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A dynamic element-activated non-semantic sparse attention method for remote sensing small object detection
Structural and functional atypicality in the temporal cortex are associated with auditory perception in maltreated children
Integrative transcriptome and genome resequencing reveals conserved flowering regulators and allelic variants in early- and late-flowering linseed (Linum usitatissimum L.) accessions
Abstract Flaxseed/Linseed ( Linum usitatissimum ) is an annual crop of economic importance due to its diverse applications in food, nutraceuticals, and industry. Flowering time is a crucial trait in linseed; however, the gene expression landscapes underlying its regulation remain poorly characterized, constraining its understanding and effective application in crop improvement. Here, an integrative transcriptome and genome resequencing approach was used to identify key flowering regulators and allelic variants. Transcriptome sequencing of reproductive tissues including floral bud at two developmental stages, flower, and two vegetative tissues (leaf and stem) in two early flowering linseed genotypes, IC0523807 and IC0525939 were performed. A total of 47.4 GB of filtered data was obtained from 20 datasets, resulting in 34,869 mapped transcripts. Differential expression analysis between vegetative tissues vs. reproductive tissues in different combinations revealed a total of 14,244 differentially expressed genes, with 67 GO and 161 KEGG enriched terms. Several DEGs were involved in auxin, cytokinin, gibberellic acid, and abscisic acid signal transduction pathways. From the 109 major Arabidopsis flowering gene orthologs identified in linseed, 54 were differentially expressed, whereas 46 of the 143 putative candidate genes reported in earlier association studies were also represented among the differentially expressed genes. Overall, by integrating a three-way strategy involving differential gene expression, flowering gene orthologs, and candidate genes for flowering, three promising genes were identified, namely, flowering locus T (FT) , the flowering repressor APETALA2-like transcription factor SCHLAFMÜTZE ( SMZ ), and a Dof-type domain-containing protein (CDF3) . The whole-genome resequencing of two early- and late-flowering genotypes was performed, unravelling allelic variations in key flowering candidate genes, including AGAMOUS-like 19 (AGL19) , della protein , flowering locus K (FLK) , Target of EAT1 (TOE1) , and Late Elongated Hypocotyl (LHY) . This study provides a detailed insight into gene expression profile of early-flowering linseed genotypes, highlighting key flowering pathways, regulators and allelic variants of flowering associated genes.
Tuning refractive indices in nematic liquid crystal via nanoparticles coupling
Pseudohypoxia induced by iron chelators preserves working memory performance in aged mice
Association of serum uric acid to high density lipoprotein cholesterol ratio with stroke
Low perceived warmth of AI agents reduces trust towards them
Abstract Artificial intelligence (AI) agents represent a new class of social actors within social and economic systems. To ensure the smooth functioning of human-AI societies, it is crucial to understand how trust between humans and AI agents is developed. The present study ( N = 400), conducted on a representative sample of U.S. residents, investigated how the fundamental dimensions of social perception may affect differences in trust towards humans and AI agents. We manipulated human and AI trustees’ warmth and competence and measured trust towards them in a trust game. Overall, AI trustees were trusted less than human trustees were, especially in the low warmth conditions. We discuss warmth as a crucial determinant of trust in the context of human-AI interactions and suggest potential implications of these results for designing trustworthy AI systems.
Empirical validation of a generative AI framework for personalized education assessment
Abstract The tension between personalized learning demands and standardized evaluation mechanisms presents a persistent challenge in contemporary education. This study proposes a comprehensive personalized education assessment framework driven by generative artificial intelligence technologies. The framework adopts a five-layer hierarchical architecture integrating data collection, processing, intelligent analysis, assessment generation, and feedback optimization components. ChatGLM3-6B, fine-tuned on 50,000 expert-curated programming feedback instances assembled through a human-in-the-loop process combining authentic instructor records, newly authored examples, and AI-assisted human-verified content, enables contextually responsive feedback generation, while dynamic learner profiling and knowledge graph modeling support precise diagnostic assessment. Empirical validation involving 449 undergraduate students in introductory Python programming courses demonstrated that the framework achieved assessment accuracy correlating at 0.847 with expert consensus (Fleiss’ κ = 0.74 for inter-rater reliability) while reducing generation time by over 99% compared to manual evaluation. Ablation experiments confirmed that knowledge graph integration contributed most substantially to accuracy improvements, with removal of this component reducing correlation by 0.055. Experimental participants exhibited significantly higher learning gains (Cohen’s d = 0.56), with particularly pronounced effects among initially lower-performing students. The framework also enhanced learner engagement and satisfaction compared to conventional assessment approaches. These findings suggest that generative AI can effectively operationalize personalized assessment at scale while maintaining pedagogical quality and transparency.
AVPDN: learning motion-robust and scale-adaptive representations for polyp detection in dynamic colonoscopy frames
(Poly)Borylated Species as Modern Reactive Groups toward Unusual Synthetic Applications
Abstract (Poly)borylated species, molecules bearing multiple carbon–boron groups, have emerged as powerful building blocks in modern synthetic chemistry owing to their rich, tunable reactivity. Their distinctive steric and electronic properties, together with their ability to participate in both ionic and radical pathways, make them uniquely versatile synthons for the selective construction of carbon─carbon and carbon─heteroatom bonds. These frameworks provide strategic opportunities for late‐stage functionalization and the assembly of architecturally complex molecules. This review highlights recent advances in the chemistry of (poly)borylated compounds, particularly molecules bearing multiple boron substituents on a single carbon site, with emphasis on their anionic, cationic, radical, alkene, 1,2‐diradical, and carbene‐based variants. It underscores the synergistic behavior of these multiple boron‐substituted reactive carbon‐centered intermediates and the diverse functionalization of C─metalloid bonds. Key reactivity modes are examined across structural classes such as gem ‐diborylalkanes, gem ‐diborylalkenes, 1,1,2‐polyborylated systems, and emerging tri‐ and tetra‐borylated frameworks. Central transformations include transition‐metal‐catalyzed cross‐couplings, stereoselective transmetalation and functionalizations, addition reactions, and polymerizations, along with boron‐masking, boron‐retentive, and boron‐eliminative strategies. Furthermore, alkylations, radical‐mediated reactions, and energy‐transfer photocatalysis pathways are critically discussed. By elucidating the mechanistic principles and synthetic potential of (poly)borylated species, this review aims to provide a unified framework to better understand their reactivity and to highlight the significant advances made since 2019 at the interface of organoboron chemistry, catalysis, and advanced materials science.
Hierarchical multi-attention neural networks for sensor fault diagnosis and mitigation in digital twins
Design of a Li-ion battery cooling system incorporating PCM, heat pipes, and liquid circuits using marine predator algorithm-enhanced ANN and multi-verse optimization
Ant colony optimization approach for sustainable end-milling with minimum quantity nano-green lubrication
Protocol of the randomized double blind sham controlled AddVNS study of transcutaneous vagus nerve stimulation mechanisms in depression
Abstract Depression is among the most prevalent mental disorders worldwide, carrying one of the highest burden of disease among all mental disorders. While invasive vagus nerve stimulation has been approved for treatment-resistant depression for decades, its clinical use is limited by surgical risks and heterogeneous clinical efficacy. Transcutaneous auricular VNS (tVNS) may offer a non-invasive alternative, but to date it remains experimental due to limited high-quality evidence, unclear biological mechanisms of action, and rudimentary knowledge on optimal stimulation parameters. To address these gaps, we initiated the AddVNS study. The AddVNS study ( Add -on t VNS in depression) is a monocentric, exploratory, prospective, randomized, double-blind, sham-controlled interventional trial conducted at the Max Planck Institute of Psychiatry’s research hospital. Adult patients with a depressive episode (ICD-10: F31–33) were assigned to receive active or sham transcutaneous vagus nerve stimulation (tVNS) in addition to treatment-as-usual (TAU) over a six-week period. Stimulation is administered three times daily (30 to 60 min each), five days per week. A deep phenotyping strategy is applied, including repeated psychophysiological measures (e.g., pupillometry, ECG, photoplethysmography, electrogastrogram) and neuroimaging (structural and functional MRI) at baseline and post-intervention, continuous actigraphy, repeated blood and stool sample acquisition (pre-, mid-, and post-intervention) for multiomic investigation, comprehensive neuropsychology including self-rated personality assessment, and closely monitored clinical evaluations. The patient-reported outcomes are collected weekly, the clinician-rated scales pre-, mid-, and post-intervention. In addition, follow-up self-ratings are obtained at 6 and 12 weeks post-tVNS. The main objective of AddVNS is to improve our understanding of the biological effects elicited by tVNS in depression. By combining rigorous methodology with an extensive and longitudinal multimodal approach, AddVNS represents the most comprehensive investigation of tVNS effects and markers in depression to date. We believe it to significantly advance our mechanistic understanding and subsequently clinical translation of this promising intervention.
Evaluation of mdh, dld, tcfA, and folE gene markers for detection of enteric fever using real-time PCR
Availability and spatial distribution of crop and forest biomass residues for biochar production in Kenya
Abstract Uptake of biochar for fuel briquetting and soil amendment is constrained in sub-Saharan Africa by inadequate knowledge of the quantity and distribution of feedstocks. This study assessed the quantities and spatial distribution of crop and forest residues available for biochar production in Kenya based on productivity data from 2021 and 2022. The residues were quantified using residue product ratios, surplus available factors and economically viable factors. Kenya produces (0.5–2.4) × 10 7 Mg y −1 of crop residues and (1.48–1.8) × 10 5 Mg y −1 of forest residues that are potentially available for biochar production. While crop and forest production are the core drivers of the availability of economically viable residues, residue to product ratios and surplus available factors are the primary drivers of residue densities. Crop residues were concentrated in counties located in western, central and southern Kenya. While all counties possess diverse types of residues, maize stalks were prevalent in all 47 counties. No county satisfied the combined requirements of high amounts of residues, high residue density and low supply uncertainties. Therefore, although Kenya has abundant and diverse residues that could produce economically viable biochar, locating production facilities will require a trade-off between counties with high residue densities, or those with supply uncertainty.