Search Results (93,660)

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition often associated with genetic syndromes. Structural variants on the long arm of chromosome 15 (15q) are recurrently implicated in syndromic ASD, yet their phenotypic spectrum remains insufficiently characterized in diverse populations. We retrospectively analyzed clinical and molecular data from three patients with ASD treated at a Brazilian public reference center who also presented neurological and systemic comorbidities. Genetic investigations included G-banded karyotyping, chromosomal microarray analysis (CMA), methylation assays, and multiplex ligation-dependent probe amplification (MLPA) when indicated. Variants were classified according to ACMG guidelines and correlated with individual phenotypes. Case 1 showed an 8.4 Mb triplication at 15q11.2–q13.1 encompassing SNRPN, UBE3A, and GABRB3, which are associated with epilepsy, delayed neuropsychomotor development, and dysmorphic traits. Case 2 presented a 418 kb duplication at 15q13.3 involving CHRNA7 and OTUD7A, a variant of uncertain significance correlated with intellectual disability, speech apraxia, and self-injurious behavior. Case 3 demonstrated extensive loss of heterozygosity at 15q11.2–q13.1 and 15q21.3–q26.2, which is compatible with maternal uniparental disomy and Prader–Willi syndrome, manifesting hypotonia, seizures, and global delay. These findings underscore the potential involvement of the 15q region in syndromic ASD and related neurological comorbidities, highlighting the diverse pathogenic mechanisms and the importance of comprehensive genomic profiling for diagnosis, counseling, and individualized care. Full article

Understanding how endangered carnivores partition spatiotemporal distribution in human-dominated landscapes is pivotal for mitigating biodiversity loss in climate-sensitive boreal ecosystems. Here, we used kernel density data derived from a 16-month camera-trap survey (140 UVL7 cameras), cold single-season (November–April) occupancy models, and MaxEnt 3.4.4 to identify the effects of biotic interactions, anthropogenic disturbance, and environmental factors on the spatiotemporal distribution of the wolverine (Gulo gulo) in Beijicun National Nature Reserve, Heilongjiang Province, China. We found that wolverines exhibited crepuscular activity patterns using night-time relative abundance index (NRAI) = 50.29% with bimodal peaks (05:00–07:00, 13:00–15:00), with dawn activity predominant during the warm season (05:00–06:00) and a bimodal activity pattern in the cold season (08:00–09:00, 14:00–15:00). Temporal overlap with prey (overlap coefficient Δ = 0.84) and competitors (Δ = 0.70) was high, but overlap with human-dominated temporal patterns was low (Δ = 0.58). Wolverines avoided human settlements and major roads, preferred moving along forest trails and gentle slopes, and avoided high-altitude deciduous forests. Populations were mainly concentrated in southern Hedong and Qianshao Forest Farms, which are characterized by high habitat integrity, high prey densities, and minimal anthropogenic disturbance. These findings suggest that wolverines may influence boreal trophic networks, especially in areas with intact prey communities, competitors, and spatial refugia from human disturbances. We recommend that habitat protection and management within the natural reserve be prioritized and that sustainable management practices for prey species be implemented to ensure the long-term survival of wolverines. Full article

With the increasing attention to China’s ecological environment protection and the prominence of lake water environment problems, the impact of soil erosion on lake ecosystems has become an important research topic for regional sustainable development. Based on the CiteSpace bibliometric method, this study systematically analyzed 225 research articles on the impact of soil erosion on the water environment of lakes in China in the core collection of Web of Science from 1998 to 2025, aiming to reveal the research hotspots, evolution trends and regional differences in this field. The results show that China occupies a dominant position in this field (209 papers), and the Chinese Academy of Sciences is the core research institution (93 papers). The research hotspots show obvious policy-driven characteristics, which are divided into slow start periods (1998–2007), accelerated growth periods (2008–2015), explosive growth periods (2016–2020) and stable development periods (2021–2025). A keyword cluster analysis identified nine main research directions, including sedimentation effect (#0 cluster), soil loss (#2 cluster) and nitrogen and phosphorus migration (#11 cluster) in the Three Gorges Reservoir area. The study found that the synergistic effects of climate change and human activities (such as land use change) are becoming a new research paradigm, and the Yangtze River Basin, the Loess Plateau and the Yunnan–Guizhou Plateau constitute the three core research areas (accounting for 72.3% of the total literature). Future research should focus on a multi-scale coupling mechanism, a climate resilience assessment and an ecological engineering effectiveness verification to support the precise implementation of lake protection policies in China. This study provides a scientific basis for the comprehensive management of the soil erosion–lake water environment system, and also contributes a Chinese perspective to the sustainable development goals (SDG6 and SDG15) of similar regions in the world. Full article

Background: Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by compulsive alcohol seeking, loss of control over drinking, and negative emotional states when not using. It has significant psychological, physiological, and social consequences, often co-occurring with mental health disorders such as depression and anxiety. Psychological resilience is gaining more recognition. Sense of coherence (SOC) could be treated as a health factor, and individual predispositions play a crucial role in fighting disease and addiction. Our study examines whether SOC and its components—comprehensibility, manageability, and meaningfulness—predict life satisfaction in patients with AUD and whether perceived stress and health behaviors mediate these relationships. Methods: The study was conducted on a sample of 100 adult patients diagnosed with alcohol use disorder. Results: We found that the higher the manageability and meaningfulness, the lower the level of perceived stress and the higher the level of preventive behavior. Notably, perceived stress emerged as a significant mediator between SOC and satisfaction with life, while health behaviors did not show a mediating effect. Conclusions: The findings emphasize the protective role of SOC in enhancing psychological well-being among individuals with AUD and suggest that interventions aimed at strengthening SOC may reduce stress and improve overall life satisfaction in this population. Full article

Compared to the traditional unidirectional power supply system, the bidirectional traction power supply system in an electrified railway offers advantages like improved traction voltage and reduced energy losses, making it more suitable for steep gradient routes. However, its increased electrical complexity necessitates advanced catenary-rail short-circuit fault calculations and relay protection calibration. This paper proposes a fault calibration approach based on deriving electrical quantities with fault distance in the railway bidirectional traction grid system. A multi-loop circuit modeling method is used to accurately model the traction grid system and impedance parameters, incorporating real loop circuits formed by the grid transmission and return conductors for the first time. The approach is validated through real-life experiments on a Chinese railway line. A case study of a direct power supply system with a return cable is used to derive electrical quantities. Faults are categorized into two sections: between the substation and the parallel station (PS), and between the PS and the section post (SP). For each section, electrical quantities are derived under unidirectional substation excitation, and the results are superimposed to obtain fault distance variation curves for currents and voltages of substation, PS, SP, and Thévenin impedance. Finally, a calibration strategy for relay protection is presented. Full article

Four biomass residues–rosemary pomace, rosemary cake, grape seed and apple pomace–were torrefied at 250, 350 and 450 °C, and the physical, chemical and structural changes were characterized. The mass and energy yield decreased with increasing torrefaction temperature; the lowest mass (~10.4%) and energy yield (~10.6%) were observed for rosemary cake torrefied at 450 °C. The HHV increased the most for all feedstocks at 350 °C, with rosemary cake reaching a peak value of 36.4 MJ/kg at 350 °C. Ash content increased with temperature due to organic mass loss, while volatiles decreased and fixed carbon increased in most samples. The FTIR spectra showed the progressive loss of hydroxyl, carbonyl and C–O functionalities and the appearance of aromatic C=C bonds, indicating the formation of the biochar. TGA and DTG analyses revealed that the torrefied samples exhibited higher initial and maximum temperatures for decomposition, confirming improved thermal stability. The TGA-FTIR analyses of gas emissions during pyrolysis and combustion showed that the emissions of CO₂, CH₄, NO_x and SO₂ decreased with increasing degree of torrefaction. Overall, 350 °C was optimal to maximize energy density. The results show that agro-industrial residues can be effectively converted into sustainable biofuels, which offer the dual benefit of reducing waste disposal problems and providing a renewable alternative. In practice, such residues could be used for decentralized power generation in rural areas, co-combustion in existing power plants, or as feedstock for advanced bioenergy systems. Full article

Nitrate leaching from agricultural soils is a major contributor to groundwater contamination and non-point source pollution. Controlling this loss remains challenging due to the complexity of soil–water–nutrient interactions under intensive farming practices. Biochar, a porous, carbon-rich material derived from biomass pyrolysis, has emerged as a promising amendment for nitrate mitigation. This review summarizes recent advances in understanding the roles of biochar in nitrate retention and transformation in soils, including both direct mechanisms—such as surface adsorption, ion exchange, and pore entrapment—and indirect mechanisms—such as enhanced microbial activity, soil structure improvement, and root system development. Field and laboratory evidence shows that biochar can reduce NO₃⁻-N leaching by 15–70%, depending on its properties, soil conditions, and application context. However, inconsistencies in performance due to differences in biochar types, soil conditions, and environmental factors remain a major barrier to widespread adoption. This review also suggests current knowledge gaps and research needs, including long-term field validation, biochar material optimization, and integration of biochar into precision nutrient management. Overall, biochar presents a multifunctional strategy for reducing nitrate leaching and promoting sustainable nitrogen management in agroecosystems. Full article

Matching drone images to satellite reference images is a critical step for achieving UAV self-localization. Existing drone visual localization datasets mainly focus on target localization, where each drone image is paired with a corresponding satellite image slice, typically with identical coverage. However, this one-to-one approach does not reflect real-world UAV self-localization needs as it cannot guarantee exact matches between drone images and satellite tiles nor reliably identify the correct satellite slice. To bridge this gap, we propose a one-to-many matching method between drone images and satellite reference tiles. First, we enhance the UAV-VisLoc dataset, making it the first in the field tailored for one-to-many imperfect matching in UAV self-localization. Second, we introduce a novel loss function, Incomp-NPair Loss, which better reflects real-world imperfect matching scenarios than traditional methods. Finally, to address challenges such as limited dataset size, training instability, and large-scale differences between drone images and satellite tiles, we adopt a Vision Transformer (ViT) baseline and integrate CNN-extracted features into its patch embedding layer. Full article

To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edge-deployment settings, this study proposes a cost-effective Cantonese ASR system based on LoRA fine-tuning and INT8 quantization. First, Whisper-tiny is parameter-efficiently fine-tuned on the Common Voice zh-HK training set using LoRA with rank = 8. Only 1.6% of the original weights are updated, reducing the character error rate (CER) from 49.5% to 11.1%, a performance close to full fine-tuning (10.3%), while cutting the training memory footprint and computational cost by approximately one order of magnitude. Next, the fine-tuned model is compressed into a 60 MB INT8 checkpoint via dynamic quantization in ONNX Runtime. On a MacBook Pro M1 Max CPU, the quantized model achieves an RTF = 0.20 (offline inference 5 × real-time) and 43% lower latency than the FP16 baseline; on an NVIDIA A10 GPU, it reaches RTF = 0.06, meeting the requirements of high-concurrency cloud services. Ablation studies confirm that the LoRA-INT8 configuration offers the best trade-off among accuracy, speed, and model size. Limitations include the absence of spontaneous-speech noise data, extreme-hardware validation, and adaptive LoRA structure optimization. Future work will incorporate large-scale self-supervised pre-training, tone-aware loss functions, AdaLoRA architecture search, and INT4/NPU quantization, and will establish an mJ/char energy–accuracy curve. The ultimate goal is to achieve CER ≤ 8%, RTF < 0.1, and mJ/char < 1 for low-power real-time Cantonese ASR in practical IoT scenarios. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

Time series clustering plays a vital role in various analytical and pattern recognition tasks by partitioning structurally similar sequences into semantically coherent groups, thereby facilitating downstream analysis. However, building high-quality clustering models remains challenging due to three key issues: (i) capturing dynamic shape variations across sequences, (ii) ensuring discriminative cluster structures, and (iii) enabling end-to-end optimization. To address these challenges, we propose KDiscShapeNet, a structure-aware clustering framework that systematically extends the classical k-Shape model. First, to enhance temporal structure modeling, we adopt Kolmogorov–Arnold Networks (KAN) as the encoder, which leverages high-order functional representations to effectively capture elastic distortions and multi-scale shape features of time series. Second, to improve intra-cluster compactness and inter-cluster separability, we incorporate a dual-loss constraint by combining Center Loss and Supervised Contrastive Loss, thus enhancing the discriminative structure of the embedding space. Third, to overcome the non-differentiability of traditional K-Shape clustering, we introduce Differentiable k-Shape, embedding the normalized cross-correlation (NCC) metric into a differentiable framework that enables joint training of the encoder and the clustering module. We evaluate KDiscShapeNet on nine benchmark datasets from the UCR Archive and the ETT suite, spanning healthcare, industrial monitoring, energy forecasting, and astronomy. On the Trace dataset, it achieves an ARI of 0.916, NMI of 0.927, and Silhouette score of 0.931; on the large-scale ETTh1 dataset, it improves ARI by 5.8% and NMI by 17.4% over the best baseline. Statistical tests confirm the significance of these improvements (p < 0.01). Overall, the results highlight the robustness and practical utility of KDiscShapeNet, offering a novel and interpretable framework for time series clustering. Full article

In southern China, the long-term irrational utilization of land resources has caused severe damage to the ecology and environment of the entire region. Serious issues such as soil degradation and water erosion have led to the decline of soil quality and productivity. In this study, the spatial distribution characteristics of soil carbon, nitrogen, and phosphorus in Zhuxi watershed, Changting County, southern China, were analyzed by coupling geostatistics with GIS. The analysis generated several important results: (1) The concentrations of soil organic matter (OM), alkali-hydrolyzable nitrogen (AN), and available phosphorus (AP) are at moderate levels, and AP exhibits local enrichment in the downstream farmland, while the concentrations of total nitrogen (TN) and total phosphorus (TP) remain at low levels. (2) The optimal theoretical model for AN is an exponential model, while other nutrients follow spherical models. Except for AP, which has a nugget effect exceeding 75%, the nugget effects of other nutrients range between 25% and 75%, indicating that their spatial distribution is moderately correlated. According to Kriging interpolation results, the distribution of OM, TN, and AN shows a clear trend of decreasing from northeast to southwest, followed by a gradual increase, which is generally consistent with the direction of rivers. The trends of TP and AP are more irregular, generally decreasing from downstream to upstream. (3) OM, TN, and AN exhibit a negative correlation with the degree of soil erosion, indicating that soil erosion is associated with the loss of carbon and nitrogen nutrients. However, the impact on phosphorus is relatively insignificant. Full article

To broaden the informational scope of existing fuzzy frameworks and enhance their flexibility in representing and processing uncertainty, we propose a novel p,q-rung triangular orthopair fuzzy number (p,q-RTOFN). To enhance the aggregation capability of fuzzy data, we develop a p,q-rung triangular orthopair fuzzy weighted power Bonferroni mean (p,q-RTOFWPBM) operator that integrates the strengths of the Bonferroni mean and power average operators. We formally establish its theorems, proofs, and key properties, including symmetry and idempotency. Furthermore, we extend the complex proportional assessment (COPRAS) method to the p,q-RTOF environment, resulting in a p,q-RTOF-PBM-COPRAS model. This model effectively incorporates both positive and negative evaluation information under uncertainty, thereby reducing information loss and improving decision accuracy. A case study on urban smart farm selection confirms the feasibility and superiority of the proposed approach. This study introduces the p,q-RTOFN framework with extended informational scope, develops a hybrid p,q-RTOFWPBM operator, and incorporates these advances into an extended COPRAS method to achieve more accurate multi-criteria decision-making under uncertainty. Full article

Proteasome subunit beta type-9 (PSMB9), a member of the proteasome beta subunit family, encodes the pivotal β1i component of the immunoproteasome. PSMB9 plays a crucial role in antigen processing and presentation; however, its comprehensive role in orchestrating a tumor-immune landscape and regulating the anti-tumor immune responses remains unexplored. Here we investigated the context-dependent functions of PSMB9 by integrating multi-omics data from The Cancer Genome Atlas, Genotype-Tissue Expression database, Human Protein Atlas, Tumor Immunotherapy Gene Expression Resource, and multiple other databases. Moreover, we explored the predictive value of PSMB9 in multiple immunotherapy cohorts and investigated its functional relevance in CAR-T therapy using genome-scale CRISPR/Cas9 screening, gene knockout cell line in vitro, and clinical cohort validation. We found widespread dysregulation in PSMB9 across cancers, predominantly upregulated in most malignancies and associated with advanced pathological stages in specific contexts. PSMB9 was also broadly and negatively correlated with tumor stemness indices. Crucially, PSMB9 expression was robustly linked to anti-tumor immunity by being significantly correlated with immune-pathway activation (e.g., IFN response, cytokine signaling), immune regulatory and immune checkpoint gene expression, and enhanced infiltration of T cells across nearly all tumor types. Consequently, elevated PSMB9 predicted superior response to immune checkpoint inhibitors in multiple cohorts, showing comparable predictive power to established predictive signatures. Furthermore, CRISPR/Cas9 screening identified PSMB9 loss as a novel mechanism of resistance to CD19 CAR T cell therapy, with PSMB9-deficient tumor cells exhibiting a survival advantage under CAR-T pressure, supported by trends in clinical CAR-T outcomes. Our study uncovers PSMB9 as a previously unrecognized critical regulator of the tumor immune landscape in a pan-cancer scope, whose expression orchestrates key immune processes within the tumor microenvironment and serves as a potent biomarker for patient prognosis. Critically, we first established PSMB9 as a novel prognostic indicator for both checkpoint blockade and CAR-T cell therapies, highlighting its dual role as a crucial immune modulator and a promising biomarker for guiding T cell-based immunotherapy strategies across diverse human cancers. Full article

Illegal wildlife trade is a major global driver of biodiversity loss, shaped by high consumer demand, transboundary networks, and uneven enforcement. In the Middle East, particularly the Gulf Cooperation Council (GCC) region, factors such as high purchasing power, cultural traditions (e.g., falconry, prestige pets), and expanding digital marketplaces sustain both legal and illegal flows. We present a nine-year (2017–2025) assessment based on weekly, repeated field surveys at the Friday Market, adjacent pet shops, and private farms, complemented by systematic monitoring of online advertisements on Haraj.com.sa. We recorded 1063 individual animals across 88 species, birds (39.4%), reptiles (52.0%), and mammals (8.6%), and analyzed prices, conservation status, and venue-specific patterns. The most frequently recorded taxa included the white-eared bulbul (Pycnonotus leucotis), common slider (Trachemys scripta), and Egyptian mastigure (Uromastyx aegyptia). Mammals, though fewer in number, commanded the highest prices, particularly cheetahs (Acinonyx jubatus) and lions (Panthera leo). About 26% of species were IUCN-listed as threatened, with CITES Appendix I taxa fetching higher prices. Findings underscore the need for real-time monitoring, targeted enforcement, and cross-border collaboration to address escalating trade in rare and protected species. Full article