Search Results (7,195)

Critical metals associated with aluminum-bearing strata have garnered increasing attention due to their considerable economic potential. Recent investigations have identified notable enrichment of Li, Ga, Zr, Nb, REEs (rare earth elements), etc., within the Upper Carboniferous Benxi Formation in the Yangquan mining area, the Northeastern Qinshui Basin, Northern China. However, their mineralogical characteristics and micro-scale modes of occurrence remain insufficiently constrained. In this study, we employed the TESCAN Integrated Mineral Analyzer (TIMA) in combination with X-ray diffraction (XRD) and clay-separation experiments to provide direct mineralogical evidence for the occurrence of Ti, Li, Ga, Zr, and REEs in claystone and aluminous claystone from the Benxi Formation, Yangquan mining area, Northeastern Qinshui Basin. Our results indicate that both lithologies are primarily composed of kaolinite and diaspore, with minor amounts of anatase and cookeite; illite is additionally present in the claystone. Titanium predominantly occurs as anatase in both lithologies, though a portion in aluminous claystone may be incorporated into kaolinite and other Ti-bearing minerals such as rutile and leucoxene. Lithium is primarily hosted by cookeite in both rock types. Mineral assemblage variations further suggest that kaolinite may have partially transformed into Li-rich chlorite (i.e., cookeite) during the transformation from aluminous claystone to claystone. Gallium is chiefly associated with diaspore and kaolinite, with a stronger correlation with diaspore in the aluminous claystone. Zircon is the sole carrier of Zr in both lithologies. Importantly, La and Ce show a consistent spatial association with O–Al–Si–Ti–P mixed aggregates in TIMA maps, particularly in aluminous claystone. Based on these spatial patterns, textural relationships, and comparisons with previous studies, phosphate minerals are inferred to be the dominant REE hosts, although minor contributions from other phases cannot be completely excluded. These findings highlight a previously underexplored mode of critical-metal enrichment in Northern Chinese bauxite-bearing strata and provide a mineralogical basis for future extraction and utilization. Full article

The filarioid nematode Dirofilaria immitis is the causative agent of heartworm disease, a major parasitic infection of canids, felids and occasionally humans. Current prevention relies on macrocyclic lactone-based chemoprophylaxis, but the emergence of drug resistance highlights the need for new intervention strategies. Here, we applied a machine learning (ML)-based framework to predict and prioritise essential genes in D. immitis in silico, using genomic, transcriptomic and functional datasets from the model organisms Caenorhabditis elegans and Drosophila melanogaster. With a curated set of 26 predictive features, we trained and evaluated multiple ML models and, using a defined threshold, we predicted 406 ‘high-priority’ essential genes. These genes showed strong transcriptional activity across developmental stages and were inferred to be enriched in pathways related to ribosome biogenesis, translation, RNA processing and signalling, underscoring their potential as anthelmintic targets. Transcriptomic analyses suggested that these genes are associated with key reproductive and neural tissues, while chromosomal mapping revealed a relatively even genomic distribution, in contrast to patterns observed in C. elegans and Dr. melanogaster. In addition, initial evidence suggested structural variation in the X chromosome compared with a recently published D. immitis assembly, indicating the importance of integrating long-read sequencing with high-throughput chromosome conformation capture (Hi-C) mapping. Overall, this study reinforces the potential of ML-guided approaches for essential gene discovery in parasitic nematodes and provides a foundation for downstream validation and therapeutic target development. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

This study examines the influence of layer thickness (0.9, 1.6, 2.4, and 4 mm) on the distribution of residual stress, microstructural evolution, and tensile properties of Cu18150/Al1060/Cu18150 multilayered composites fabricated via a combined cast-rolling and hot-rolling technique. The grain refinement, dislocation density, and residual stress gradients across the interfaces were characterized and analyzed using integrated electron backscatter diffraction and kernel average misorientation mapping. The results demonstrated that specimens with a lower layer thickness (0.9–1.6 mm) possess a significantly improved tensile strength of 351 MPa, which is mainly due to the significant grain refinement and the presence of compressive residual stresses at the region of the Al/Cu interfaces. However, tensile strength decreased to 261 MPa in specimens with thicker layers (4 mm), accompanied by improved ductility, e.g., elongation of 30%. This is associated with a reduction in the degrees of interfacial constraint and the formation of more homogeneous deformation structures that accommodate a larger strain. The intermediate layer thickness of 2.4 mm offers an optimal compromise, achieving a tensile strength of 317 MPa while maintaining balanced mechanical performance. These results emphasize the importance of layer thickness in controlling such stress profiles and optimizing the mechanical behavior of hybrid metal composites, providing useful guidance on the design and fabrication of superior structural-form materials. Full article

The purpose of this paper is to determine the local dimension of the critical locus of a generic singularity. We use combinatorial methods to calculate this dimension in terms of a convex object associated with the singularity, called the Newton polyhedron. In this article, we prove that the local dimension of the critical locus of a generic singularity $f:({\mathbb{C}}^n,0)⟶(\mathbb{C},0),$ $n\le 4,$ is equal to the combinatorial dimension of the Newton polyhedron of the gradient mapping $\nabla f.$ Therefore, there is some symmetry between combinatorial properties of the Newton polyhedron of a generic singularity and geometric properties of its critical locus. Full article

Background: Dominantly acting variants in TUBB2B have primarily been associated with cortical dysplasia complex with other brain malformations 7 (CDCBM7), a disorder in which cortical brain abnormalities are typically linked to developmental delay/intellectual disability (DD/ID) and seizures. While the majority of TUBB2B pathogenic variants have been linked to isolated CDCBM7, only one family with CDCBM7 and congenital fibrosis of the extraocular muscles (CFEOM) has been reported so far. We describe a second individual with a severe phenotype of CFEOM combined with CDCBM7 carrying a pathogenic TUBB2B missense variant previously reported in two individuals with isolated CDCBM7. Methods: A trio-based WGS analysis was performed. The structural impact of the identified substitution was assessed by using the UCSF Chimera (v.1.17.3) software and PyMOL docking plugin DockingPie tool. Results: WGS analysis identified a de novo missense TUBB2B variant (p.Ile202Thr, NM_178012.5), previously associated with isolated CDCBM7. Structural analysis and docking simulations revealed that Ile202 contributes to establishing a proper hydrophobic environment required to stabilize GTP/GDP in the β-tubulin pocket. p.Ile202Thr was predicted to disrupt these interactions. Conclusions: Our findings broaden the mutational spectrum of TUBB2B-related CFEOM, targeting a different functional domain of the protein, and further document the occurrence of phenotypic heterogeneity. We also highlight the limitations of exome sequencing in accurately mapping TUBB2B coding exons due to its high sequence homology with TUBB2A and suggest targeted or genome analyses when clinical suspicion is strong. Full article

Plant-parasitic nematodes (PPNs) pose a significant threat to agricultural production and global food security. To mitigate this challenge, quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have been extensively employed in crop resistance breeding research. These methods have identified resistance-related genes and genetic markers, offering a solid scientific basis and practical tools for resistance breeding. This review summarizes recent advances in QTL and GWAS applications for enhancing resistance to cyst nematodes (Heterodera glycines, H. filipjevi, and H. avenae), root-knot nematodes (Meloidogyne graminicola and M. incognita), and root-lesion nematodes (Pratylenchus spp.). It also evaluates the commercial deployment of resistance genes, discusses integrated breeding strategies, and highlights future research directions toward developing durable nematode-resistant crops. Full article

Children with Autism Spectrum Disorder (ASD) often experience cognitive difficulties that impact learning. This study explores the use of electroencephalogram data collected with the MUSE 2 headband during task-based cognitive sessions to understand how cognitive states in children with ASD change across three structured tasks: Shape Matching, Shape Sorting, and Number Matching. Following signal preprocessing using Independent Component Analysis (ICA), power across various frequency bands was extracted using the Welch method. These features were used to analyze cognitive states in children with ASD in comparison to typically developing (TD) peers. To capture dynamic changes in attention over time, Morlet wavelet transform was applied, revealing distinct brain signal patterns. Machine learning classifiers were then developed to accurately distinguish between ASD and TD groups using the EEG data. Models included Support Vector Machine, K-Nearest Neighbors, Random Forest, an Ensemble method, and a Neural Network. Among these, the Ensemble method achieved the highest accuracy at 0.90. Feature importance analysis was conducted to identify the most influential EEG features contributing to classification performance. Based on these findings, an ASD map was generated to visually highlight the key EEG regions associated with ASD-related cognitive patterns. These findings highlight the potential of EEG-based models to capture ASD-specific neural and attentional patterns during learning, supporting their application in developing more personalized educational approaches. However, due to the limited sample size and participant heterogeneity, these findings should be considered exploratory. Future studies with larger samples are needed to validate and generalize the results. Full article

The tubulin polymerization promoting proteins (TPPPs) are a small family of conserved proteins originally characterized as microtubule binding proteins. TPPP1, the first identified member, both binds to and bundles microtubules. Its homologs, TPPP2 and TPPP3, are encoded by separate genes on distinct chromosomes but both lack the N-terminal tail present in TPPP1. Functional studies revealed that TPPP3 retains comparable microtubule binding and bundling capacity to TPPP1, whereas TPPP2 displays markedly reduced binding and no bundling activity. Intriguingly, TPPP3 has been implicated in many different diseases. In this review, we summarize the current findings on TPPP3 and its dysregulation in various diseases including cancer, reproductive dysfunction, musculoskeletal conditions, endothelial dysfunction, and neurodegenerative diseases. Full article

Commercial computer-aided design (CAD) software is often expensive. This paper examines the use of product manufacturing information (PMI) web visualization to address the challenges faced by production site personnel and external partners collaborating on product development. These individuals need to be able to view or query PMI in model-based definition models without having to install professional CAD software. A detailed analysis of the relationships between PMI entity attributes in standard for the exchange of product model data (STEP) AP242 files was conducted. An algorithm for the automatic parsing and mapping of PMI semantics to a web browser is presented. Using linear sizes as an example, this paper introduces a prototype system with the following features: PMI web visualization; automatic linkage of PMI to associated geometry; browser-native rendering without the need for dedicated applications; and integration of graphical presentation and semantic representation. The effectiveness and feasibility of the prototype system are validated through case studies. However, the system has limitations when handling large assemblies with compound tolerances, curved dimension placements, and overlapping annotations, which presents areas for future research. Full article

Leaf rust, a devastating fungal disease caused by Puccinia triticina (Pt), severely impacts wheat quality and yield. Identifying genetic loci for wheat leaf rust resistance, developing molecular markers, and breeding resistant varieties is the most environmentally friendly and economical strategy for disease control. This study utilized a recombinant inbred line (RIL) population of Doumai and Shi4185, combined with the wheat 90 K single nucleotide polymorphisms (SNPs) chip data and maximum disease severity (MDS) of leaf rust from four environments, to identify adult plant resistance (APR) loci through linkage mapping. Additionally, kompetitive allele-specific PCR (KASP) markers suitable for breeding were developed, and genetic effects were validated in a natural population. In this study, 5 quantitative trait loci (QTL) on chromosomes 1B (2), 2A and 7B (2) were identified through inclusive composite interval mapping, and named as QLr.lfnu-1BL1, QLr.lfnu-1BL2, QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2, respectively, explaining 4.54–8.91% of the phenotypic variances. The resistance alleles of QLr.lfnu-1BL1 and QLr.lfnu-1BL2 originated from Doumai, while the resistance alleles of QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 came from Shi4185. Among these, QLr.lfnu-1BL2, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 overlapped with previously reported loci, whereas QLr.lfnu-1BL1 and QLr.lfnu-2AL are likely to be novel. Two KASP markers, QLr.lfnu-2AL and QLr.lfnu-7BL, were significantly associated with leaf rust resistance in a diverse panel of 150 wheat varieties mainly from China. Totally, 34 potential candidate genes encoded the NLR proteins, receptor-like kinases, signaling kinases and transcription factors were selected as candidate genes for the resistance loci. These findings will provide stable QTL, available breeding KASP markers and candidate genes, and will accelerate the progresses of wheat leaf rust resistance improvement through marker-assisted selection breeding. Full article

Background: Tumor-associated macrophages (TAMs) are essential regulators of the glioblastoma (GBM) microenvironment; their functional heterogeneity and interaction networks are not fully elucidated. We identify RNF135 as a novel TAM-enriched gene associated with immune activation and adverse prognosis in GBM. Methods: To evaluate RNF135’s expression profile, prognostic significance, and functional pathways, extensive transcriptome analyses from TCGA and CGGA cohorts were conducted. The immunological landscape and cellular origin of RNF135 were outlined using single-cell RNA-seq analyses and bulk RNA-seq immune deconvolution (MCP-counter, xCell and ssGSEA). Cell–cell communication networks between tumor cells and RNF135-positive and -negative tumor-associated macrophage subsets were mapped using CellChat. Results: RNF135 predicted a poor overall survival and was markedly upregulated in GBM tissues. Functional enrichment analyses showed that increased cytokine signaling, interferon response, and innate immune activation were characteristics of RNF135-high samples. Immune infiltration profiling showed a strong correlation between the abundance of T cells and macrophages and RNF135 expression. According to the single-cell analyses, RNF135 was primarily expressed in TAMs, specifically in proliferation, phagocytic, and transitional subtypes. RNF135-positive TAMs demonstrated significantly improved intercellular communication with aggressive tumor subtypes in comparison to RNF135-negative TAMs. This was facilitated by upregulated signaling pathways such as MHC-II, CD39, ApoE, and most notably, the CCL signaling axis. The CCL3/CCL3L3–CCR1 ligand–receptor pair was identified as a major mechanistic driver of TAM–TAM crosstalk. High RNF135 expression was also linked to greater sensitivity to Selumetinib, a selective MEK1/2 inhibitor that targets the MAPK/ERK pathway, according to drug sensitivity analysis. Conclusions: RNF135 defines a TAM phenotype in GBM that is both immunologically active and immunosuppressive. This phenotype promotes inflammatory signaling and communication between cells in the tumor microenvironment. Targeting the CCL–CCR1 axis or combining RNF135-guided immunomodulation with certain inhibitors could be a promising therapeutic strategies for GBM. Full article

Spawning habitat partitioning can be important for maintaining sympatric fish species. Likewise, critical spawning habitat loss may challenge the long-term persistence of sympatric fish species. The Bois Brule River, Wisconsin, USA, is a spring-fed, western Lake Superior tributary that supports five naturally reproducing populations of salmonids (native brook trout Salvelinus fontinalis; introduced brown trout Salmo trutta, rainbow trout Oncorhynchus mykiss, coho salmon O. kisutch, and chinook salmon O. tshawytscha). Given increases in recreational angler use and predicted climate-associated changes to trout stream habitat, a better understanding of species interactions during spawning is important to guide future management and conservation of these anthropogenically derived sympatric native and introduced salmonids. Our aim was to establish whether there was partitioning or overlapping in the redd site location preferences among native and introduced salmonids inhabiting the Bois Brule River. We mapped species-specific redd locations by canoe over a 15.3 river km section known to be important for salmonid spawning and evaluated physical, flow, and thermal conditions of these habitats of the Bois Brule River during 2021–2022. We found that spring spawning rainbow trout and fall spawning pacific salmonids and brown trout used the same spawning locations on mid-channel, larger gravel reefs downstream of riffle sections. Native brook trout spawned on smaller substrates with lower streamflow on the edges of the channel, with the highest spawning activity occurring in littoral areas of lentic portions of the river. Our findings provide valuable knowledge of critical spawning habitats for sympatric salmonids that may inform habitat conservation and enhancement efforts in the Bois Brule River and other Great Lakes tributaries with similar sympatric, naturally reproducing salmonids populations. Full article

Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article

Microwave pretreatment (MWP) has emerged as a promising strategy to enhance the pyrolysis of lignocellulosic biomass due to its rapid, volumetric, and selective heating. By disrupting the recalcitrant structure of cellulose, hemicellulose, and lignin, MWP improves biomass deconstruction, increases carbohydrate accessibility, and enhances yields of bio-oil, syngas, and biochar. When combined with complementary pretreatments—such as alkali, acid, hydrothermal, ultrasonic, or ionic-liquid methods—MWP further reduces activation energies, facilitating more efficient saccharification and thermal conversion. This review systematically evaluates scientific progress in this field through bibliometric analysis, mapping research trends, evolution, and collaborative networks. Key research questions are addressed regarding the technical advantages of MWP, the physicochemical transformations induced in biomass, and associated environmental benefits. Findings indicate that microwave irradiation promotes hemicellulose depolymerization, reduces cellulose crystallinity, and weakens lignin–carbohydrate linkages, which facilitates subsequent thermal decomposition and contributes to improved pyrolysis efficiency and product quality. From an environmental perspective, MWP contributes to energy savings, mitigates greenhouse gas emissions, and supports the integration of renewable electricity in biomass conversion. Full article