Search Results (4,001)

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Objective: Visceral adipose tissue is a primary driver of insulin resistance and dysglycemia in type 2 diabetes (T2D), yet its clinical assessment remains challenging. This study aimed to validate neck circumference (NC) as a novel, practical anthropometric biomarker for estimating visceral fat area (VFA) and identifying metabolic risk in a T2D cohort, facilitating its integration into public health and primary care screening strategies. Methods: In a cross-sectional study of 1139 T2D patients, we collected data on NC, biochemical parameters (fasting plasma glucose, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides), and precisely measured VFA and subcutaneous fat area (SFA) via bioelectrical impedance analysis (Omron HDS-2000). We employed Pearson’s correlation and multivariate logistic regression to analyze the relationship between NC and metabolic indicators. Receiver operating characteristic (ROC) curve analysis was used to establish sex-specific NC cut-off values for predicting abnormal VFA. Results: The cohort comprised 687 (60.3%) males and 452 (39.7%) females. NC demonstrated strong positive correlations with VFA (p < 0.001), as did body mass index (BMI), waist–hip ratio (WHR), and SFA. In males, NC was further positively correlated with key metabolic biomarkers including fasting insulin, Insulin Resistance Index, triglycerides, and creatinine. ROC analysis identified NC > 39.5 cm for males and >35.5 cm for females as the optimal cut-off points for detecting abnormal visceral adiposity, highlighting its diagnostic utility. Conclusions: NC serves as a highly accessible and effective biomarker for visceral adiposity and associated metabolic dysfunction in patients with T2D. The established sex-specific cut-off values provide a simple, non-invasive tool for risk stratification in clinical and public health settings, enabling early intervention and improved management of metabolic disease. Full article

Background and Objectives: Chronic kidney disease (CKD) and maintenance hemodialysis (HD) are frequently associated with psychological distress and impaired health-related quality of life (HRQoL). However, the relationships between depressive, anxiety, and stress symptoms, clinical factors, and HRQoL remain insufficiently understood in routine care. This study aimed to assess the prevalence of psychological distress and to explore cross-sectional correlates of kidney disease-specific and generic HRQoL in Romanian patients receiving long-term HD, providing one of the first detailed characterizations of these relationships in an Eastern European maintenance HD cohort. Materials and Methods: This single-center cross-sectional study included 125 adult patients undergoing maintenance HD for at least one year. Baseline assessment comprised socioeconomic, demographic and clinical and paraclinical data, including Charlson Comorbidity Index (CCI), dialysis adequacy (spKt/V), cognitive function, psychological distress assessed with the Depression, Anxiety and Stress Scale (DASS-21R), and HRQoL evaluated using the Kidney Disease Quality of Life Short Form (KDQOL-SF™ 1.3). HRQoL domains and physical and mental component summary scores (PCS, MCS) were analyzed using descriptive statistics, correlation analyses, and multivariable linear regression. Follow-up assessments at approximately one year were summarized descriptively. Results: Disease-specific HRQoL revealed marked impairment in perceived disease burden and work status, while physical HRQoL was substantially reduced (PCS 36.5 ± 9.6). Mental HRQoL was relatively preserved (MCS 48.8 ± 8.8). At baseline, 48.0% of patients reported at least mild depressive symptoms, 34.4% anxiety symptoms, and 44.0% stress symptoms. spKt/V showed a modest association with PCS. Psychological distress demonstrated weak associations with HRQoL; stress was independently associated with lower MCS, with limited explained variance (R² ≤ 0.15). Conclusions: Psychological distress is common among Romanian HD patients and is cross-sectionally associated with markedly impaired physical HRQoL. While the present design does not allow causal inferences, these findings support the implementation of routine psychological screening and the consideration of targeted psychosocial interventions in HD care. Full article

The time-scale mismatch between rapid influent fluctuations and slow biochemical responses hinders the stability of wastewater treatment plants (WWTPs). Existing models often fail to capture shock signals due to noise interference (“signal pollution”). To address this, we propose the HD-MAED-LSTM model, which employs a “decompose-and-conquer” strategy. Targeting the dynamic characteristics of different components, this study innovatively designs heterogeneous attention mechanisms: utilizing Long-term Dependency Attention to capture the global evolution of the trend component, employing Multi-scale Periodic Attention to reinforce the cyclic patterns of the seasonal component, and using Gated Anomaly Attention to keenly capture sudden shocks in the residual component. In a case study, the effectiveness of the proposed model was validated based on one year of operational data from a large-scale industrial WWTP. HD-MAED-LSTM outperformed baseline models such as Transformer and LSTM in the medium-to-long-term (10-h) prediction of COD, TN, and TP, clearly demonstrating the positive role of differentiated modeling. Notably, in the core task of shock load early warning, the model achieved an F1-Score of 0.83 (superior to Transformer’s 0.77 and LSTM’s 0.67), and a Mean Directional Accuracy (MDA) as high as 0.93. Ablation studies confirm that the specialized attention mechanism is the key performance driver, reducing the Mean Absolute Error (MAE) by 56.7%. This framework provides precise support for shifting WWTPs from passive response to proactive control. Full article

Background: Despite widespread proton pump inhibitor (PPI) use in hemodialysis (HD), evidence on cancer risk in high-risk populations remains scarce. We investigated the association between PPI use and incident cancer in a population-based cohort of patients receiving HD. Methods: We used data from the 4th–7th HD quality assessments from South Korea and data linked to claims and death. We classified patients by PPI prescription over 1 year, including No-Prescription (no PPI during the year, n = 37,934); Short (PPI for <60 days, n = 9909); and Long (PPI for ≥60 days, n = 18,108) groups. Any cancer-free survival and overall survival by PPI use were evaluated. Results: The 5-year cancer-free rates for any cancer were 89.6%, 88.5%, and 88.1% in the No-Prescription, Short, and Long groups, respectively. The 5-year patient survival rate was 42.2%, 43.8%, and 40.3% in the No-Prescription, Short, and Long groups, respectively. Patients prescribed PPI had a higher cancer risk than those without a PPI prescription. However, survival among patients with cancer did not differ significantly across the three groups. The Long group had a higher risk of pancreatic and renal cancers than the other two groups. The No-Prescription group had lower risks of thyroid, prostate, and liver cancers than the other groups. Conclusions: In our study, long-term PPI use was associated with higher overall cancer risk, particularly pancreatic and renal cancers, compared with the No-Prescription group. Although PPI prescription did not significantly affect cancer-specific survival, the findings suggest that prolonged PPI use may contribute to cancer development in this population. Full article

Continuous human-mediated introduction of colonies and queens promotes genetic introgression and reshapes the genetic diversity and structure of local honeybee populations. According to reports, multiple non-native honeybee colonies and queens have been introduced into the DL region, leading to continuous genetic introgression. Here, we assessed the effects of continuous introgression on indigenous Apis cerana in the DL region using mtDNA and genome-wide SNP markers. We sequenced the mitochondrial tRNA ^leu-COII from 217 individuals sampled at 7 DL sites and identified 26 haplotypes defined by 18 polymorphic sites. The ΦST values indicated no internal differentiation within the Apis cerana populations in the DL region. Phylogenetic, network, ABBA-BABA test, and f3 statistic suggested introgression from both northern and southern sources. The f4-ratio indicates that approximately 16% of the ancestry in the DL group is derived from the Aba group. Genetic diversity varied widely within the DL region (Hd: 0.2907–0.8220; π: 0.0009–0.0038; K: 0.3140–1.3980), indicating different stages of introgression. The genetic structure within the DL group appears to be unstable, necessitating long-term monitoring of evolutionary processes and genetic diversity dynamics in A. c. cerana for further insights. Full article

Biosurfactants produced by halophilic bacteria are gaining attention as eco-friendly and biocompatible alternatives to synthetic surfactants due to their high surface activity, stability under extreme conditions, and intrinsic antimicrobial properties. These amphiphilic biomolecules hold great promise for bioremediation, biomedical, and pharmaceutical applications. In this study, moderately halophilic bacteria capable of biosurfactant production were isolated from saline mud collected at the Burgas solar salterns (Bulgaria). The halophilic microbiota was enriched in Bushnell–Haas (BH) medium containing 10% NaCl amended with different carbon sources. Primary screening in BH liquid medium evaluated the isolates’ ability to degrade n-hexadecane while at the same time producing biosurfactants. Thirty halophilic bacterial strains were isolated on BH agar plates supplemented with 2% n-hexadecane, 2% olive oil, or 2% glycerol. Four isolates—BS7OL, BS8OL, BS9GL, and BS10HD—with strong emulsifying activity (E₂₄ = 56%) and reduced surface tension in the range of 27.3–45 mN/m were derived after 7 days of batch fermentation. Strain BS10HD was chosen as the most potent biosurfactant producer. Its phylogenetic affiliation was determined by 16S rRNA gene sequence analysis; according to the nucleotide sequence, it was assigned to Halomonas ventosae. The extract material was analysed by thin-layer chromatography (TLC) and Fourier transform infrared spectroscopy (FTIR). Upon spraying the TLC plate with ninhydrin reagent, the appearance of a pink spot indicated the presence of amine functional groups. FTIR analysis showed characteristic peaks for both lipid and peptide functional groups. Based on the observed physicochemical properties and analytical data, it can be suggested that the biosurfactant produced by Halomonas ventosae BS10HD is a lipopeptide compound. Full article

The highly squinted mode, as an operational configuration of synthetic aperture radar (SAR), fulfills specific remote sensing demands. Under equivalent conditions, it necessitates a higher pulse repetition frequency (PRF) than the side-looking mode but produces inferior imaging quality, thereby constraining its widespread application. By applying the sparse SAR imaging method to highly squinted SAR systems, imaging quality can be enhanced while simultaneously reducing PRF requirements and expanding swath. Hyperparameters in sparse SAR imaging critically influence reconstruction quality and computational efficiency, making hyperparameter optimization (HPO) a persistent research focus. Inspired by HPO techniques in the deep unfolding network (DUN), we modified the iterative soft-thresholding algorithm (ISTA) employed in fast sparse SAR reconstruction based on approximate observation operators. Our adaptation enables adaptive regularization parameter tuning during iterations while accelerating convergence. To improve the robustness of this enhanced algorithm under realistic SAR echoes with noise, we integrated hypergradient descent (HD) to automatically adjust the ISTA step size after regularization parameter convergence, thereby mitigating overfitting. The proposed method, named Hyper-ISTA-GHD, adaptively selects regularization parameters and step sizes. It achieves high-precision, rapid imaging for highly squinted SAR. Owing to its training-free iterative minimization framework, this approach exhibits superior generalization capabilities compared to existing DUN methods and demonstrates broad applicability across diverse SAR imaging modes and scene characteristics. Simulations show that the hyperparameter selection and reconstruction results of the proposed method are almost consistent with the optimal values of traditional methods under different signal-to-noise ratios and sampling rates, but the time consumption is only one-tenth of that of traditional methods. Comparative experiments on the generalization performance with DUN show that the generalization performance of the proposed method is significantly better than DUN in extremely sparse scenarios. Full article

Annual influenza vaccination remains critical for mitigating severe illness and reducing healthcare strain, particularly among high-risk populations. Despite advancements in vaccine platforms, the comparative efficacy of novel vaccines—such as high-dose (HD-IIV), recombinant (rIV), cell-based (cIV), and adjuvanted (aIV) influenza vaccines—versus standard-dose non-adjuvanted (SD-IIV) vaccines remains a public health concern. Traditional Relative Vaccine Efficacy (rVE) metrics, though robust, may overestimate population-level benefits. This short communication explores alternative comparative efficacy measures: risk difference (ΔRD) and number needed to vaccinate (ΔNNV). Analysis of data derived from randomized controlled trials (RCTs), or robust pragmatic trials, shows that while rVE values for newer vaccines often indicate superior efficacy, ΔRD and ΔNNV highlight the limits in incremental protection at the population level, with ΔRD generally below 10 cases per 1000 vaccinated. These findings underline the sustained relevance of SD-IIV in immunization programs and emphasize the need for broader vaccine coverage to highlight the benefits of vaccination and enhance population health outcomes. Full article

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition protocols, and patient populations differ from those in the training data. This study investigates how such center-related domain shift affects automated breast DCE-MRI tumor segmentation on the multi-center MAMA-MIA dataset. Methods: We trained a standard 3D U-Net for primary tumor segmentation under two evaluation settings. First, we constructed a random patient-wise split that mixes cases from the three main MAMA-MIA center groups (ISPY2, DUKE, NACT) and used this as an in-distribution reference. Second, we designed a balanced leave-one-center-out cross-validation (LoCoCV) protocol in which each center is held out in turn, while training, validation, and test sets are matched in size across folds. Performance was assessed using the Dice similarity coefficient, 95th percentile Hausdorff distance (HD95), sensitivity, specificity, and related overlap measures. Results: On the mixed-center random split, the best three-channel model achieved a mean Dice of about 0.68 and a mean HD95 of about 19.7 mm on the held-out test set, indicating good volumetric overlap and boundary accuracy when training and test distributions match. Under balanced LoCoCV, the one-channel model reached a mean Dice of about 0.45 and a mean HD95 of about 41 mm on unseen centers, with similar averages for the three-channel variant. Compared with the random split baseline, Dice and sensitivity decreased, while HD95 nearly doubled, showing that boundary errors become larger and segmentations less reliable when the model is applied to new centers. Conclusions: A model that performs well on mixed-center random splits can still suffer a substantial loss of accuracy on completely unseen institutions. The balanced LoCoCV design makes this out-of-distribution penalty visible by separating center-related effects from sample size effects. These findings highlight the need for robust multi-center training strategies and explicit cross-center validation before deploying breast DCE-MRI segmentation models in clinical practice. Full article

Background: Neoprene knee sleeves are commonly used to enhance joint stability and mechanical performance during resistance training. However, the specific influence of sleeve density on the force–velocity–power (F–V–P) profile during multi-joint lower-body exercises such as the back squat remains unclear. This study aimed to compare the theoretical F–V–P parameters derived from back squat performance while wearing low-density (LD) versus high-density (HD) knee sleeves. Methods: Fifteen resistance-trained males completed an incremental back squat test under both LD and HD conditions. A linear position transducer recorded barbell displacement and velocity. Individual force–velocity relationships were modelled to determine maximal theoretical force (F₀), velocity (V₀), power (Pmax), and the F–V slope. Paired-sample t-tests, linear mixed models, and Cohen’s d effect sizes were calculated. Clinical relevance was assessed using a threshold defined as 0.2 × the standard deviation of the HD condition. Bayesian analyses were conducted to estimate the probability and magnitude of the observed effects. Results: No statistically significant differences were observed between sleeve conditions for F₀, V₀, Pmax, or F–V slope (p > 0.05, d ≤ 0.37). Nonetheless, HD sleeves yielded slightly higher mean values for F₀, V₀, and Pmax, exceeding the predefined threshold for practical relevance. Bayesian models showed moderate probabilities (~0.80) that HD sleeves outperformed LD, though with limited chances of crossing the clinical significance threshold. Conclusions: Although HD sleeves do not produce systematic changes in F–V–P parameters, their increased material stiffness may provide small yet practically meaningful mechanical advantages in high-force resistance training contexts. Full article

Although the impact of COVID-19, caused by SARS-CoV-2, may appear to have diminished in recent years, the emergence of new variants still continues to cause significant global health and economic challenges. While numerous metabolomic studies have explored serum-based alterations linked to the infection, investigations utilizing urine as a biological matrix remain notably limited. This gap is especially significant given the potential advantages of urine, a non-invasive and easily obtainable biofluid, in clinical settings. In the context of patients in intensive care units (ICUs), temporal monitoring through such non-invasive samples may offer a practical and effective approach for tracking disease progression and tailoring therapeutic interventions. This study retrospectively explored the longitudinal metabolomic alterations in COVID-19 patients admitted to the ICU, stratified into three prognostic outcome groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. A total of 32 urine samples, collected at four distinct time points per patient during April 2020 and preserved at −80 °C, were analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy for comprehensive metabolic profiling. Statistical evaluation using two-way ANOVA and ANOVA–Simultaneous Component Analysis (ASCA) identified significant prognostic variations (p < 0.05) in the levels of taurine, 3-hydroxyvaleric acid and formic acid. Complementary supervised classification via random forest modeling yielded moderate predictive performance with out-of-bag error rate of 40.6% based on prognostic categories. Particularly, taurine, 3-hydroxyvaleric acid and formic acid levels were highest in the PS group. However, no significant temporal changes were observed for any metabolite in analyses. Additionally, metabolic pathway analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the “taurine and hypotaurine metabolism” pathway as the most significantly affected (p < 0.05) across prognostic classifications. Harnessing urinary metabolomics, as indicated in our preliminary study, could offer valuable insights into the dynamic metabolic responses of ICU patients, thereby facilitating more personalized and responsive critical care strategies in COVID-19 patients. Full article

Background: Short tandem repeats (STRs) are highly variable sequences present along the human genome, including the Y-chromosome. Y-STRs are exclusive to males, and the haplotypes they define are informative. Objectives: Twenty-six Y-STR loci were genotyped in 252 males from Northern Portugal to characterise Y-chromosome genetic variation using the Investigator Argus Y28 QS Kit. Methods: The kit mentioned was used to amplify male DNA samples, and capillary electrophoresis was used to analyze the fragments. Forensic parameters and haplotype diversity were computed, and samples’ haplogroups were predicted. A multidimensional scaling (MDS) plot was used to graphically represent the R_ST genetic distances, including reference populations. Results: A total of 250 different haplotypes were observed, including 248 unique ones, yielding a very high haplotype diversity (HD = 0.999) and discriminatory power (DP = 0.992). Haplogroup analysis indicated a predominance of R1b (58.7%), followed by E1b1b, I and J, pointing to a population history shaped by Mediterranean and North African gene flow. Comparative analysis between Portugal and 5 other populations showed greater genetic affinity with Spain and Italy, while revealing marked differentiation from Greece, Morocco, and former Portuguese colonies. Conclusions: The results confirm that the Northern Portuguese Population exhibits high Y-STR variability and robust forensic resolution. The dataset was submitted to the YHRD database, enhancing the representation of the Portuguese population and underscoring the value of the 26 locus panel for applications in forensic science, genealogy, and population genetics. Full article

Brain-derived growth factor, BDNF, has critical roles in a wide variety of neuronal aspects, including cell survival, differentiation, and synaptic function after their maturation. TrkB, a high-affinity receptor for BDNF, is a major contributor in these neuronal aspects, and its functions are exerted via stimulating intracellular signaling pathways including the mitogen-activated protein kinase (MAPK) pathways. As a family of MAPKs, the functions of ERK1/2, p38MAPK, and JNKs have been extensively studied using in vivo and in vitro neuronal systems. ERK 1/2, a major serine-threonine kinase and belonging to the MAPK family, also works as a downstream molecule after activation of the BDNF/TrkB system. Interestingly, growing evidence has demonstrated that ERK1/2 signaling exerts a positive or negative influence on neurons in both healthy and pathological conditions in the central nervous system (CNS). Indeed, activation of ERK 1/2 stimulated by the BDNF/TrkB system is involved in the regulation of synaptic plasticity. On the other hand, overactivation of ERK1/2 signaling under pathological conditions is closely related to neurodegeneration. Furthermore, cell stress activates p38MAPKs and JNK signaling, contributing to the progression of neurodegeneration. In this review, we show how MAPK pathway signaling affects neuronal fate, including cell survival or cell death, in the CNS. Moreover, we discuss the involvement of overactivation of MAPK signaling in the neurodegeneration observed in Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). Full article

End-stage chronic kidney disease markedly increases susceptibility to infections due to compromised immune function and other physiological alterations. Bacteremia is responsible for higher mortality rates in hemodialysis patients compared to the general population. Our study aimed to investigate the incidence and clinical outcomes among patients with end-stage CKD and associated infections. The study retrospectively analyzed admitted patients between 1 January 2023 and 31 May 2025. Among 56 hospitalized patients with CKD and infection (30 hemodialysis [HD], 26 non-HD), baseline comorbidity profiles were broadly comparable. Microbiology was frequently positive (46/56, 82.1%), dominated by Staphylococcus aureus (25/98, 25.5%), Klebsiella pneumoniae (19.98, 19.4%), and Escherichia coli (15/98, 15.3%). Crude in-hospital mortality was higher in HD (46.7% vs. 15.4%; p = 0.012; RR 3.03). In multivariable logistic regression, HD remained independently associated with death (adjusted OR 38.22, 95% CI 1.55–940.53; p = 0.026), alongside hypotension (OR 17.55, 1.46–210.92; p = 0.024) and male sex (OR 4.41, 1.29–15.11; p = 0.018); model performance was strong (AUC 0.867). In this single-center cohort of infected patients with end-stage CKD, maintenance hemodialysis was independently associated with higher in-hospital mortality, even after adjustment for age, sex, comorbidity burden, hypotension, and length of stay; hypotension and male sex were additional risk factors. LOS and most presenting features did not differ meaningfully by dialysis status. Our findings also emphasize the urgent necessity for heightened surveillance of local antimicrobial resistance patterns and underscore the profound vulnerability of hemodialysis patients to severe infectious outcomes, which is exacerbated by immunosuppressive conditions and the limited efficacy of available therapeutic options against resistant pathogens. Full article