mutations of the KIT or PDGFRA oncogenes but also by loss of the metabolic complex succinate dehydrogenase (SDH), which leads to DNA hypermethylation; this study shows that in SDH-deficient tumours, displacement of CTCF insulators by DNA methylation activates oncogene expression, illustrating how epigenetic alterations can drive oncogenic signalling in the absence of kinase mutations.

In socially monogamous prairie voles ( Microtus ochrogaster ), parental behaviors not only occur in mothers and fathers, but also exist in some virgin males. In contrast, the other virgin males display aggressive behaviors towards conspecific pups. However, little is known about the molecular underpinnings of this behavioral dichotomy, such as gene expression changes and their regulatory mechanisms. To address this, we profiled the transcriptome and DNA methylome of hippocampal dentate gyrus of four prairie vole groups, namely attacker virgin males, parental virgin males, fathers, and mothers. While we found a concordant gene expression pattern between parental virgin males and fathers, the attacker virgin males have a more deviated transcriptome. Moreover, numerous DNA methylation changes were found in pair-wise comparisons among the four groups. We found some DNA methylation changes overlapping with transcription differences, across gene-bodies and promoter regions. Furthermore, the gene expression changes and methylome alterations are selectively enriched in certain biological pathways, such as Wnt signaling, which suggest a canonical transcription regulatory role of DNA methylation in paternal behavior. Therefore, our study presents an integrated view of prairie vole dentate gyrus transcriptome and epigenome that provides a DNA epigenetic based molecular insight of paternal behavior.

This study extends the classical circular restricted three-body problem (CR3BP) by introducing a dominant central primary, forming a collinear restricted four-body problem (CR4BP) that better reflects the dynamics of real planetary systems. The model remains dynamically consistent and non-degenerate when the central mass parameter μ 0 lies in (½, 1) and the peripheral mass μ satisfies 0 <  μ  < ½ (1 – μ 0 ). It generalizes to the CR3BP by setting μ 0  = 0, recovering classical results. The system exhibits six libration points: four collinear and two symmetric non-collinear points forming an isosceles triangle with the peripheral primaries. Non-collinear points emerge via a saddle-node bifurcation at a critical μ  =  μ c and as μ increases further within the range μ c  <  μ  < ½ (1 – μ 0 ), these points move away from the x -axis and gradually align closer to the y -axis, while remaining symmetric with respect to the x -axis. The stability analysis reveals that collinear libration points L 1 , L 3 and L 4 are linearly unstable under all conditions while L 2 is stable in the interval 0 <  μ  <  μ * where μ * is a critical threshold for L 2 . The non-collinear points are linearly stable within a defined interval μ c  <  μ  <  μ c 1 . Finally, these results are applied to the Saturn–Janus–Epimetheus system to illustrate the model’s practical relevance.