Search Results (4,625)

Background/Objectives: Southeastern Europe and Croatia have served as a genetic crossroads between the Near East and Europe since prehistoric times, shaped by numerous and repeated migrations. By integrating 19 newly generated ancient genomes with 285 previously published ancient genomes from Croatia, we investigated patterns of maternal and paternal landscapes from the Neolithic, Bronze, and Iron Ages through to the Antiquity and medieval periods, as well as the modern Croatian population. Methods: Ancient DNA extraction from human remains and library preparation were conducted in dedicated clean-room facilities, followed by high-throughput sequencing on the Illumina platform. Sequencing data were analyzed with established pipelines to determine mitochondrial and Y-chromosomal haplogroups and the genetic sex of individuals. Results: New ancient data reveal a predominantly European maternal profile, dominated by haplogroups H, U, and HV0, whereas Y-chromosomal lineages are characterized by J subclades and R1a, with limited representation of R1b and the absence of I2a. When combined with published ancient Croatian genomes, the results reveal similar haplogroup diversity and patterns, as well as the expansion of mtDNA haplogroup H over time and a substantial increase in Y-chromosome R1a and I2a haplogroup frequency from the prehistoric to the modern period. Conclusions: Although the analyzed samples are heterogeneous and originate from different historical periods, their genetic signatures conform to the broader patterns expected for the region. In a wider context, the ancient Croatian mitochondrial data reveal stronger genetic persistence from prehistory to modern times, unlike paternal lineages, which show significantly higher divergence. Full article

Small-object detection in UAV remote sensing faces common challenges such as tiny target size, blurred features, and severe background interference. Furthermore, single imaging modalities exhibit limited representation capability in complex environments. To address these issues, this paper proposes CTU-YOLO, a UAV-based small-object detection algorithm built upon cross-modal feature fusion and lightweight upsampling. The algorithm incorporates a dynamic and adaptive cross-modal feature fusion (DCFF) module, which achieves efficient feature alignment and fusion by combining frequency-domain analysis with convolutional operations. Additionally, a lightweight upsampling module (LUS) is introduced, integrating dynamic sampling and depthwise separable convolution to enhance the recovery of fine details for small objects. Experiments on the DroneVehicle and LLVIP datasets demonstrate that CTU-YOLO achieves 73.9% mAP on DroneVehicle and 96.9% AP on LLVIP, outperforming existing mainstream methods. Meanwhile, the model possesses only 4.2 MB parameters and 13.8 GFLOPs computational cost, with inference speeds reaching 129.9 FPS on DroneVehicle and 135.1 FPS on LLVIP. This exhibits an excellent lightweight design and real-time performance while maintaining high accuracy. Ablation studies confirm that both the DCFF and LUS modules contribute significantly to performance gains. Visualization analysis further indicates that the proposed method can accurately preserve the structure of small objects even under nighttime, low-light, and multi-scale background conditions, demonstrating strong robustness. Full article

On 2021, in the intensive care unit of a County Emergency Hospital where oxygen therapy treatment was applied to COVID patients, located in the municipality of Ploiesti, Prahova County, a fire occurred that resulted in the destruction by burning of the ICU room, the death of two people, and the injury of a medical professional. In order to elucidate the accelerating causes of the combustion phenomenon of materials in the ICU room, a combustion stand was designed whose atmosphere can be controlled in terms of achieving high oxygen concentrations of 40% vol., in accordance with the treatment schemes applied to the patients and with the configuration of the room and the frequency of use of the access door. In this experimental stand, a series of combustion tests of flexible polyurethane foam samples were performed, which highlighted the acceleration of combustion and the complete consumption of the mass. The purpose of this work is to explain the rapidity of the fire in a hospital ward, both with experimental methods and with the help of FDS. Full article

This study presents an integrated, low-cost system for measuring extremely small displacements in Ionic Polymer–Metal Composite (IPMC) actuators operating in aqueous environments. A custom optical setup was developed, combining a glass tank, a tubular microscope with a 10× achromatic objective, a digital USB camera and uniform LED backlighting, enabling side-view imaging of the actuator with high contrast. The microscopy system achieves a spatial sampling of 0.536 μm/pixel on the horizontal axis and 0.518 μm/pixel on the vertical axis, while lens distortion is limited to a maximum edge deviation of +0.015 μm/pixel (≈+2.8%), ensuring consistent geometric magnification across the field of view. On the image-processing side, a predictive grid-based tracking algorithm is introduced to localize the free tip of the IPMC. The method combines edge detection, Harris corners and a constant-length geometric constraint with an adaptive search over selected grid cells. On 1920 × 1080-pixel frames, the proposed algorithm achieves a mean processing time of about 10 ms per frame and a frame-level detection accuracy of approximately 99% (98.3–99.4% depending on the allowed search radius) for actuation frequencies below 2 Hz, enabling real-time monitoring at 30 fps. In parallel, dedicated electronic circuitry for supply and load monitoring provides overvoltage, undervoltage, open-circuit and short-circuit detection in 100 injected fault events, all faults were detected and no spurious triggers over 3 h of nominal operation. The proposed microscopy and tracking framework offer a compact, reproducible and high-resolution alternative to laser-based or Digital Image Correlation techniques for IPMC displacement characterization and can be extended to other micro-displacement sensing applications in submerged or challenging environments. Full article

Background: Obesity is a worldwide public health problem, affecting organs and systems. It is also a cardiovascular risk factor, which facilitates the development of diseases, such as arterial hypertension, dyslipidemia, and diabetes, which are used as criteria for the diagnosis of metabolically unhealthy obesity. Objective: To analyze the association between blood pressure and metabolic health factors, stress level, and nutrient intake in overweight and obese university students through mediation analysis. Methods: A quantitative, non-experimental, cross-sectional, correlational, and quantitative study was conducted in a sample of 230 obese/overweight university students selected by a multistage mass random sampling method. To evaluate habitual dietary intakes, a CFCA food frequency questionnaire was applied; a DASS-21 scale was used to evaluate stress; blood pressure and anthropometric data were collected; insulin levels, lipid profile, and glucose were determined using fasting blood samples. Statistical analysis was performed using univariate methods (frequencies, trend, and dispersion measures) and a mediational model. Results: The majority were young people aged 18 years (18.7%), with morning and afternoon shifts (60%), overweight (76.1%), and obese (23.9%). Not all obese people have arterial hypertension; however, an increase in BMI increases the risk of suffering from this disease. Model 1 showed that certain types of stress and sex at birth have an important relationship with diastolic blood pressure, mediated in some cases by weight. In Model 2, weight is a significant mediator in the relationship between moderate stress and systolic BP, and between sex at birth and systolic BP, thus allowing us to contribute to the understanding of how these variables are interrelated. Conclusions: This suggests that severe stress and sex at birth not only affect BP directly, but also do so through their effect on weight. Thus, both pathways contribute to understanding the relationship between stress, sex at birth, and diastolic and systolic blood pressure. Nevertheless, the results of this study provide empirical knowledge to design evidence-based prevention and treatment strategies. Full article

Solid-state microwave technology has emerged as an alternative to conventional magnetron-based microwave systems due to its precise frequency control and potential to improve heating uniformity. The objective of this study was to evaluate the effects of solid-state microwave cooking at 912–913 MHz on the quality characteristics of beef steak and minced beef in comparison with conventional oven cooking and traditional microwave cooking (2450 MHz). Meat samples were cooked to an internal temperature of 75 °C, and cooking time, weight loss, moisture content, lipid oxidation (TBARS), total soluble protein (TSP), color attributes, and texture properties were evaluated. Solid-state microwave cooking resulted in shorter cooking times compared to conventional oven cooking. However, it caused significantly higher cooking loss in beef steak (48.1%) compared to conventional (34.8%) and microwave cooking (42.4%) (p ≤ 0.05). In minced beef, solid-state microwave cooking led to significantly higher TBARS values (1.56 mg MDA/kg) than conventional cooking (1.07 mg MDA/kg) (p ≤ 0.05), indicating increased lipid oxidation. No significant differences were observed among cooking methods for total soluble protein content and several texture parameters (p > 0.05). Solid-state microwave cooking produced improved color development compared to traditional microwave cooking. Overall, solid-state microwave cooking shows potential advantages in processing time and color formation; however, increased cooking loss and lipid oxidation indicate that optimization of processing conditions is necessary to limit undesirable quality changes. Full article

Traditional water quality monitoring methods are limited in providing timely chlorophyll-a (Chl-a) assessments in small inland reservoirs. This study presents a rapid Chl-a retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-a maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs. Full article

Federated learning effectively addresses the issues of data privacy and communication overhead in traditional deep learning through distributed local training. However, its open architecture is seriously threatened by backdoor attacks, where malicious clients can implant triggers to control the global model. To address these issues, this paper proposes a novel three-stage defense mechanism based on local clients. First, through text readability analysis, each client’s local data is independently evaluated to construct a global scoring distribution model, and a dynamic threshold is used to precisely locate and remove suspicious samples with low readability. Second, frequency analysis and perturbation are performed on the remaining data to identify and disrupt triggers based on specific words while preserving the basic semantics of the text. Third, n-gram distribution analysis is employed to detect and remove samples containing abnormally high-frequency word sequences, which may correspond to complex backdoor attack patterns. Experimental results show that this method can effectively defend against various backdoor attacks with minimal impact on model accuracy, providing a new solution for the security of federated learning. Full article

Background and Objectives: Cervical adenocarcinoma (CAC) is a histologically distinct subtype of cervical cancer with a rising incidence in many regions. While the roles of key driver mutations are known, a comprehensive understanding of its genomic landscape, particularly variations across different populations and tumor stages, remains incomplete. This study aims to characterize the somatic genomic landscape of CAC by identifying recurrent mutations, copy number alterations (CNAs), and patterns of co-occurrence, with a focus on variations across racial groups and between primary and metastatic tumors. Materials and Methods: We conducted a comprehensive genomic analysis of 102 tumor samples from 99 patients diagnosed with cervical adenocarcinoma using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) database. Results: The most frequently mutated genes were PIK3CA (25.5%), TP53 (21.6%), ARID1A (20.6%), and KRAS (16.7%). Significant amplification of ERBB2 was also observed (n = 3; 4.83%). Our analysis revealed notable genomic disparities across racial groups, with TP53 mutations being significantly more frequent in White patients compared to Asian and Black patients (p = 0.0236). Furthermore, we identified significant co-occurrence between mutations in KRAS and MSH2 (p = 0.011) as well as ATM and STK11 (p = 0.037). In comparing tumor types, mutations in BCL6 were found to be significantly enriched in metastatic samples. Conclusions: This study validates the primary drivers of cervical adenocarcinoma and reveals novel findings, including notable racial disparities in TP53 mutation frequency and unique patterns of co-occurring mutations. These findings highlight the genomic heterogeneity of the disease and suggest that ancestry and tumor evolution may influence its molecular pathogenesis, offering potential avenues for the development of targeted therapies and personalized biomarkers. Full article

From a psychoneuroimmunology standpoint, stress and cigarette smoking are plausible modulators of periodontal inflammation through neuroendocrine–immune pathways and cytokine networks. Interleukin-18 (IL-1 family), interleukin-21 (common γ-chain cytokine), and interleukin-15 (tissue-resident lymphocyte activation/homeostasis) are mechanistically relevant candidates to characterize in relation to these exposures. We aimed to quantify serum IL-15, IL-18, and IL-21 and examine their associations with stress, smoking, and periodontal status in Mexican adults. Methods: Cross-sectional study (n = 65; 18–60 years; 70.8% female). Smoking status (23.1% smokers) and periodontal status were recorded; due to low periodontitis frequency (n = 3), periodontal status was analyzed as healthy (23.1%) versus periodontal disease (76.9%; gingivitis + periodontitis). Stress was assessed using the 18-item Symptomatic Stress Questionnaire and dichotomized as no/low stress (0–10; 52.3%) versus pathological stress (11–54; 47.7%). Systolic and diastolic blood pressure were recorded. IL-15, IL-18, and IL-21 were measured in serum by immunoassay. Analyses used medians (IQR), Mann–Whitney U tests with rank-biserial effect sizes, and exploratory Benjamini–Hochberg false discovery rate (FDR) adjustment across the nine primary cytokine-by-contrast tests; correlations with age and diastolic blood pressure were exploratory. Results: Cytokine distributions were right-skewed, particularly for IL-21. Across smoking, stress, and periodontal-status contrasts, no comparison met q < 0.05 after FDR adjustment. Effect-size patterns were heterogeneous rather than uniformly monotonic across exposures (e.g., IL-18 showed higher central tendency in healthy vs. periodontal disease; IL-21 showed higher central tendency in no/low stress vs. pathological stress), indicating substantial inter-individual variability in circulating cytokines within this cohort. Conclusions: In this exploratory cross-sectional sample, serum IL-15, IL-18, and IL-21 did not show robust, multiplicity-resistant differences by smoking, stress, or periodontal status. The findings provide a transparent description of distributional properties and hypothesis-generating patterns that motivate larger, longitudinal studies with repeated cytokine sampling, standardized periodontal assessment, and improved control of key confounders to clarify the relevance of these cytokines to periodontal inflammation under behavioral exposures. Full article

Background: Identifying factors associated with cognitive impairment among older adults is critical. This study investigates both concurrent and longitudinal associations between sleep quality, sleep duration, and cognitive performance among older adults in China, with particular emphasis on the moderating role of fruit and/or vegetable intake (FVI), a factor rarely examined in previous research. Methods: We pooled five waves of a specially designed nationwide sample of adults aged 65 years or older (N = 64,690; mean age: 86.3 years; men: 43.5%) in 2005, 2008, 2011, 2014, and 2018 in China. Cognitive impairment was assessed by the Mini-Mental State Examination. Among the sample, 10.7% were cognitively impaired. FVI was dichotomized into frequent (almost daily) versus infrequent (other low frequencies). Sleep hours were grouped into short (≤6 h), normal (7–9 h), and long (≥10 h) durations. Both concurrent and cross-lagged analyses were performed after adjusting for a wide set of covariates (demographics, socioeconomic status, family/social connections, health practices, disability, self-rated health, and chronic conditions). Analyses were further stratified by gender, age group, and urban–rural residence. Results: When all covariates were present (the full model), good sleep quality was associated with 22% lower odds of the prevalence of cognitive impairment, whereas the long sleep duration was associated with 24% higher odds as compared with the normal sleep duration. Although the short sleep duration was not associated with the prevalence of cognitive impairment in the full model, it was associated with 8% higher odds of cognitive impairment when health condition was not controlled for. Interaction analyses revealed that frequent FVI buffered the adverse cognitive effects of poor sleep quality and both short and long sleep durations. Subgroup analyses further show similar patterns across subpopulations, with more pronounced protective associations in older women and the oldest-old. Conclusions: Good sleep quality, normal sleep durations, and frequent FVI jointly contribute to better cognitive functioning at older ages. While the observed relationships are largely concurrent rather than causal, promoting both healthy sleep and dietary habits may be important for cognitive health among older adults. Full article

Background/Objectives: Caffeine citrate represents the standard pharmacological intervention for apnea of prematurity (AOP) and episodes of intermittent hypoxia (IH). Despite its widespread use, consensus regarding the necessity of routine serum monitoring, optimal dosing protocols, and precise clinical indications remains elusive. The primary objective of this investigation was to evaluate the longitudinal trajectory of serum caffeine concentrations in preterm infants and to analyze their correlation with the incidence of AOP and IH episodes. Furthermore, we sought to determine whether blood caffeine concentrations varied significantly across gestational ages throughout the postnatal period. Methods: This multicenter, prospective observational study enrolled preterm infants with a gestational age of ≤30 weeks. Participants were administered a standard loading dose of caffeine citrate within the first 24 h of life, followed by a standardized maintenance regimen. Serum caffeine levels were quantified on a weekly basis. The cohort was stratified into two distinct groups based on gestational age: Group 1 (23–27 weeks) and Group 2 (28–30 weeks). Results: The study yielded 588 serum caffeine measurements from a cohort of 104 preterm infants, characterized by a median gestational age of 28 weeks (range: 23–30 weeks) and a mean birth weight of 1034 ± 296 g. Statistical analysis revealed no significant disparities in serum caffeine concentrations across gestational age groups (p > 0.05). Notably, during the third week of life, infants with apneic episodes demonstrated significantly lower caffeine levels than those without apnea (p = 0.016). Furthermore, a significant negative correlation was identified between serum caffeine concentrations and the frequency of IH episodes during the third, fourth, and fifth weeks of life across multiple oxygen saturation thresholds. Conclusions: While serum caffeine concentrations in preterm infants did not vary significantly with gestational age, lower levels were associated with a higher incidence of AOP and IH episodes. These results suggest that while routine monitoring or dose adjustment based solely on gestational age may not be warranted, maintaining adequate serum levels is critical for symptom management. Future research should prioritize randomized controlled trials with expanded sample sizes, extended follow-up periods, and a rigorous analysis of adverse effects. Full article

In nuclear power plant fault diagnosis, traditional machine learning models (e.g., SVM and KNN) require full retraining on the entire dataset whenever new fault categories are introduced, resulting in prohibitive computational overhead. Deep learning models, on the other hand, are prone to catastrophic forgetting under incremental learning settings, making it difficult to simultaneously preserve recognition performance on both old and newly added classes. In addition, nuclear power plant fault data typically exhibit significant class imbalance, further constraining model performance. To address these issues, this study employs SHAP-XGBoost to construct a feature evaluation system, enabling feature extraction and interpretable analysis on the NPPAD simulation dataset, thereby enhancing the model’s capability to learn new features. To mitigate insufficient temporal feature capture and sample imbalance among incremental classes, we propose a cascaded spatiotemporal feature extraction network: LSTM is used to capture local dependencies, and its hidden states are passed as position-aware inputs to a Transformer for modeling global relationships, thus alleviating Transformer overfitting on short sequences. By further integrating frequency-domain analysis, an improved Adaptive Time–Frequency Network (ATFNet) is developed to enhance the robustness of discriminating complex fault patterns. Experimental results show that the proposed method achieves an average accuracy of 91.36% across five incremental learning stages, representing an improvement of approximately 20.7% over baseline models, effectively mitigating the problem of catastrophic forgetting. Full article

Background: ApoE is a major regulator of lipid metabolism and glycaemic control. The aim of the current study is to investigate the ApoE gene polymorphisms among Black South Africans with and without type 2 diabetes mellitus (T2DM) and associate them with their lipid profile. Methods: A cross-sectional case–control study was conducted among 107 participants, divided into two groups: patients with T2DM (n = 65) and non-diabetic controls (n = 42). Blood samples were collected for analysis of glycated haemoglobin, lipid profile, nitric oxide, high-sensitivity C-reactive protein and DNA genotyping using the MALDI-TOF. Continuous variables were analysed using Student’s t-test or one-way analysis of variance (ANOVA). Genotype and allele frequencies were compared using Fisher’s exact tests. Results: The ApoE3 allele was the most prevalent among both groups, observed in 55.47% in T2DM patients and 52.38% in the non-diabetic group, followed by E4 and E2. HWE analysis revealed a deviation from equilibrium [χ² (3) = 9.137, p = 0.0275]. TG levels differed significantly across ApoE alleles (F = 3.68, p = 0.03), with higher TG concentrations observed among E3 allele carriers and E4 allele carriers. Poor glycaemic control (HbA1c ≥ 7.0%) predominated across all ApoE alleles. Among E3 allele carriers, 75.0% of participants exhibited poor glycaemic control, whereas only 25.0% achieved good glycaemic control (p = 0.002). Conclusions: ApoE polymorphisms are associated with allele-specific heterogeneity in lipid metabolism and glycaemic control among individuals with T2DM, underscoring the complex, context-dependent role of genetic variation in metabolic dysregulation within African populations. Full article

Background: Early administration of antibiotics is commonly recommended for pneumonia in intensive care unit (ICU) patients. However, the clinical benefit of very early empirical treatment remains uncertain and may reflect differences in illness severity, baseline risk, or care pathways, particularly in non-septic or hemodynamically stable ICU populations. Methods: We performed a retrospective cohort study using the Medical Information Mart for Intensive Care IV (v2.2) database to evaluate the observational association between antibiotic timing and in-hospital mortality among adult ICU patients with pneumonia. Patients were categorized as receiving early (<3 h) or delayed (≥3 h) antibiotic therapy after ICU admission. A multivariable logistic regression model adjusted only for age and sex. Given the absence of detailed severity-of-illness measures, no causal inference was intended, and all analyses were considered hypothesis-generating. Additional analyses exploring antibiotic class, dosing frequency, and combination therapy were conducted in an exploratory manner, given substantial variation in sample sizes and a high risk of confounding by indication, misclassification, immortal-time, and survivorship bias. Results: Among 7569 ICU patients with pneumonia, 56.5% received antibiotics within three hours of ICU admission. Early antibiotic initiation was associated with higher in-hospital mortality than delayed therapy (26.1% vs. 21.5%; OR 1.30, 95% CI 1.16–1.44; p < 0.001). Because validated severity-of-illness measures were unavailable, residual confounding and confounding by indication are likely and may largely explain this association. A potential signal of increased mortality was observed in patients receiving ≥3 doses of levofloxacin (OR 4.39, 95% CI 1.13–17.02); however, this subgroup was small and the finding is highly susceptible to survivorship and indication bias. Mortality appeared lower in patients receiving two or three antibiotics compared with monotherapy, but marked group imbalances, lack of restriction or stratification, and clinical selection effects limit interpretability. Regimens involving ≥4 agents were rare and primarily associated with prolonged ICU length of stay rather than a clear mortality difference. Conclusions: In this large retrospective ICU cohort, very early antibiotic administration for pneumonia was observationally associated with higher in-hospital mortality. Causality cannot be inferred, and early treatment likely represents a marker of higher baseline risk or clinical urgency rather than a harmful exposure. These findings challenge the assumption that earlier antibiotic initiation is uniformly beneficial and underscore the importance of individualized, stewardship-aligned, and context-dependent decision-making regarding antimicrobial timing and intensity in critically ill patients. Full article