Search Results (19,682)

In this study, we present a professional development programme for teachers launched to introduce Computer Science and Informatics (CSI) in primary education in Slovenia. The study aims to examine which CSI core concepts teachers most frequently choose to integrate into their lessons when given the freedom to select the topics within the framework, and to explore how students engage with and respond to these activities, as reported in teachers’ reflections. This study is based on reflective feedback from forty-seven teachers from seven primary schools who implemented interdisciplinary lessons that integrate CSI content into existing primary school curricula. Qualitative data from 152 reflections were used to support our research findings. The results show that teachers most frequently introduced the concepts from the content area of algorithms and programming. In contrast, content areas such as computing systems, networks and the internet, data and analysis, and impacts of computing received less attention. Teachers reported that students were motivated and engaged, although some challenges emerged, including difficulties in solving tasks or following instructions. As this pilot study reports on the first year of a two-year initiative, the findings provide preliminary insights into how a structured professional development programme for teachers can support interdisciplinary approaches in CSI education. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Background: Cancer’s multifactorial complexity demands innovative polypharmacological strategies that can simultaneously target multiple oncogenic pathways. Natural products, with their inherent chemical diversity, offer promising multi-target therapeutic potential. This study comprehensively investigates the anticancer mechanisms of Tunisian Myrtus communis essential oils (McEOs) using an integrated computational-experimental framework to elucidate their polypharmacological basis and therapeutic potential. Methods: McEO composition was characterized via GC-MS analysis. Antiproliferative activity was evaluated against HeLa (cervical), MCF-7 (breast), and Raji (lymphoma) cancer cell lines using MTT assays. A multi-scale computational pipeline integrated network pharmacology, molecular docking against eight key oncoproteins, and 100 ns all-atom molecular dynamics simulations to elucidate molecular mechanisms and target interactions. Results: GC-MS revealed a 1,8-cineole-rich chemotype (38.94%) containing significant sesquiterpenes. McEO demonstrated potent differential cytotoxicity: HeLa (IC₅₀ = 8.12 μg/mL) > MCF-7 (IC₅₀ = 19.59 μg/mL) > Raji cells (IC₅₀ = 27.32 μg/mL). Network pharmacology quantitatively explained this differential sensitivity through target overlap analysis, showing higher associations with breast (23%) and cervical (18.3%) versus lymphoma (5.5%) cancer pathways. Molecular docking identified spathulenol as a high-affinity Androgen Receptor (AR) antagonist (XP GScore: −9.650 kcal/mol). Molecular dynamics simulations confirmed exceptional spathulenol-AR complex stability, maintaining critical hydrogen bonding with Asn705 for 96% of simulation time. Conclusions: McEO exerts sophisticated multi-target anticancer effects through synergistic constituent interactions, notably spathulenol’s potent AR antagonism. This integrated computational-experimental approach validates McEO’s polypharmacological basis and supports its therapeutic potential, particularly for hormone-dependent malignancies, while establishing a robust framework for natural product bioactivity deconvolution. Full article

Background: Prolonged fever (PF) is one of the most challenging clinical conditions due to its complex molecular mechanisms and limited effective treatments. Objective: The current study aimed to explore the mechanism of action of Mo-Ha-Rak (MHR), a Thai traditional polyherbal remedy, in PF treatment. Methods: Integration of network pharmacology, molecular docking, and inhibition of nitric oxide (NO) production in LPS-induced RAW264.7 macrophages approaches were used. Results: The study identified 86 potential active compounds, 131 potential therapeutic targets, and 9 hub genes for MHR. Key targets with the highest degree of connectivity in PF, including TNF, IL6, IL1B, PTGS2, STAT3, and NFKB1, are closely associated with arachidonic acid metabolism pathways, which play critical roles in infections, inflammation, cell proliferation, and apoptosis in the PF microenvironment. Molecular docking analysis suggested that core compounds exhibited strong binding affinities for four key targets, viz. TNF, IL6, IL1B, and PTGS2, with binding energies ranging from −4.1 to −9.8 kJ/mol. MHR exhibited dose-dependent reduction of NO production at concentrations of 10–100 µg/mL. Among the biomarkers of MHR tested, ellagic acid, loureirin A, resveratrol, and rhein showed potential to inhibit NO production. Conclusions: This study demonstrates that MHR exerts its therapeutic effects on PF through a complex network of multiple compounds, targets, and pathways. These findings highlight the mechanisms of PF and the role of MHR in modulating the arachidonic acid metabolism pathway, which underlies the development of fever. Full article

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

Background/Objectives: The clinical management of adolescent idiopathic scoliosis (AIS) is hindered by the inability to accurately predict curve progression. Although skeletal maturity and the initial Cobb angle are established predictors of progression, their combined predictive accuracy remains limited. This study aimed to develop a robust and interpretable artificial intelligence (AI) system using deep learning (DL) models to predict the progression of scoliosis using only standing frontal radiographs. Methods: We conducted a multicenter study involving 542 patients with AIS. After excluding 52 borderline progression cases (6–9° progression in the Cobb angle), 294 and 196 patients were assigned to progression (≥10° increase) and non-progression (≤5° increase) groups, respectively, considering a 2-year follow-up. Frontal whole spinal radiographs were preprocessed using histogram equalization and divided into two regions of interest (ROIs) (ROI 1, skull base–femoral head; ROI 2, C7–iliac crest). Six pretrained DL models, including convolutional neural networks (CNNs) and transformer-based models, were trained on the radiograph images. Gradient-weighted class activation mapping (Grad-CAM) was further performed for model interpretation. Results: Ensemble models outperformed individual ones, with the average ensemble model achieving area under the curve (AUC) values of 0.769 for ROI 1 and 0.755 for ROI 2. Grad-CAM revealed that the CNNs tended to focus on the local curve apex, whereas the transformer-based models demonstrated global attention across the spine, ribs, and pelvis. Models trained on ROI 2 performed comparably with respect to those using ROI 1, supporting the feasibility of image standardization without a loss of accuracy. Conclusions: This study establishes the clinical potential of transformer-based DL models for predicting the progression of scoliosis using only plain radiographs. Our multicenter approach, high AUC values, and interpretable architectures support the integration of AI into clinical decision-making for the early treatment of AIS. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

To avoid continuous cropping problems with Bupleurum, we screened suitable preceding crops for rotation with Bupleurum through different crop rotations. Therefore, the objective of this study was to find out the relationships between microbial community characteristics, soil properties, and enzyme activities under four different rotation patterns, including fallow-Bupleurum (CK), maize-Bupleurum (M), soybean-Bupleurum (So), and sunflower-Bupleurum (Su). Results indicated that under all four rotation patterns, So treatment significantly enhanced soil nutrients and enzyme activities compared to CK. So not only optimized the composition of soil bacterial and fungal communities but markedly enhanced microbial α diversity. Additionally, So exhibited high similarity in bacterial and fungal community composition with M, and featured complex symbiotic relationships within the soil microbial network. While no clear discrepancies were detected in the abundance of the top twenty metabolic pathways in the predictive functions of bacterial and fungal communities across four rotation patterns, the metabolic pathway function MET-SAM-PWY (methionine synthesis pathway) in bacterial communities and the metabolic pathway function VALSYN-PWY (valine synthesis pathway) in fungal communities were particularly prominent under the So rotation pattern. RDA suggested that soil properties (available phosphorus and pH) and enzyme activities (sucrase and alkaline phosphatase activities) were the driving forces for bacterial community composition, while soil properties (soil organic matter and available potassium) and enzyme activities (sucrase and catalase activities) regulated fungal community composition. Hence, the soybean-Bupleurum rotation pattern represents a cultivation practice more beneficial for the sustainable development of the bupleurum industry, which can significantly improve soil fertility and the micro-ecological environment. Full article

Human remains detection (HRD) dogs are vital tools in forensic science and disaster response, but their training is limited by the restricted availability of human material. Synthetic odorants such as Sigma Pseudo™ formulations provide safer, standardized alternatives, yet current products reproduce only a fraction of the volatile organic compound (VOC) profile of decomposition. In particular, sulfur-containing volatiles, which are highly odor-active and consistently present in human remains, are often missing, reducing biological fidelity. Here, we integrate analytical chemistry with canine olfactory genetics and molecular biology to explain these limitations. Dogs possess one of the largest olfactory receptor (OR) repertoires among mammals, with high allelic diversity and specialized trace amine-associated receptors (TAARs) tuned to cadaveric amines. Together with olfactory binding proteins (OBPs) and ciliary signal transduction cascades, these molecular mechanisms highlight why incomplete VOC mixtures may fail to activate the full receptor network required for reliable odor imprinting. We propose the “sulfur gap hypothesis” and suggest hybrid training strategies combining improved synthetics with ethically sourced biological samples to enhance HRD dog performance. Full article

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

Background: Isorhamnetin (ISO), a dietary O-methylated flavonol, was evaluated for hypoglycemic activity and mechanism in a streptozotocin (STZ) model of type 1 diabetes. Methods: We conducted untargeted plasma metabolomics (ESI±), network integration and docking, and measured pancreatic PI3K, phosphorylated AKT, and COX-2; INS-1 β cells challenged with the PI3K inhibitor LY294002 were used to assess viability, intracellular ROS, and PI3K phosphorylation. Results: ISO lowered fasting glycemia, increased circulating insulin, improved dyslipidemia by reducing low-density lipoprotein cholesterol (LDL-C), and preserved islet architecture. Untargeted plasma metabolomics (ESI±) indicated broad remodeling with enrichment of arachidonic-, linoleic-, starch/sucrose- and glycerophospholipid pathways. Network integration and docking prioritized targets converging on PI3K/AKT and COX-2/eicosanoid signaling. Consistently, in pancreatic tissue, ISO increased PI3K, phosphorylated AKT, and reduced COX-2. In INS-1 beta cells challenged with the PI3K inhibitor LY294002, ISO improved viability, decreased intracellular ROS, and partially restored PI3K phosphorylation at 4 µM. Conclusions: Together, these data indicate that ISO exerts hypoglycemic effects while supporting β-cell integrity through activation of PI3K/AKT and tempering of COX-2–linked lipid-mediator pathways. ISO therefore emerges as a food-derived adjunct candidate for autoimmune diabetes, and the work motivates targeted lipidomics and in vivo pathway interrogation in future studies. Full article

SHORT INTERNODE (SHI)-Related Sequence (SRS) transcription factors play crucial roles in plant growth, development, and stress responses and have been extensively studied in various plant species. However, the molecular functions and regulatory mechanisms of SRS genes in the economically important tropical fruit crop pitaya (Hylocereus undatus) remain poorly understood. This study identified 9 HuSRS genes in pitaya via bioinformatics analysis, with subcellular localization predicting nuclear distributions for all. Gene structure analysis showed 1–4 exons, and conserved motifs (RING-type zinc finger and IXGH domains) were shared across subclasses. Phylogenetic analysis classified the HuSRS genes into three subfamilies. Subfamily I (HuSRS1–HuSRS4) is closely related to poplar and tomato homologs and subfamily III (HuSRS6–HuSRS8) contains a recently duplicated paralogous pair (HuSRS7/HuSRS8) and shows affinity to rice SRS genes. Protein structure prediction revealed dominance of random coils, α-helices, and extended strands, with spatial similarity correlating to subfamily classification. Interaction networks showed HuSRS1, HuSRS2, HuSRS7 and HuSRS8 interact with functional proteins in transcription and hormone signaling. Promoter analysis identified abundant light/hormone/stress-responsive elements, with HuSRS5 harboring the most motifs. Transcriptome and qPCR analyses revealed spatiotemporal expression patterns: HuSRS4, HuSRS5, and HuSRS7 exhibited significantly higher expression levels in callus (WG), which may be associated with dedifferentiation capacity. In seedlings, HuSRS9 exhibited extremely high transcriptional accumulation in stem segments, while HuSRS1, HuSRS5, HuSRS7 and HuSRS8 were highly active in cotyledons. This study systematically analyzed the characteristics of the SRS gene family in pitaya, revealing its evolutionary conservation and spatio-temporal expression differences. The research results have laid a foundation for in-depth exploration of the function of the SRS gene in the tissue culture and molecular breeding of pitaya. Full article

Caries is the most prevalent chronic disease affecting oral health in preschool children. In this 12-month prospective cohort study of 3–4-year-olds, we investigated the community-level bacterial–fungal interkingdom interactome and its role in cariogenic microenvironments, using 16S rRNA gene (bacterial) sequencing and ITS2 gene (fungal) sequencing of unstimulated saliva. Longitudinal analysis identified 19 key bacterial and fungal species that were associated with both caries progression and clinical features. Salivary bacteria Desulfovibrio, Bacteroides heparinolyticus, Alloprevotella, Anaerobiospirillum, and fungus Candida tropicalis not only showed altered abundances during caries development but also correlated with severity of caries, establishing diagnostic microbial signatures for caries prediction. The salivary mycobiome exhibited highly active and complex intra-network interactions in the caries-active state, suggesting that fungal networks may drive the broader community-wide microbiota interaction network in the caries state. Metabolic profiling further revealed distinct pathway shifts before and after caries onset. The findings demonstrate that caries progression follows ecological succession governed by cross-domain interactions. This study highlighted the fungal network’s important role in driving dysbiosis, advancing the current understanding of early childhood caries beyond bacterial-centric models, and also highlighted fungi not only as modulators but as active contributors to cariogenesis, which could guide future antimicrobial strategies. Full article

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations. Full article