Numerical Simulation Study on Mechanical Characteristics of Submarine Cable During Laying Process

[Introduction] Based on an offshore wind farm construction project in Qidong, Jiangsu Province, the numerical simulation study on mechanical characteristics of submarine cable during laying process was carried out and the local stress distribution of the suspension span of submarine cables under different trenching depths was analyzed. [Method] A numerical model of submarine cable laying based on ABAQUS numerical simulation software was established. [Result] Results show that the stress of the lifting point at the trailing edge of the submarine cable increases significantly during the burial process compared with that before the burial operation. This is the most dangerous position as the cable at this position has the largest stress during the whole burial process. The trenching depth has a significant effect on the stress at the lifting point at the trailing edge of the cable. With the increase of the trenching depth, the stress at the lifting point at the trailing edge increases correspondingly. [Conclusion] The selection of cable material at the lifting point should be protected in the cable laying operation, and the yield stress parameters of the cable should be mainly based on the stress analysis results of the cable in the burial operation. The research results can provide a reference for submarine cable laying operation

Factors associated with suicidal attempts in female patients with mood disorder

AimThis study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD).MethodThe study included 396 female participants who were diagnosed with MD Diagnostic Group (F30–F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model.ResultLASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (β = −0.02, SE = 0.02), social dysfunction (β = 1.72, SE = 0.24), time interval between first onset and first dose (β = 0.03, SE = 0.01), polarity at onset (β = −1.13, SE = 0.25), and times of hospitalization (β = −0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71–0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750–0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks.ConclusionFive factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts

Development and validation of a prediction nomogram for non-suicidal self-injury in female patients with mood disorder

BackgroundNon-suicidal self-injury (NSSI) is a highly prevalent behavioral problem among people with mental disorders that can result in numerous adverse outcomes. The present study aimed to systematically analyze the risk factors associated with NSSI to investigate a predictive model for female patients with mood disorders.MethodsA cross-sectional survey among 396 female patients was analyzed. All participants met the mood disorder diagnostic groups (F30–F39) based on the Diseases and Related Health Problems 10th Revision (ICD-10). The Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test were used to assess the differences of demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses was then used to identify the risk factors of NSSI. A nomogram was further used to construct a prediction model.ResultsAfter LASSO regression selection, 6 variables remained significant predictors of NSSI. Psychotic symptom at first-episode (β = 0.59) and social dysfunction (β = 1.06) increased the risk of NSSI. Meanwhile, stable marital status (β = −0.48), later age of onset (β = −0.01), no depression at onset (β = −1.13), and timely hospitalizations (β = −0.10) can decrease the risk of NSSI. The C-index of the nomogram was 0.73 in the internal bootstrap validation sets, indicated that the nomogram had a good consistency.ConclusionOur findings suggest that the demographic information and clinical characteristics of NSSI can be used in a nomogram to predict the risk of NSSI in Chinese female patients with mood disorders

Activation-Induced T Helper Cell Death Contributes to Th1/Th2 Polarization following Murine Schistosoma japonicum Infection

In chronic infectious diseases, such as schistosomiasis, pathogen growth and immunopathology are affected by the induction of a proper balanced Th1/Th2 response to the pathogen and by antigen-triggered activation-induced T cell death. Here, by using S. japonicum infection or schistosome antigens-immunized mouse model, or antigens in vitro stimulation, we report that during the early stage of S. japonicum infection, nonegg antigens trigger Th2 cell apoptosis via the granzyme B signal pathway, contributing to Th1 polarization, which is thought to be associated with worm clearance and severe schistosomiasis. Meanwhile, after the adult worms lay their eggs, the egg antigens trigger Th1 cell apoptosis via the caspase pathway, contributing to Th2 polarization, which is associated with mild pathology and enhanced survival of both worms and their hosts. Thus, our study suggests that S. japonicum antigen-induced Th1 and Th2 cell apoptosis involves the Th1/Th2 shift and favorites both hosts and parasites

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

BackgroundMajor Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.MethodsIn this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.ResultsRegression analysis found significant predictors of MDD to be beta2 (16–24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24–40 Hz) power in the Medial Occipital Cortex (MOC) (B = −12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12–16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8–12 Hz, B = 9.004, p = 0.013) and theta (4–8 Hz, B = −13.582, p = 0.008) with eyes closed were also significant predictors.ConclusionThe study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.LimitationsThe research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

Tumor-penetrating peptide fused EGFR single-domain antibody enhances cancer drug penetration into 3D multicellular spheroids and facilitates effective gastric cancer therapy

Human tumors, including gastric cancer, frequently express high levels of epidermal growth factor receptors (EGFRs), which are associated with a poor prognosis. Targeted delivery of anticancer drugs to cancerous tissues shows potential in sparing unaffected tissues. However, it has been a major challenge for drug penetration in solid tumor tissues due to the complicated tumor microenvironment. We have constructed a recombinant protein named anti-EGFR-iRGD consisting of an anti-EGFR VHH (the variable domain from the heavy chain of the antibody) fused to iRGD, a tumor-specific binding peptide with high permeability. Anti-EGFR-iRGD, which targets EGFR and αvβ3, spreads extensively throughout both the multicellular spheroids and the tumor mass. The recombinant protein anti-EGFR-iRGD also exhibited antitumor activity in tumor cell lines, multicellular spheroids, and mice. Moreover, anti-EGFR-iRGD could improve anticancer drugs, such as doxorubicin (DOX), bevacizumab, nanoparticle permeability and efficacy in multicellular spheroids. This study draws attention to the importance of iRGD peptide in the therapeutic approach of anti-EGFR-iRGD. As a consequence, anti-EGFR-iRGD could be a drug candidate for cancer treatment and a useful adjunct of other anticancer drugs