Search Results (2,589)

Background/Objectives: Vancomycin-resistant (VRE) and vancomycin-variable (VVE) Enterococcus spp. represent an increasing clinical challenge due to limited treatment options and the potential for undetected dissemination of such resistance genes. Data on Enterococci genomic epidemiology in healthcare settings remain rather limited. Our study aimed to investigate vancomycin resistance determinants in Enterococcus spp., clonal structure, and occurrence of VVE using whole-genome sequencing (WGS) in Latvia. Methods: Clinical isolates collected from hospitalised patients in two tertiary-level hospitals in Latvia (2021–2024) were analysed using WGS following routine laboratory identification. Vancomycin resistance determinants were identified in silico, along with MLST and cgMLST genotyping. Results: Of 532 sequenced isolates, 482 met the quality and inclusion criteria. E. faecalis (56.64%) and E. faecium (40.25%) predominated. Among 125 isolates carrying vancomycin resistance genes, vanB (54.40%) was the most frequent, followed by vanA (38.20%) and vanC (6.40%); vanC was restricted to E. gallinarum and E. casseliflavus. Vancomycin resistance was more prevalent in E. faecium (51.03%) than in E. faecalis (6.59%). cgMLST identified outbreak clusters among E. faecium ST80 and ST78 with complex type-specific resistance patterns and hospital specificity. E. faecalis showed polyclonal endemicity with the vanB gene present in different clades. Three (0.62%) vancomycin-variable E. faecium (VVE) isolates were identified in one hospital, harbouring vanA-type gene clusters comprising vanHAX but lacking the sensory gene vanS and the regulatory gene vanR. Conclusions: The VanB gene predominated in both hospitals, driven by clonal expansion of hospital-adapted E. faecium ST80/ST78, contrasting with earlier vanA predominance in Europe but aligning with recent regional vanB trends. The detection of VVE highlights clinically relevant genotype–phenotype discordance, underscoring the importance of integrating genomic surveillance with routine phenotypic testing to detect cryptic resistance and guide effective antimicrobial therapy. Full article

Manual classification of breast cancer is resource-intensive, slow, and subject to inter-observer variability, motivating automated deep learning solutions. Most current methods rely on unimodal imaging data and struggle with domain generalization (DG) across varied clinical environments. We propose a Dual-Stage Multimodal Alignment approach that integrates breast ultrasound (US) imagery with clinical text reports to improve diagnostic stability. The method proceeds in two stages: (1) Local Correlation Alignment (LCA), which aligns fine-grained visual features with textual embeddings to capture localized lesion attributes, and (2) Global Attention Alignment (GAA), which applies multi-head self-attention to the joint visual–textual sequence to encourage domain-invariant representations. We evaluate the approach on a harmonized, leakage-free repository of 6880 images aggregated from six public US datasets (BUS-CoT, BrEaST, BUS-BRA, BUS-UCLM, BLUI, BUSI) under three protocols: independent benchmarking on BUS-CoT, pooled cross-dataset evaluation, and zero-shot domain generalization on unseen unimodal target domains. On the BUS-CoT benchmark, the 198M-parameter model reaches 0.8177 accuracy and 0.8852 AUC, on par with the 7-billion-parameter Qwen2.5-VL-7B with chain-of-thought reasoning (0.8064 accuracy, 0.8354 AUC) while using roughly 1/35 the parameter count. In the pooled setting, it is competitive with single-domain state-of-the-art methods on individual subsets (e.g., 0.9576 AUC on BUSI, 0.8741 accuracy on BUS-BRA). Under zero-shot transfer without clinical text, per-domain AUC ranges from 0.7360 to 0.8060 across four unseen targets, providing a lower bound under cross-scanner shift. These results indicate that task-specific multimodal alignment can rival large vision-language models in breast US diagnosis at a fraction of the parameter count. Full article

Special education teachers often lack training to implement research-evaluated writing programs with fidelity, which contributes to insufficient instruction for students with disabilities. This study addresses a research gap: the limited documentation of implementation fidelity in French spelling interventions that integrate assistive technologies (ATs) for learners aged 9–12 with dysorthographia. Grounded in a theoretical foundation that coordinates alphabetic, orthographic, and morphographic processes within an explicit instruction sequence (explanation, modeling, guided practice, and independent application), the program aligned text-to-speech and word prediction with targeted spelling goals. Using a mixed-methods design, six elementary students participated in a single-case protocol with a transformative sequential design over 20 weeks. Four teachers received targeted training (theoretical + practical) and delivered explicit, individualized instruction during a 10-week intervention. Content analysis of teacher and researcher logs showed high, yet context-responsive, fidelity with variations by student profile, school context, and teacher. Converging quantitative and qualitative patterns suggest improvements in word-level accuracy/fluency and highlight training/coaching as a driver of fidelity. The discussion provides actionable implications for professional learning, school scheduling and dosage protection, and future research that multimodalizes fidelity evidence and instruments AT orchestration across the writing cycle. Full article

Background: Previous work from our group demonstrated an association between immunohistochemical detection of Human cytomegalovirus (HCMV) late antigen and poor event-free survival (EFS) in pediatric medulloblastoma. Whole-genome sequencing (WGS) further identified increased abundance of HCMV-aligned reads at the UL88 locus, particularly in Group 3 tumors, a molecular subgroup associated with aggressive clinical behavior and poor prognosis. Methods: We performed an integrated multi-omics analysis of pediatric medulloblastoma using WGS (n = 39) and RNA sequencing (RNA-seq; n = 28) datasets. RNA-seq data were filtered using stringent alignment criteria (MAPQ ≥ 20) and compared with fetal brain (n = 12), adult brain (n = 12), and HCMV-infected cell culture controls (n = 3). Only high-confidence uniquely aligned reads were retained to reduce nonspecific and multi-mapped viral alignments. Sequencing reads were aligned to the HCMV Merlin reference genome (NC_006273.2) using a standardized analytical pipeline. A subset of 28 cases with matched tumor WGS, tumor RNA-seq, and germline WGS data was used for integrated multi-omics analyses. Orthogonal validation analyses were performed in Group 3 tumors using independent genomic and transcriptomic approaches. Exploratory survival analyses were conducted in a combined cohort (n = 84) integrating genomic and immunohistochemical datasets. Results: Recurrent low-level HCMV-aligned molecular signals were identified across medulloblastoma datasets. Reads aligning to UL76, UL88, and UL99 were the most consistently detected HCMV-associated late-gene signals across RNA-seq and WGS datasets. A composite HCMV late-gene signature (UL76–UL88–UL99) showed higher levels in Group 3 tumors than in other molecular subgroups (p < 0.05 in WGS analyses). Orthogonal analyses demonstrated concordant low-level HCMV-associated genomic and transcriptomic signals enriched in tumors with MYC-associated activation and chromosome 17 imbalance. In the combined cohort (n = 84), elevated HCMV-associated signal assessed by immunohistochemistry and genomic profiling was associated with reduced EFS (median 55 vs. 147 months; log-rank p < 0.001). The subgroup classified as HCMV-high Group 3 demonstrated the strongest association with adverse outcome in exploratory multivariable analyses (HR = 6.43, p = 0.002). Conclusions: This study identifies recurrent low-level HCMV-associated genomic and transcriptomic signals across pediatric medulloblastoma datasets, with preferential enrichment in biologically aggressive Group 3 tumors. Although the extremely low abundance of viral-aligned reads precludes definitive evidence of productive viral infection, the reproducible detection of HCMV-associated molecular signatures across independent sequencing platforms supports further investigation into a potential oncomodulatory association in pediatric medulloblastoma. Additional validation using optimized viral detection methodologies, independent cohorts, and mechanistic studies will be necessary to clarify the biological and clinical significance of these findings. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article

One of the major agricultural, nutritional, and medicinal resource in the plant kingdom is the family of Cucurbitaceae, which is also recognized for its richness in carotenoids, terpenoids and triterpenoids. Mevalonate diphosphate decarboxylase (MVD) plays a crucial role in the mevalonate pathway by catalyzing a key step in isoprenoid biosynthesis, which is important for plant growth as well as for responses to biotic and abiotic stresses. Despite its metabolic relevance, comparative analyses of the MVD gene and protein in cucurbits remain limited. Therefore, this study aimed to identify and characterize MVD gene and protein in Cucurbitaceae using in silico approaches. Homology searches, multiple sequence alignment, phylogenetic and selective pressure analyses, physicochemical characterization, structural prediction, conserved motif analysis, cis-regulatory element prediction, and public expression profiling were performed. The predicted proteins showed high conservation in amino acid sequence, motif organization, and structural conformation, with lengths ranging from 398 to 424 aa. Descriptive FPKM-based transcriptomic profiles in Cucumis sativus showed higher MVD expression values in reproductive tissues and an apparent increase under powdery mildew infection. These findings suggest a potential role of the MVD gene in the Cucurbitaceae family and provide an exploratory framework for future studies on terpenoid and triterpenoid metabolism, promoter regulation, and stress-associated transcriptional responses. Full article

Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems. Full article

As digital transformation continues to reshape organizational operations, cultivating sustainable enterprise resource planning (ERP) competencies has become increasingly important for aligning higher education with industry needs. However, ERP learning is often characterized by high levels of complexity and may present substantial cognitive challenges for novice learners. Grounded in the People–Process–Technology (PPT) framework, this study conceptualizes Core ERP Competencies through three dimensions: System Operation Skills (technology dimension), Business Process Understanding (process dimension), and Perceived Overall ERP Capability (integration dimension). Drawing upon Cognitive Load Theory and pedagogical scaffolding principles, this study conceptualizes System Experience Configuration (SEC) as an instructional configuration framework that operationalizes different allocations of structured instructor guidance and autonomous system practice during flipped-classroom learning activities. A field-based quasi-experimental design was implemented in an undergraduate ERP course, involving unit-specific between-class comparisons across different SEC exposure sequences. Three levels of SEC were examined: Moderate, High, and Very High. To maintain an exploratory and theory-informed approach, non-directional hypotheses were developed to investigate whether different SEC conditions were associated with differences in ERP learning outcomes. Objective learning outcomes were assessed through unit-specific performance measures of System Operation Skills and Business Process Understanding, while Perceived Overall ERP Capability was evaluated through student self-reports after experiencing all three SEC conditions. The findings indicate that learning outcomes did not consistently improve as the proportion of autonomous practice increased. Across several unit-specific comparisons, the High SEC condition was associated with stronger performance than the Very High SEC condition, suggesting that extensive reductions in structured instructional support were not consistently associated with superior outcomes for novice ERP learners. At the same time, the results varied across competency dimensions and instructional units, indicating that the relationship between instructional guidance and learning outcomes may not be adequately explained by the assumption that increasing autonomous practice consistently improves performance. Given the design of the study, these findings should be interpreted as evidence of associations between instructional configurations and learning outcomes rather than as definitive causal effects. The findings are particularly relevant to hybrid information systems courses that combine hands-on practice with conceptual understanding, such as ERP and database education. Rather than assuming that increased practice time invariably leads to superior learning outcomes, educators may need to consider how different balances between guidance and autonomous practice support different dimensions of ERP learning. This study contributes empirical evidence regarding instructional configuration decisions in flipped ERP learning environments and provides practical implications for designing balanced learning experiences in complex digital learning contexts. Full article

Background: Gene fusions play a pivotal role in the pathogenesis and classification of hematologic malignancies. RNA sequencing (RNA-seq) has emerged as a powerful tool for detecting gene fusions; however, many clinical studies have focused on targeted RNA-seq, and optimal parameters for whole transcriptome RNA-seq remain uncertain. Methods: We retrospectively analyzed whole RNA-seq data from 301 patients diagnosed with acute leukemia between October 2022 and May 2025 to characterize the landscape of pathogenic gene fusions. Fusions were identified using the Arriba algorithm, and subsampling analyses were performed on cases with recurrent fusions to determine the minimum sequencing output required for reliable detection. Results: Pathogenic gene fusions were identified in 113 of 301 patients (37.5%). Whole RNA-seq detected fusions that were not identifiable by conventional assays, including UBTF::ATXN7L3, and highlighted frequent fusion events, such as ZNF384 rearrangements. Subsampling analysis demonstrated that a sequencing output ≥ 100 million reads (moderate confidence) or ≥300 million reads (high confidence) was sufficient for 100% detection of recurrent fusions. Conclusions: Whole RNA-seq reliably detects clinically relevant gene fusions in acute leukemia, aligns well with conventional karyotyping results, and surpasses targeted RNA-seq in comprehensiveness. A sequencing output of at least 100 million reads is recommended for clinical fusion detection. Full article

Reliable long-range onboard perception is a prerequisite for future railway safety systems, where potential obstacles must be recognized under long braking distances, sparse far-field returns, and strongly constrained rail-corridor geometry. This paper presents Rail-BEV as an initial reproducible baseline study for LiDAR-centric, sensor-aware bird’s-eye-view (BEV) railway obstacle perception. LiDAR is used as the primary geometric sensing modality, while a front-center RGB camera provides lightweight auxiliary visual evidence through calibrated LiDAR-to-image projection. The aligned geometric and visual cues are organized within a unified railway-oriented BEV backend that integrates geometry-aware fusion, rail-geometry prediction, and lightweight inference-time structural refinement. Evaluation was conducted on a scene-isolated railway benchmark with range-stratified center-distance matching, and all model variants were assessed on independent test sequences rather than on validation-selected checkpoints. Compared with CenterPoint and BEVFusion baselines evaluated under the same settings, Rail-BEV achieved the highest overall mAP of 0.6669, with particularly improved long-range pedestrian perception. The controlled ablation further shows that front-view RGB evidence improves the LiDAR-only baseline from 0.5612 to 0.5750 mAP, while ROI-based rail-corridor refinement further increases mAP to 0.5916 and Rail-BEV mIoU to 0.1193. These results indicate that LiDAR-centered sensing, lightweight visual assistance, and coarse rail-aware structural reasoning can be jointly organized to support reproducible long-range railway obstacle perception. This study also clarifies the remaining limitations in rail-geometry quality, calibration robustness, sensor degradation, and strict railway-oriented localization. Full article

The melon fly, Zeugodacus cucurbitae (Coquillett), is a globally significant agricultural pest causing substantial economic losses. Odorant-binding proteins (OBPs) are critical of the insect olfactory system, yet their specific physiological functions in Z. cucurbitae remain largely uncharacterized. In this study, we functionally characterized ZcucOBP14 and investigated its putative involvement in host chemoreception and insecticide tolerance. Sequence alignment and phylogenetic analysis indicated that ZcucOBP14 belongs to the Minus-C OBP subfamily, and quantitative reverse transcription PCR (RT-qPCR) showed that it was predominantly expressed in both the head and abdomen. Fluorescence binding assays revealed that ZcucOBP14 exhibited broad binding affinity to 11 host plant volatiles, three sex pheromones, and two insecticides. Subsequent electroantennography (EAG) and behavioral bioassays identified isopulegol, 1-hexanol, linalool, and α-pinene as key ligands regulating the behavioral responses of Z. cucurbitae. RNA interference (RNAi)-mediated knockdown of ZcucOBP14 significantly reduced EAG responses to key ligands, eliminated behavioral preference, and increased insecticide-induced mortality by 20%. Molecular docking further identified that Tyr71, Ile67, Trp50, Val107, Phe116 and Leu70 were critical residues involved in ligand interactions. Collectively, these findings highlight the indispensable role of ZcucOBP14 in olfactory perception and its contribution to insecticide tolerance, laying a solid theoretical foundation for the development of novel behavior-modifying agents, attractants, and optimized integrated pest management (IPM) strategies against this pest. Full article

To address the dual challenges of scarce historical time-series data and limited representational capacity of standalone models in pavement performance prediction, this study proposes an Engineering-heuristic-constrained Perturbation Data Augmentation Framework and a hybrid Bidirectional Long Short-Term Memory–Extreme Gradient Boosting (BiLSTM–XGBoost) model. The augmentation framework generates high-quality virtual samples by applying controlled perturbations aligned with engineering variability—to both covariates (e.g., traffic volume and layer thickness) and Pavement Condition Index (PCI) sequences—while enforcing the physical constraint of monotonic year-on-year deterioration. This expands 10 typical road sections into 1200 training samples. A two-stage prediction architecture is then developed: BiLSTM first extracts high-order temporal features from historical PCI sequences; these features are then fused with covariates and engineering features as input to XGBoost for final regression. Evaluated on an independent test set, the hybrid model outperforms the standalone models and the ANN model, achieving an R² of 0.771, with RMSE, MAE, and MAPE as low as 2.043, 1.706, and 1.859%, respectively. This work provides an accurate and practical tool for pavement performance prediction under data scarcity, supporting informed decision-making in pavement management systems. Full article

We propose a novel targetless extrinsic calibration method for wide-baseline and wide-angle fisheye cameras, which are mounted on a driving vehicle for surround view monitoring. Sequences of image frames from three fisheye cameras are obtained, and the object instance and depth around the vehicle are used for calibration. Thus, the proposed method can be applied to online vehicle camera calibration. Fisheye images are first transformed into the cylindrical coordinate system by considering the panoramic formation of the cameras. Then, the state-of-the-art object detection and monocular depth estimation models are applied to the cylindrical images. Vehicle instances matched across different views are reconstructed into 3D point clouds, and their depths are scaled by employing the pose geometry of the front camera. The per-point depths and global scale are then jointly optimized to achieve accurate cross-view alignment and extrinsic calibration. Experiments on both real-world and synthetic video datasets show that the proposed method achieves higher accuracy than COLMAP and DUSt3R under challenging conditions such as wide baselines and low frame rates, without requiring an artificial calibration target. Full article

Early wildfire monitoring in power transmission corridors requires reliable detection of weak fire and smoke cues under complex field conditions and strict edge-computing constraints. To address these issues, this paper proposes an edge-deployable RGB–thermal framework based on visible and thermal infrared (TIR) imaging for unmanned aerial vehicle (UAV)-based corridor monitoring, including a spatial detector, YOLO-MMSC, and a temporal-enhanced version, YOLO-MMSC-T. The study also establishes a self-collected corridor-oriented RGB–thermal (RGB–T) dataset to complement public wildfire data. Unlike existing RGB–thermal wildfire datasets that mainly focus on forest or wildland fire scenes, the proposed dataset is specifically organized for complex-background power transmission-corridor monitoring, including continuous UAV sequences, nighttime conditions, smoke/vegetation occlusion, long-range small targets, and hard-negative interference. To the best of our knowledge, this is the first self-collected RGB–thermal wildfire dataset designed for this specific application scenario. The framework integrates a mobile inverted bottleneck convolution (MBConv) lightweight backbone, a Shallow Detail Fusion Module (SDFM) for shallow cross-modal alignment and denoising, a Content-Guided Attention (CGA) module for adaptive fusion, and normalized Wasserstein distance (NWD)-based box regression for long-range small-target localization. Experiments on public and self-collected datasets show that YOLO-MMSC achieves 94.6% mAP@0.5, 95.0% precision, and 93.9% recall while running at 60 FPS on Jetson Orin NX. With temporal fine-tuning, YOLO-MMSC-T reaches a continuous detection rate (CDR) of 95.6% with a jitter index of $2.8\times {10}^{-3}$. Field experiments using a DJI Matrice 4T further indicate a practical operating altitude of 120–180 m. These results support lightweight RGB–thermal remote sensing for real-time wildfire monitoring in complex transmission-corridor environments. Full article

The extraordinary genetic diversity of human immunodeficiency virus type 1 (HIV-1), driven by high mutation and recombination rates, poses significant challenges for diagnostics, therapy, and vaccine development. While variable regions enable immune escape, hyperconserved regions are critical for viral function and represent promising targets for novel therapeutic interventions. This study aimed to develop and validate a bioinformatic algorithm for quantitative assessment of sequence conservation and automated identification of functionally significant conserved regions across all major HIV-1 proteins. A total of 1119 full-length HIV-1 genome sequences representing major subtypes (A1, A2, A6, B, C, D, F1, F2, G, H, J, K) were analyzed. Normalized Shannon entropy (S-index) was calculated for each alignment column. Statistical thresholds for conserved regions were established using 95% confidence intervals derived from bootstrap resampling. Two complementary algorithms, clustering and local maxima detection, were applied to identify conserved regions, which were subsequently mapped to known functional domains based on literature data. Protein conservation varied markedly, with S_m values ranging from 0.784 (Vpu) to 0.920 (Pol). Gag, Pol, and Vpr demonstrated the highest overall conservation, while Env, Rev, Tat, and Vpu exhibited pronounced variability interspersed with conserved domains. In total, 25 conserved regions in Gag, 49 in Pol, 28 in Env, and 6–4 regions in accessory proteins (Vif, Vpr, Rev, Tat, Nef, Vpu) were identified. These regions corresponded to critical functional elements including enzyme catalytic centers, zinc fingers, receptor-binding sites, protein interaction interfaces, and membrane-anchoring domains. The developed computational framework enables statistically grounded identification of evolutionarily constrained regions across analyzed HIV-1 subtypes. The identified conserved regions represent candidate sites for further investigation and may inform downstream studies focused on antiviral target prioritization, immunogen design, and diagnostic assay development. However, their translational applicability requires additional analytical, structural, and experimental validation. Full article