Search Results (134)

Background/Objectives: FAM111A is a trypsin-like serine protease that has emerged as a regulator of DNA replication, and is directly related to genome stability, protein homeostasis, antiviral defense and cancer progression. Pathogenic variants in FAM111A are correlated with genetic syndromes such as Kenny–Caffey syndrome type 2 (KCS2) and gracile bone dysplasia/osteocraniostenosis (GCLEB/OCS). This study focuses on the evolutionary, genetic, and structural analysis of FAM111A, in order to identify key regions and candidate pharmacological targets that are related to this enzyme’s function. Methods: The methodology of this in silico study includes separate analyses at the sequence, structural and functional levels. Initially, data mining was carried out using NCBI/Protein (2025), and then data filtering was performed in order to identify representative FAM111A sequences for several species. Sequence analysis was then executed through multiple alignments and phylogenetic analyses. Through this, conserved domains and motifs were identified. For structural analysis, human pathogenic mutations and protein structures were identified through searches in biological databases including PDB and ClinVar, and then all data were analyzed in order to identify candidate pharmacological targets related to FAM111A function. Results: Approximately 1850 FAM111A protein sequences were retrieved for several species, and after filtering processes a dataset of 85 representative sequences was generated. Evolutionary analysis indicates that FAM111A originated in early metazoans, with progressive domain specialization leading to mammal-restricted acquisition of regulatory elements, including the PIP-box PCNA (proliferating cell nuclear antigen) interacting peptide and UBL (ubiquitin-like) domains. The ubiquitin-like/DNA binding domain and catalytic serine protease domain (SPD) are the most conserved, containing seven highly conserved motifs. The structural analysis was based on two protein structures and 34 critical mutations that accumulate in two distinct regions. Finally, by combining the results, six pharmacological targets and 100 inhibitors are proposed. Conclusions: Advancing the structural and function characterization of FAM111A, coupled with pharmacological target identification and evolutionary insights, will be critical to validate this underexplored protease as a therapeutic genetic target in genetic disorders, cancer, and antiviral responses. Full article

This text systematically investigates the Laotudingzi monzogranite (a gold-hosting intrusion) and the Dongbaligou gold ore deposit in the Laoling gold ore belt through comprehensive geochronological, whole-rock geochemical (macroelement and microelement), strontium-neodymium-lead-hafnium isotopic and in situ sulfur-lead isotopic analysis of pyrite, combined with hydrogen-oxygen isotopic studies of hydrothermal quartz. The results demonstrate a significant Early–Middle Jurassic magmatic-mineralization event in southern Jilin Province (Ji’nan). The gold mine is structurally controlled by detachment fractures within the Laoling metamorphic core complex, which developed in an extended environment. The metallogenic materials are primarily derived from adakitic magma, supporting a “decratonic-type” genetic model. By integrating geochronological, geochemical, and isotopic datasets from the ore-related intrusions and gold deposits, as well as fluid inclusion characteristics, we elucidate the metallogenic mechanism linking Jurassic gold mineralization to subduction-related cratonic destruction. The process involved lower crustal thickening induced by Paleo-Pacific Plate subduction, lithospheric destabilization via gravitational foundering and delamination, and syn-extensional magmatism that sourced ore-forming fluids during cratonic lithosphere thinning. This work establishes a genetic framework connecting plate subduction, lithospheric removal, and gold endowment in convergent margin settings. Full article

Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset. Full article

The probability integral method is the primary technique for predicting mining-induced subsidence in China, and its predictive accuracy strongly depends on the precision of the model parameters. To improve the accuracy and stability of parameter inversion and to overcome the convergence randomness of the Genetic Algorithm (GA) in global search, as well as the tendency of the BFGS quasi-Newton method (BFGS) to converge to local optima in non-convex optimization problems, a hybrid GA–BFGS optimization algorithm is proposed for inverting the parameters of the probability integral model. This hybrid approach combines the global exploration capability of GA with the fast local refinement of BFGS, resulting in a more efficient and robust parameter optimization process. Simulation results under ideal conditions without model error demonstrate that the proposed GA–BFGS algorithm outperforms pattern search (PS), GA, and BFGS in terms of inversion accuracy, convergence stability, and robustness to noise and outliers. In engineering applications, the inversion accuracy is reduced compared with simulation experiments, which can be attributed to complex geological conditions and inherent model uncertainties. Therefore, further improvements in subsidence prediction accuracy require not only refined inversion algorithms but also the development of more accurate prediction models that explicitly account for site-specific geological and mining conditions. Full article

The issue of water disasters in the mining floor is extremely severe. Despite significant progress in the on-site monitoring and identification of water inrush channels, research on the spatial development characteristics of cracks and the temporal evolution patterns remains insufficient, resulting in the incomplete development of microseismic-based water disaster early warning theory and practice. Based on this, the present study first derives the expressions for the diameter and length of water inrush channels according to the damage characteristics of microseismic events and the glazed porcelain shape features of the channels. A theoretical model for the correlation between microseismic-water inrush volume is established, and the relationship between microseismic and water level is revealed. Analysis of field monitoring data further indicates that when high-energy microseismic features (such as single high-energy events and higher daily cumulative energy) are detected, the aquifer water level begins to decline, followed by high water inrush events. Therefore, a decrease in water level accompanied by high-energy microseismic features can serve as an important early warning marker for water disasters. Finally, advanced machine learning methods are applied, in which the optimal index combination for floor water inrush prediction is obtained through the genetic algorithm, and the weights of each index are determined by integrating the analytic hierarchy process with the random forest model. Field engineering verification demonstrates that the integrated early warning system performs significantly better than any single monitoring indicator, and all high-water-inrush events are successfully predicted within four days. Full article

Total Soluble Solids (TSS) in tomatoes is a core indicator for evaluating fruit quality and processing characteristics. Its composition mainly consists of soluble sugars (such as fructose and glucose) and organic acids (such as citric acid and malic acid). The contents of sugars and acids and their ratio directly affect the flavor and nutritional value. Cultivated tomatoes have a TSS of 4–6%, compared with 10–15% in wild varieties. In recent years, with the advancement of molecular biology and genomics technologies, significant progress has been made in the research on the regulatory mechanisms of tomato fruit TSS and major sugars and acids, including the identification of major quantitative trait locus (QTLs) (Lin5, SlALMT9), functional characterization via CRISPR/Cas9 and elucidation of the transporter network. Breaking the negative correlation between TSS and yield remains a major bottleneck in breeding. Analyzing the mechanism by which environmental factors regulate the TSS and optimizing cultivation measures are crucial for increasing the TSS content in tomatoes. The deep integration of cutting-edge technologies (such as Genome-wide association studies (GWAS), metabolome-wide association studies (mGWAS), Genomic selection (GS), genome editing, and crop modeling) with design breeding is expected to accelerate the development of high-TSS tomato varieties. This paper reviews the current research status from the following four aspects: QTL mapping related to tomato TSS and mining of major genes, metabolic and transport mechanisms of major sugars and acids and key genes, the influence of environmental factors on TSS, and application of genetic improvement strategies and technologies. Full article

Edible mushrooms have gained global popularity due to their nutritional value, medicinal properties, bioactive compounds and industrial applications. Despite their long-standing roles in ecology, nutrition, and traditional medicine, their additional functions in cultivation, breeding, and classification processes are still in their infancy due to technological constraints. The advent of Artificial Intelligence (AI) technologies has transformed the cultivation process of mushrooms, genetic breeding, and classification methods. However, the analysis of the application of AI in the mushroom production cycle is currently scattered and unorganized. This comprehensive review explores the application of AI technologies in mushroom cultivation, breeding, and classification. Four databases (Scopus, IEEE Xplore, Web of Science, and PubMed) and one search engine (Google Scholar) were used to perform a thorough review of the literature on the utility of AI in various aspects of the mushroom production cycle, including intelligent environmental control, disease detection, yield prediction, germplasm characterization, genotype–phenotype integration, genome editing, gene mining, multi-omics, automatic species identification and grading. In order to fully realize the potential of these edge-cutting AI technologies in transforming mushroom breeding, classification, and cultivation, this review addresses challenges and future perspectives while calling for interdisciplinary approaches and multimodal fusion. Full article

Stomata on the leaves of wheat serve as important gateways for gas exchange with the external environment. Their morphological characteristics, such as size and density, are closely related to physiological processes like photosynthesis and transpiration. However, due to the limitations of existing analysis methods, the efficiency of analyzing and mining stomatal phenotypes and their associated genes still requires improvement. To enhance the accuracy and efficiency of stomatal phenotype traits analysis and to uncover the related key genes, this study selected 210 wheat varieties. A novel semantic segmentation model based on transformer for wheat stomata, called Wheat Stoma Former (WSF), was proposed. This model enables fully automated and highly efficient stomatal mask extraction and accurately analyzes phenotypic traits such as the length, width, area, and number of stomata on both the adaxial (Ad) and abaxial (Ab) surfaces of wheat leaves based on the mask images. The model evaluation results indicate that coefficients of determination (R²) between the predicted values and the actual measurements for stomatal length, width, area, and number were 0.88, 0.86, 0.81, and 0.93, respectively, demonstrating the model’s high precision and effectiveness in stomatal phenotypic trait analysis. The phenotypic data were combined with sequencing data from the wheat 660 K SNP chip and subjected to a genome-wide association study (GWAS) to analyze the genetic basis of stomatal traits, including length, width, and number, on both adaxial and abaxial surfaces. A total of 36 SNP peak loci significantly associated with stomatal traits were identified. Through candidate gene identification and functional analysis, two genes—TraesCS2B02G178000 (on chromosome 2B, related to stomatal number on the abaxial surface) and TraesCS6A02G290600 (on chromosome 6A, related to stomatal length on the adaxial surface)—were found to be associated with stomatal traits involved in regulating stomatal movement and closure, respectively. In conclusion, our WSF model demonstrates valuable advances in accurate and efficient stomatal phenotyping for locating genes related to stomatal traits in wheat and provides breeders with accurate phenotypic data for the selection and breeding of water-efficient wheat varieties. Full article

Rice (Oryza sativa L.) is one of the major food crops. Yield and quality are affected by premature leaf senescence, a complex and tightly regulated developmental process. To elucidate the molecular regulatory mechanism controlling rice leaf senescence, the integrative transcriptome, metabolome and weighted gene co-expression network analysis (WGCNA) of flag leaves in five development stages (FL1–FL5) was performed. In this study, a total of 9412 differential expressed genes (DEGs) were identified. To further mine DEGs related to leaf senescence, a total of five stage-specific modules were characterized by WGCNA. Among them, two modules displayed continuous down-regulated and up-regulated trends from stages FL1 to FL5, which were considered to be highly negatively and positively correlated with the senescence trait, respectively. GO enrichment results showed that the genes clustered in stage-specific modules were significantly enriched in a vast number of senescence-associated biological processes. Furthermore, large numbers of senescence-related genes were identified, mainly participating in transcription regulation, hormone pathways, degradation of chlorophyll, ROS metabolism, senescence-associated genes (SAGs), and others. Most importantly, a total of 40 hub genes associated with leaf senescence were identified. In addition, the metabolome analysis showed that a total of 309 differential metabolites (DMs) were identified by WGCNA. The integrative transcriptome and metabolome analysis identified a key hub gene OsBELH4A based on the correlation analysis conducted between 40 hub genes and 309 DMs. The results of function validation showed that OsBELH4A overexpression lines displayed delayed leaf senescence, and significantly increased grain number per plant and grain number per panicle. By contrast, its knockout lines displayed premature leaf senescence and reduced grain yield. Exogenous hormone treatment showed that OsBELH4A significantly responded to SA and auxin. These findings provide novel insights into leaf senescence, and further contribute to providing genetic resources for the breeding of crops resistant to premature senescence. Full article

Background: Facing global wheat production pressures such as environmental degradation and reduced cultivated land, breeding innovation is urgent to boost yields. Genomic selection (GS) is a useful wheat breeding technology to make the breeding process more efficient, increasing the genetic gain per unit time and cost. Precise genomic estimated breeding value (GEBV) via genome-wide markers is usually hampered by high-dimensional genomic data. Methods: To address this, we propose GRE, a framework combining genome-wide association study (GWAS)’s biological significance and random forest (RF)’s prediction efficiency for an explainable machine learning GS model. First, GRE identifies significant SNPs affecting wheat yield traits by comparison of the constructed 24 SNP subsets (intersection/union) selected by leveraging GWAS and RF, to analyze the marker scale’s impact. Furthermore, GRE compares six GS algorithms (GBLUP and five machine learning models), evaluating performance via prediction accuracy (Pearson correlation coefficient, PCC) and error. Additionally, GRE leverages Shapley additive explanations (SHAP) explainable techniques to overcome traditional GS models’ “black box” limitation, enabling cross-scale quantitative analysis and revealing how significant SNPs affect yield traits. Results: Results show that XGBoost and ElasticNet perform best in the union (383 SNPs) of GWAS and RF’s TOP 200 SNPs, with high accuracy (PCC > 0.864) and stability (standard deviation, SD < 0.005), and the significant SNPs identified by XGBoost are precisely explained by their main and interaction effects on wheat yield by SHAP. Conclusions: This study provides tool support for intelligent breeding chip design, important trait gene mining, and GS technology field transformation, aiding global agricultural sustainable productivity. Full article

Heavy metals (HMs) including cadmium (Cd), lead (Pb), arsenic (As), zinc (Zn), copper (Cu), chromium (Cr), and nickel (Ni) pose significant challenges to bioenergy crop production due to their persistence, toxicity, and bioaccumulation in soils and plants. This study not only summarizes the mechanisms of HM absorption, translocation, and accumulation in bioenergy crops, but also critically assesses their impact on crop development, biomass quality, and biorefinery processes. Heavy metals disrupt key physiological processes and modify lignocellulosic composition, which is important for biofuel and biogas production. Global soil contamination from sources like industrial emissions, mining, and agricultural activities exacerbates these problems, posing a threat to both energy security and environmental sustainability. Sustainable management strategies, including phytoremediation, microbial bioremediation, soil amendments, and genetic engineering, are explored to mitigate HM effects while enhancing crop resilience. This review emphasizes the importance of integrating techniques to balance bioenergy production with environmental and human health and safety, including the use of HM-tolerant crop varieties, enhanced biorefinery processes, and robust policy frameworks. Future research should focus on developing scalable remediation technologies and interdisciplinary solutions that align with the United Nations’ Sustainable Development Goals and meet global bioenergy needs. Full article

Breast cancer represents a significant public health concern in both Western countries and Asia. Accurate and early detection is critical to improving long-term patient survival. For physicians to understand the classification and decision rules and to evaluate their results, it is preferable to use white box approaches to develop prediction models. This paper proposes a novel classification technique for extracting malignant prediction rules from training datasets containing numerical and binary nominal attributes. The classification technique introduced in this study facilitates the discovery of breast cancer patterns by integrating a real-coded genetic algorithm, an adaptive directed mutation operator, and a two-level malignant-rule-mining process. The experimental results, compared with existing rule-based methods from previous studies, demonstrate that the proposed approach generates simple and interpretable decision rules and effectively identifies patterns that lead to accurate breast cancer classification. Full article

Sjögren’s Disease (SjD), Rheumatoid Arthritis (RA), and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with overlapping genetic features, yet the etiologies of these diseases are poorly understood. Using these rheumatic diseases as an example of proof of concept, our aim was to develop a tool that simplifies analysis of gene–disease associations applicable to any disease and to perform comparisons. This tool is meant to provide insights into associated gene symbols and gene expression data to identify candidate biomarkers in common among these diseases. The Diseasesv2.0 and GTExv8 databases were utilized for data collection, providing searchable disease names, affiliated gene symbols, confidence scores (ranging from 0 to 5, with 5 being the most confident), and gene expression across the panel of 54 tissue types present in GTExv8. Data infrastructure was established on a Postgres database using Plotlyv5.17.0 and Streamlitv1.27.2 Python packages. The resulting database was used to investigate the genetic associations among SjD, RA, and SLE, including confidence scores from 2.50 to 5.00. STRINGv12 analysis determined significant pathways (FDR < 0.05). Analysis using our tool revealed the following refined gene associations for each disease: SjD based on ‘Sjogren’ search term (n = 12 genes), RA (n = 231 genes), and SLE (n = 137 genes). We found seven genes in common, namely, CD4, CD8A, IL6, IL17A, TNFS13B, TNF, and TRIM21. With the exception of IL17A, these genes were expressed in tissue types known or suggested to be affected by SjD. STRINGv12 determined significant KEGG pathways involving interleukin signaling, cytokine signaling, and the immune system. We developed a tool that simplifies the data mining process, allowing users to search for diseases of interest and view common gene associations and gene expression. Some of the genes identified through our tool may be further explored to better understand SjD pathogenesis and systemic impact. Full article

Nuclear receptor subfamily 4 group A member 1 (NR4A1) plays a crucial role in regulating various physiological processes. As gene mining for reproductive traits is essential, this study aimed to investigate the mRNA expression, genetic variation, and association of the NR4A1 gene with goat litter size. We examined the mRNA expression levels of the NR4A1 gene in eight different tissues of female Shaanbei White Cashmere (SBWC) goats (n = 6). Then, a novel 11-bp insertion/deletion (InDel) variant was genotyped in 1136 SBWC goats, 87 SNPs were identified through resequencing (n = 120), and selection signal analysis was undertaken. The NR4A1 gene was expressed in all examined tissues, including the ovary and the oviduct, suggesting its role in goat reproduction. Both the 11-bp InDel and 13 SNP variants showed significant association with litter size. Additionally, four potential transcription factor binding sites were predicted within the insertion allele, which may contribute to increased litter size. Selection signal analysis revealed strong pressure on the NR4A1 gene region in the Cashmere goat population. These findings suggest that NR4A1 is a promising candidate gene for improving litter size in goats and could be utilized as a genetic marker in breeding programs. Full article

The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems. Full article