Browse Articles
Discover research articles across all indexed journals
Hybrid net powered large scale audit of dataset licensing and attribution practices for enhanced transparency and compliance
Functional characterization of angiogenesis genes in hepatocellular carcinoma via integrated bulk and single cell RNA sequencing
Causal connectivity maps derived from single-pulse interleaved TMS/fMRI
Benefit and risk associated with interleukin-6 receptor inhibitor administration during severe COVID-19: a retrospective multicentric study
Signaling network exploration of microRNA140-5p in response to TMJ-OA pathological changes
Machine learning for individual epigenetic fingerprints as predictors of well-being in young adults
Sustainable cooling solutions in Dubai: the impact of incident radiation and panel angles on solar AC performance
Abstract The rising demand for air conditioning units driven by climate change underscores the importance of using sustainable energy sources, which help reduce greenhouse gas emissions and mitigate environmental impact. The present work focuses on the design and construction of a solar air conditioning system by integrating photovoltaic panels or solar thermal collectors to power the cooling cycle, providing a sustainable and eco-friendly alternative to conventional systems. A solar air conditioning unit entails the design, integration, and performance evaluation of a system that harnesses solar energy to provide space cooling. This study focuses on the design and construction of a solar air conditioning unit for cooling a defined space, with performance evaluated under varying solar radiation levels and panel tilt angles. Thermal comfort parameters and power factors were analyzed to determine the system’s overall efficiency. Experiments were carried out under Dubai’s climatic conditions by varying the incident solar radiation from 700 to 1400 W/m 2 and adjusting the panel inclination angle between 15° and 25° which determined the performance parameters and the thermal comfort. The unit achieved maximum values for moisture removal rate, thermal efficiency, and solar coefficient of performance at 0.74 g/s, 95%, and 1.03, respectively. Power ratios were observed to decrease with increasing incident radiation. A solar panel tilt angle of 25° yielded the highest moisture removal rate 0.78 g/s, solar coefficient of performance 1.1, and solar direct consumption ratio 0.61. Thermal comfort parameters, including the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), were calculated to be − 0.21 and 12.7%, respectively, both falling within acceptable comfort ranges.
Structural, optical, and magnetic characterization of Cu–Zn–Ni spinel ferrite nanoparticles with antibacterial potential
Abstract One of the most pressing challenges in biomedical applications is the growing prevalence of bacteria that are resistant to multiple antibiotics. Metal-based nanoparticles are emerging as a promising strategy to address this problem, which is the focus of the present work. Cu 0.15 Zn 0.2 Ni 0.65 Fe 2 O 4 nano-ferrite was synthesized via the co-precipitation method. The chosen cation ratio preserves the spinel phase while Ni improves magnetic response, and Zn enhances magnetic softness and site stability. For comparison, single-cation ferrites NiFe 2 O 4 , ZnFe 2 O 4 , and CuFe 2 O 4 were synthesized using the same procedure to enable a consistent evaluation of antibacterial activity. All ferrites were characterized using XRD and FTIR. Additional analyses including UV–Vis, SEM, EDX, XPS, TEM, VSM, and Atomic Absorption Spectroscopy (AAS) were performed for Cu 0.15 Zn 0.2 Ni 0.65 Fe 2 O 4 sample. XRD confirmed a cubic spinel phase for all ferrites. FTIR provided further evidence of cation redistribution of tetrahedral and octahedral sites. AAS verified the availability of Cu 2+ , Zn 2+ , and Ni 2+ ions, supporting their contribution to antibacterial activity. VSM showed soft magnetic behavior with ~ 54.3 emu/g saturation magnetization. Antibacterial tests demonstrated that Cu 0.15 Zn 0.2 Ni 0.65 Fe 2 O 4 exhibits stronger inhibitory activity against S. aureus and E. coli at both low and high concentrations. At 500 μg/mL, the inhibition zone reached ~ 20 mm for S. aureus and ~ 17 mm for E. coli , The MIC values were found to be 40 μg/mL for S. aureus and 80 μg/mL for E. coli , indicating stronger sensitivity of Gram-positive bacteria. After establishing its individual performance, comparison has been obtained with single-cation ferrites. Across all trials, Cu 0.15 Zn 0.2 Ni 0.65 Fe 2 O 4 consistently produced larger inhibition zones, showing clear superiority. The superior antibacterial activity is attributed to the synergistic incorporation of Cu 2+ , Zn 2+ , and Ni 2+ within a single spinel lattice, giving Cu 0.15 Zn 0.2 Ni 0.65 Fe 2 O 4 strong intrinsic antibacterial activity and improving performance over single-cation ferrites, confirming its novelty and potential for biomedical applications.
The Microflora Danica atlas of Danish environmental microbiomes
A hierarchical fusion framework for vehicle to grid energy management using predictive intelligence and learning based pricing
A causal discovery-based adaptive fusion algorithm for multi-source heterogeneous knowledge graphs
Abstract Multi-source heterogeneous knowledge graph fusion faces significant challenges due to schema heterogeneity, entity conflicts, and relationship inconsistencies across different knowledge sources. This paper proposes CausalFusion, a novel adaptive fusion algorithm that leverages causal discovery principles to guide the knowledge graph integration process. The algorithm incorporates a constraint-based causal discovery component specifically designed for relational data, an adaptive weight learning mechanism that dynamically adjusts source contributions based on causal strength, and a conflict resolution strategy that prioritizes causal consistency over statistical correlation. Experimental evaluation on benchmark datasets including DBpedia, Freebase, YAGO, and Wikidata demonstrates significant improvements in fusion quality, with the proposed method achieving 91.2% precision and 88.7% recall, outperforming state-of-the-art baselines by 1.9% and 1.5% respectively. The results validate the effectiveness of incorporating causal inference into knowledge graph fusion, particularly for preserving meaningful causal relationships while resolving heterogeneity conflicts.
Explainable artificial intelligence for sedimentary facies segmentation
Integrative genomic analysis reveals DHX58 as a key player in gastric cancer
Gastric cancer (GC) is a major global health burden with limited treatment options. Identifying the molecular mechanisms underlying GC progression is critical for developing novel therapeutic strategies. We integrated whole-genome bisulfite sequencing and bulk RNA sequencing to identify hub genes involved in GC. Functional annotations were performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, supplemented by data from The Cancer Genome Atlas. Key findings were validated using western blotting, immunohistochemistry, quantitative real-time PCR, and methylation-specific PCR. DExH-box helicase 58 ( DHX58 ) was identified as a hypomethylated, highly expressed hub gene in GC. Mechanistically, DHX58 expression is regulated by the transcription factor CCAAT enhancer–binding protein α. Immune profiling further implicated DHX58 in the development of resistance to GC immunotherapy. Our study revealed that DHX58 hypomethylation drives its overexpression in GC, making it a promising therapeutic target. These findings offer new insights into the pathogenesis of GC and suggest potential immunotherapeutic approaches.
Integrating social cognitive theory with machine learning to predict MSM-women sexual behavior: a multicenter random forest model development study in China
Geometric and singularities insights of swept surfaces via the Bishop frame in Euclidean 3-Space
This study investigates the geometry and singular behavior of swept surfaces generated by the move of Bishop frame along a spatial curve in Euclidean 3-space. The surface is defined as the envelope of a family of unit spheres whose centers trace an axial trajectory, with the contact points forming great circles within a prescribed plane. A parametric representation is established to highlight the dependence of the surface on both the axial and profile curves. Key geometric features including the coefficients of the first fundamental form, unit normal vectors, and curvature characteristics are analyzed, revealing that the profile curves act as planar geodesics and curvature lines. We further examine singularities, offset surfaces, and parabolic curves, deriving conditions for smoothness, geodesicity, and convexity. Special attention is given to the criteria under which the surface becomes developable, particularly when it reduces to a cylinder, cone, or tangent surface. Several illustrative examples, including those arising from mate curves of slant helices and circular trajectories, demonstrate the resulting geometric phenomena.
Rare genetic variants confer a high risk of ADHD and implicate neuronal biology
Abstract Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a large genetic component 1 . It affects around 5% of children and 2.5% of adults 2 , and is associated with several severe outcomes 3–11 . Common genetic variants associated with the disorder have been identified 12,13 , but the role of rare variants in ADHD is mostly unknown. Here, by analysing rare coding variants in exome-sequencing data from 8,895 individuals with ADHD and 53,780 control individuals, we identify three genes ( MAP1A , ANO8 and ANK2 ; P < 3.07 × 10 −6 ; odds ratios 5.55–15.13) that are implicated in ADHD. The protein–protein interaction networks of these three genes were enriched for rare-variant risk genes of other neurodevelopmental disorders, and for genes involved in cytoskeleton organization, synapse function and RNA processing. Top associated rare-variant risk genes showed increased expression across pre- and postnatal brain developmental stages and in several neuronal cell types, including GABAergic (γ-aminobutyric-acid-producing) and dopaminergic neurons. Deleterious variants were associated with lower socioeconomic status and lower levels of education in individuals with ADHD, and a decrease of 2.25 intelligence quotient (IQ) points per rare deleterious variant in a sample of adults with ADHD ( n = 962). Individuals with ADHD and intellectual disability showed an increased load of rare variants overall, whereas other psychiatric comorbidities had an increased load only for specific gene sets associated with those comorbidities. This suggests that psychiatric comorbidity in ADHD is driven mainly by rare variants in specific genes, rather than by a general increased load across constrained genes.
Development and optimization of a female-specific Biomechanical model for biodynamic response analysis: a comparison with male biomechanical models
Abstract Whole-body vibration exposure is a critical factor affecting human health and comfort, particularly for individuals operating on/off-road vehicles. Prior studies have focused on male biomechanical models. This study intentions to develop a new female-specific biomechanical model to analyze and optimize biodynamic responses under vertical vibration conditions. The objective is to introduce a ten degrees-of-freedom (dofs) biomechanical model tailored for the female body, considering the average weight of human beings. The new model has compared against existing male-oriented models to evaluate its effectiveness. The female body is divided into ten key segments: head, pelvis thorax, abdomen, left upper arm, left hand, left forearm, right upper arm, right forearm, and right hand. Mechanical properties are adjusted based on female-specific mass distribution, stiffness, and damping characteristics. The Firefly Algorithm is used for parameter optimization. The biodynamic responses, including seat-to-head transmissibility, apparent mass, and driving point mechanical impedance, are evaluated and compared with previous male models. The optimized female model exhibits distinct biodynamic response characteristics due to anatomical and biomechanical differences. The goodness of fit analysis indicates improved predictive accuracy for female subjects, suggesting the necessity for gender-specific modelling in vibration analysis.
Integrative multi-omics analyses identify key genes and elucidate bidirectional regulatory mechanisms in thyroid dysfunction
Objective Hyperthyroidism and hypothyroidism are globally prevalent endocrine disorders, with their pathogenesis involving multifactorial mechanisms including genetics, immunity, and metabolism. Although genome-wide association studies (GWAS) have identified risk genes such as PDE8B, critical gaps remain in the annotation of causal variants in non-coding regions, characterization of tissue-specific regulatory networks, and understanding of ethnic heterogeneity. This study aimed to systematically identify genes associated with hyperthyroidism and hypothyroidism and unravel their underlying molecular mechanisms through multi-omics integration. Methods We included data from the ThyroidOmics Consortium, comprising 1,840 hyperthyroidism cases (49,983 controls) and 3,340 hypothyroidism cases (49,983 controls). Core candidate genes were prioritized using a combination of SMR-HEIDI analysis, cross-tissue transcriptome-wide association study (TWAS), mBAT-combo rare variant analysis, and polygenic priority score (PoPS). GTEx colocalization (coloc) analysis was used to validate tissue-specific colocalization between these candidate genes and disease signals. Phenome-wide association study (PheWAS), KEGG pathway enrichment, and protein-protein interaction (PPI) network analyses were performed to explore gene functions, with potential targeted drugs predicted using the Drug Signatures Database (DSigDB). Results Cross-validation by four methods identified FAM227B, PDE8B, and PDE10A as key genes for hyperthyroidism, and PDE8B as the critical gene for hypothyroidism. GTEx coloc analysis (with PP4 > 0.8 as the threshold) confirmed significant colocalization: FAM227B with hyperthyroidism signals in the adrenal gland, lung, and minor salivary gland; PDE8B with both hyperthyroidism and hypothyroidism signals in thyroid tissue; and PDE10A with hyperthyroidism signals in thyroid tissue. As a core member of the phosphodiesterase family, PDE8B exhibited bidirectional regulatory characteristics in thyroid hormone synthesis via the cAMP signaling pathway and nucleotide metabolism network: its inhibition promoted hormone synthesis in hypothyroidism, while its interaction with PDE10A suppressed overactive cAMP signaling in hyperthyroidism. PheWAS linked FAM227B to cardiovascular diseases and PDE10A to neurological pathways. KEGG enrichment analysis highlighted the “morphine addiction” pathway (p = 6.12 × 10 ⁻ ⁵), suggesting potential neuroendocrine crosstalk. Notably, potential drugs targeting FAM227B, PDE8B, and PDE10A were identified. Conclusion Through multi-omics integration, this study identifies PDE8B as a central gene associated with thyroid dysfunction, characterized by tissue-specific colocalization, and elucidates its critical roles in signaling pathways, comorbidity associations, and drug targeting. These findings provide insights into the bidirectional regulatory mechanisms of hyperthyroidism and hypothyroidism and a theoretical basis for developing phosphodiesterase family-based precision therapies. It should be noted that all samples in this study are of European ancestry, and the generalizability of the results in other ethnic groups remains to be verified.