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Grazing reverses climate-induced soil carbon gains on the Tibetan Plateau
Bovine lactoferrin drives cell cycle arrest and alters the transcriptomic profile of NSCLC cells
Dominant ionic currents in rabbit ventricular action potential dynamics
Mathematical models of cardiac cell electrical activity include numerous parameters, making calibration to experimental data and individual-specific modeling challenging. This study applies Sobol sensitivity analysis, a global variance-decomposition method, to identify the most influential parameters in the Shannon model of rabbit ventricular myocyte action potential (AP). The analysis highlights the background chloride current (IClb) as the dominant determinant of AP variability. Additionally, the inward rectifier potassium current (IK1), fast/slow delayed rectifier potassium currents (IKr, IKs), sodium-calcium exchanger current (INaCa), the slow component of the transient outward potassium current (Itos), and L-type calcium current (ICaL) significantly affect AP biomarkers, including duration, plateau potential, and resting potential. Exploiting these results, a hierarchical reduction of the model is performed and demonstrates that retaining only six key parameters can capture sufficiently well individual biomarkers, with a coefficient of determination exceeding 0.9 for selected cases. These findings improve the utility of the Shannon model for personalized simulations, aiding applications like digital twins and drug response predictions in biomedical research.
Single junction CsPbBr3 solar cell coupled with electrolyzer for solar water splitting
Assessing alpha lattice design for heat stress indices and yield stability in wheat genotypes
Features extraction based on Naive Bayes algorithm and TF-IDF for news classification
The rapid proliferation of online news demands robust automated classification systems to enhance information organization and personalized recommendation. Although traditional methods like TF-IDF with Naive Bayes provide foundational solutions, their limitations in capturing semantic nuances and handling real-time demands hinder practical applications. This study proposes a hybrid news classification framework that integrates classical machine learning with modern advances in NLP to address these challenges. Our methodology introduces three key innovations: (1) Domain-Specific Feature Engineering, combining tailored n-grams and entity-aware TF-IDF weighting to amplify discriminative terms; (2) BERT-Guided Feature Selection, leveraging distilled BERT to identify contextually important words and resolve rare-term ambiguities; and (3) Computationally Efficient Deployment, achieving 95.2% of the accuracy of BERT at 1/52.4th of the inference cost. Evaluated on a balanced corpus of Sina News articles in 11 categories, the system demonstrates a test precision of 95.12% (vs. 84.43% for SVM+TF-IDF baseline), with statistically significant improvements confirmed by 5-fold cross-validation(p < 0.01). The critical findings reveal strong performance in distinguishing semantically distinct categories, while exposing challenges in fine-grained differentiation. The efficiency of the framework (2.1 inference latency) and scalability (linear utilization of CPU resources) validate its practicality for real-world deployment. This work bridges the gap between traditional feature engineering and transformer-based models, offering a cost-effective solution for news platforms. Future research will explore hierarchical classification and the adaptation of dynamic topics to further refine semantic boundaries.
De novo pyrimidine biosynthesis inhibition synergizes with BCL-XL targeting in pancreatic cancer
Abstract Oncogenic KRAS induces metabolic rewiring in pancreatic ductal adenocarcinoma (PDAC) characterized, in part, by dependency on de novo pyrimidine biosynthesis. Pharmacologic inhibition of dihydroorotate dehydrogenase (DHODH), an enzyme in the de novo pyrimidine synthesis pathway, delays pancreatic tumor growth; however, limited monotherapy efficacy suggests that compensatory pathways may drive resistance. Here, we use an integrated metabolomic, proteomic and in vitro and in vivo DHODH inhibitor-anchored genetic screening approach to identify compensatory pathways to DHODH inhibition (DHODHi) and targets for combination therapy strategies. We demonstrate that DHODHi alters the apoptotic regulatory proteome thereby enhancing sensitivity to inhibitors of the anti-apoptotic BCL2L1 (BCL-XL) protein. Co-targeting DHODH and BCL-XL synergistically induces apoptosis in PDAC cells and patient-derived organoids. The combination of DHODH inhibition with Brequinar and BCL-XL degradation by DT2216, a proteolysis targeting chimera (PROTAC), significantly inhibits PDAC tumor growth. These data define mechanisms of adaptation to DHODHi and support combination therapy targeting BCL-XL in PDAC.
Phytochemical and biological characterization of extracted natural colors from fruits of monotheca buxifolia
Evaluation of health effect on workers exposed to methyl bromide with prefrontal event-related potential
Methyl bromide (MB) is a potent fumigant used to control pests in soil and agricultural products. As an ozone-depleting substance, MB has been largely replaced by safer alternatives. MB is highly toxic to humans and has been shown to adversely affect asymptomatic workers’ central and autonomic nervous systems and vascular health. However, its impact on perceptual and cognitive abilities remains underexplored. In this study, we examined the effects of MB exposure on cognitive functions in asymptomatic workers. Event-related potential (ERP) indices, which reflect perceptual and cognitive processes, and urinary bromide ion (Br-) concentrations were assessed in 32 fumigators (study group) and 18 inspectors (control group) before and after fumigation. Post-work ERP latency and amplitude changes in inspectors were significant (P < 0.01), similar to those observed in healthy individuals. In contrast, ERP changes in fumigators were not significant compared to pre-work values; this suggests that MB negatively impacts cognitive health. Additionally, Br‑ levels in fumigators rose sharply after work (P < 0.001), while inspectors showed no such increase. The elevated Br- levels and nonenhanced ERP indices in fumigators after MB exposure indicate adverse health effects despite the absence of symptoms.
Imaging a light-induced molecular elimination reaction with an X-ray free-electron laser
Lycium barbarum glycopeptide mitigates retinal ischemia-reperfusion injury through its anti-inflammatory, anti- senescence, and anti-apoptosis properties
Association between lifestyle-related factors and low back pain: Evidence from a Japanese population–based study
Low back pain (LBP) is a major public health issue, and lifestyle-related factors (LRFs) are increasingly recognized as key contributors to LBP. However, comprehensive studies using recent data concerning the association between LBP and LRFs remain limited. In this study, a nationally representative sample of Japanese adults were surveyed to evaluate the relationship between LRFs and LBP and to explore how these factors relate to both the severity and chronicity of LBP. A cross-sectional nationwide survey was conducted among 5000 randomly selected Japanese adults aged 20–90 years; valid responses were obtained from 2188. Participants were analyzed using three different methods: (1) those with or without current LBP, (2) those with no/mild or moderate/severe pain, and (3) those with or without chronic LBP. Key LRFs included body mass index, alcohol consumption, smoking, exercise habits, comorbidities (dyslipidemia, diabetes, and hypertension), and self-image regarding body shape. Multivariable logistic regression analysis revealed that current LBP was significantly associated with body mass index (odds ratio [OR]=1.04, 95% confidence interval [CI]: 1.00–1.07), alcohol consumption (OR=1.37, 95% CI: 1.04–1.80), smoking (OR=1.63, 95% CI: 1.21–2.20), and dyslipidemia (OR=1.51, 95% CI: 1.06–2.13), and the severity of LBP was associated with smoking (OR=1.77, 95% CI: 1.19–2.64), lack of exercise (OR=1.55, 95% CI: 1.10–2.15), and dyslipidemia (OR=1.64, 95% CI: 1.06–2.55). In addition, smoking was the only LRF significantly associated with chronic LBP (OR=1.70, 95% CI: 1.23–2.34). Multiple LRFs are associated with the prevalence of LBP. Stratified analysis provided deeper insight into specific risk factors for LBP. In particular, dyslipidemia is linked to pain severity, whereas smoking is associated with both severity and chronicity. Future longitudinal studies should focus on the influence of these key LRFs on onset, severity, and chronicity of LBP.
Slip-actuated bionic tactile sensing system with dynamic DC generator integrated E-textile for dexterous robotic manipulation
Brain functional connectivity characteristics at various levels of inhibitory function in elderly individuals with cognitive impairment
Field study on routine procedures for navel care in neonatal calves on dairy farms in Eastern Germany
Clean conditions and prophylactic measures around calving are essential for the health and welfare of calves. Therefore, the objective of this study was to evaluate the association of different navel care (NC) practices on the occurrence of omphalitis in neonatal dairy calves. Between December 2016 and July 2019, 196 dairy farms in Eastern Germany were visited once within a large-scale cross-sectional study. 1,967 calves aged five to 21 days were clinically examined, including palpation of the external umbilicus for inflammation signs. Furthermore, information on animal health and farm management, including the implementation of NC, was obtained through interviews with the farm or herd manager. Causal diagrams were drawn, containing variables considering NC (practice of NC, method of application, preparation applied, frequency of NC, time of first NC, wearing gloves during NC) as influence variables, omphalitis as target variable, and all potential confounders to perform multivariable statistical analyses at animal level. Over one-fourth of all calves examined showed omphalitis signs (n = 525 calves, 26.7%). The odds of omphalitis tended to increase (OR = 2.3) if no NC was performed compared to regular NC. Almost half of all other variables analysed seemed relevant for the occurrence of omphalitis. Administering the preparation into the umbilical cord reduced the odds of omphalitis by 62% compared to no NC. Repeated applications tended to decrease the odds of omphalitis by 44% compared to single applications. Furthermore, wearing gloves during NC tended to increase the odds of omphalitis by 30% compared to not wearing gloves. Neither the preparation applied, the method of application, nor the timing of NC had an impact on the omphalitis occurrence. Considering these results, different NC practices influence the odds of omphalitis in neonatal dairy calves. Nevertheless, further investigations are necessary regarding the application procedure of NC during the daily farm routine.
Transposons and accessory genes drive adaptation in a clonally evolving fungal pathogen
Accurate recognition of UAVs on multi-scenario perception with YOLOv9-CAG
Investigating the impact of social media images on users’ sentiments towards sociopolitical events based on deep artificial intelligence
This paper presents the findings of the research aimed at investigating the influence of visual content, posted on social media in shaping users’ sentiments towards specific sociopolitical events. The study analyzed various sociopolitical topics by examining posts containing relevant hashtags and keywords, along with their associated images and comments. Using advanced machine learning and deep learning methods for sentiment analysis, textual data were classified to determine the expressed sentiments. Additionally, the correlation between posted visual content and user sentiments has been studied. A particular emphasis was placed on understanding how these visuals impact users’ attitudes toward the events. The research resulted in a comprehensive dataset comprising labeled images and their comments, offering valuable insights into the dynamics of public opinion formation through social media. This study investigates the influence of social media images on user sentiment toward sociopolitical events using deep learning-based sentiment analysis. By analyzing posts from movements such as Black Lives Matter, Women’s March, Climate Change Protests, and Anti-war Demonstrations, we identified a strong correlation between visual content and public sentiment. Our results reveal that Anti-war Demonstrations exhibit the highest correlation (PLCC: 0.709, SROCC: 0.723), while Climate Change Protests display the lowest alignment (PLCC: 0.531, SROCC: 0.611). Overall, the study finds a consistent positive correlation (PLCC range: 0.615–0.709, SROCC: 0.611–0.723) across movements, indicating the significant role of visual content in shaping the public opinion.