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Stability analysis of dam grouting reinforcement based on FLAC3D

Ill reservoirs have significant safety hazards, it is urgent to grout and strengthen the dangerous reservoir dams. In order to effectively evaluate the stability of the dam after grouting reinforcement, this paper carried out numerical simulation analysis work. Firstly, the action mechanism of penetration grouting, compression grouting and splitting grouting is described, and it is pointed out that these modes of action are intermixed and interconverted, and they may exist simultaneously in the same grouting process. Next, based on the FLAC3D finite difference program, the analysis and calculation of the stability of seepage control reinforcement of a dangerous reservoir in Guangxi Province were carried out. The calculation results show that, a complete grout curtain zone is formed inside the dam body after grouting reinforcement treatment, the dam stress is adjusted twice, the area of plastic zone decreased 23%, and the stability of the dam body is significantly enhanced. This article has certain guiding significance for the analysis and evaluation of safety and stability of similar projects.

Fcer1g and St3gal1: Macrophage-associated angiogenesis biomarkers and therapeutic targets in sepsis-induced acute lung injury

Background Acute lung injury (ALI) involves the release of growth factors and inflammatory mediators from damaged pulmonary tissues, fostering endothelial cell proliferation, migration, and vascular lumen formation, thereby driving pathological angiogenesis. Macrophages contribute to angiogenesis and vascular homeostasis, but their dysregulation in pathological states worsens vascular dysfunction. This study aims to identify macrophage-associated angiogenesis-related genes as novel diagnostic biomarkers and therapeutic targets for sepsis-associated ALI (SALI). Methods Transcriptomic datasets from the GEO database were analyzed using differential expression profiling and weighted gene co-expression network analysis (WGCNA) to identify candidate genes. These candidates were compared with macrophage- and angiogenesis-related gene sets from GENECARDS for functional prioritization. Three machine learning algorithms (LASSO regression, random forest, and SVM) were employed to refine predictive biomarkers, followed by immune infiltration analysis (via CIBERSORT) to assess correlations with immune subsets. Single-cell RNA sequencing and RT-PCR were used for spatial validation of gene expression. Results Two macrophage-associated angiogenesis-related genes, Fcer1g (FCER1G) and St3gal1 (ST3GAL1), were identified as key biomarkers. Both genes showed significant upregulation in the training cohort (p < 0.001) and independent validation sets (p < 0.05), with robust diagnostic accuracy (AUC > 0.85). Immune correlation analysis indicated strong positive associations with macrophage infiltration (p < 0.01), particularly M2-polarized subsets. scRNA-seq confirmed their predominant expression in macrophage clusters, with increased activity in SALI tissues (log2FC > 2.0, p < 0.001). Conclusions In mouse in vivo studies, Fcer1g and St3gal1 were shown to precisely mediate intricate macrophage-endothelial cell interactions via glycoimmune signaling pathways at the molecular level. This interaction finely modulates endothelial cell activation and drives angiogenic remodeling, critically impacting SALI progression. Given the physiological and pathological parallels between mice and humans, our findings offer a theoretical underpinning for subsequent human – oriented research. Moving forward, efforts should focus on verifying the expression patterns, action mechanisms, and diagnostic/therapeutic potential of these genes in relation to human SALI – associated signatures.

Evaluating cognitive depth of AI-generated multiple-choice questions with Bloom’s Taxonomy

Introduction While LLMs are used to generate medical and dental MCQs, their alignment with Bloom’s Taxonomy remains unexplored. Materials and Methods Five widely used LLMs, including ChatGPT-4o (OpenAI), Copilot Pro (Microsoft), Claude Sonnet 4 (Anthropic), Grok 3 (xAI), and DeepSeek R1 (DeepSeek) were evaluated. Each model generated 60 MCQs (total 300) based on content from an oral and maxillofacial anatomy textbook across the five cognitive levels of Bloom’s Taxonomy. Two independent investigators assessed each item using a 5-point Likert scale for remembering, understanding, applying, analyzing, and evaluating/creating. Inter-rater reliability was measured using weighted Cohen’s kappa. Model performance and inter-model differences were analyzed using the Kruskal–Wallis test. Results Inter-rater reliability was moderate to strong (kappa = 0.74–0.86). Median scores for remembering, understanding, applying, and evaluating/creating were above 4 across all LLMs, while the analyzing level scored a median of 3.5 for ChatGPT-4o and DeepSeek R1. No significant difference was found between models in remembering and understanding levels (p > 0.05). Claude Sonnet 4 outperformed the other models at the applying, analyzing, and evaluating/creating levels (p = 0.01, 0.003, and 0.005, respectively). Within-model analysis showed that only Copilot Pro and Claude Sonnet 4 consistently aligned with Bloom’s cognitive levels across all categories. In contrast, ChatGPT-4o, DeepSeek R1, and Grok 3 performed significantly better at the lower cognitive levels (p = 0.00, 0.00, and 0.001, respectively). Conclusions All LLMs performed well at lower cognitive levels, while Claude Sonnet 4 achieved the highest alignment at higher-order levels.

Association between hemoglobin/red blood cell distribution width ratio and acute kidney injury in sepsis and heart failure patients

Introduction The hemoglobin/red blood cell distribution width (Hb/RDW) ratio has emerged as a potential biomarker for acute kidney injury (AKI), particularly in patients with cardiovascular conditions. This study investigated the relationship between Hb/RDW ratio and AKI incidence in critically ill patients diagnosed with sepsis and heart failure (HF). Methods A retrospective study was conducted with 119 critically ill patients with sepsis and 83 patients with HF, analyzed according to the presence or absence of kidney injury. Multivariable logistic regression identified independent predictors of AKI. Outcomes between higher and lower Hb/RDW groups were compared. Results Patients who developed AKI showed higher C-reactive protein levels, elevated RDW (15.7 ± 2.2 vs. 14.9 ± 1.8; p = 0.01), and higher SAPS 3 scores, along with markedly lower Hb concentrations and Hb/RDW ratios (75.1 ± 1.6 vs. 85.5 ± 1.9; p < 0.001). In multivariable analysis, serum urea (OR 1.016; 95% CI 1.005–1.027 per mg/dL), SAPS 3, and Hb/RDW ratio (OR 0.977; 95% CI 0.959–0.996) were independently associated with AKI. Patients with a lower Hb/RDW ratio had higher frequencies of AKI, kidney-replacement therapy, red-cell transfusion, and mortality. During a 7-year follow-up, progression to dialysis-dependent stage V chronic kidney disease (CKD-V) occurred in 10.8% of HF patients and 2.5% of sepsis patients, indicating that a lower Hb/RDW ratio was also associated with worse long-term renal outcomes. Conclusion The Hb/RDW ratio is independently associated with AKI and may also reflect long-term kidney prognosis, representing a cost-effective and readily available ICU marker to identify patients at risk for both acute and chronic renal deterioration in sepsis or HF.

Disposable non-enzymatic impedimetric biosensor using Mn-doped ZnS-chitosan nanocomposite for tetracycline detection

Monitoring antibiotic residues in aquaculture water is critical for food safety, environmental protection, and antimicrobial stewardship. Here, we present a proof-of-concept disposable, non-enzymatic impedimetric biosensor for the rapid and selective detection of tetracycline. The sensor employs interdigitated electrodes functionalized with a manganese-doped zinc sulfide-chitosan nanocomposite, providing a stable, conductive, and environmentally friendly sensing interface. The successful synthesis of the nanocomposite was confirmed using scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy. Using electrochemical impedance spectroscopy, the device exhibits a linear response over the range of 62.5–1000 nM tetracycline, with a limit of detection of 42 nM and a limit of quantification of 138 nM. It also displays strong selectivity over other common antibiotics, including ampicillin, amoxicillin, cephalexin, doxycycline, penicillin, and non-antibiotic interferent, glucose, as well as excellent reproducibility and operational stability under repeated measurements. The sensor can detect tetracycline in lake, tap, and bottled water with linear responses across the same concentration range. The combination of biocompatible, low-cost materials and simple fabrication supports single-use deployment and scalability. These results demonstrate the potential of manganese-doped zinc sulfide-chitosan nanocomposite-based impedimetric biosensors as practical platforms for on-site monitoring of antibiotic residues in aquaculture water.

Child training in the Child ViReal Support Program: Combining iVR-based cognitive training and CBT techniques in a pilot study

Introduction Attention deficits are common among school-aged children and affect their social, academic, and family lives, making it necessary to receive adequate support and intervention strategies. Objectives This study aimed (a) to evaluate whether a multilevel intervention program –combining parent training, child training based on a cognitive-behavioral approach, and iVR-based cognitive training—could improve attention, executive functions, and psychosocial adjustment in children with attention deficits, and (b) to examine whether outcomes differed according to the sequence in which the intervention components were delivered. This pilot study was conducted as a precursor to a larger study. Methods Sixteen families were randomly assigned to two groups following a sequential intervention design, with each group receiving the same components in a different order. Hence, families in the PC group received parent training first, followed by child training, while families in the CP group began with child training, followed by parent training (Clinical Trials Registry NCT05391698). Results After participating in the intervention program, children demonstrated enhanced attentional and inhibitory control and sustained attention, as measured by fewer omission and commission errors, faster hit reaction time, and improved executive score. They also exhibited gains in working memory, processing speed, and planning and programming abilities and reported reduced behavioral problems. Emotional competence improved significantly only for the PC group. A significant time X group interaction was found for school competence, with the PC group showing a more pronounced improvement over time than the CP group. In contrast, no significant changes were observed in alerting and orienting scores, reaction time variability, social competence or self-perception skills. Conclusions The findings have important implications, suggesting that multilevel intervention programs – particularly those integrating iVR-based cognitive training – may positively impact specific cognitive and psychosocial outcomes in children with attention deficits. As a pilot study, these results provide preliminary evidence and methodological insights to guide the design and rationale of future larger-scale studies.

Editorial Note: Gene/QTL discovery for Anthracnose in common bean (Phaseolus vulgaris L.) from North-western Himalayas

Diphtheria seroprotection among Indonesian children: Community Health Surveys Riskesdas 2007, 2013 and 2018

Diphtheria remains a public health concern in Indonesia despite long-standing inclusion of diphtheria-containing vaccines in the national immunization program. This study assessed temporal trends in diphtheria immunity among Indonesian children aged 1–14 years, identified vulnerable age groups, and examined factors associated with seroprotection. We analyzed diphtheria IgG antibody data measured by ELISA from the Indonesian Community Health Surveys (Riskesdas) conducted in 2007, 2013, and 2018. The analysis included 6,622 children (2007), 7,110 (2013), and 7,203 (2018). Bivariate and multivariate logistic regression analyses were performed to identify determinants of seroprotection. Across all surveys, diphtheria IgG levels were lowest at ages 1–6 years, increased at ages 7–10 years, and declined again from age 11 years onward. Overall seroprotection ranged from 71.1% to 83.6%, with a two- to threefold increase in long-term protection observed in 2018 compared with 2007 and 2013. In multivariate analyses, complete DTP immunization consistently remained the strongest independent predictor of seroprotection among children aged 1–4 years (p < 0.05). Among children aged 1–14 years, maternal education (Riskesdas 2007) and household economic status (Riskesdas 2018) were also associated with seroprotection. Following the introduction of the DTP4 booster, higher diphtheria IgG concentrations (GMC 0.48 IU/mL to 0.77 IU/mL) were observed among children aged 1–2 years old in 2018 compared with earlier surveys (GMC 0.18 IU/mL to 0.33 IU/mL). Diphtheria immunity among Indonesian children remains suboptimal, with the highest vulnerability at ages 5–6 years and evidence of waning immunity after ten years of age. Ensuring complete routine DTP immunization is critical, and booster strategies may be considered to sustain long-term population immunity.

The relationship between platelet count and postoperative acute kidney injury and long-term prognosis in neurosurgical critically ill patients: A retrospective study

Objective This study was designed to investigate the associations among platelet (PLT) count upon admission to the intensive care unit (ICU), postoperative acute kidney injury (AKI), and long-term prognosis (one-year mortality risk) in patients undergoing neurosurgical operations. Methods This study conducted a retrospective analysis based on the MIMIC-IV database, including patients who underwent neurosurgery and were admitted to the ICU. Platelet count information at admission was collected. The primary endpoint were AKI within 7 days of ICU admission and mortality within one year after surgery. For the primary endpoint, a multivariate logistic regression model combined with restricted cubic spline was used for statistical analysis to explore the potential association between platelet count and AKI, and subgroup analysis was conducted to assess the stability of the results. For the other endpoint, a restricted cubic spline was used to construct a visualization relationship graph, and a Cox multivariate regression model was further established and a cumulative mortality curve was drawn. Sensitivity analyses were performed using both the first recorded platelet count following ICU admission and the 24-hour mean platelet count to assess the robustness of our findings. Additionally, an independent validation cohort was constructed using the MIMIC-III database to conduct external validation. Results A total of 1605 patients were included in this study, with a median age of 60 years, among whom 875 were male (54.5%). Among the 1605 patients, 607 (37.82%) developed acute kidney injury within 7 days of hospitalization. Logistic regression analysis showed that compared with patients in the first quartile of platelet count (Q1 ≤ 179), those in the third quartile (Q3 > 234) had a significantly lower risk of developing acute kidney injury within 7 days of hospitalization (Model 1:HR = 0.42, 95% CI: 0.33–0.54; Model 2: HR = 0.67, 95% CI: 0.59–0.76; Model 3: HR = 0.57, 95% CI: 0.43–0.74; Model 4: HR = 0.62, 95%CI: 0.47–082; all P-values < 0.050). Furthermore, when the platelet count is below 204 × 10⁹/L, the risk of AKI occurrence in patients increases significantly. Cox multivariate regression analysis of the secondary endpoint showed that both relatively low platelet count (≤ 179) and relatively high platelet count (> 234) were associated with an increased risk of death within 1 year, with the former association being particularly significant (HR = 1.53, 95% CI: 1.17–2, P = 0.002). Sensitivity analyses yielded directionally consistent results. In the MIMIC-III external validation cohort, the associations between platelet levels and risks of AKI as well as long-term mortality remained generally consistent. Conclusion In neurosurgical patients admitted to the ICU, early platelet levels within the first 24 hours were associated with the incidence of AKI within 7 days and long-term outcomes. A lower platelet count during the early ICU period was associated with an increased risk of AKI and poorer prognosis. This association appeared to be non-linear, the range of 179–234 × 10⁹/L corresponded to a lower risk or better prognosis. Platelet count can be a potential tool for risk stratification, but it is not sufficient to support clinical intervention decisions based on causal relationships.

New approach for health assessment of high voltage motor using experimental case studies

This paper introduces a new approach for comprehensive Health Index (HI) assessment of high-voltage (HV) induction motors used in oil and gas plants. The proposed method is designed to be practical, relying on readily available operational and maintenance data without requiring the installation of additional sensors. It accounts for real-world limitations in data acquisition and incorporates internationally recognized criteria from IEC, IEEE, and CIGRE standards. The Health Index calculation integrates both conventional diagnostic test results, such as Partial Discharge (PD), Insulation Resistance (IR), Polarization Index (PI), Tan Delta (TD), vibration measurements, and complementary information/ Conditional Factors CF, including physical condition, maintenance history recorded in the Standard Assessment Procedure (SAP), and aging factors. Condition ratings, weighting factors, and parameter-specific scoring are systematically applied to provide a balanced assessment. By adopting a multi-criteria analysis framework, the proposed method consolidates diverse parameters into a unified, condition-based Health Index. The significance of this work lies in its ability to support proactive asset management, minimize unexpected failures, and ensure the reliable and efficient operation of HV motors. Experimental case studies further demonstrate the applicability and robustness of the approach.

A transparent AI assurance and benchmarking framework for EEG seizure detection on TUSZ seeded with a reproducible gradient-boosting ensemble

Airborne geophysical imaging of freshwater reservoir beneath the eastern margin of Great Salt Lake

Fuzzy-based multi-objective scheduling for human–robot manufacturing systems

Abstract This study addresses the optimization of production planning and scheduling for human–robot interaction in a fuzzy environment, a critical challenge in modern manufacturing, especially under fluctuating market demand. The proposed model simultaneously determines production quantities, inventory/shortage levels, human–robot task allocation, and job sequencing. All decisions are optimized in a multi-period, multi-product setting. Three objective functions are considered: maximizing net present value, minimizing maximum completion time, and minimizing total early and tardy times. To handle uncertainties in demand and processing times, a pessimistic (credibility-constrained) fuzzy programming approach is employed. The model is solved using the epsilon-constraint method for small-scale problems and metaheuristic algorithms (NSGA-II, MOPSO, and MOWOA) for larger instances. Sensitivity analyses reveal that reducing completion times increases costs, lowering net present value, while higher uncertainty rates increase production times and shortages, reducing net present value. A 4% increase in bank interest rate reduces net present value by 15.68%, with no impact on completion or early/tardy times. The MOWOA algorithm demonstrates superior performance in generating efficient solutions for large-scale problems, offering practical insights for optimizing human–robot collaboration in manufacturing.

Estrous cycle modulates fasting-induced torpor propensity via hypothalamic estrogen signalling

Abstract Torpor is a state of transient hypometabolism and hypothermia that is engaged by many species in adverse conditions such as food scarcity. Chemo- or optoactivation of neurons in the hypothalamic preoptic area (POA) drives torpor-like hypometabolism and hypothermia. Estrogens, principally estradiol, modulate thermogenesis and energy balance by central actions, and POA neurons express the canonical estrogen receptor ERα. We explored the role of POA estrogen signalling in fasting-induced torpor in the mouse. We found that torpor depth and duration vary across the estrus cycle in mice, whereby the torpor response is greatest during the diestrus phase in which circulating estradiol is at its peak. Exogenous estradiol lengthens torpor bouts in female mice, but not in males. Knockdown of ERα within the POA blunts torpor in female mice, suggesting that estradiol acting via ERα modulates the activity of hypothalamic neurons that generate torpor. We speculate that this cyclical oscillation in torpor propensity which is lowest in the estrous phase may be an adaptive change that preserves reproduction during periods of moderate environmental stress.

Hidden Markov model analysis of fluorescence blinking in fluorescently labeled DNA

Abstract We investigate the transition processes between the emitting (ON) and non-emitting (OFF) states of fluorescent molecules using a machine-learning approach. In fluorescently labeled DNA, continuous fluorescence is observed under irradiation; however, the system occasionally transitions to a non-emitting state, often associated with a charge-separated configuration. The resulting fluorescence trajectories exhibit characteristic blinking behavior —alternating ON and OFF states— which is heavily obscured by various sources of noise, making reliable state classification challenging. Because such trajectories represent typical stochastic time-series data, advanced analytical techniques are required. In this study, we apply a hidden Markov model to extract hidden ON/OFF states from noisy fluorescence trajectories using the forward-filtered backward-blocking Gibbs sampling algorithm, and construct probability density functions of the ON- and OFF-state durations to characterize the blinking dynamics. From these distributions, the characteristic relaxation times are evaluated as 17.6 ms for the ON state and 7.8 ms for the OFF state. The relatively long OFF period indicates that the charge-separated state in the DNA-ATTO655 system is fairly stable, suggesting suppressed charge recombination. In addition, we discuss the characteristic timescale of the light absorption–emission process in the ON state in terms of the average photon count per time bin. These results provide new insights into the fluorescence dynamics of single DNA-fluorophore systems. Finally, we discuss the detailed conditions required for reliable time-series analysis in terms of the photon-count histogram shape and the time-bin width used in the trajectories.

A genomic approach for accurate identification of closely related species with next-generation sequencing samples

Pan-cancer analysis reveals the oncogenic and immunomodulatory roles of PTGFRN across human cancers

Predicting wire electrical discharge machined surface roughness of C355/silicon nitride/graphene hybrid nanocomposites using simulation, statistical and machine learning techniques

Abstract This study employed machine learning (ML) and optimization approaches, with support vector regression (SVR), artificial neural networks (ANNs) simulations and response surface methodology to study surface roughness of wire electrical discharge machined/machining of (WEDM) aluminum alloy C355 hybrid composite samples. The samples were strengthened by silicon nitride (Si₃N₄) and graphene nanoparticles (GNPs). The composites surface roughness was investigated using real-time WEDM experiments conducted with varied control settings, including servo-voltage, maximum current, wire feed rate and on/off pulses. The grid-based search approach was used to modify the support vector machine variables, and the layers (input-hidden-output) of the ANN architectural design were achieved. The correlation coefficient and mean absolute percentage error (MAPE) were used to assess the generated models’ prediction ability. SVR outperformed ANN ( R  = 0.991350) and RSM ( R  = 0.985320) in terms of accuracy, with an R-value of 0.997603 and the lowest MAPE of 0.0748. According to ANOVA results, peak current was the most significant WEDM parameter, accounting for 60.21% of the variation in surface roughness. The suggested method, combining support vector machine and ANN algorithm, can efficiently and accurately analyze and predict WEDM surface roughness on aluminum alloy C355 with Si₃N₄ and GNPs hybrid composites. Hence, this innovative study leveraged application of simulation, statistical and ML techniques to advance substrative manufacturing/WEDM process for the benefits of machining industries.

The effects of short-term blood flow restriction training on knee function and quality of life in older adults patients with tibial plateau fractures

Urinary 6-sulfatoxymelatonin as a predictive biomarker for brain injury in very preterm infants

Abstract Brain injury in preterm infants (BIPI) remains a significant clinical challenge with limited diagnostic biomarkers. This study aimed to investigate urinary 6-sulfatoxymelatonin (6-SMT) levels as a potential noninvasive biomarker for brain injury in very preterm infants. A prospective cohort study was conducted with 127 very preterm infants admitted to our hospital from January to December 2024. Infants were categorized into brain-injury and control group based on neuroimaging findings. Urinary 6-SMT concentrations were measured on postnatal days 1, 3, and 7 using ELISA. Clinical parameters and perinatal risk factors were evaluated. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic efficacy, and conditional logistic regression analysis was performed to evaluate the association between urinary 6-SMT levels and brain injury. Compared with the control group ( n  = 97), infants with brain injury ( n  = 30) exhibited significantly lower gestational age (GA: 29.36 vs. 30.43 weeks, p  = 0.028) and birth weight (BW: 1.21 vs. 1.34 kg, p  = 0.030), significantly lower rates of acidosis (10.0% vs. 12.4%, p  = 0.023) and antenatal magnesium sulfate (MgSO₄) exposure (30.0% vs. 60.8%, p  = 0.003), and significantly higher rates of early infection (46.7% vs. 21.6%, p  = 0.007) and asphyxia (30.0% vs. 1.0%, p  = 0.014). Crucially, urinary 6-SMT levels were markedly lower in the brain injury group on all measurement days (day 1: 558.51 vs. 813.86 pg/mL, p  = 0.015; day 3: 722.62 vs. 938.48 pg/mL, p  < 0.001; Day 7: 796.81 vs. 1034.48 pg/mL, p  = 0.014). ROC analysis identified day 3 urinary 6-SMT (cut-off 762.46 pg/mL) as the best single marker (AUC = 0.714, sensitivity 73.2%, specificity 70.0%), while a combined model integrating levels from days 1, 3, and 7 achieved superior diagnostic performance (AUC = 0.764, sensitivity 78.4%, specificity 66.7%). Urinary 6-SMT levels progressively increased during the first postnatal week and correlated positively with GA and BW on days 1 and 3, but correlations weakened by day 7. After 1:1 matching on GA and BW, conditional logistic regression analysis confirmed a significant protective association between higher day 3 urinary 6-SMT levels and reduced brain injury risk (adjusted OR = 0.996, p  = 0.004). Reduced urinary 6-SMT concentrations in very preterm neonates are significantly associated with brain injury. Serial urinary 6-SMT measurements, particularly when combined across multiple time points, demonstrate promising diagnostic potential as a noninvasive biomarker for BIPI. These findings suggest that melatonin deficiency may contribute to the pathophysiology of preterm brain injury and warrant further investigation for clinical translation.