Seismic Earth Pressures of Retaining Wall from Large Shaking Table Tests

To ascertain seismic response of retaining wall in the Wenchuan earthquake, large shaking table tests are performed and an acceleration record is acted in 3 directions. In the tests, acceleration time history recorded at Wolong station in the Wenchuan earthquake is used to excite the model wall. Results from the tests show that the location of dynamic resultant earth pressure is 0.35–0.49 H from toe of the wall for road shoulder retaining wall on rock foundation, 0.33–0.42 H for embankment retaining wall on rock foundation, and 0.46–0.77 H for road shoulder retaining wall on soil foundation. Besides, dynamic earth pressure increases with the increase of ground shaking from 0.1 g to 0.9 g and the relationship is nonlinear. The distribution is closed to for PGA less than 0.4 g but larger for PGA larger than and equal to 0.4 g, especially on the soil foundation. After the comparison of measured earth pressures and theoretical results by pseudodynamic method and pseudostatic method, results of the former are consistent with those of the shaking table test, but results of the latter method are smaller than measured

Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits

The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

Therapeutic Angiogenesis of PLGA-Heparin Nanoparticle in Mouse Ischemic Limb

Objective. To evaluate the possibility and efficacy of the nanoparticle encapsulating heparin as a novel delivery system to treat ischemic disease. Methods. Firstly, to synthesize the PLGA heparin and test the surface morphology, the average diameter, the loading efficiency, and the release time in vitro, then inject the PLGA heparin into mouse ischemic limbs to observe the perfusion recovery with LDPI at the time of postischemic 7, 14, 21, and 28 days, and, finally, test the expression of VEGF and HGF, the number of the neovessels and record the necrotic score of ischemic limbs. Results. The surface morphology of the PLGA heparin was smooth, the average diameter was 297 nm, the loading efficiency was 5.35%, and the release period maintained for 14 days. In animal experiment, the perfusion recovery, HGF expression level, and capillary density in PLGA-heparin group were significantly higher than that in control group, and this was consistent with less ischemic limb necrosis. Conclusion. Nanoparticle encapsulating heparin could be successful and efficient in ischemic disease. The therapeutic angiogenesis of PLGA heparin might be due to the prolongation of its biological effects with stimulating growth factor expression

Smoking susceptibility and its predictors among adolescents in China: Evidence from Ningbo City

Susceptibility to smoking is a risk factor of actual adolescent smoking behaviors. This study aimed to estimate the rate of smoking susceptibility and its predictors in China with a sample of 4,695 junior high school students in Ningbo, China. Core questions from the Global Youth Tobacco Survey (GYTS) were adapted to the China context and administered to these students. The rate of smoking susceptibility, measured by “Do you foresee yourself taking up smoking in the next 12 months”, is 6.1%. Results from logistic regression suggested that among boys, adolescents’ health knowledge that smoking can cause lung cancer (OR=2.73), the belief that smoking can help people relax (OR=2.32), and self-report of never having seen anti-smoking information on campus (OR=1.80) predicted increased susceptibility to smoking. Conversely, the belief that boys who smoke are less attractive (OR=0.64), that parents will have a problem with their child smoking (OR=0.50), having no friends or classmates who smoke (OR=0.22), and not seeing teachers smoke in the previous week (OR=0.61) predicted decreased susceptibility to smoking. Findings for girls were similar. This study suggested the need for comprehensive programs aiming to improve family, peer, and school environments to decrease smoking susceptibility among adolescents

Smoking Experimentation among Elementary School Students in China: Influences from Peers, Families, and the School Environment

The aim of this study was to investigate experimentation with smoking among primary school students in China. Data were acquired from a recent survey of 4,073 students in grades 4 to 6 (ages 9–12) in 11 primary schools of Ningbo City. The questions were adapted from the Global Youth Tobacco Survey (GYTS). Results suggest that although the Chinese Ministry of Education (MOE) encourages smoke-free schools, experimentation with cigarettes remains a serious problem among primary school students in China. Peers, family members, and the school environment play important roles in influencing smoking experimentation among students. Having a friend who smoked, seeing a family member smoke, and observing a teacher smoking on campus predicted a higher risk of experimentation with smoking; the exposure to anti-tobacco materials at school predicted a lower risk of experimentation with smoking. The evidence suggests that public health practitioners and policymakers should seek to ensure the implementation of smoke-free policies and that intervention should target young people, families, and communities to curb the commencement of smoking among children and adolescents in China

Generative AI for brain image computing and brain network computing: a review

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial

Antimicrobial Resistance, Virulence, and Genetic Characterization of Methicillin-Resistant Staphylococcus aureus Recovered from Ready-to-Eat (RTE) Food in China: A New Challenge for Food Safety

The objective of the present study was to determine the prevalence, antimicrobial resistance, virulence profiles, and molecular characteristics of methicillin-resistant Staphylococcus aureus (MRSA) obtained from ready-to-eat (RTE) foods in China. Two hundred seventy-six RTE food-associated S. aureus isolates were collected from 25 provinces across China in 2018, then characterized by antimicrobial susceptibility testing, virulence factors detecting, multilocus sequence typing (MLST), spa typing, SCC mec typing and pulsed-field gel electrophoresis (PFGE). Two hundred fifty isolates (90.6%) were resistant to at least one antimicrobial agent; 73 (26.4%) isolates were multi-drug resistant (MDR). Thirty MRSA isolates were identified, among which nine toxin genes ( sea, seb, sec, sed, seh, selk, sell, selq , and tsst-1 ) were detected. Sixty percent (18/30) of the MRSA isolates harbored multiple toxin genes. Four virulence gene patterns were identified, with seb-selk-selq (30/30) being the most common pattern. Thirteen sequence types, as well as 13 spa and 4 SCC mec types were found among 30 MRSA isolates. The most prevalent MRSA lineages were CC59-t437-SCC mec IV/V (23.3% [7/30]), CC398-t011-SCC mec V (23.3% [7/30]), and CC1-t114-SCC mec IV (16.7% [5/30]). Our findings highlight the importance for the identification of prevalent clones, assessment of drug-resistance and virulence, and formulation of food safety measures for public health

AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts

Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep learning method achieved a micro-F1 score of 62.34% in the hierarchical text classification task. Meanwhile, in the text summarization task, GPT-4 attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the experimental deep learning model's performance. However, it may suffer from an issue of hallucination. We have made all models and codes publicly available to support further research in this field

Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psychological intervention. In this paper, we introduce two novel datasets from Chinese social media: SOS-HL-1K for suicidal risk classification and SocialCD-3K for cognitive distortions detection. The SOS-HL-1K dataset contained 1,249 posts and SocialCD-3K dataset was a multi-label classification dataset that containing 3,407 posts. We propose a comprehensive evaluation using two supervised learning methods and eight large language models (LLMs) on the proposed datasets. From the prompt engineering perspective, we experimented with two types of prompt strategies, including four zero-shot and five few-shot strategies. We also evaluated the performance of the LLMs after fine-tuning on the proposed tasks. The experimental results show that there is still a huge gap between LLMs relying only on prompt engineering and supervised learning. In the suicide classification task, this gap is 6.95% points in F1-score, while in the cognitive distortion task, the gap is even more pronounced, reaching 31.53% points in F1-score. However, after fine-tuning, this difference is significantly reduced. In the suicide and cognitive distortion classification tasks, the gap decreases to 4.31% and 3.14%, respectively. This research highlights the potential of LLMs in psychological contexts, but supervised learning remains necessary for more challenging tasks. All datasets and code are made available.Comment: 10 page