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PURPOSE: Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable. METHODS: We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index. RESULTS: This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways. CONCLUSION: Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

BackgroundThe additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant's true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies.ResultsThrough extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model.ConclusionsOverall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs.

Jharia, one of the oldest and most significant mining areas, is facing the issue of subsidence for different reasons, such as coal fires, underground mining, and soil sensitivity towards seasonal change. This paper concentrates on the subsidence analysis of the Eastern region of the Jharia Coalfield from January 2018 to January 2021. Sentinal-1 SLC SAR imagery is used for multi-image sparse point processing and generating time series. Data processing is done using SARPROZ software. Further, the PS points inside the study area are filtered, and a classification map is drawn using ArcGIS software. The yearly percentage change of different classified zones is examined. The percentage change is significant for subsiding and uplifting zones but minimal for stable zones. Different critically and highly subsiding areas are identified by overlaying the classification zones on the topographic map. The seasonal variation of cumulative displacement is also examined, and patterns are analyzed. Different regions have different sensitivity towards seasonal variation. Overall, this paper provides insightful deformation results over Eastern Jharia for 2018–2021.

Abstract Background The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant’s true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. Results Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model. Conclusions Overall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs.

Abstract The escalating global cancer burden underscores the urgent need for more effective therapeutic strategies. Phytochemicals, naturally occurring compounds in plants, have garnered attention for their potential in cancer chemoprevention and chemotherapy. Their ability to modulate molecular mechanisms and influence cell signaling pathways offers a promising avenue for cancer management. This review aims to synthesize current knowledge on phytochemicals’ chemopreventive and chemotherapeutic potential, focusing on their molecular mechanisms of action and impacts on cell signaling pathways involved in cancer. A systematic literature search was conducted across major databases, including PubMed/Medline, Web of Science, Scopus, and Google Scholar. The search strategy uses Medical Subject Headings (MeSH) and free‐text terms using Boolean operators to capture relevant studies. Inclusion criteria targeted original research and reviews on the effects of phytochemicals in cancer, with a specific focus on molecular mechanisms. Phytochemicals, including flavonoids, polyphenols, and terpenoids, demonstrated significant anticancer properties by inducing cell cycle arrest, apoptosis, and autophagy. They modulate critical cell signaling pathways, such as cyclooxygenase‐2, nuclear factor kappa B, and various growth factor‐related pathways, and rectify epigenetic alterations, contributing to their chemopreventive and therapeutic effects. Phytochemicals represent a valuable resource for developing novel cancer prevention and treatment strategies; their actions on molecular mechanisms and cell signaling pathways underscore their potential in cancer prevention and combat. Further research is warranted to translate these findings into clinical applications, optimizing phytochemical‐based interventions for cancer management.

Abstract Wound healing involves a carefully regulated sequence of events, encompassing pro-inflammatory and anti-inflammatory stages, tissue regeneration, and remodeling. However, in individuals with diabetes, this process gets disrupted due to dysregulation caused by elevated glucose levels and pro-inflammatory cytokines in the bloodstream. Consequently, the pro-inflammatory stage is prolonged, while the anti-inflammatory phase is delayed, leading to impaired tissue regeneration and remodeling with extended healing time. Furthermore, the increased glucose levels in open wounds create an environment conducive to microbial growth and tissue sepsis, which can escalate to the point of limb amputation. Managing diabetic wounds requires meticulous care and monitoring due to the lack of widely available preventative and therapeutic measures. Existing clinical interventions have limitations, such as slow recovery rates, high costs, and inefficient drug delivery methods. Therefore, exploring alternative avenues to develop effective wound-healing treatments is essential. Nature offers a vast array of resources in the form of secondary metabolites, notably polyphenols, known for their antimicrobial, anti-inflammatory, antioxidant, glucose-regulating, and cell growth-promoting properties. Additionally, nanoparticles synthesized through environmentally friendly methods hold promise for wound healing applications in diabetic and non-diabetic conditions. This review provides a comprehensive discussion and summary of the potential wound-healing abilities of specific natural polyphenols and their nanoparticles. It explores the mechanisms of action underlying their efficacy and presents effective formulations for promoting wound-healing activity. Graphical abstract

Introduction: Coeliac Disease (CD) is a systemic immune disorder caused by gluten in genetically susceptible individuals. A serological screening assay for CD has been designed to detect Immunoglobulin A (IgA) and IgG anti-tissue Transglutaminase (a-tTG) and IgA and IgG Deamidated Gliadin Peptide antibodies (a-DGP) simultaneously. The seronegative gap can be closed when these two antigens are combined on a single solid phase. This is primarily because untreated CD children who are negative for antibodies of one of the antigens may exhibit a positive result for the other. Aim: To determine the diagnostic parameters {sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV)} of tTG-IgA and htTG-DGP (Coeliac fusion- a combination of IgA and IgG to human tTG and synthetic DGP) for the diagnosis of CD in clinically symptomatic children. Materials and Methods: This cross-sectional study was conducted at the Paediatric Outpatient Department (OPD) of Santokhba Durlabhji Memorial Hospital, Jaipur, Rajasthan, India from April 2023 to November 2023. The study population comprised 45 children (age >6 months and

Abstract A substantial variation in the land cover dynamics has been observed as a consequence of increasing urban expansion. Polarimetric synthetic aperture radar (PolSAR) data is widely being used for land cover studies in urban areas due to its all-weather, day-and-night imaging capabilities. However, in densely built-up areas, challenge arises with buildings having large Azimuth Orientation Angles (AOAs). These buildings are often misclassified as vegetation due to the depolarization of radar signal causing volumetric scattering response from the structures. This study addresses this issue by proposing an approach that integrates polarimetric information with interferometric SAR (InSAR) coherence to improve the differentiation between urban structures and vegetated areas, enhancing the accuracy of urban land-cover classification. Vegetated areas exhibit lower temporal coherence due to changes in the orientation of its leaves and branches caused by wind, seasonal variations, growth phenology, and other factors. In contrast, urban structures, being relatively stable targets, maintain high temporal coherence values. In present research various decomposition and scattering parameters were evaluated, along with PolInSAR coherence derived from L-band (ALOS-2) and C-band (RADARSAT-2), using two machine learning algorithms namely, Random Forest (RF) and Convolutional Neural Network (CNN). The C-band RADARSAT-2 data, particularly with six-component decomposition parameters, performed better, achieving an overall accuracy as 85.85% using RF algorithm. To further improve classification results, optical datasets from Landsat constellation were fused with SAR parameters using Gram-Schmidt fusion technique. This fusion led to significant improvements, achieving an overall accuracy of 94.50% and kappa statistics of 0.92, when CNN algorithm was applied to the fused optical and C-band RADARSAT-2 dataset. These results demonstrate the effectiveness of combining PolInSAR and optical data for more accurate urban land-cover classification, particularly in complex urban environments.