Search Results (52,169)

Restriction–modification (RM) systems contribute to genome plasticity in Mycoplasma hominis, a facultative pathogen with an extremely small but highly heterogeneous genome. The MhoVII RM system, which contains a fusion of two methyltransferases (MTases), M1 and M2, was recently identified within a family of Type II RM systems, but its specificity and biological function remained unknown. Phylogenetic analysis revealed that M1 and M2 belong to distinct MTase classes clustering within the YhdJ and MTaseD12 branches, respectively. In this study, the dissemination, expression and function of the MhoVII system was analyzed in detail using Oxford Nanopore-based methylation analysis, recombinant expression of the individual RM components in Escherichia coli, and methylation-sensitive restriction assays. It was thus possible to demonstrate that M1 and M2 methylate the complementary non-palindromic motifs GATG and CATC, and that the associated restriction endonuclease cleaves only DNA lacking 6mA methylation at these sites. The transcriptional analysis of mid-to-late logarithmic cultures indicated a polycistronic organization of the MhoVII genes, and GATG/CATC-driven methylation analysis revealed culture-dependent methylation differences, suggesting a post-transcriptional regulation, whereas in the infection of HeLa cells, MhoVII transcription was highest at the beginning and was then gradually downregulated in the later stages of infection. These findings establish MhoVII as a previously uncharacterized Type II RM system. Full article

LRRC8 channels are volume-regulated anion channels (VRACs) activated by cellular swelling, which mediate regulatory volume decrease in many cell types. Recently, it has been shown that these channels contribute to the release of glutamate from astrocytes. Since enhanced extracellular glutamate concentrations produce hyperexcitability, and microdialysis revealed elevated levels of the transmitter in the brains of epileptic patients, we asked whether astroglial glutamate release through LRRC8/VRACs might contribute to the initiation of experimental temporal lobe epilepsy (TLE). Patch clamp, pharmacological, and single-cell transcript analyses were performed in the hippocampus of controls and mice with inducible deletion of LRRC8a in astrocytes. In addition, these mice were exposed to our unilateral intracortical kainate model of TLE. Tonic currents were recorded from CA1 pyramidal neurons as a measure of glutamate release. Our data show that neither expression of LRRC8a nor the amplitude of tonic currents was altered 4 h after status epilepticus-induced TLE. These findings do not suggest that increased astroglial glutamate release through LRRC8 channels contributes to the initiation of experimental TLE. Full article

This study merged archival data from three separate experiments to investigate the typology of individuals who benefit most and least from gratitude letter writing interventions (N = 487). First, k-means clustering of pre- to post-intervention changes in affect revealed three distinct groups: Buffered, Mixed Feelings, and Backfired. The Buffered cluster comprised individuals who, on average, experienced decreases in negative affect (e.g., less frustration) but no changes in positive emotions (e.g., joyful). The Mixed Feelings cluster experienced increases in positive affect, alongside self-conscious emotions, particularly indebtedness, which became more closely aligned with uplifting emotional states following the intervention. The Backfired cluster experienced decreases in positive feelings and increases in negative affect. Next, differences in individual characteristics across clusters indicated that those in the Buffered cluster were relatively more neurotic, had higher baseline negative feelings, and lower trait gratitude. Individuals in the Mixed Feelings cluster tended to be more dispositionally grateful and seemed to invest more effort into the activity. Finally, individuals in the Backfired cluster were also relatively more grateful and had higher baseline positive affect. These findings contribute to understanding individual differences in the effectiveness of gratitude letter interventions and highlight opportunities to tailor such activities to promote personal growth. Full article

Deep learning–based pansharpening methods for remote sensing have advanced rapidly in recent years. However, current methods still face three limitations that directly affect reconstruction quality. Content adaptivity is often implemented as an isolated step, which prevents effective interaction across scales and feature domains. Dynamic multi-scale mechanisms also remain constrained, since their scale selection is usually guided by global statistics and ignores regional heterogeneity. Moreover, frequency and spatial cues are commonly fused in a static manner, leading to an imbalance between global structural enhancement and local texture preservation. To address these issues, we design three complementary modules. We utilize the Adaptive Convolution Unit (ACU) to generate content-aware kernels through local feature clustering, thereby achieving fine-grained adaptation to diverse ground structures. We also develop the Multi-Scale Receptive Field Selection Unit (MSRFU), a module providing flexible scale modeling by selecting informative branches at varying receptive fields. Meanwhile, we incorporate the Frequency–Spatial Attention Unit (FSAU), designed to dynamically fuse spatial representations with frequency information. This effectively strengthens detail reconstruction while minimizing spectral distortion. Specifically, we propose the Hybrid Attention Fusion Network (HAFNet), which employs the Hybrid Attention-Driven Residual Block (HARB) as the fundamental utility to dynamically integrate the above three specialized components. This design enables dynamic content adaptivity, multi-scale responsiveness, and cross-domain feature fusion within a unified framework. Experiments on public benchmarks confirm the effectiveness of each component and demonstrate HAFNet’s state-of-the-art performance. Full article

Toll-like receptors (TLRs) are essential pattern recognition receptors of the innate immune system and play critical roles in pathogen invasion in teleosts. In this study, we identified and characterized full-length open reading frames of three TLRs belonging to the TLR11 subfamily from Schizothorax prenanti, termed spTLR19 (2868 bp), spTLR20 (2835 bp), and spTLR21 (2946 bp), encoding 955, 944, and 981 amino acids, respectively. All three proteins exhibited the conserved domain architecture typical of TLRs, comprising a leucine-rich repeat (LRR) domain, a transmembrane region, and a Toll/IL-1 receptor (TIR) domain. Phylogenetic and homology analyses revealed that spTLR19 and spTLR20 clustered most closely with their homologues from Cyprinus carpio, while spTLR21 showed the highest similarity to Onychostoma macrolepis TLR21. Expression profiling showed that these TLRs were ubiquitously expressed across examined tissues, with relatively higher expression in immune-related tissues such as spleen and gills. Furthermore, challenge with Streptococcus agalactiae and Aeromonas veronii significantly up-regulated the expression of spTLR19, spTLR20, and spTLR21 in spleen, liver, and gills, suggesting their involvement in antibacterial immune responses. These findings enhance the functional understanding of the teleost TLR11 subfamily and provide a foundation for elucidating disease resistance and immune regulation in S. prenanti. Full article

The content and career cluster of agriculture, food, and natural resources (AFNR) provides opportunities for K-12 teachers to engage students to solve complex authentic problems that blend science, technology, engineering, and mathematics (STEM), yet limited research has been conducted on how to effectively leverage teaching and learning to integrate STEM using the context of AFNR through the school-based agricultural education program. This conceptual paper was developed through a collaborative sensemaking process focused on systems thinking as a way of knowing to integrate STEM within the contexts of AFNR, utilizing the SBAE program in the United States. A comprehensive career and technical education (CTE) program model of SBAE develops secondary education students’ career readiness skills through classroom and laboratory instruction, leadership development, and supervised agricultural experiences. The literature was reviewed to describe the current status of integrated STEM in SBAE, including learning by doing, solving real-world problems, application of content knowledge in out-of-school and community-based settings, learner-centered pedagogies, and development of career readiness skills for the workforce. By employing systems thinking as the theoretical framework and integrated STEM as a conceptual framework, the authors engaged in collaborative sensemaking of their professional and scholarly experiences and proposed findings and discussion of a three-model framework (i.e., teacher, program, and learning approach) to support integrated STEM education through AFNR and SBAE. Limitations of the framework are also discussed. The AFNR career cluster was used as the context to discuss how the three-model framework (i.e., teacher, program, and learning approach) of integrated STEM through AFNR could be operationalized for SBAE. Discussion and implications of the three-model framework for other career clusters in career and technical education (CTE) and non-formal education in community settings are presented. Conclusions and recommendations are provided for advancing STEM integration in SBAE for teacher development, program development, and research. Full article

Opuntia ficus-indica L. is a prominent crop in Mexico, requiring advanced non-destructive technologies for the real-time monitoring and quality control of fresh commercial cladodes. The primary research objective of this study was to develop and validate high-precision mathematical models that correlate hyperspectral signatures (400–1000 nm) with the specific nutritional, morphological, and antioxidant attributes of fresh cladodes (cultivar Villanueva) at their peak commercial maturity. By combining hyperspectral imaging (HSI) with machine learning algorithms, including K-Means clustering for image preprocessing and Partial Least Squares Regression (PLSR) for predictive modeling, this study successfully predicted the concentrations of 10 minerals (N, P, K, Ca, Mg, Fe, B, Mn, Zn, and Cu), chlorophylls (a, b, and Total), and antioxidant capacities (ABTS, FRAP, and DPPH). The innovative nature of this work lies in the simultaneous non-destructive quantification of 17 distinct variables from a single scan, achieving coefficients of determination (R²) as high as 0.988 for Phosphorus and Chlorophyll b. The practical applicability of this research provides a viable replacement for time-consuming and destructive laboratory acid digestion, enabling producers to implement automated, high-throughput sorting lines for quality assurance. Furthermore, this study establishes a framework for interdisciplinary collaborations between agricultural engineers, data scientists for algorithm optimization, and food scientists to enhance the functional value chain of Opuntia products. Full article

K-Means is a well-known algorithm for unsupervised clustering, very often used due to its simplicity and efficiency. Its long-time widespread use has stimulated researchers to investigate its properties further. A critical property concerns K-Means’s strong dependence on the seeding method adopted to initialize centroids. Poor initialization causes K-Means to get stuck in a local sub-optimal solution. This paper proposes DPCCs—Density Peaks of Candidate Centroids—a novel seeding method for K-Means. DPCC rests on genetic concepts and density peaks to define an initialization solution close to the optimal one. First, a population of J elitist candidate solutions, that is, solutions capable of yielding a reduced clustering cost, is built. Although none of these particular solutions can be near the optimal one, candidate centroids, as experimentally confirmed, tend to thicken around ground truth centroids. Therefore, subsequent generations of the population are created by repeating the k-nearest neighbors (kNNs) procedure for different values of the k parameter, and estimating density through the reverse nearest neighbors (RNNs) relationship of each centroid. Centroid density peaks are then exploited to rearrange the population solutions toward extracting a candidate solution, which is finally optimized by K-Means. The paper describes the design and operation of DPCC, which is currently implemented in parallel Java. The clustering effectiveness of DPCC is demonstrated by applications to both benchmark and real-world datasets. Results are compared with those of other competing algorithms. Full article

This paper presents a computational framework for constructing and analysing a focal legislative citation network. A depth-limited expansion strategy generates subgraphs of the network that capture the local structural environment of a seed Act while avoiding the global hub dominance present in whole-corpus analyses. Centrality measures and community detection show how the seed Act’s perceived influence changes with network radius. To incorporate semantic information, we develop and apply an Large Language Model (LLM)-assisted topic modelling method in which representative keywords and LLM-generated summaries form a compact text representation that is converted into a Term Frequency-Inverse Document Frequency (TF–IDF) document–term matrix. Although demonstrated on New Zealand’s mental health legislation, the framework generalises to any legislative corpus or jurisdiction. Integrating graph-theoretic structure with LLM-assisted semantic modelling provides a scalable approach for analysing legislative systems, identifying domain-specific clusters, and supporting computational studies of legal evolution and policy impact. Full article

The grapevine (Vitis vinifera L.) is one of the most important horticultural crops, with thousands of varieties cultivated worldwide. In this study, we analyzed chloroplast SNV markers using a whole-genome shotgun sequencing approach to investigate the genetic diversity and phylogeny of 409 cultivated V. vinifera accessions originating from nine countries across Southeast and Central Europe, as well as a heterogeneous set of additional accessions maintained by INRAE. Shotgun sequencing allowed high coverage, enabling the detection of 93 SNVs across 24 chloroplast genes, including 11 non-synonymous variants. The ycf1 gene showed the highest variability, consistent with its role in species differentiation. Haplotype analysis revealed 102 distinct haplotypes, with clear geographic structuring: ATT predominated in the eastern Mediterranean, ATA in western Europe, and GTA mainly in a heterogeneous group of varieties from a French collection. To validate the shotgun approach, seven SNV markers were analyzed using target capture sequencing, confirming the accuracy of detected variants with only minimal discrepancies, which is mostly attributable to homopolymeric regions and low-frequency alleles. Phylogenetic analyses using both trees and networks delineated three major haplotype clusters, reflecting human-mediated dispersal of grapevine cultivars through historical viticultural practices. This study represents the largest chloroplast genome analysis of cultivated V. vinifera to date, providing a large cpDNA resource for assessing chloroplast diversity and maternal haplotype structure in cultivated grapevine. The results highlight the power of combining high-throughput sequencing and chloroplast genomics for population-level studies in perennial crops. Full article

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care. Full article

In recent years, the mortality due to diabetes has increased around the world. In particular, diabetes is the second leading cause of mortality in Mexico, with a heterogeneous distribution of mortality rates at the municipality level. The objective of this study is the analysis of clusters of municipalities with similar values for sociodemographic indices and diabetes mortality. In this sense, an application is presented that was developed using a data science methodology and a machine learning algorithm called fuzzy c-means. For this research, 4,604,360 death certificates from 2019 to 2023 were assessed, among other official data. As a result of the analysis, two key indicators related to diabetes mortality were found, i.e., one is the percentage of population in poverty and the other is population density. The main results of this research are as follows: a direct correlation was found between population density and mortality, and an inverse correlation was found between population in poverty and mortality. In the study interval, it was observed that the cluster with less mortality showed an increase in mortality rate year after year. Finally, we consider that the tendencies found can be useful to public health authorities for optimizing the distribution of resources for treating diabetes and reducing diabetes-related mortality. Full article

Image-Based Visual Servoing (IBVS) systems often suffer from instability due to measurement noise, modeling errors, and external disturbances. To address these issues, this study proposes a Visual Predictive Control framework integrating Radial Basis Function (RBF) and Extended Kalman Filter (EKF) coupled state-disturbance estimation and task-oriented K-means clustering. First, a feedback linearization Model Predictive Control (MPC) law is designed to handle system nonlinearities and physical constraints. Second, a coupled estimation mechanism is established where the EKF suppresses noise while the RBF network learns lumped disturbances. Crucially, to optimize network efficiency, a task-oriented K-means clustering method is introduced to select RBF centers based on the nominal IBVS path. Lyapunov analysis confirms the Uniformly Ultimately Bounded (UUB) stability. Simulation results demonstrate that the proposed method significantly reduces estimation errors and improves tracking accuracy compared to traditional schemes. Ultimately, this approach enhances the robustness and engineering practicality of robotic visual servoing through the deep coordination of control and estimation. Full article

Xinjiang is an arid and semi-arid region where ecosystems are fragile, and monitoring how its ecology changes over time is critical for its sustainable development. In this study, a Remote Sensing Ecological Index (RSEI) was established for Xinjiang from 2000 to 2025. To understand temporal and spatial changes in ecological quality, we conducted spatial autocorrelation analysis, Theil–Sen median trend analysis, a Mann–Kendall trend test, and Hurst exponent analysis. We also used Geodetector to determine which factors affect the RSEI. The main results were as follows: (1) The RSEI in Xinjiang remained low, with a mean value between 0.285 and 0.336. Mountainous areas had higher values, basins had lower values, and spatial clustering was strong (Moran’s I index: 0.81–0.86). (2) H-H clusters expanded and then shrank, while L-L clusters grew after 2015. Areas with excellent ecological grades increased, but so did areas with poor grades, indicating that improvement and degradation both exist. (3) Most areas were stable, but 19.13% showed persistent degradation, indicating that these areas need more attention. (4) Land surface temperature (q = 0.624) and land cover (q = 0.576) were the main driving factors, and factor interactions showed enhanced effects. The results of this study could provide a scientific basis for ecosystem protection and restoration in Xinjiang. Full article

In modern intelligent manufacturing, spindle thermal errors are critical to machining accuracy. To address this, we propose a two-stage prediction framework. First, for feature selection, an enhanced Red-Billed Magpie Optimization algorithm (RBMO-X) optimizes the parameters of a hybrid convolutional neural network (DLTK). Concurrently, PSO-optimized HDBSCAN clustering combined with Pearson correlation selects optimal temperature-sensitive points. The DLTK network integrates LSTM, deformable convolution, Transformer, and Fourier KAN modules for robust spatiotemporal feature extraction. The experimental results demonstrate significant improvements. The proposed feature selection method improves the Silhouette index by 32.39% and increases BWP by 49.16%. Using the selected points reduces prediction RMSE by 31.89% compared to random selection. The final RBMO-X-DLTK model achieves an RMSE of 0.181 μm, an MAE of 0.128 μm, and an R² score of 0.9978, outperforming seven benchmark models (e.g., BP, LSTM, CNN-LSTM). In practical validation, the model enabled an average thermal error reduction of 89%. This integrated approach provides a robust and accurate solution for spindle thermal error prediction, demonstrating strong generalization capability. Full article