Search Results (8,873)

Malaria, caused by Plasmodium parasites, remains a global health crisis, necessitating novel therapeutic strategies targeting host–parasite interactions. During liver-stage infection, parasites exploit host vesicular trafficking machinery, particularly SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins that mediate membrane fusion. Using a CRISPR/Cas9 knockout system in HeLa cells combined with advanced microscopy of Plasmodium berghei-infected HeLa cells, we identified specific endolysosomal SNAREs including Vesicle-Associated Membrane Protein 7 (VAMP7), Vesicle-Associated Membrane Protein 8 (VAMP8), Vesicle Transport Through Interaction With T-SNAREs 1B (Vti1B), and Syntaxin 7 (Stx7) to be recruited to the parasitophorous vacuole membrane (PVM) with distinct temporal profiles. This demonstrates the parasite’s precise manipulation of host endolysosomal trafficking pathways. VAMP7 and Vti1B were localized to the PVM within 30 min post-infection, suggesting potential roles during invasion, while VAMP8 and Stx7 appeared later around 24 h post infection (hpi), coinciding with increased nutrient acquisition. Single gene deletions showed minimal impact, but combinatorial knockouts (KO) revealed critical redundancy. VAMP7-VAMP8 as well as VAMP7–Vti1B double KO significantly reduced parasite infection and growth, with Vti1B playing a dominant role. Triple KO phenotypes mirrored VAMP7-Vti1B disruption, underscoring Vti1B’s dominant role. SNARE depletion also impaired the lysosome–PVM association and LAMP1 positive vesicle recruitment. Our findings indicate Plasmodium hijacks a coordinated host SNARE network to fuse lysosomes with the PVM for nutrient uptake. Targeting Vti1B-containing complexes disrupts this pathway without host cell toxicity, offering a promising host-directed antimalarial approach. Full article

This paper proposes a unified approach based on maximal consistent blocks (MCBs) to address the problem of incomplete single-label and multi-label dimensional reduction. The matrix computation method for maximal consistent blocks is improved by introducing a dynamic multi-row detection mechanism and optimizing the block size determination criteria. The complete set of maximal consistent blocks can be efficiently obtained via matrix intersection operations. For incomplete single-label decision information systems, an attribute reduction algorithm is designed based on maximal consistent blocks. Redundant attributes are eliminated by preserving the upper and lower approximation distributions of decision classes. In the multi-label scenario, a complementary decision reduct method integrating coarse and fine decision functions is proposed, and a unified solution paradigm is adopted to accomplish multi-label dimensional reduction. The effectiveness in classification (F1-score, Ranking Loss, Hamming Loss), reduction performance, and runtime efficiency is validated via statistical tests, scalability studies, structured missingness studies, and comparisons with four representative baselines on Birds, Scene, and Yeast datasets (5%/10%/15% missing rates). Full article

Identifying the major themes and recurring motifs of an author’s long novels is a basic task in literary studies. To support this task in a scalable way while retaining within-novel narrative variation, we model an author corpus as a third-order nonnegative tensor indexed by work × narrative segment × vocabulary. For such narrative tensors, we propose a tailored nonnegative tensor factorization model that reduces redundancy among topics via an orthogonality-promoting penalty and promotes smooth topic variation along contiguous narrative segments via an ${\ell }_2$ total-variation penalty. We develop a block proximal linearization algorithm for the resulting optimization problem and show that every limit point of the generated sequence satisfies the KKT conditions. Experiments on Toni Morrison’s long novels, including comparisons of the results of the proposed model with those of NMF and LDA, suggest that the cross-work themes extracted by the proposed approach exhibit qualitative patterns broadly consistent with thematic concerns discussed in existing literary scholarship. Additional experiments on the corpora of Ernest Hemingway and Graham Swift provide further validation of the proposed model. Full article

Eukaryotic microorganisms, including microalgae, protists, fungi, and micro-metazoans, act as drivers of energy flow and nutrient cycling, collectively forming the microbial food loop, and also serve as important indicators of environmental health. To investigate the seasonal variation in eukaryotic microorganisms in a mussel farming area, a total of 96 seawater samples were collected from surface and bottom layers of water across different seasons. High-throughput sequencing of the 18S rRNA gene was employed to characterize shifts in microbial community structure and identify key influencing factors. Our results indicated significant seasonal differences in eukaryotic microbial communities between surface and bottom waters. Redundancy Analysis (RDA) revealed that seasonal variations in community structure were primarily driven by environmental factors such as temperature, dissolved oxygen (DO), and salinity. Co-occurrence network analysis indicated that surface water networks exhibited higher numbers of nodes and edges, as well as greater modularity, suggesting more distinct niche differentiation and higher natural connectivity within the community. These findings provide fundamental data for understanding the response mechanisms of eukaryotic microbial communities to seasonal changes in the mussel cultivation area of Gouqi Island. Full article

Background/Objectives: Epiblepharon is a common congenital eyelid anomaly in East Asian children, often associated with redundant skin and orbicularis oculi muscle overriding the eyelid margin. Recent studies have suggested that systemic factors such as body mass index (BMI) may contribute to its development. This study aimed to investigate the relationship between BMI and epiblepharon and to analyze the correlation between BMI and skin-fold height as a marker of eyelid structural redundancy. Methods: This retrospective comparative study included 100 pediatric patients (54 males, 46 females) aged 3–13 years who underwent surgical correction for lower eyelid epiblepharon and 100 age-matched controls without the condition. Preoperative height, weight, and skin-fold height were analyzed. Intergroup comparisons were performed using independent t-tests, and correlations between BMI and skin-fold height were evaluated using Spearman correlation. Results: There were no significant differences in overall BMI, obesity index, or prevalence of obesity defined as BMI ≥ 95th percentile between groups. Boys aged 7–8 years demonstrated significantly higher BMI in the epiblepharon group, and boys aged 9–10 years showed a significantly higher obesity index in the epiblepharon group, whereas boys aged 3–4 years showed significantly lower BMI. No significant differences were observed in girls. BMI was not independently associated with epiblepharon in multivariate logistic regression analysis (OR 1.06, 95% CI 0.96–1.16, p = 0.278). Among patients with epiblepharon, BMI showed a significant negative correlation with skin-fold height (r = −0.410, p < 0.001), suggesting increased orbicularis muscle redundancy in obese children. Conclusions: BMI was not independently associated with the presence of epiblepharon; however, age-specific differences were observed in certain male subgroups. Higher BMI was correlated with lower skin-fold height among affected patients, suggesting that adiposity may influence eyelid morphology in specific developmental stages. Further longitudinal studies are warranted to clarify the age-dependent relationship between obesity and epiblepharon. Full article

Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article

RGB-D salient object detection (RGB-D SOD) aims to accurately localize and segment visually salient objects by jointly leveraging RGB images and depth maps. Some existing methods rely on static fusion strategies with fixed paths and weights, which treat all regions equally and fail to capture the varying importance of different regions and modalities. Although some attention-based methods alleviate the limitations of static fusion by assigning adaptive weights to different regions and modalities, the quality of RGB and depth data may degrade in real-world scenarios due to sensor noise, illumination changes, or environmental interference. These attention-based methods often overlook inter-modality quality differences and complementarity, making them prone to over-relying on a certain modality, which can lead to noise introduction, feature conflicts, and performance degradation. To address these limitations, this paper proposes a novel dynamic feature routing and fusion framework for RGB-D SOD, which adaptively adjusts the fusion strategy according to the quality of input modalities. To enable modality quality awareness, the proposed method characterizes the modality complementarity between RGB and depth features in a task-driven manner inspired by information-theoretic principles. We introduce a task-relevance scoring function which is integrated with a mutual information estimator to quantify such complementarity, and emphasizes task-relevant features while suppressing redundancy. A dynamic routing module is then designed to perform feature selection guided by the captured complementarity. In addition, we propose a novel cross-modal fusion module to adaptively fuse the features selected by the dynamic routing module, which effectively enhances complementary representations while suppressing redundant features and noise interference. Extensive experiments conducted on seven public RGB-D SOD benchmark datasets demonstrate that the proposed method consistently achieves competitive performance, outperforming existing methods by an average of approximately 1% across multiple evaluation metrics. Notably, in challenging scenarios with severe modality quality degradation, the proposed method outperforms existing best-performing methods by up to 1.8%, demonstrating strong robustness against cluttered backgrounds, complex object structures, and diverse object scales. Overall, the proposed dynamic fusion framework provides a novel solution to modality quality imbalance in RGB-D salient object detection. Full article

Agent-based systems built on large language models (LLMs) increasingly rely on complex internal reasoning processes, tool interactions, and memory mechanisms. However, the internal decision-making dynamics of such agents remain difficult to observe, analyze, and compare in a systematic manner. To address this limitation, we present AgentSec, a curated dataset of structured agent interaction traces designed to support the analysis of agent-level reasoning and action behaviors. The dataset consists of 30 deterministic and non-redundant scenario instances, each capturing a complete agent interaction session under a fixed and validated schema. Quantitatively, the 30 released sessions comprise 67 decision nodes and 45 tool calls (73.3% successful), with provenance graphs exhibiting an average depth of 4.53 (max 7) and a maximum branching factor of 3. Scenarios are organized according to a predefined taxonomy of agent behavioral patterns, including tool success and failure modes, fallback strategies, memory conflicts and overwrites, decision rollbacks, and provenance branching structures. Each scenario encodes a distinct analytical case rather than a parametric variation, enabling focused and interpretable study of agent decision-making processes. AgentSec provides detailed records of decision traces, tool calls, memory updates, and provenance relations, and is intended to facilitate reproducible research on agent behavior analysis, auditing, and evaluation. The dataset is released alongside its schema, scenario manifest, and validation tooling to support reuse and extension by the research community. Rather than serving as a large-scale performance benchmark, AgentSec is explicitly designed as a diagnostic and unit-test suite for auditing agent-level reasoning logic and provenance consistency under controlled structural conditions. Full article

To address the limited expressiveness in current speech synthesis caused by coarse-grained prosody modeling and simplistic feature fusion strategies, a joint prosody modeling framework and a nonlinear fusion method named KAFusion are proposed, based on the Kolmogorov–Arnold (KA) representation theorem. The joint modeling integrates pitch and energy as prosodic priors with text encodings to jointly guide duration prediction, enabling explicit control over speech rate and tone. During feature fusion, KAFusion facilitates nonlinear interactions among features through its nested inner and outer functions. Information entropy serves as the quantitative metric, and both theoretical and experimental results demonstrate the fusion module’s efficacy in suppressing redundancy while preserving task-critical content. Evaluations on the AISHELL3 dataset show a 5.8% improvement in MOS over the baseline. Ablation studies further validate the effectiveness of the proposed components, where KAFusion achieves an output entropy of 3.47, which is 18.4% higher than that of linear fusion (2.93) and indicates richer information content. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

High-precision and real-time monitoring of forest fires is a critical requirement in disaster prevention and mitigation. During fire evolution, significant stage-wise variations occur, which make it difficult for conventional semantic segmentation models to simultaneously achieve robust multi-scale feature extraction and strong interference resistance. To address this issue, this paper proposes a stage-aware multi-head segmentation network, termed SAMS-Net. The proposed network decouples fire-stage recognition from pixel-level segmentation and employs a Hard-Switch Routing mechanism to dynamically activate the stage-specific decoder that matches the current fire phase during inference, while pruning irrelevant branches to reduce computational redundancy. Experimental results show that SAMS-Net achieves 76.16% mIoU, 81.30% Dice, and 90.31% PA, outperforming mainstream segmentation models such as FCN, U-Net++, DeepLabV3, and YOLOv9-Seg. In challenging stages, particularly the early and recession phases, the segmentation performance improves by more than 10% compared with the second-best model. Meanwhile, the proposed method maintains high accuracy with a real-time inference speed of 75.8 FPS. These results support the effectiveness of SAMS-Net for flame-and-ember foreground segmentation on the constructed multi-stage forest-fire benchmark dataset. Broader generalization across independent datasets and real-world deployment scenarios will be further investigated in future work. Full article

People with special needs often face barriers to participating in adapted outdoor leisure physical activities. A participatory action research project involving a nonprofit organization, a citizen with motor disabilities, and researchers aimed to co-develop a digital platform connecting people with special needs interested in outdoor leisure physical activities with trained volunteers. The adopted co-design methodology followed four stages: (1) Exploration (identifying users’ needs and preferences), (2) Co-design (defining key information and platform features), (3) Validation (prioritizing features), and (4) Development (implementing and testing the platform). This article focuses on stages 2, 3, and 4. During stage 2, key information and features were identified to support matching people with special needs and volunteers and informing users about adapted outdoor leisure physical activities. In stage 3, these elements were prioritized using eight key considerations, including technological (e.g., ease of use), environmental (e.g., avoiding redundancy with existing initiatives), organizational (e.g., availability of human resources), and financial factors (e.g., grant planning). Stage 4 resulted in the launch of Tandem Actif, followed by user testing to document user experience and guide improvements. This article details the application of co-design within a participatory action research project aimed at promoting safe, ethical, and accessible participation in outdoor leisure physical activities for people with special needs. Full article

Atopic dermatitis (AD) is a chronic inflammatory skin disorder that is significantly contributed to by epigenetics. We developed a machine learning-based framework to identify DNA methylation biomarkers associated with AD classification and severity. Genome-wide methylation data from peripheral blood were processed using four feature selection algorithms: coarse approximation linear function (CALF), elastic net (EN), minimum redundancy maximum relevance (mRMR), and recursive feature elimination with cross-validation (RFECV). The integrative framework identified a central panel of 8 CpG sites that achieved an area under the curve (AUC) of 1.00 in the test set. This panel demonstrated high disease specificity, showing poor classification performance for systemic lupus erythematosus (AUC = 0.46), Crohn’s disease (AUC = 0.50), and oral squamous cell carcinoma (AUC = 0.58). Severity prediction using RFECV-selected 63 CpG sites (RFE63) achieved high accuracy across classifiers, with Random Forest (accuracy = 0.94) outperforming the others. The functional enrichment of CpG-associated genes highlighted key immune-related transcriptional regulators, including STAT5A, RUNX1, MEIS1, and PAX4. These genes are linked to chromatin remodeling, T helper cell differentiation, and interleukin-2 regulation, which are critical in AD pathogenesis and severity. Our findings demonstrate the utility of machine learning-integrated epigenomics in identifying robust, disease-specific biomarkers for AD diagnosis and monitoring, offering new insights into the molecular mechanisms underlying childhood AD. However, further validation in large-scale independent cohorts is required to confirm their clinical robustness and generalizability. Full article

Straw returning is an effective means of improving soil structure and increasing soil organic matter content. However, few studies have been conducted on the effects of corn straw returning on the soil microorganism community in soybean crops. In this paper, taking conventional combined tillage (CT) as a control, the effects of no-tillage with straw mulching (NTS), no-tillage with stubble retention (NT), and deep plowing with straw incorporation (DT) on soil bacterial community under a corn–soybean rotation system were studied. The results showed that the contents of soil total nitrogen, total phosphorus, available phosphorus, the activities of soil urease and acid phosphatase, and soil bacterial richness and diversity in the NTS treatment were significantly higher than those in other treatments. Moreover, the NTS treatment increased the abundance of Acidobacteriota and MND1 (unclassified bacterial genus) in the soil. The number of unique OTUs in the NTS treatment was the greatest (26.67%), with that of the CT treatment being the smallest (7.22%). Redundancy analysis (RDA) revealed that soil total nitrogen, total phosphorus, and available phosphorus are the key driving changes in bacterial community. Consequently, NTS treatment was the optimal approach for both soil fertility improvement and bacterial community optimization. This approach combines straw mulching and no-tillage, which not only exerts the nutrient supply effect of straw but also reduces the impact of soil disturbance on microbial habitats. Full article

The remaining useful life (RUL) prediction of lithium-ion batteries remains challenging for hybrid models due to high computational redundancy and hyperparameter sensitivity under complex operating conditions. To address these issues, this paper proposes a novel hybrid framework that integrates a CNN–BiLSTM–Attention network with a Crested Porcupine Optimizer (CPO). The key innovation lies in the simultaneous co-optimization of model structure and parameters: a group-level structured pruning strategy eliminates redundant convolutional kernels to reduce complexity, while the CPO algorithm dynamically optimizes critical hyperparameters (e.g., BiLSTM hidden nodes, attention dimensions) using RMSE as the fitness function. This dual optimization achieves a balance between model lightweighting and predictive accuracy. Experimental results on real-world electric vehicle datasets demonstrate that the optimized model reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 60.7% and 55.2%, respectively, compared to the baseline CNN–BiLSTM–Attention model. Furthermore, the model maintains high robustness across multiple datasets (R² > 0.996), validating its strong generalization capability in small-sample and high-noise scenarios. Full article