Search Results (9,587)

Early diagnosis of lung cancer, essential for reducing its high mortality rate, is currently challenging, partly due to the lack of specific biomarkers. Here, we attempted to develop a noninvasive and potentially sensitive screening method based on the proteomic analysis of unstimulated and stimulated saliva samples, collected by passive drooling and salivary swabs, respectively, from healthy heavy smokers enrolled in a nonprofit screening project. Protein content analyzed before and after sample cryopreservation for various periods and the associated two-dimensional electrophoresis revealed that protein extraction after short-term cryopreservation prevented the loss of detectable proteins. Mass spectrometric analysis of these electrophoretically resolved proteins revealed the presence of salivary proteins whose levels may be dysregulated in various types of lung cancer. Finally, in pilot experiments conducted on stimulated saliva from a patient with a lung cancer nodule, we detected altered content or selective presence of proteins involved in lung carcinogenesis, such as serpin B3 or the proteins S100A14 and aldoketoreductase-A1, respectively. While acknowledging that these findings require further validation, we believe that the use of saliva and related proteomic analyses may contribute to the identification of potential early lung cancer biomarkers, which could hopefully improve clinical management of the tumor and patient survival. Full article

Understanding the genetic basis of high-performance animals is vital for biological insight and breeding. This study aimed to identify genetic factors distinguishing champion gamecocks (Gallus gallus domesticus) from less successful ones, representing the first study to link transcriptomic profiles directly to competitive outcomes. Using RNA sequencing on non-invasive feather samples, we compared gene expression between high-performing (≥80% win rate) and low-performing (≤20% win rate) cohorts. Our analysis identified 441 differentially expressed genes. Notably, the high-performing cocks exhibited the significant upregulation of genes integral to muscle development and repair (e.g., SYNPO2, POPDC2) and enhanced neural function (NRN1). Conversely, several genes involved in neural development pathways, including CNTNAP2 and GFRA4, were significantly downregulated in the high-performing group. These findings suggest that a rooster’s competitive success is not determined by a single factor, but by a complex interplay of superior muscular capabilities and uniquely regulated neurological pathways. The identified genes provide a set of potential biomarkers that could inform selective breeding strategies aimed at enhancing performance traits in gamecocks. Full article

In addressing the challenges posed by high costs, low accuracy, and cumbersome operations in mycotoxin detection, a novel aptamer-antibody sandwich chemiluminescent biosensor for detecting aflatoxin B₁ (AFB₁) was developed. The indirect competition between AFB₁, aflatoxin B₁-ovomucoid complete antigen (AFB₁-OVA), and rabbit anti-ovomucoid (OVA) antibody results in the formation of a sandwich complex. This sandwich assay is linked to a horseradish peroxidase-labeled antibody, which catalyzes luminol chemiluminescence for the indirect detection of AFB₁. The biosensor was designed to operate with high precision, low cost, and a low detection limit for AFB₁, which is contingent upon experimental conditions such as pH, reagent concentration, temperature, and incubation time. The optimization of pH, aptamer concentration, competitive incubation time, competitive incubation temperature, and HRP-labeled antibody concentration was instrumental in achieving these objectives. Experimental findings demonstrated that the sensor’s detection limit was 0.067 ng/mL, exhibiting excellent linearity (R² = 0.99679) within the concentration range of 0.25–10 ng/mL. The recovery rate of spiked samples ranged from 94.4% to 108.05%. This sensor boasts a low detection limit, straightforward operation, and minimal cost, thus offering a novel solution for developing cost-effective, high-precision mycotoxin detection methods. Full article

In this study, a laboratory-precision four-high micro-rolling mill was employed to investigate the influence of grain size on the deformation behavior and fracture mechanism of a micro-rolled Cu/Al composite ultra-thin sheet. Analytical testing techniques including scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM+EDS), X-ray diffraction (XRD), and unidirectional tensile experiments were utilized. The experimental results indicate that the grain size of the Cu/Al composite ultra-thin sheet increases with increasing annealing temperature and extended holding time while undergoing the first and second micro-rolling processes. Under identical annealing conditions, secondary micro-rolling leads to an increase in the grain size of Cu, while the growth rate of Al grains is reduced. Tensile tests and fracture surface observations reveal that as the annealing temperature increases, the grain size of the once-micro-rolled Cu/Al composite ultra-thin sheet also increases. When annealing at 400 °C for 40 min, the elongation reaches a maximum of 25.6%, with a tensile strength of 106.3 MPa. For the second micro-rolled samples, a maximum tensile strength of 114.8 MPa is achieved after annealing at a temperature of 360 °C for an 80 min holding time, although the elongation is significantly lower at 3.4%. This indicates that the fracture mode of the once-micro-rolled ultra-thin Cu/Al composite sheet is ductile fracture, whereas that of the second micro-rolled sample is brittle fracture. Full article

Aim: Both the arterial and venous systems independently predict mortality in septic shock, yet no bedside tools are able to integrate their assessment. Risk stratification becomes challenging when arterial parameters suggest favourable outcomes while venous parameters indicate poor prognosis, or vice versa. To address this gap, we developed the VTI-VeXUS index and conducted this proof-of-concept study to test its association with mortality. Methods: We conducted a prospective cohort study in two ICUs, enrolling adult patients with septic shock. We calculated the VTI-VeXUS index (VTI/[VeXUS+1]) from ultrasound measurements obtained within 24 h of ICU admission and stratified patients as having a high or low VTI-VeXUS index based on a cutoff of 11. We evaluated the primary outcome of mortality at 30 days using survival analysis. Results: We enrolled 62 patients. Patients with a low VTI-VeXUS index had higher rates of left ventricular dysfunction (32.3% vs. 3.2%, p = 0.006), right ventricular dysfunction (35.5% vs. 0.0%, p < 0.001), lower stroke volume (54.0 mL vs. 62.0 mL, p = 0.005), and increased 30-day mortality (adjusted HR: 3.86, 95% CI 1.23 to 12.14). Conclusions: In this exploratory proof-of-concept study, a low VTI-VeXUS index was associated with ventricular dysfunction and increased mortality. While limited by small sample size and univariate analysis, these findings suggest this novel integrated metric warrants validation in larger prospective studies. Full article

This study focuses on the development and evaluation of automatic speech recognition (ASR) systems for Kazakh child speech, an underexplored domain in both linguistic and computational research. A specialized acoustic corpus was constructed for children aged 2 to 8 years, incorporating age-related vocabulary stratification and gender variation to capture phonetic and prosodic diversity. The data were collected from three sources: a custom-designed Telegram bot, high-quality Dictaphone recordings, and naturalistic speech samples recorded in home and preschool environments. Four ASR models, Whisper, DeepSpeech, ESPnet, and Vosk, were evaluated. Whisper, ESPnet, and DeepSpeech were fine-tuned on the curated corpus, while Vosk was applied in its standard pretrained configuration. Performance was measured using five evaluation metrics: Word Error Rate (WER), BLEU, Translation Edit Rate (TER), Character Similarity Rate (CSRF2), and Accuracy. The results indicate that ESPnet achieved the highest accuracy (32%) and the lowest WER (0.242) for sentences, while Whisper performed well in semantically rich utterances (Accuracy = 33%; WER = 0.416). Vosk demonstrated the best performance on short words (Accuracy = 68%) and yielded the highest BLEU score (0.600) for short words. DeepSpeech showed moderate improvements in accuracy, particularly for short words (Accuracy = 60%), but faced challenges with longer utterances, achieving an Accuracy of 25% for sentences. These findings emphasize the critical importance of age-appropriate corpora and domain-specific adaptation when developing ASR systems for low-resource child speech, particularly in educational and therapeutic contexts. Full article

Plant pruning is the selective removal of specific plant parts to enhance growth, shape, and health. In this work, the effects of pruning were evaluated regarding the physiological parameters, maturity, quality, and harvest indices and the nutritional quality features of twelve chayote [Sechium edule (Jacq.) Sw] (Cucurbitaceae) varieties. GC-FID approaches were utilized to determine CO₂ assimilation rates. The results demonstrated that pruning upregulated the leaf temperature and conductance but decreased transpiration and CO₂ assimilation rates within the evaluated period (06:30 a.m.–16:23 p.m.). It was noted that the implementation of pruning also impacted samples with enhanced photosynthetically active radiation activity, with a positive correlation with CO₂ assimilation. The macro- and micronutrient content was higher in samples with an epidermis, especially for S. edule var. albus spinosum. Nevertheless, the analyzed samples presented low (5–10 mL CO₂ kg⁻¹ h⁻¹), medium (10–15 mL CO₂ kg⁻¹ h⁻¹), and high levels (15–20 mL CO₂ kg⁻¹ h⁻¹) of respiratory intensity and weight loss (7–17%)—effects attributed to botanical differences between the studied chayote varieties. This work demonstrates, for the first time, the effects of pruning in chayote orchards and expands the knowledge regarding the implementation of effective approaches to produce plants with culinary, cultural, and medicinal implications. Further approaches are required to determine the effects of pruning on chayote after harvest. Full article

Autonomous driving is a complex and fast-evolving domain at the intersection of robotics, machine learning, and control systems. This paper provides a systematic review of recent developments in reinforcement learning (RL) and imitation learning (IL) approaches for autonomous vehicle control, with a dedicated focus on the CARLA simulator, an open-source, high-fidelity platform that has become a standard for learning-based autonomous vehicle (AV) research. We analyze RL-based and IL-based studies, extracting and comparing their formulations of state, action, and reward spaces. Special attention is given to the design of reward functions, control architectures, and integration pipelines. Comparative graphs and diagrams illustrate performance trade-offs. We further highlight gaps in generalization to real-world driving scenarios, robustness under dynamic environments, and scalability of agent architectures. Despite rapid progress, existing autonomous driving systems exhibit significant limitations. For instance, studies show that end-to-end reinforcement learning (RL) models can suffer from performance degradation of up to 35% when exposed to unseen weather or town conditions, and imitation learning (IL) agents trained solely on expert demonstrations exhibit up to 40% higher collision rates in novel environments. Furthermore, reward misspecification remains a critical issue—over 20% of reported failures in simulated environments stem from poorly calibrated reward signals. Generalization gaps, especially in RL, also manifest in task-specific overfitting, with agents failing up to 60% of the time when faced with dynamic obstacles not encountered during training. These persistent shortcomings underscore the need for more robust and sample-efficient learning strategies. Finally, we discuss hybrid paradigms that integrate IL and RL, such as Generative Adversarial IL, and propose future research directions. Full article

Background: Cardiovascular diseases are the leading cause of death in Europe. The cardiovascular risk factors (CVRFs) with the greatest clinical impact are high blood pressure (HBP), type 2 diabetes mellitus (DM2), smoking, overweight, obesity, dyslipidaemia, and sedentary lifestyle. The objective of this review was to compare the prevalence of the different CVRFs according to population-based studies conducted in the European Union. Methods: This systematic review aimed to assess the prevalence of CVRFs in the European Union. A comprehensive search strategy was employed, including databases such as PubMed, Scopus, and Web of Science, using specific keywords related to cardiovascular risk factors, prevalence, and European countries. The quality of the reviewed studies was assessed using established criteria, categorising them as low, moderate, or high quality. Results: A total of 26 studies from Belgium, Croatia, Cyprus, Czech Republic, Estonia, France, Germany, Greece, Hungary, Ireland, Italy, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Romania, Spain, and Sweden were included. The findings revealed considerable variability in the prevalence of CVRFs across the European Union, with overweight, hypercholesterolemia, and hypertension being the most frequently reported. Prevalence rates varied notably by country, age group, and sample characteristics. The majority of the included studies were of moderate methodological quality, underscoring the need for more rigorous research to better support evidence-based policies and targeted health interventions. Conclusions: There are differences between the studies presented, ranging from the sex ratio, the age studied, to even the CVRFs included. This also means that the prevalence of each CVRF varies by country. Full article

Transformers are critical equipment in power transmission and distribution systems, and the condition of their windings significantly impacts their reliable operation. Therefore, the fault diagnosis of transformer windings is of great importance. Addressing the challenge of limited fault samples in traditional diagnostic methods, this study proposes a small-sample fault diagnosis method for transformer windings. This method combines data augmentation using the Gramian angular field (GAF) and generative adversarial networks (GAN) with a deep residual network (ResNet). First, by establishing a transformer winding fault simulation experiment platform, frequency response curves for three types of faults—axial displacement, bulging and warping, and cake-to-cake short circuits—and different fault regions were obtained using the frequency response analysis method (FRA). Second, a frequency response curve image conversion technique based on the Gramian angular field was proposed, converting the frequency response curves into Gramian angular summation field (GASF) and Gramian angular difference field (GADF) images using the Gramian angular field. Next, we introduce several improved GANs to augment the frequency response data and evaluate the quality of the generated samples. We compared and analysed the diagnostic accuracy of ResNet34 networks trained using different GAF–GAN combination datasets for winding fault types, and we proposed a transformer winding small-sample fault diagnosis method based on GAF-GAN-ResNet34, which can achieve a fault identification accuracy rate of 96.88% even when using only 28 real samples. Finally, we applied the proposed fault diagnosis method to on-site transformers to verify its classification performance under small-sample conditions. The results show that, even with insufficient fault samples, the proposed method can achieve high diagnostic accuracy. Full article

Background: Nursing students usually face excessive academic and clinical demands that negatively impact their sleep quality. Physical activity (PA) has been proposed to enhance sleep, yet few investigations have focused on this correlation within the Saudi environment. The purpose of this research was to determine the relationship between PA and sleep quality in Saudi nursing students. Methodology: A cross-sectional study was conducted among a sample of 554 nursing students from different universities in Saudi Arabia. The International Physical Activity Questionnaire–Short Form (IPAQ-SF) and the Pittsburgh Sleep Quality Index (PSQI) were used to collect data. Snowball sampling was used to recruit the participants through social media. Descriptive statistics, Pearson correlation, and inferential tests were employed for the analysis. Results: Most participants were female (85.1%) and aged 18–34 years (95.5%). LPA levels were reported by 59.6% of students, and only 8.2% engaged in VPA. The majority (91%) reported poor sleep duration (<5 h), and 57.4% had poor sleep efficiency. Overall, 86.7% of students experienced poor sleep quality. Gender and GPA were significantly associated with both PA and sleep quality. Female students and those with lower GPAs were more likely to report LPA and poor sleep. Marital status was also associated with higher levels of VPA. No significant associations were found with age, academic year, region, income, or parental education. A statistically significant positive correlation was found between PA and sleep quality (r = 0.192, p < 0.001), suggesting that increased PA is modestly associated with better sleep. Conclusion: The research shows a high rate of poor sleep and low physical activity in nursing students, indicating an alarming health trend. Although PA was linked significantly to better sleep, the modest strength indicates the necessity for multi-component interventions. Structured PA programs and sleep hygiene education should be incorporated into nursing curricula by universities to foster students’ well-being and academic performance. Full article

This study proposes a novel framework, Mamba-DQN, which integrates the state space-based time-series encoder Mamba-SSM into the Deep Q-Network (DQN) architecture to improve reinforcement learning performance in dynamic environments. Conventional reinforcement learning models primarily rely on instantaneous state information, limiting their ability to effectively capture temporal dependencies. To address this limitation, the proposed Mamba-DQN generates latent representations that summarize temporal information from state sequences and utilizes them for both Q-value estimation and Prioritized Experience Replay (PER), thereby enhancing the adaptability of policy learning and improving sample efficiency. The Mamba-SSM offers linear computational complexity and is optimized for parallel processing, enabling real-time learning and policy updates even in environments characterized by high state transition rates. The effectiveness of the proposed framework was validated through experiments conducted in environments with strong temporal dependencies and sparse rewards. Experimental results demonstrate that Mamba-DQN achieves superior stability and efficiency in policy learning compared to conventional DQN, LSTM-DQN, and Transformer-DQN models. Full article

The oasis area of the Cherchen River Basin (OACRB) is located in the southeast edge of the Tarim Basin in Xinjiang, China. High boron (B) groundwater is observed in the OACRB according to 40 groundwater samples collected in May 2023. Identification of the chemical characteristics and B sources of groundwater in the OACRB is of great significance for the sustainable development and utilization of groundwater resources and the protection of animals, plants and human health. To explore the chemical characteristics and main B sources of groundwater, Piper three-line diagram, Gibbs diagram, correlation analysis, hydrogeochemical simulation and absolute principal component analysis (PCA-APCS-MLR) were used for analysis. The contribution of different factors to groundwater B was quantitatively evaluated. The results showed that the groundwater is weakly alkaline (with an average pH of 7.94) and mainly brackish water and saline water with Cl⁻ and Na⁺ as the main anions and cations. The groundwater is dominated by SO₄ · Cl-Na type. The average concentration (ρ) of groundwater B in the study area was 1.48 mg·L⁻¹ with the over-standard rate was 45.0%. The APCS-MLR receptor model analysis revealed that groundwater chemical components including B were mainly derived from leaching-enrichment, human activity, primary geological factors, and unknown sources. Groundwater B is obviously greater than the standard limit, which is mainly due to agricultural activities (fertilizers and pesticides) and unknown sources. Full article

Avian haemosporidians are globally distributed protozoan parasites transmitted to birds by dipteran vectors. The effects of haemosporidian infections are wide-ranging and generally manifest as negative impacts on avian survival and fitness. The study of avian haemosporidians has grown considerably over the past 20 years, such that inter-regional and global effects can be explored. However, considerable gaps in intra-regional studies remain; such studies are critical to broader assessments. In this study, we conducted a community survey of avian haemosporidians found in birds in Cát Tiên National Park, Vietnam. We quantified relative parasite abundance and host associations, and compared our results to data from nearby countries. We also assessed the broader geographic distributions of previously described haemosporidian lineages that we identified in our sample. We screened 320 birds and detected infections in just 28 (8.8%). Infections were either Haemoproteus (12, 3.8%) or Plasmodium (16, 5.0%); we detected no Leucocytozoon infections. We recovered 23 haemosporidian lineages, of which 19 are new, suggesting a high degree of parasite endemism. While our positive detection rate is considerably lower than expected, the high proportion of new versus previously described lineages highlights the need for a continued focus on localized studies to broaden our knowledge of intra- and inter-regional distributions as the field seeks the mechanisms underlying generalized patterns of parasite distribution. Full article

Heavy metal pollution in rice fields is a major concern; however, little research has addressed its exposure and risk to waterbirds inhabiting rice fields. We investigated the accumulation of heavy metals (Cd, Pb, As, Cr, Cu, and Zn) in sediment, water, food, feces, feathers, and eggshell samples collected from different nesting sites (Chongwei Village and Wuji Village) of little egrets (Egretta garzetta) on Hainan Island, China, and compared the differences in their breeding parameters and eggshell quality. Higher levels of heavy metals were observed in all samples except feces from Wuji Village compared to those from Chongwei Village. As, Cd, and Pb exhibited little bioaccumulation in all feather and eggshell samples, whereas Cr concentrations in feather samples from both heronries and eggshell samples in Wuji Village exceeded the toxicity threshold in birds, indicating that the high maternal Cr was transferred to eggs in Wuji Village. Significantly lower hatching and breeding success rates were observed in Wuji Village than in Chongwei Village, which may be closely related to Cr contamination. This study revealed that waterbirds breeding in rice fields are under threat from heavy metal contamination and highlighted the suitability of bird feathers and eggshells as biomonitors of the environment. Full article