Search Results (827)

This study evaluated the effects of expected progeny difference (EPD) grade and feeding system on carcass performance in Hanwoo steers using a large-scale field dataset collected under commercial production conditions. Records from 4561 steers (1466 fed total mixed fermented feed [TMF] and 3095 on a conventional separate-feeding system) across 269 farms in Korea from January 2023 to May 2025 were analyzed. Expected progeny difference grades for carcass weight (CWT), backfat thickness (BFT), ribeye area (REA), and marbling score (MBS) were classified A-D. Carcass performance significantly differed among EPD grades. Compared with grade D, grade A steers exhibited greater CWT (45.2 kg), less BFT (3.44 mm), greater REA (10.77 cm²), and greater MBS (1.57 units). Genetically superior animals reached slaughter age earlier. Steers fed TMF demonstrated higher CWT, BFT, REA, and MBS than conventionally fed steers. No significant interaction between EPD grade and feeding system was found for any carcass trait. These results indicate that EPD grades consistently predict carcass performance across different feeding environments, while TMF improves the absolute level of carcass traits. This large field dataset demonstrates that integrating Hanwoo EPD information with appropriate feeding management may support more efficient and profitable carcass production under commercial farm conditions. Full article

Background/Objectives: Alzheimer’s disease (AD) disproportionately affects women, who account for nearly two-thirds of affected individuals worldwide. This sex imbalance cannot be explained by longevity alone and likely reflects complex interactions among biological sex, endocrine aging, genetic susceptibility, and brain-specific mechanisms of vulnerability. Neuroimaging has played a pivotal role in characterizing these sex-related differences in vivo, enabling the assessment of amyloid-β deposition, tau propagation, neurodegeneration, cerebral glucose metabolism, and network reorganization. This invited review examines AD through a rigorously sex-specific neuroimaging perspective, with particular emphasis on implications for women’s brain health. Methods: We integrated evidence from structural MRI, FDG-PET, amyloid-PET, tau-PET, estrogen receptor PET, diffusion MRI, and fluid biomarkers, together with epidemiological, molecular, genetic, and endocrine studies. The review focuses on female-specific trajectories of AD initiation and progression, highlighting the contribution of neuroendocrine aging, menopause, metabolic dysfunction, and sex-modulated genetic risk factors. Results: Available evidence indicates that women exhibit distinct biological and neuroimaging signatures across the AD continuum. Menopause emerges as a critical neuroendocrine transition associated with metabolic decline, altered brain connectivity, increased amyloid and tau vulnerability, and progressive neurodegeneration. Female-specific patterns of tau propagation and sex-dependent interactions with genetic risk factors further contribute to differential disease trajectories. Advanced multimodal neuroimaging approaches have substantially improved the characterization of these mechanisms and their relationship with cognitive decline and clinical progression. Conclusions: A sex-specific neuroimaging framework is essential to improve understanding of AD pathophysiology and to advance precision medicine approaches tailored to women’s brain health. Recognition of endocrine aging and female-specific biological vulnerability may inform earlier identification of at-risk individuals and the development of targeted prevention and treatment strategies. Future research should prioritize sex-aware longitudinal studies and multimodal biomarker integration to optimize personalized interventions in AD. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to bridge microstructural features with engineering-scale mechanical behaviour. However, their practical application is hindered by two major challenges: high computational costs of physics-based simulations, and the labour-intensive, trial-and-error nature of parameter calibration. These challenges are amplified in additively manufactured (AM) materials, where location-dependent properties require calibration to be repeated at multiple points to produce a detailed property map. Additionally, a limited understanding of how individual parameters of the CP models influence stress–strain predictions across the strain spectrum compounds these issues, making it challenging to utilise CP models for efficient materials design. To address these limitations, we developed an integrated framework that combines deep neural network (DNN) surrogates, optimisation algorithms (OAs), and explainable AI (XAI) techniques. We also utilised experimental tensile data from AM AlSi10Mg alloy as ground truth since AM materials are expected to benefit the most from our investigation. We demonstrate that, by using OAs such as a Natural Evolutionary Strategy or a Genetic Algorithm, the calibration process can be made more accurate and significantly accelerated. We also investigated the utility of employing deep neural network (DNN) surrogates of CP simulations in the calibration process. The fast-solving DNN surrogates achieved substantial time savings in the absence of OAs, i.e., during exhaustive parameter searches mandated by trial-and-error strategies. However, their effectiveness in parameter discovery was context-dependent when used in conjunction with OAs, since OAs can sometimes converge with fewer simulations than required for DNN training. Furthermore, we applied Shapley Additive exPlanations (SHAP), an XAI method, which revealed intricate interactions among some CP parameters, offering insight into why conventional trial-and-error calibration approaches often prove challenging. Our study contributes to strengthening the practical relevance of CP models for modelling-informed materials engineering and optimisation applications. Finally, our integrated framework offers broad applicability beyond materials modelling, enabling accelerated discovery of tuneable parameters in phenomenological models and providing deeper insight into their contributions to predictions. Full article

Background: Parenting stress is a known risk factor for children’s social anxiety, yet the mediating and moderating mechanisms underlying this relationship remain underexplored, particularly regarding gene–environment interactions. This quantitative, cross-sectional study, grounded in diathesis-stress and family process theories, examined whether maladaptive parenting mediates the link between parenting stress and children’s social anxiety, and whether FKBP5 gene variation moderates this mediation. Methods: A sample of 1774 fourth- to sixth-grade students (aged 10–14 years) and their parents participated. Parenting stress and maladaptive parenting were parent-reported, children’s social anxiety was self-reported, and children’s FKBP5-related cumulative genetic score was derived from four SNPs (rs4713916, rs1360780, rs3800373, rs9296158). Moderated mediation analyses were conducted. Results: Parenting stress was significantly and positively associated with children’s social anxiety. Maladaptive parenting partially mediated this relationship. The FKBP5 showed a marginally significant moderating effect, with simple slope analysis suggesting parenting stress was more strongly associated with child social anxiety among children with higher genetic risk. No moderating effect was found for the path from maladaptive parenting to social anxiety. Conclusions: Parenting stress is associated with children’s social anxiety both directly and indirectly through maladaptive parenting, with FKBP5-related cumulative genetic risk potentially moderating the direct effect. These findings offer preliminary evidence that may inform preventive interventions targeting parenting stress, although replication is needed. Full article

Background/Objectives: IL23R encodes a pivotal component of the IL-23/Th17 signaling axis and represents a validated genetic susceptibility locus for inflammatory bowel disease (IBD), psoriasis, and ankylosing spondylitis. Despite extensive GWAS data, the functional consequences of the full spectrum of IL23R missense single-nucleotide variants (SNVs) have not been systematically characterized. This study aimed to identify high-risk missense SNVs through a multi-tool in silico pipeline. Methods: A total of 723 missense SNVs from NCBI dbSNP were verified against transcript NM_144701.3/Q5VWK5-1 (629 aa) using Ensembl VEP (GRCh38). Sequential filtering was performed using applied SIFT, PolyPhen-2, PROVEAN, E-SNPs&GO, MutPred2, and ConSurf (grade ≥ 7); AlphaMissense and FATHMM-MKL were used as independent annotation layers. Protein stability was assessed with MuPro and DynaMut2 (AlphaFold2 AF-Q5VWK5-F1-v6; pLDDT = 68.19); structural characterization was performed with Project HOPE, and interaction networks were constructed using STRING and GeneMANIA. Results: Sequential filtering identified 37 high-risk missense variants. MuPro predicted destabilizing effects for 36/37 variants, with concordant DynaMut2 results for 35/37. Project HOPE identified disulfide bond disruption in 11 variants, charge-altering substitutions in 8, and glycine/proline backbone conformational changes in 11. STRING analysis identified IL12RB1 (0.999), IL23A (0.999), JAK2 (0.995), IL12B (0.986), and STAT3 (0.980) as the leading IL23R interactors. The protective variant R381Q was appropriately characterized as neutral by PROVEAN (−1.16) and AlphaMissense (likely_benign), supporting the specificity of the pipeline. Conclusions: Comprehensive in silico analysis identified 37 high-risk IL23R missense candidates with convergent computational evidence of predicted deleteriousness, predominantly involving cysteine bridge disruption, charge alteration, and glycine/proline backbone conformational changes. These variants are presented as prioritized candidates for future functional validation and may inform subsequent investigations of IBD susceptibility and IL-23 pathway pharmacogenomics. Full article

Global food production systems are increasingly challenged by population growth, climate change, water scarcity, and environmental degradation, necessitating the adoption of sustainable, resource-efficient food production strategies. Aquaponic systems integrate recirculating aquaculture with hydroponic crop cultivation, enabling nutrient recycling and improved water-use efficiency. Simultaneously, CRISPR/Cas9 genome-editing technology has emerged as a powerful tool for precise genetic improvement of economically important aquaculture traits. This review critically evaluates current progress in CRISPR/Cas9 applications in aquaculture, with emphasis on Nile tilapia (Oreochromis niloticus). Evidence from peer-reviewed studies indicates that targeted modification of genes associated with growth regulation, disease resistance, nutrient metabolism, feed efficiency, and stress tolerance can significantly enhance fish productivity and physiological resilience. Genes involved in hypoxia adaptation and nitrogen metabolism may further improve environmental performance in intensive recirculating systems by reducing ammonia accumulation and enhancing nutrient utilization. However, most genome-editing studies have been conducted under laboratory or conventional aquaculture conditions, with limited information available regarding the long-term performance, ecological interactions, microbial dynamics, and biosafety of genome-edited fish in aquaponic environments. Technical limitations including off-target effects, mosaicism, delivery efficiency, regulatory uncertainty, and public acceptance continue to constrain large-scale implementation. In the short term, CRISPR/Cas9 applications are likely to focus on practical trait enhancement under controlled aquaculture systems, whereas longer-term research may explore fish lines specifically optimized for nutrient cycling, environmental resilience, and integrated aquaponic sustainability. Overall, CRISPR/Cas9-mediated genome editing represents a promising but still emerging strategy for improving sustainable aquaculture and aquaponic food production systems. Full article

Background: The population of Serbia faces a significant burden from cardiovascular diseases (CVDs). This study aimed to computationally investigate genetic factors that contribute to the prevalence of these diseases by examining the possible involvement of common variants on lipid metabolism. Methods: We examined how a variant prevalent in the Serbian population, chr7:g.56019730G>A in the PSPH gene, affects the phosphoserine phosphatase (PSP) protein interaction network, particularly involved in lipid metabolism. The Informational Spectrum Method (ISM), method for the analysis of protein sequence based on amplitude changes, was applied to single out the top 10 affected interactors. Their further functional annotation identified the pathways in which they jointly participate with PSP. An additional strategy encompassed the investigation of variant combinations in all analyzed genes and potential relevance of prevalent variant combinations on lipid metabolism. Results: The PSP interactions affected by the R49W variant, such as SHMT1/2, were primarily in pathways associated with serine, glycine, and sphingolipid metabolism, highly relevant for CVD etiology. Further, we identified frequent variant combinations within the LRCH1, CEP126, PIK3CG, and PIKFYVE genes in the Serbian cohort. Conclusions: This study underscores the importance of investigating genetic variant combinations in complex diseases, and provides a hypothesis generating foundation for future research into the relationship between these genes and cardiovascular diseases. Full article

Dietary emulsifiers, common in processed and ultra-processed foods, improve food texture and shelf life but may affect gut health by interacting with the microbiota and intestinal barrier. While emulsifiers have long been considered safe, growing evidence links their presence in ultra-processed foods to chronic disease risk. This review aims to evaluate the current understanding of the factors and mechanisms underlying individual differences in intestinal mucosal susceptibility to dietary emulsifiers. A search of PubMed and Embase through February 2026 identified eight relevant studies. Overall, the available evidence indicates a heterogeneous and highly individualized host response to dietary emulsifiers. These differences appear to be strongly influenced by the gut microbiota and its functional properties, while animal studies further suggest that host factors such as sex-related differences in microbial composition may also contribute to variability in response. Importantly, not all emulsifiers have the same effects, underscoring compound-specific impacts on gut physiology. The findings demonstrate that sensitivity to dietary emulsifiers varies substantially between individuals, challenging the long-standing assumption that these additives are universally safe. Given the multifactorial nature of this susceptibility, particularly the role of the gut microbiota, future research should adopt an integrative approach that combines microbial profiling with host genetics, immune responses, and early-life exposures. Such efforts will be essential to identify at-risk individuals and to inform more personalized dietary recommendations aimed at preserving intestinal health and reducing disease risk. Importantly, there is a clear need for larger, well-powered studies that can validate and expand upon these initial observations. Full article

The cellulose synthase gene superfamily encompasses two major groups, CesA and Csl, which are vital for synthesizing cellulose and hemicellulose in plant cell walls and fundamental to plant growth and developmental regulation. Madhuca pasquieri is a rare tree with high timber value. Currently, there is no relevant report on the identification and characterization of the CesA/Csl gene family in M. pasquieri. In this study, based on the high-quality genome of M. pasquieri, 47 members of the CesA/Csl superfamily were identified and classified into seven subfamilies, including CesA, CslA, CslB, CslC, CslD, CslE and CslG. Cis-acting elements were identified via analysis of the 2000 bp upstream sequences of MpCesA, suggesting extensive involvement in biotic and abiotic stress regulation. Based on the transcriptome data of five growth periods, the expression of the CesA/Csl family was analyzed. Combined with phylogenetic information, it is inferred that MpCesA4/7b/7a/8b may regulate the secondary wall, while MpCesA1/3b/6b may regulate the primary wall. Protein–protein interaction showed that MpCesA4/7b/8a were in the core site. Finally, we constructed the cellulose synthase complex (MpCesA4/7b/8b) model using AlphaFold3, which suggests that MpCesA4/7b/8b may form a complex on the plasma membrane to carry out cellulose synthesis. This study has a limitation in that the complex and its expression lack experimental validation, and only data analysis is provided as a reference, offering some directions for future research. In summary, the systematic characterization of the MpCesA/Csl gene family provides important insights into cell wall formation, genetic enhancement, and future biotechnological applications of this species. Full article

The extraordinary genetic diversity of human immunodeficiency virus type 1 (HIV-1), driven by high mutation and recombination rates, poses significant challenges for diagnostics, therapy, and vaccine development. While variable regions enable immune escape, hyperconserved regions are critical for viral function and represent promising targets for novel therapeutic interventions. This study aimed to develop and validate a bioinformatic algorithm for quantitative assessment of sequence conservation and automated identification of functionally significant conserved regions across all major HIV-1 proteins. A total of 1119 full-length HIV-1 genome sequences representing major subtypes (A1, A2, A6, B, C, D, F1, F2, G, H, J, K) were analyzed. Normalized Shannon entropy (S-index) was calculated for each alignment column. Statistical thresholds for conserved regions were established using 95% confidence intervals derived from bootstrap resampling. Two complementary algorithms, clustering and local maxima detection, were applied to identify conserved regions, which were subsequently mapped to known functional domains based on literature data. Protein conservation varied markedly, with S_m values ranging from 0.784 (Vpu) to 0.920 (Pol). Gag, Pol, and Vpr demonstrated the highest overall conservation, while Env, Rev, Tat, and Vpu exhibited pronounced variability interspersed with conserved domains. In total, 25 conserved regions in Gag, 49 in Pol, 28 in Env, and 6–4 regions in accessory proteins (Vif, Vpr, Rev, Tat, Nef, Vpu) were identified. These regions corresponded to critical functional elements including enzyme catalytic centers, zinc fingers, receptor-binding sites, protein interaction interfaces, and membrane-anchoring domains. The developed computational framework enables statistically grounded identification of evolutionarily constrained regions across analyzed HIV-1 subtypes. The identified conserved regions represent candidate sites for further investigation and may inform downstream studies focused on antiviral target prioritization, immunogen design, and diagnostic assay development. However, their translational applicability requires additional analytical, structural, and experimental validation. Full article

The genetic architecture of wheat yield-related traits is complex due to their polygenic control, relatively low heritability, and strong genotype–environment interactions. Commonly used characteristics of wheat productivity include thousand-grain weight (TGW), grain weight per ear (GWE), and grain number per ear (GNE). To identify stable loci associated with productivity-related traits in common wheat, we performed QTL analysis using two mapping populations derived from crosses between contrasting cultivars. The populations were phenotyped for GNE, GWE, and TGW over two years. In addition, GWAS was conducted using a cultivar panel phenotyped for yield and GWE over ten years, and for GNE, GWE, and TGW over two years. The most reproducible loci were located on chromosomes 2D, 4A, 5A, 5B, 6A, 6B, and 7A. From these regions, 16 SNPs were selected for KASP marker development. Validation in an independent panel of 296 spring common wheat varieties phenotyped over three years identified three most informative markers: wsnp_Ex_c16175_24619793 (4A), wsnp_Ex_c2171_4072995 (5A), and BS00034554_51 (6B), all consistently associated with TGW and additionally associated with GWE, GNE, or yield in individual years. These markers may be useful for marker-assisted selection of wheat productivity-related traits. Full article

Dataset-Based Damage Detection (DBDD) methods are effective for structures with geometric complexity, strong interaction effects, and operational variability, such as liquid-containing tanks with changing fluid levels. In many DBDD applications, accurate damage identification relies on high-fidelity numerical models and extensive simulations across numerous damage scenarios, which results in substantial computational cost. Although finite element modeling is well suited for these problems, fine-meshed models are computationally expensive, whereas coarse-meshed models can introduce errors in extracted dynamic features and reduce damage detection accuracy. This paper proposes an efficient DBDD framework that reduces computational demand while preserving detection reliability. A genetic algorithm is used to select a compact set of highly informative dynamic features that are extracted from a coarse-meshed finite element model and are suitable for training a machine-learning damage classification model. By combining a reduced-cost numerical model with an optimized feature set, the proposed approach decreases simulation time and database storage requirements while maintaining accurate damage prediction. The method is validated on a fluid-filled cylindrical tank under multiple fluid levels, demonstrating robust damage detection performance under simulated response variability (different FEM solvers, mesh sizes, ±5% material perturbations, and SNR 40–10 dB noise). These results indicate that the proposed framework is a practical and accurate solution for structural health monitoring of complex engineering systems. Full article

Pediatric neurodevelopmental disorders (NDDs) encompass a highly heterogeneous group of conditions characterized by complex interactions among genetic, molecular, developmental, and environmental factors. Growing evidence increasingly supports an important role for neuroglial dysfunction, including disturbances in astrocytic, microglial, and oligodendroglial biology, in the pathophysiology of disorders such as autism spectrum disorder, global developmental delay, intellectual disability, and rare neurogenetic syndromes. At the same time, artificial intelligence (AI)-assisted analytical approaches are becoming increasingly relevant in pediatric diagnostics through integration of multidimensional datasets, including clinical phenotypes, neuroimaging, genomic sequencing, and molecular biomarkers. This review examines the evolving intersection of neuroglial biology and AI-based analytical methods in pediatric NDDs. Current understanding of neuroglial mechanisms underlying disease vulnerability and developmental heterogeneity is discussed alongside emerging applications of machine learning, deep phenotyping platforms, radiogenomics, and large language models in diagnostic interpretation and clinical decision support. Important translational and ethical challenges, including algorithmic bias, interpretability limitations, data governance, and disparities in data accessibility, are also considered. Overall, integration of neuroglial research with AI-assisted analytical frameworks may contribute to more biologically informed interpretation of pediatric neurodevelopmental disorders and support ongoing development of increasingly individualized diagnostic approaches. Full article

Beyond its function as a carrier of hereditary information, recent research has uncovered novel properties of extracellular DNA, including its role in the adaptation to the environment when released from plants. The secreted DNA has been shown to exert insecticidal effects against insect pests, which play an adaptive role in plant-insect interactions, particularly in regulating populations of economically important sap-feeding insects. The molecular mechanisms underlying this insecticidal effect are underinvestigated and remain largely unknown. Therefore, there is a need for more efforts to uncover these mechanisms to better understand plant–pest interactions, which would provide new insights into natural pest control strategies and inspire biotechnological applications. In the current study, we show that Pittosporum tobira (P. tobira) secretes single-stranded DNA (ssDNA) that exerts an insecticidal effect on Coccus hesperidum (C. hesperidum). We collected extracellular DNA from P. tobira leaves and tested its potential insecticidal effect by applying it to C. hesperidum, which is a well-known pest that causes damage to P. tobira. Our results revealed that the outermost layer of the leaf cuticle of P. tobira predominantly contains ssDNA of approximately 100 nt in length, originating from both chloroplast and nuclear genomes. This DNA exhibited pronounced insecticidal activity against C. hesperidum, with chloroplast-derived sequences significantly enriched compared to the total DNA in intact plant cells. These findings suggest that the microevolution of the P. tobira nucleome and plastome contributed to the formation of extracellular DNA with insecticidal properties (eci-DNA), which is part of its defense strategy against insect pests. Moreover, in this article, for the first time, we show that antisense DNA (illustrated with oligonucleotide insecticide Coccus-11) is capable of activating insect retrotransposons and upregulating their RT-RNase H, a crucial enzyme for the DNA containment mechanism and successful action of oligonucleotide insecticides. Notably, the laboratory-developed ssDNA-based ‘genetic zipper’ technology, designed for sustainable pest management, possesses characteristics similar to eci-DNA found in nature, highlighting a potential natural parallel to this biotechnological approach for sustainable pest management. Full article

Introduction/Background: Metformin is the first-line therapy for type 2 diabetes (T2D); however, a considerable inter-individual variability in glycemic response is observed among patients. This heterogeneity suggests that metformin’s effects depend not only on drug exposure but also on the underlying metabolic and genetic factors. Methods: We applied a Gene–Environment interaction Mendelian Randomization (MR-G×E) in a cohort of 2743 individuals to investigate whether genetically influenced metabolite-HbA1c associations differ by metformin use. Metabolites associated with metformin response were used to establish metabolite-specific polygenic risk scores (PRSs) using metabolome-wide association study (mGWAS) variants. Generated PRS were used as genetic instruments within a one-sample, modified two-stage least squares model. An interaction term between PRS and metformin use was included to assess treatment-dependent genetic effects, adjusting for age, sex, body mass index, and genetic ancestry (principal components). Results: Metformin use significantly modified genetically influenced associations between 18 metabolites and HbA1c. Positive and negative PRS-metformin interaction effects indicated attenuation, strengthening or reversal of baseline genetic associations under treatment. Several amino acid metabolites, palmitoyl sphingomyelin (d18:1/16:0), and carbohydrate-related metabolite 1,5-anhydroglucitol showed specific patterns under metformin use. Interestingly, several metabolites (creatinine, gamma glutamylcitrulline, N-acetylthreonine, 3-methyl-2-oxovalerate, glycerol-3-phosphate, 1-(1-enyl-palmitoyl)-GPC (P-16:0), 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2), sphingomyelin (d18:1/22:1, d18:2/22:0, d16:1/24:1), fructose, and methyl-glucopyranoside (alpha + beta)) showed no basal causal association with HbA1c but exhibited significant interaction effect with metformin use, suggesting metabolic association only in the presence of metformin. Conclusions: These findings indicate that metformin modifies the genetically influenced metabolite-HbA1c relationships, exhibiting treatment-dependent metabolic effects that are not detectable with standard MR approaches. Incorporating pharmacological context into causal inference provides new insights into the metabolic basis for the variable metformin response and helps inform precision strategies for T2D management. Full article