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Abstract Formyl peptide receptors (FPR), part of the G-protein coupled receptor superfamily, are pivotal in directing phagocyte migration towards chemotactic signals from bacteria and host tissues. Although their roles in acute bacterial infections are well-documented, their involvement in immunity against tuberculosis (TB) remains unexplored. Here, we investigate the functions of Fpr1 and Fpr2 in defense against Mycobacterium tuberculosis (Mtb), the causative agent of TB. Elevated levels of Fpr1 and Fpr2 were found in the lungs of mice, rabbits and peripheral blood of humans infected with Mtb, suggesting a crucial role in the immune response. The effects of Fpr1 and Fpr2 deletion on bacterial load, lung damage, and cellular inflammation were assessed in a murine TB model utilizing hypervirulent strain of Mtb from the W-Beijing lineage. While Fpr2 deletion had no impact on disease outcome, Fpr1-deficient mice demonstrated improved bacterial control, especially by macrophages. Bone marrow-derived macrophages from these Fpr1 −/− mice exhibited an enhanced ability to contain bacterial growth over time. Contrarily, treating genetically susceptible mice with Fpr1-specific inhibitors caused impaired early bacterial control, corresponding with increased Mtb persistence in necrotic neutrophils. Furthermore, ex vivo assays revealed that Fpr1 −/− neutrophils were unable to restrain Mtb growth, indicating a differential function of Fpr1 among myeloid cells. These findings highlight the distinct and complex roles of Fpr1 in myeloid cell-mediated immunity against Mtb infection, underscoring the need for further research into these mechanisms for a better understanding of TB immunity.

Abstract A 60-day feeding trial was conducted to evaluate the effects of dietary Anabaena blue-green algae (ABGA) meal on the growth performance, digestibility, and physio-metabolic responses of Catla catla fingerlings (initial average weight 9.45 ± 0.15 g). Six iso-nitrogenous (30% crude protein) and iso-caloric (378.09 Kcal. digestible energy/100 g) diets were formulated: a control diet (A0, 0% ABGA) and five experimental diets with varying ABGA inclusion levels (A3: 3%, A6: 6%, A9: 9%, A12: 12%, A15: 15%). The results demonstrated that there were no significant differences (P > 0.05) in percentage weight gain (PWG), specific growth rate (SGR), protein efficiency ratio (PER), and feed conversion ratio (FCR) among the experimental groups. Additionally, dietary ABGA did not significantly affect (P > 0.05) body carcass composition among different groups. However, amylase activity significantly decreased (P 0.05) across all groups. Notably, oxidative stress responses (SOD; superoxide dismutase and CAT; catalase), carbohydrate metabolic enzymes (LDH; lactate dehydrogenase and MDH; malate dehydrogenase), and serum glucose levels increased significantly (P 0.05) observed in serum total protein, albumin/globulin (A/G) ratio, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) activities among the experimental groups. Hematological parameters revealed that RBC (red blood cell) count, hemoglobin (Hb) concentration, and packed cell volume (PCV) significantly decreased (P

Abstract The creation of new alloys with improved qualities has become essential in modern industries for high-performance materials. This work’s main objective is to use vacuum arc melting (VAM) to synthesize two different high-entropy alloys (HEAs): AlCrFeNiCu and AlCrFeNiCo. The mechanical properties, phase composition, grain boundaries, and alloy composition of the HEAs were studied. The predominant crystal structure, the Body-Centred Cubic (BCC) phase was obtained for both alloys. Significantly, the Co-containing HEA showed a smaller particle size than the Cu-containing HEA, which led to a 14.21% increase in microhardness. It indicates that the Co-based HEA will likely perform better than the Cu-based HEA in applications prone to abrasion, and indentation, and requiring high hardness levels based on the observed microstructure and hardness parameters. According to wear surface morphology studies, main effects analysis and ANOVA show that increasing loads and sliding distances increase wear rate, whereas sliding velocity has less effect. The best wear rate-reducing parameters are 10 N, 0.5 m/s, and 500 m for Cu-containing HEAs, and the same can be predicted using regression analysis. The study categorizes the intricate worn surface structure by describing different surface properties and wear mechanisms, such as grooves, delamination, adhesive wear, and pitting.

Abstract The crude leaf extract of Phragmanthera macrosolen L. has been utilized for the first time as an effective reducing, capping and stabilizing agent to synthesize silver oxide nanoparticles (Ag2O NPs) through a green approach. The prepared Ag2O NPs were analyzed by scanning electron-microscopy (SEM), High Resolution Transmission electron microscope (HR-TEM), X-ray diffractions (XRD), Fourier transforms infrared (FTIR) spectroscopy, energy dispersive spectroscopy (EDS), and Ultra-violet visible spectrometry (UV-Vis). The biosynthesized Ag2O NPs applied on gram-positive (S. aureus) and gram-negative (E. coli) bacterial types. FTIR spectral peaks indicate that the phytochemicals in the extract are responsible for the formation of Ag2O nanoparticles. The XRD result shown, poly-crystalline nanoparticles and the average crystalline size calculated was 45.8 nm. UV-Vis analysis shows absorbance existed at 590.5 nm, and the energy band gap calculated through the Tauc relation was 2.1 eV. The SEM images gave a globular and some rod like morphology with diameter of particle obtained between 20.11 and 46.50 nm with some hollow cubic microstructure of Ag2O NPs which makes it suitable for antimicrobial application. The EDS confirms the elemental composition and existence of Ag and O2. The HR-TEM images, specific area electron diffraction (SAED), and XRD patterns confirmed the morphology of Ag2O NPs mean 45.84 nm and polycrystalline nature of the nanoparticles. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 15 and 30 µl respectively for the samples tested. In this study, it was observed that Ag2O NPs highly sensitive to E. coli than s. aureus. The present study revealed that the possible use of green synthesized Ag2O NPs as potential antibacterial agent.

Abstract With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

Abstract The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Abstract Ocean mesoscale eddies, with $$20-300$$ 20 - 300 km size, present in energetic regions of the global ocean, are known to impact local and remote atmospheric weather. The impact of eddies in the Mediterranean Sea on the local weather, however, remains largely unknown. Here, we study this impact during an extreme weather event observed over Israel on January $$8-10$$ 8 - 10 , 2020, resulting in heavy rains and floods. To do so, we designed a set of coupled and forced numerical simulations with a horizontal resolution of 5 km in both ocean and atmosphere. The coupled simulation successfully reproduces the main characteristics of the torrential rains observed during the event, whereas the forced simulations exhibit poorer performance. Our results emphasize the importance of mesoscale air-sea coupling in supplying moisture to the atmosphere, via mechanisms involving both sea surface temperature and surface currents associated with sea eddies. Extreme weather events are intensified by the Mediterranean Sea eddies, especially through atmospheric meso-cyclones.

Abstract White matter hyperintensities index structural abnormalities in the cerebral white matter, including axonal damage. The latter may promote atrophy of the cerebral cortex, a key feature of dementia. Here, we report a study of 51,065 individuals from 10 cohorts demonstrating that higher white matter hyperintensity volume associates with lower cortical thickness. The meta-GWAS of white matter hyperintensities-associated cortical ‘atrophy’ identifies 20 genome-wide significant loci, and enrichment in genes specific to vascular cell types, astrocytes, and oligodendrocytes. White matter hyperintensities-associated cortical ‘atrophy’ showed positive genetic correlations with vascular-risk traits and plasma biomarkers of neurodegeneration, and negative genetic correlations with cognitive functioning. 15 of the 20 loci regulated the expression of 54 genes in the cerebral cortex that, together with their co-expressed genes, were enriched in biological processes of axonal cytoskeleton and intracellular transport. The white matter hyperintensities-cortical thickness associations were most pronounced in cortical regions with higher expression of genes specific to excitatory neurons with long-range axons traversing through the white matter. The meta-GWAS-based polygenic risk score predicts vascular and all-cause dementia in an independent sample of 500,348 individuals. Thus, the genetics of white matter hyperintensities-related cortical atrophy involves vascular and neuronal processes and increases dementia risk.

Abstract Antimicrobial peptides have garnered increasing attention as potential alternatives due to their broad-spectrum antimicrobial activity and low propensity for developing resistance. This is for the first time; proteome sequences of Aegle marmelos were subjected to in-silico digestion and AMP prediction were performed using DBAASP server. After screening the peptides on the basis of different physiochemical property, peptide sequence GKEAATKAIKEWGQPKSKITH (AM1) shows the maximum binding affinity with − 10.2 Kcal/mol in comparison with the standard drug (Trimethoprim) with − 7.4 kcal/mol and − 6.8 Kcal/mol for DHFR and SaTrmK enzyme respectively. Molecular dynamics simulation performed for 300ns, it has been found that peptide was able to stabilize the protein more effectively, analysed by RMSD, RMSF, and other statistical analysis. Free binding energy for DHFR and SaTrmK interaction from MMPBSA analysis with peptide was found to be -47.69 and − 44.32 Kcal/mol and for Trimethoprim to be -13.85 Kcal/mol and − 11.67 Kcal/mol respectively. Further in-vitro study was performed against Methicillin Susceptible Staphylococcus aureus (MSSA), Methicillin Resistant Staphylococcus aureus (MRSA), Multi-Drug Resistant Staphylococcus aureus (MDR-SA) strain, where MIC values found to be 2, 4, and 8.5 µg/ml lesser in comparison to trimethoprim which has higher MIC values 2.5, 5, and 9.5 µg/ml respectively. Thus, our study provides the insight for the further in-vivo study of the peptides against multi-drug resistant S. aureus.

Abstract In this study, we examine the third-order fractional nonlinear Schrödinger equation (FNLSE) in $$(1+1)$$ -dimensional, by employing the analytical methodology of the new extended direct algebraic method (NEDAM) alongside optical soliton solutions. In order to better understand high-order nonlinear wave behaviors in such systems, the researched model captures the physical and mathematical properties of nonlinear dispersive waves, with applications in plasma physics and optics. With the aid of above mentioned approach, we rigorously assess the novel optical soliton solutions in the form of dark, bright–dark, dark–bright, periodic, singular, rational, mixed trigonometric and hyperbolic forms. Additionally, stability assessments using conserved quantities, such as Hamiltonian property, and consistency checks were used to validate the solutions. The dynamic structure of the governing model is further examined using chaos, bifurcation, and sensitivity analysis. With the appropriate parameter values, 2D, 3D, and contour plots can all be utilized to graphically show the data. This work advances our knowledge of nonlinear wave propagation in Bose–Einstein condensates, ultrafast fibre optics, and plasma physics, among other areas with higher-order chromatic effects.

Abstract In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Abstract Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient’s admission data to recommend safe and effective medications. These systems are categorised into two types: instance-based and longitudinal-based. Instance-based models only consider the current admission, while longitudinal models consider the patient’s medical history. Electronic Health Records are used to incorporate medical history into longitudinal models. This project proposes a novel Knowledge Graph-Driven Medicine Recommendation System using Graph Neural Networks, KGDNet, that utilises longitudinal EHR data along with ontologies and Drug-Drug Interaction knowledge to construct admission-wise clinical and medicine Knowledge Graphs for every patient. Recurrent Neural Networks are employed to model a patient’s historical data, and Graph Neural Networks are used to learn embeddings from the Knowledge Graphs. A Transformer-based Attention mechanism is then used to generate medication recommendations for the patient, considering their current clinical state, medication history, and joint medical records. The model is evaluated on the MIMIC-IV EHR data and outperforms existing methods in terms of precision, recall, F1 score, Jaccard score, and Drug-Drug Interaction control. An ablation study on our models various inputs and components to provide evidence for the importance of each component in providing the best performance. Case study is also performed to demonstrate the real-world effectiveness of KGDNet.

Abstract Chronic demyelination and oligodendrocyte loss deprive neurons of crucial support. It is the degeneration of neurons and their connections that drives progressive disability in demyelinating disease. However, whether chronic demyelination triggers neurodegeneration and how it may do so remain unclear. We characterize two genetic mouse models of inducible demyelination, one distinguished by effective remyelination and the other by remyelination failure and chronic demyelination. While both demyelinating lines feature axonal damage, mice with blocked remyelination have elevated neuronal apoptosis and altered microglial inflammation, whereas mice with efficient remyelination do not feature neuronal apoptosis and have improved functional recovery. Remyelination incapable mice show increased activation of kinases downstream of dual leucine zipper kinase (DLK) and phosphorylation of c-Jun in neuronal nuclei. Pharmacological inhibition or genetic disruption of DLK block c-Jun phosphorylation and the apoptosis of demyelinated neurons. Together, we demonstrate that remyelination is associated with neuroprotection and identify DLK inhibition as protective strategy for chronically demyelinated neurons.

Abstract While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-grained annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained language models to showcase the feasibility of training named entity recognition models. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

Abstract The present study focuses on the green synthesis of a novel Z-scheme SnS₂/HAp photocatalyst using Ocimum tenuiflorum (tulsi) leaf extract as a stabilizing agent. This approach not only emphasizes sustainability but also adds value to waste by extracting hydroxyapatite (HAp) from Labeo rohita fish scales, addressing the challenge of their disposal. The synthesized photocatalyst was thoroughly characterized using a range of analytical techniques to evaluate its crystal structure, optical properties, morphology, and elemental composition. The photocatalytic activity of the SnS₂/HAp composite was assessed through the degradation of gentian violet (GV) dye, a representative organic pollutant. Various reaction parameters were optimized to enhance the degradation efficiency, and the photocatalyst’s performance was further tested across different water matrices. Under optimal conditions, the SnS₂/HAp photocatalyst achieved a maximum photodegradation efficiency of 97.49% with a rate constant of 0.0494 min− 1 for GV dye. Additionally, it exhibited an efficiency greater than 70% against other emerging pollutants via advanced oxidation processes (AOP). The enhanced photocatalytic activity was attributed to the formation of a Z-Scheme heterojunction between SnS2 and HAp, which enhanced the charge separation efficiency and delayed the charge recombination. The study also demonstrated the photocatalyst’s remarkable reusability, maintaining high performance over five cycles and across various water environments. This highlights its potential as a sustainable solution for the removal of organic pollutants from aqueous streams. Finally, a Z-scheme electron transport mechanism is proposed to explain the photodegradation process of GV dye using the SnS₂/HAp photocatalyst.

Abstract The identification of malaria infection using microscope images of blood smears is considered as a ‘gold standard’. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using ‘cvui’ library to interact with the OpenVINO’s inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported.

Abstract Among biomimetic strategies shaping engineering designs, molecularly imprinted polymer (MIP) technology stands out, involving chemically synthesised receptors emulating natural antigen-antibody interactions. These versatile ‘designer polymers’ with remarkable stability and low cost, are pivotal for in vitro diagnostics. Amid the recent global health crisis, we probed MIPs’ potential to capture SARS-CoV-2 virions. Large biotemplates complicate MIP design, influencing generated binding site specificity. To precisely structure recognition sites within polymers, we innovated an epitope imprinting method supplemented by in silico polymerization component screening. A viral surface Spike protein informed epitope selection was targeted for MIP development. A novel multi-monomer docking approach (MMSD) was employed to simulate classical receptor-ligand interactions, mimicking binding reinforcement across multiple amino acids. Around 40 monomer combinations were docked to the epitope sequence and top performers experimentally validated via rapid fluorescence binding assays. Notably, high imprinting factor polymers correlated with MMSD predictions, promising rational MIP design applicable to diverse viral pathologies.

Abstract Herbal medicines are important for ensuring sustainable development goals (SDGs) in healthcare, particularly in developing countries with high rates of antimicrobial resistance (AMR) and little access to medical facilities. Thymus vulgaris is a widely used herbal medicinal plant known for its secondary metabolites and antimicrobial properties. The present study involved a comprehensive examination of the isolation, characterization, and antibacterial activity of Thymus vulgaris obtained from Ethiopia. The aerial part of the plant Thymus vulgaris was successively extracted with hexane, chloroform, and methanol based on differences in polarity. Phytochemical screening tests conducted against hexane, chloroform and MeOH crude extracts indicated the presence of some secondary metabolites. Based on the thin-layer chromatography tests, the chloroform extract was subjected to column chromatography, yielding Tv-2 compounds, namely 5-isopropyl-2-methylphenol. The structures of the compounds were elucidated via spectroscopic methods (UV–Vis, FT-IR and NMR). We investigated the antibacterial properties of hexane crude extract, chloroform crude extract, MeOH crude extract, and isolated fractions derived from T. vulgaris against various bacterial strains. This study contributes to a better understanding of the bioactive components present in Thymus vulgaris crude extracts and their potential role in tackling microbial infections.

Abstract The present study investigates the temporal effects of flaxseed supplementation on boar semen quality, antioxidant status, and in-vivo fertility under high-temperature humidity index (THI) conditions in a sub-tropical climate. Twelve Hampshire crossbreed boars were randomly assigned to control and treatment groups, with the treatment group receiving flaxseed oil supplementation. Semen samples were collected and analyzed for semen quality parameters, sperm kinematics, and antioxidant status. Fertility outcomes were assessed through in-vivo mating trials. Flaxseed supplementation resulted in time dependent significant improvements in semen volume, sperm concentration, total and progressive sperm motility, sperm quality parameters, and antioxidant status. Fertility outcomes, including farrowing rates and litter sizes, were also enhanced in the flaxseed-supplemented group. These findings highlight the potential of flaxseed supplementation to improve boar fertility under high ambient stress conditions, with implications for optimizing reproductive performance in swine production systems.

Abstract Stroke remains a critical global health issue, significantly impacting India with substantial contributions to mortality and disability. This study comprehensively analyses stroke incidence, mortality, and disability-adjusted life years (DALYs) across India from 1990 to 2021, using the latest Global Burden of Disease (GBD) 2021 data. We evaluate how Universal Health Coverage (UHC), health expenditure, human development index (HDI), and gross national income (GNI) influence stroke outcomes. Our findings reveal significant regional disparities, with higher stroke rates in urban areas and states like Goa and Kerala. Higher health expenditure and HDI are linked to lower stroke rates, while higher GNI per capita correlates with increased stroke incidence, likely due to lifestyle changes. Risk factors include air pollution, tobacco use, dietary risks, and high blood pressure. Air pollution notably impacts stroke mortality in Bihar and Jharkhand, while tobacco use is a major risk factor in Mizoram and Manipur. Dietary risks and hypertension are prevalent in Maharashtra and Jammu & Kashmir. The study highlights the need for targeted public health strategies addressing regional disparities and socioeconomic factors. Policymakers should focus on lifestyle modification programs, public awareness campaigns, and enhanced access to quality stroke care to reduce stroke-related morbidity and mortality effectively.

Abstract Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes.

Abstract DNA characterisation in people with tuberculosis (TB) is critical for diagnostic and microbiome evaluations. However, extracellular DNA, more frequent in people on chemotherapy, confounds results. We evaluated whether nucleic acid dyes [propidium monoazide (PMA), PEMAX] and DNaseI could reduce this. PCR [16S Mycobacterium tuberculosis complex (Mtb) qPCR, Xpert MTB/RIF] was done on dilution series of untreated and treated (PMA, PEMAX, DNaseI) Mtb. Separately, 16S rRNA gene qPCR and sequencing were done on untreated and treated sputa before (Cohort A: 11 TB-negatives, 9 TB-positives; Cohort B: 19 TB-positives, PEMAX only) and 24-weeks after chemotherapy (Cohort B). PMA and PEMAX reduced PCR-detected Mtb DNA for dilution series and Cohort A sputum versus untreated controls, suggesting non-intact Mtb is present before treatment-start. PEMAX enabled sequencing-based Mycobacterium-detection in 7/12 (58%) TB-positive sputa where no such reads otherwise occurred. In Cohort A, PMA- and PEMAX-treated versus untreated sputa had decreased α- and increased β-diversities. In Cohort B, β-diversity differences between timepoints were only detected with PEMAX. DNaseI had negligible effects. PMA and PEMAX (but not DNaseI) reduced extracellular DNA in PCR and improved pathogen detection by sequencing. PEMAX additionally detected chemotherapy-associated taxonomic changes that would otherwise be missed. Dyes enhance microbiome evaluations especially during chemotherapy.

Abstract Invasive cervical cancers (ICC), caused by HPV infections, have a heterogeneous molecular landscape. We investigate the detection, timing, and HPV type specificity of somatic mutations in 3929 HPV-positive exfoliated cervical cell samples from individuals undergoing cervical screening in the U.S. using deep targeted sequencing in ICC cases, precancers, and HPV-positive controls. We discover a subset of hotspot mutations rare in controls (2.6%) but significantly more prevalent in precancers, particularly glandular precancer lesions (10.2%), and cancers (25.7%), supporting their involvement in ICC carcinogenesis. Hotspot mutations differ by HPV type, and HPV18/45-positive ICC are more likely to have multiple hotspot mutations compared to HPV16-positive ICC. The proportion of cells containing hotspot mutations is higher (i.e., higher variant allele fraction) in ICC and mutations are detectable up to 6 years prior to cancer diagnosis. Our findings demonstrate the feasibility of using exfoliated cervical cells for detection of somatic mutations as potential diagnostic biomarkers.

Abstract Soil contamination with heavy metals presents a substantial environmental peril, necessitating the exploration of innovative remediation approaches. This research aimed to investigate the efficiency of nano-silica in stabilizing heavy metals in a calcareous heavy metal-contaminated soil. The soil was treated with five nano-silica levels of 0, 100, 200, 500, and 1000 mg/kg and incubated for two months. The results showed that nano-silica had a specific surface area of 179.68 $${\text{m}}^{2}/\text{g}$$ m 2 / g . At 1000 mg/kg, the DTPA-extractable concentrations of Pb, Zn, Cu, Ni, and Cr decreased by 12%, 11%, 11.6%, 10%, and 9.5% compared to the controls, respectively. Additionally, as the nano-silica application rate increased, both soil pH and specific surface area increased. The augmentation of nano-silica adsorbent in the soil led to a decline in the exchangeable (EX) and carbonate-bound fractions of Pb, Cu, Zn, Ni, and Cr, while the distribution of heavy metals in fractions bonded with Fe–Mn oxides, organic matter, and residue increased. The use of 1000 mg/kg nano-silica resulted in an 8.0% reduction in EX Pb, 4.5% in EX Cu, 7.3% in EX Zn, 7.1% in EX Ni, and 7.9% in EX Cr compared to the control treatment. Overall, our study highlights the potential of nano silica as a promising remediation strategy for addressing heavy metal pollution in contaminated soils, offering sustainable solutions for environmental restoration and ecosystem protection.

Abstract Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed “DecXpert” a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85–0.93) and 85% specificity (95% CI 0.82–0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88–0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.

Abstract The research introduces a novel method for creating drug-loaded hydrogel beads that target anti-aging, anti-oxidative, and anti-inflammatory effects, addressing the interconnected processes underlying various pathological conditions. The study focuses on the development of hydrogel beads containing anti-aging compounds, antioxidants, and anti-inflammatory drugs to effectively mitigate various processes. The synthesis, characterization and in vitro evaluations, and potential applications of these multifunctional hydrogel beads are discussed. A polymeric alginate-orange peel extract (1:1) hydrogel was synthesized for encapsulating fish oil. Beads prepared with variable fish oil concentrations (0.1, 0.3, and 0.5 ml) were characterized, showing no significant decrease in size i.e., 0.5 mm and a reduction in pore size from 23 to 12 µm. Encapsulation efficiency reached up to 98% within 2 min, with controlled release achieved upto 45 to 120 min with increasing oil concentration, indicating potential for sustained delivery. Fourier-transform infrared spectroscopy confirmed successful encapsulation by revealing peak shifting, interaction between constituents. In vitro degradation studies showed the hydrogel's biodegradability improved from 30 to 120 min, alongside anti-inflammatory, anti-oxidative, anti-collagenase and anti-elastase activities, cell proliferation rate enhanced after entrapping fish oil. In conclusion, the synthesized hydrogel beads are a promising drug delivery vehicle because they provide stable and effective oil encapsulation with controlled release for notable anti-aging and regenerative potential. Targeted delivery for inflammatory and oxidative stress-related illnesses is one set of potential uses. Further research may optimize this system for broader applications in drug delivery and tissue engineering.

Abstract This research addresses the scalable and inexpensive synthesis of α-Fe2O3via hydrothermal method without any precipitating agent as well as the enhancement of solar driven photocatalytic and H2 production through doping different chromium proportions. Competency of α-Fe2O3, both pure and doped with chromium, to function as photocatalyst was evaluated by its interaction with multiple dyes, which was real-time monitored utilizing (Internet of Things) IoT technique. By adding chromium, the rate of deterioration increased substantially from 15 to 94% for TB under sunlight in a remarkably brief 20 min by employing a very small amount of Cr0.8Fe1.2O3 (0.3 g/L), as evidenced by high degree of mineralization i.e. 85% and LC-HRMS. Also, the rapid breakdown of Trypan Blue (TB) was indicated by BOD5/COD ratio. Moreover, Cr-doped α-Fe2O3 displays excellent H2 production (~ 132 μmol h−1 g−1) as compared to α-Fe2O3. This work highlights the potential utilization of Cr-doped α-Fe2O3 for the purification of industrial waste water and green energy harvesting.

Abstract COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings.

Abstract The presence of defects at the interface between the perovskite film and the carrier transport layer poses significant challenges to the performance and stability of perovskite solar cells (PSCs). Addressing this issue, we introduce a dual host-guest (DHG) complexation strategy to modulate both the bulk and interfacial properties of FAPbI3-rich PSCs. Through NMR spectroscopy, a synergistic effect of the dual treatment is observed. Additionally, electro-optical characterizations demonstrate that the DHG strategy not only passivates defects but also enhances carrier extraction and transport. Remarkably, employing the DHG strategy yields PSCs with power conversion efficiencies (PCE) of 25.89% (certified at 25.53%). Furthermore, these DHG-modified PSCs exhibit enhanced operational stability, retaining over 96.6% of their initial PCE of 25.55% after 1050 hours of continuous operation under one-sun illumination, which was the highest initial value in the recently reported articles. This work establishes a promising pathway for stabilizing high-efficiency perovskite photovoltaics through supramolecular engineering, marking a significant advancement in the field.

Abstract Novel chimeric antigen receptor (CAR) T-cell approaches are needed to improve therapeutic efficacy in solid tumors. High-risk neuroblastoma is an aggressive pediatric solid tumor that expresses cell-surface GPC2 and GD2 with a tumor microenvironment infiltrated by CD16a-expressing innate immune cells. Here we engineer T-cells to express a GPC2-directed CAR and simultaneously secrete a bispecific innate immune cell engager (BiCE) targeting both GD2 and CD16a. In vitro, GPC2.CAR-GD2.BiCE T-cells induce GPC2-dependent cytotoxicity and secrete GD2.BiCE that promotes GD2-dependent activation of antitumor innate immunity. In vivo, GPC2.CAR-GD2.BiCE T-cells locally deliver GD2.BiCE and increase intratumor retention of NK-cells. In mice bearing neuroblastoma patient-derived xenografts and reconstituted with human CD16a-expressing immune cells, GD2.BiCEs enhance GPC2.CAR antitumor efficacy. A CAR.BiCE strategy should be considered for tumor histologies where antigen escape limits CAR efficacy, especially for solid tumors like neuroblastoma that are infiltrated by innate immune cells.

Abstract Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction.

Abstract Understanding the structure and diversity of microbiomes is critical to establishing olives in non-traditional production areas. Limited studies have investigated soil and root-associated microbiota dynamics in olives across seasons or locations in the United States. We explored the composition and spatiotemporal patterns of the olive-associated microbial communities and specificity in two niches (rhizosphere and root endosphere), seasons (spring, summer, and fall), and domains (bacteria and fungi) in the microbiome of the olive cultivar Arbequina across three olive orchards in Texas. Phylum Proteobacteria, followed by Actinobacteriota, dominated the bacterial populations in the rhizosphere and endosphere. Rubrobacter and Actinophytocola were dominant taxa in the rhizosphere and root endosphere at the genus level. Among fungal communities, phylum Ascomycota was prevalent in the rhizosphere and endosphere, while members of the Chaetomiaceae family outnumbered other taxa in the root endosphere. As per the alpha diversity indices, the rhizosphere at Moulton showed much higher richness and diversity than other places, which predicted a significant difference in rhizosphere between locations for bacterial diversity and richness. There was no significant variation in the bacterial diversity in the niches and the fungal diversity within the root endosphere between locations. Beta diversity analysis confirmed the effect of compartments—in influencing community differences. Microbial diversity was apparent within the endosphere and rhizosphere. The seasons influenced only the rhizosphere fungal diversity, contrasting the bacterial diversity in either niche. The research provided a comprehensive overview of the microbial diversity in olive trees' rhizosphere and root endosphere. The abundance and composition of OTUs associated with the rhizosphere soil of Arbequina suggest its role as a source reservoir in defining the potential endophytes.

Abstract Roaming is an unconventional type of molecular reaction where fragments, instead of immediately dissociating, remain weakly bound due to long-range Coulombic interactions. Due to its prevalence and ability to form new molecular compounds, roaming is fundamental to photochemical reactions in small molecules. However, the neutral character of the roaming fragment and its indeterminate trajectory make it difficult to identify experimentally. Here, we introduce an approach to image roaming, utilizing intense, femtosecond IR radiation combined with Coulomb explosion imaging to directly reconstruct the momentum vector of the neutral roaming H2, a precursor to $${{{{{{{{\rm{H}}}}}}}}_{3}}^{+}$$ H 3 + formation, in acetonitrile, CH3CN. This technique provides a kinematically complete picture of the underlying molecular dynamics and yields an unambiguous experimental signature of roaming. We corroborate these findings with quantum chemistry calculations, resolving this unique dissociative process.

Abstract Post-Acute sequelae of SARS-CoV-2 (PASC) is a multisystem disorder causing persistent musculoskeletal deconditioning and reduced lower extremity strength. Electrical stimulation (E-Stim) to the gastrocnemius muscle can enhance strength outcomes by increasing the frequency of muscle fiber activation. We investigated its effect on individuals with PASC. Participants were randomized into intervention (IG) or control (CG) groups. The IG self-administered daily one-hour E-Stim to both their gastrocnemius muscles using a functional device over 4-week, while the CG used a sham device. Primary outcomes were ankle dorsiflexion strength assessed via dynamometry during maximum voluntary contractions, and gastrocnemius voluntary activation (GVA) via surface electromyography. The secondary outcome assessed activities of daily living (ADL), instrumental ADL, and mobility queries. Percentage improvement was calculated. Eighteen patients were analyzed (IG = 10; CG = 8). After 4 week, the IG showed a significantly higher improvement in ankle dorsiflexion strength (222.64%) compared to the CG (51.27%, p = 0.002). Additionally, the IG’s ankle dorsiflexion strength improvement significantly correlated with GVA improvement (rho = 0.782) at 4 week. The secondary outcomes did not reveal significant changes in neither group. Self-administered gastrocnemius E-Stim improves ankle dorsiflexion strength in individuals with PASC. However, larger sample sizes and longer interventions are needed to validate these findings.

Abstract Herein, a hydrothermal etching approach was used to generate an innovative CuS/O,N-CNT composite. The hydrothermal etching of g-C3N4 led to the creation of O,N-CNT, with ethanol as the oxygen source. The SEM and TEM characterizations confirmed the formation of CNT, whereas the XPS analysis proved the doping of oxygen and nitrogen in the CNT matrix along with the incorporation of CuS. Under sun irradiation, the produced CuS/O,N-CNT showed outstanding photocatalytic efficiency, eliminating methyl orange and methylene blue dyes with 97.21% and 98.11% efficacy, respectively. Adding hydrothermally etched O,N-CNT increased light absorption and charge migration kinetics, as can be studied from the UV-DRS and PL analysis; hence, the observed improvements in light absorption and charge transfer pathways contributed to the CuS/O,N-CNT composite's enhanced photocatalytic activity, indicating its potential for efficient elimination of organic contaminants under solar irradiation. The catalyst demonstrated high reusability performance up to six cycles and significantly degraded other dyes. Scavenger analysis, along with VB-XPS and UV-DRS analysis, aid in developing a photocatalytic mechanism that confirms the participation of hydroxyl and superoxide radicals in the degradation process.

Abstract This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Abstract Mosquitoes are important vectors for the transmission of several infectious diseases that lead to huge morbidity and mortality. The exhaustive use of synthetic insecticides has led to widespread resistance and environmental pollution. Using essential oils and nano-emulsions as novel insecticides is a promising alternative approach for controlling vector borne diseases. In the current study, Lantana camara EO and NE were evaluated for their larvicidal and pupicidal activities against Anopheles culicifacies. The inhibitory effect of EO and NE on AChE, NSE (α/β), and GST was also evaluated and compared. GC-MS analysis of oil displayed 61 major peaks. The stable nano-emulsion with an observed hydrodynamic diameter of 147.62 nm was formed using the o/w method. The nano-emulsion exhibited good larvicidal (LC50 50.35 ppm and LC90 222.84 ppm) and pupicidal (LC50 54.82 ppm and LC90 174.58 ppm) activities. Biochemical evaluations revealed that LCEO and LCNE inhibited AChE, NSE (α/β), and GST, displaying LCNE to be a potent binder to AChE and NSE enzyme, whereas LCEO showed higher binding potency towards GST. The nano-emulsion provides us with novel opportunities to target different mosquito enzymes with improved insecticidal efficacy. Due to its natural origin, it can be further developed as a safer and more potent larvicide/insecticide capable of combating emerging insecticide resistance.

Abstract Exfoliation of two-dimensional (2D) nanosheets from three-dimensional (3D) non-layered, non-van der Waals crystals represents an emerging strategy for materials engineering that could significantly increase the library of 2D materials. Yet, the exfoliation mechanism in which nanosheets are derived from crystals that are not intrinsically layered remains unclear. Here, we show that planar defects in the starting 3D boron material promote the exfoliation of 2D boron sheets—by combining liquid-phase exfoliation, aberration-corrected scanning transmission electron microscopy, Raman spectroscopy, and density functional theory calculations. We demonstrate that 2D boron nanosheets consist of a planar arrangement of icosahedral sub-units cleaved along the {001} planes of β-rhombohedral boron. Correspondingly, intrinsic stacking faults in 3D boron form parallel layers of faulted planes in the same orientation as the exfoliated nanosheets, reducing the {001} cleavage energy. Planar defects represent a potential engineerable pathway for exfoliating 2D sheets from 3D boron and, more broadly, the other covalently bonded materials.

Abstract Few studies have focused sufficiently on the intricate link between functional health and depression among older people aged 60 and above in India. Therefore, the current study investigates the association between functional health and depression among older Indian adults through the mediating role of social disengagement and loneliness and the moderating role of living arrangements using recent data from the Longitudinal Aging Study in India (LASI: 2017–2018). Composite International Diagnostic Interview-Short Form (CIDI-SF) for depression, the Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) for functional health, and the indoor/outdoor activities, visits, and religious events for social disengagement were used. The feelings of loneliness and living arrangements were measured using single-item questions and surveys/interviews of household members. Bivariate analysis, logistic regression, and a Partial Least Squares-Structural Equation Model were adopted. The results show that older persons with functional health had 1.85 times higher odds of depression; similarly, those not engaging in social activities and experiencing loneliness were more likely to feel depressed. Living with someone was negatively linked to depression. A significant moderation by living arrangements in the functional health-depression relationship was also observed. The results also indicate significant mediating roles of social disengagement and loneliness, with 22.0% and 3.08% mediation effects, respectively. Therefore, this study recommends the provision of housing and social interaction among older people.

Abstract This study investigates the use of carbonized Himalayan Chir Pine Biomass, known as Chir Pine Activated Carbon (CPAC), as an eco-friendly and cost-effective adsorbent for efficient industrial dye removal, with a focus on environmental sustainability. By applying different additive treatments, four adsorbents (C1, C2, C3, and C4) were formulated. CPAC was synthesized through pyrolysis and characterized using various analytical techniques including FE-SEM, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). The adsorption capacity of CPAC was evaluated using Malachite Green (MG) dye as a model contaminant. FE-SEM images revealed high porosity (~ 10 µm) and a high surface area (119.886 m2/g) as confirmed by BET testing. CPAC effectively removed MG dye within 30 min at a solution pH of 7. Langmuir and Freundlich isotherm models indicated both monolayer and multilayer adsorption, while kinetic models suggested chemisorption. The regeneration efficiency was assessed using 0.1 N HCl over five consecutive cycles, with C4 demonstrating a high regeneration tendency of 85% and only a 9% reduction in adsorption ability after the fifth cycle. The developed CPAC shows excellent potential for use in the textile, paper, and leather industries for industrial dye adsorption, contributing to the protection of aquatic ecosystems. Additionally, CPAC can be utilized in other water and air purification applications.

Abstract Breast invasive carcinoma (BRCA) is the most malignant and leading cause of death in women. Global efforts are ongoing for improvement in early detection, prevention, and treatment. In this milieu, a comprehensive analysis of RNA-sequencing data of 1097 BRCA samples and 114 normal adjacent tissues is done to identify dysregulated genes in major molecular classes of BRCA in various clinical stages. Significantly enriched pathways in distinct molecular classes of BRCA have been identified. Pathways such as interferon signaling, tryptophan degradation, granulocyte adhesion & diapedesis, and catecholamine biosynthesis were found to be significantly enriched in Estrogen/Progesterone Receptor positive/Human Epidermal Growth Factor Receptor 2 negative, pathways such as RAR activation, adipogenesis, the role of JAK1/2 in interferon signaling, TGF-β and STAT3 signaling intricated in Estrogen/Progesterone Receptor negative/Human Epidermal Growth Factor Receptor 2 positive and pathways as IL-1/IL-8, TNFR1/TNFR2, TWEAK, and relaxin signaling were found in triple-negative breast cancer. The dysregulated genes were clustered based on their mutation frequency which revealed nine mutated clusters, some of which were well characterized in cancer while others were less characterized. Each cluster was analyzed in detail which led to the identification of NLGN3, MAML2, TTN, SYNE1, ANK2 as candidate genes in BRCA. They are central hubs in the protein–protein-interaction network, indicating their important regulatory roles. Experimentally, the Real-Time Quantitative Reverse Transcription PCR and western blot confirmed our computational predictions in cell lines. Further, immunohistochemistry corroborated the results in ~ 100 tissue samples. We could experimentally show that the NLGN3 & ANK2 have tumor-suppressor roles in BRCA as shown by cell viability assay, transwell migration, colony forming and wound healing assay. The cell viability and migration was found to be significantly reduced in MCF7 and MDA-MB-231 cell lines in which the selected genes were over-expressed as compared to control cell lines. The wound healing assay also demonstrated a significant decrease in wound closure at 12 h and 24 h time intervals in MCF7 & MDA-MB-231 cells. These findings established the tumor suppressor roles of NLGN3 & ANK2 in BRCA. This will have important ramifications for the therapeutics discovery against BRCA.

Abstract Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality’s strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG’s resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature’s image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

Abstract Whether the Main Himalayan Thrust can host a single surface-rupturing event in the Himalaya with a rupture length of > 700 km remains controversial. Previous paleoseismological studies in the Darjeeling-Sikkim Himalaya (DSH) suggested medieval surface-rupturing earthquakes, correlating them with the eleventh–thirteenth century events from Nepal and Bhutan and extending the coseismic rupture length > 700 km. Conversely, there is no rupture evidence of the 1714 Bhutan and 1934 Bihar–Nepal earthquakes in the DSH, resulting in a discrepancy in the rupture extent of the great earthquakes. Consequently, we conducted a paleoseismological investigation across a ~ 10 m-high fault scarp on the Himalayan Frontal Thrust at Chenga village, DSH, revealing a surface-faulting event during 1313–395 BCE. We suggest that the DSH is a 150 km-long independent segment bounded by a transverse ridge and fault and has a recurrence interval of ~ 949–1963 years, which is significantly larger than Nepal (~ 700–900 years) and Bhutan Himalaya (~ 339–761 years).

Abstract Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

Abstract Opioids are the gold standard for the treatment of chronic pain but are limited by adverse side effects. In our earlier work, we showed that Heat shock protein 90 (Hsp90) has a crucial role in regulating opioid signaling in spinal cord; Hsp90 inhibition in spinal cord enhances opioid anti-nociception. Building on these findings, we injected the non-selective Hsp90 inhibitor KU-32 by the intrathecal route into male and female CD-1 mice, showing that morphine anti-nociceptive potency was boosted by 1.9–3.5-fold in acute and chronic pain models. At the same time, tolerance was reduced from 21-fold to 2.9 fold and established tolerance was rescued, while the potency of constipation and reward was unchanged. These results demonstrate that spinal Hsp90 inhibition can improve the therapeutic index of morphine. However, we also found that systemic non-selective Hsp90 inhibition blocked opioid pain relief. To avoid this effect, we used selective small molecule inhibitors and CRISPR gene editing to identify 3 Hsp90 isoforms active in spinal cord (Hsp90α, Hsp90β, and Grp94) while only Hsp90α was active in brain. We thus hypothesized that a systemically delivered selective inhibitor to Hsp90β or Grp94 could selectively inhibit spinal cord Hsp90 activity, resulting in enhanced opioid therapy. We tested this hypothesis using intravenous delivery of KUNB106 (Hsp90β) and KUNG65 (Grp94), showing that both drugs enhanced morphine anti-nociceptive potency while rescuing tolerance. Together, these results suggest that selective inhibition of spinal cord Hsp90 isoforms is a novel, translationally feasible strategy to improve the therapeutic index of opioids.

Abstract Municipal solid waste compost, the circular economy's closed-loop product often contains excessive amounts of toxic heavy metals, leading to market rejection and disposal as waste material. To address this issue, the study develops a novel approach based on: (i) utilizing plant-based biodegradable chelating agent, l-glutamic acid, N,N-diacetic acid (GLDA) to remediate heavy metals from contaminated MSW compost, (ii) comparative assessment of GLDA removal efficiency at optimal conditions with conventional nonbiodegradable chelator EDTA, and (iii) enhanced pre- and post-leaching to evaluate the mobility, toxicity, and bioavailability of heavy metals. The impact of treatment variables, such as GLDA concentration, pH, and retention time, on the removal of heavy metals was investigated. The process was optimized using response surface methodology to achieve the highest removal effectiveness. The findings indicated that under optimal conditions (GLDA concentration of 150 mM, pH of 2.9, retention time for 120 min), the maximum removal efficiencies were as follows: Cd-90.32%, Cu-81.96%, Pb-91.62%, and Zn-80.34%. This process followed a pseudo-second-order kinetic equation. Following GLDA-assisted leaching, the geochemical fractions were studied and the distribution highlighted Cd, Cu, and Pb's potential remobilization in exchangeable fractions, while Zn displayed integration with the compost matrix. GLDA-assisted leaching and subsequent fractions illustrated transformation and stability. Therefore, this process could be a sustainable alternative for industrial applications (agricultural fertilizers and bioenergy) and social benefits (waste reduction, urban landscaping, and carbon sequestration) as it has controlled environmental footprints. Hence, the proposed remediation strategy, chemically assisted leaching, could be a practical option for extracting heavy metals from MSW compost, thereby boosting circular economy.

Abstract Leguminous crop Tephrosia candida has high biomass production and contains a substantial quantity of nutrients within its biomass. Starting in 2019, a long-term study was done to find the best Tephrosia candida dose for mulching in guava orchards. The study had four treatments: T1 = 3.0 kg dry biomass m−2 of the plant basin, T2 = 2.0 kg, T3 = 1.0 kg, and T4 = control (no mulch). Every year, the treatments imposed in the month of August. The third year (2021–2022) results indicated that mulching with 3 kg of biomass m−2 increased trunk diameter, fruit yield, fruit weight, specific leaf area, total leaf chlorophyll, and leaf macro- and micro-nutrients. At 3.0 kg m−2, mulching improved soil properties such as EC, available nitrogen, available phosphorus, exchangeable potassium, DTPA extractable micronutrients (Fe, Zn, Cu, and Mn), total organic carbon (Ctoc), soil organic carbon (Csoc), organic carbon fractions, and microbial biomass carbon between 0–0.15 m and 0.15–0.30 m. There was an increasing trend in dehydrogenase activity (DHA) and fluorescein diacetate (FDA). The Tephrosia leaf litter exhibited decay constants of 1.27 year−1, and the carbon content was 40.11%. Therefore, applying Tephrosia biomass mulching at a rate of 3.0 kg m−2 is a viable long-term solution for enhancing soil fertility and sequestering carbon.

Abstract Previous work has shown that environmental variables affect SARS-CoV-2 transmission, but it is unclear whether different strains show similar environmental responses. Here we leverage genetic data on the transmission of three (Alpha, Delta and Omicron BA.1) variants of SARS-CoV-2 throughout England, to unpick the roles that climate and public-health interventions play in the circulation of this virus. We find evidence for enhanced transmission of the virus in colder conditions in the first variant selective sweep (of Alpha, in winter), but limited evidence of an impact of climate in either the second (of Delta, in the summer, when vaccines were prevalent) or third sweep (of Omicron, in the winter, during a successful booster-vaccination campaign). We argue that the results for Alpha are to be expected if the impact of climate is non-linear: we find evidence of an asymptotic impact of temperature on the alpha variant transmission rate. That is, at lower temperatures, the influence of temperature on transmission is much higher than at warmer temperatures. As with the initial spread of SARS-CoV-2, however, the overwhelming majority of variation in disease transmission is explained by the intrinsic biology of the virus and public-health mitigation measures. Specifically, when vaccination rates are high, a major driver of the spread of a new variant is it’s ability to evade immunity, and any climate effects are secondary (as evidenced for Delta and Omicron). Climate alone cannot describe the transmission dynamics of emerging SARS-CoV-2 variants.

Abstract The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R $$^2$$ 2 of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R $$^2$$ 2 of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc.

Desired modifications of surfaces at the nanoscale may be achieved using energetic ion beams. In the present work, a complete study of self-assembled ripple pattern fabrication on Si and Ge by 100 keV Ar+ ion beam bombardment is discussed. The irradiation was performed in the ion fluence range of ≈3 × 1017 to 9 × 1017 ions/cm2 and at an incident angle of θ ≈ 60° with respect to the surface normal. The investigation focuses on topographical studies of pattern formation using atomic force microscopy, and induced damage profiles inside Si and Ge by Rutherford backscattering spectrometry and transmission electron microscopy. The ripple wavelength was found to scale with ion fluence, and energetic ions created more defects inside Si as compared to that of Ge. Although earlier reports suggested that Ge is resistant to structural changes upon Ar+ ion irradiation, in the present case, a ripple pattern is observed on both Si and Ge. The irradiated Si and Ge targets clearly show visible damage peaks between channel numbers (1000–1100) for Si and (1500–1600) for Ge. The clustering of defects leads to a subsequent increase of the damage peak in irradiated samples (for an ion fluence of ≈9 × 1017 ions/cm2) compared to that in unirradiated samples.