Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectories and influencing factors in adolescent procrastination behavior throughout the COVID-19 pandemic: a four-wave prospective longitudinal study

Background Despite the growing attention given to adolescent behavior problems, little is known about the trajectories and factors that have influenced adolescent procrastination during the COVID-19 pandemic. This study monitors changes in procrastination behavior among Chinese adolescents during the pandemic and identifies vulnerable groups. Methods A four-wave study using a representative sample of 11-to 18-year-olds in China was conducted, with baseline data collected in June 2020 (n = 4,156; 49% girls) and follow-ups in December 2020 (n = 3,392; 50% girls), August 2021 (n = 2,380; 48% girls), and October 2021 (n = 1,485; 49% girls). Procrastination behavior was assessed using the General Procrastination Scale. Latent growth curve models, latent growth mixture modes, and multivariate logistic regression models were used to describe the trajectory of procrastination and identify predictors of deterioration. Results The proportion and overall trends of adolescent procrastination increased with the pandemic. Higher parental over-protection was a contributing factor to the higher baseline levels leading to the faster growth of adolescent procrastination. The model identified three distinct trajectories of low-increasing [including 2,057 participants (49.5%)], moderate-stable [including 1,879 participants (45.2%)], and high-decreasing procrastination [including 220 participants (5.3%)]. More daily leisure screen-time, lower frequency of exercise weekly, and dissatisfaction with distance learning were the top three risk factors for moderate-stable and high-decreasing procrastination compared to low-increasing procrastination. Adolescents with mothers with a higher level of education were more liable to be high-decreasing procrastination than moderate-stable procrastination. Conclusion The proportion and overall trends of adolescent procrastination increased with the pandemic. The categories of procrastination among adolescents during that time period were probed. Also, the study further clarified the risk factors for severe and moderate procrastination relative to no procrastination. Thus, effective procrastination prevention and intervention strategies need to be implemented to support adolescents, particularly those at risk.
A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root

Recent clinical studies have suggested that introducing 3D patient-specific aortic root models into the pre-operative assessment procedure of transcatheter aortic valve replacement (TAVR) would reduce the incident rate of peri-operative complications. Tradition manual segmentation is labor-intensive and low-efficient, which cannot meet the clinical demands of processing large data volumes. Recent developments in machine learning provided a viable way for accurate and efficient medical image segmentation for 3D patient-specific models automatically. This study quantitively evaluated the auto segmentation quality and efficiency of the four popular segmentation-dedicated three-dimensional (3D) convolutional neural network (CNN) architectures, including 3D UNet, VNet, 3D Res-UNet and SegResNet. All the CNNs were implemented in PyTorch platform, and low-dose CTA image sets of 98 anonymized patients were retrospectively selected from the database for training and testing of the CNNs. The results showed that despite all four 3D CNNs having similar recall, Dice similarity coefficient (DSC), and Jaccard index on the segmentation of the aortic root, the Hausdorff distance (HD) of the segmentation results from 3D Res-UNet is 8.56 ± 2.28, which is only 9.8% higher than that of VNet, but 25.5% and 86.4% lower than that of 3D UNet and SegResNet, respectively. In addition, 3D Res-UNet and VNet also performed better in the 3D deviation location of interest analysis focusing on the aortic valve and the bottom of the aortic root. Although 3D Res-UNet and VNet are evenly matched in the aspect of classical segmentation quality evaluation metrics and 3D deviation location of interest analysis, 3D Res-UNet is the most efficient CNN architecture with an average segmentation time of 0.10 ± 0.04 s, which is 91.2%, 95.3% and 64.3% faster than 3D UNet, VNet and SegResNet, respectively. The results from this study suggested that 3D Res-UNet is a suitable candidate for accurate and fast automatic aortic root segmentation for pre-operative assessment of TAVR.
Determinants of psychotherapists’ attitudes to online psychotherapy

Introduction Online psychotherapy is a form of work that is becoming more and more popular. Public health problems, such as COVID-19, forced mental health professionals and patients to incorporate new methodologies such as the use of electronic media and internet to provide follow-up, treatment and also supervision. The aim of this study was to investigate which factors shape the therapists’ attitudes toward online psychotherapy during a pandemic taking into account: (1) attitudes toward the COVID-19 pandemic (fear of contagion, pandemic fatigue, etc.), (2) personal characteristics of the psychotherapists (age, gender, feeling of efficacy, anxiety, depression, etc.), and (3) characteristics of the psychotherapeutic practice (guideline procedure, client age group, professional experience, etc). Materials and methods Study participants were 177 psychotherapists from four European countries: Poland (n = 48), Germany (n = 44), Sweden (n = 49), and Portugal (n = 36). Data were collected by means of an individual online survey through the original questionnaire and the standardized scales: a modified version of the Attitudes toward Psychological Online Interventions Scale (APOI), Fear of Contagion by COVID-19 Scale (FCS COVID-19), Pandemic Fatigue Scale (PFS), Hospital Anxiety and Depression Scale (HADS), Social Support Questionnaire (F-SozU K-14), and the Sense of Efficiency Test (SET). Results Determinants that impacted psychotherapists’ attitudes toward online therapy were: COVID-19 belief in prevention—keeping distance and hand disinfection, pandemic behavioral fatigue, previous online therapy experience (including voice call), working with youth and adults. Our study showed that belief in the sense of prevention in the form of taking care of hand disinfection before the session, pandemic behavioral fatigue and experience in working with adults were significant predictors of negative attitudes of therapists toward online psychological interventions. On the other hand, belief in the sense of prevention in the form of keeping distance during the session had a positive effect on general attitudes toward therapy conducted via the internet. Discussion The online therapy boom during the COVID-19 pandemic has spawned a powerful tool for psychotherapists. More research in this area and training of psychotherapists are needed for online psychological interventions to become an effective therapy format that is accepted by patients and therapists alike.
Motor learning in developmental coordination disorder: behavioral and neuroimaging study

Developmental coordination disorder (DCD) is characterized by motor learning deficits that are poorly understood within whole-body activities context. Here we present results of one of the largest non-randomized interventional trials combining brain imaging and motion capture techniques to examine motor skill acquisition and its underpinning mechanisms in adolescents with and without DCD. A total of 86 adolescents with low fitness levels (including 48 with DCD) were trained on a novel stepping task for a duration of 7 weeks. Motor performance during the stepping task was assessed under single and dual-task conditions. Concurrent cortical activation in the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). Additionally, structural and functional magnetic resonance imaging (MRI) was conducted during a similar stepping task at the beginning of the trial. The results indicate that adolescents with DCD performed similarly to their peers with lower levels of fitness in the novel stepping task and demonstrated the ability to learn and improve motor performance. Both groups showed significant improvements in both tasks and under single- and dual-task conditions at post-intervention and follow-up compared to baseline. While both groups initially made more errors in the Stroop task under dual-task conditions, at follow-up, a significant difference between single- and dual-task conditions was observed only in the DCD group. Notably, differences in prefrontal activation patterns between the groups emerged at different time points and task conditions. Adolescents with DCD exhibited distinct prefrontal activation responses during the learning and performance of a motor task, particularly when complexity was increased by concurrent cognitive tasks. Furthermore, a relationship was observed between MRI brain structure and function measures and initial performance in the novel stepping task. Overall, these findings suggest that strategies that address task and environmental complexities, while simultaneously enhancing brain activity through a range of tasks, offer opportunities to increase the participation of adolescents with low fitness in physical activity and sports.
Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease

Background Fatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren’s Syndrome and Systemic Lupus Erythematosus and an important predictor of patient’s health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue. Method Observational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features. Results Cluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models. Conclusion These results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue.
Intelligent grid load forecasting based on BERT network model in low-carbon economy

In recent years, the reduction of high carbon emissions has become a paramount objective for industries worldwide. In response, enterprises and industries are actively pursuing low-carbon transformations. Within this context, power systems have a pivotal role, as they are the primary drivers of national development. Efficient energy scheduling and utilization have therefore become critical concerns. The convergence of smart grid technology and artificial intelligence has propelled transformer load forecasting to the forefront of enterprise power demand management. Traditional forecasting methods relying on regression analysis and support vector machines are ill-equipped to handle the growing complexity and diversity of load forecasting requirements. This paper presents a BERT-based power load forecasting method that leverages natural language processing and image processing techniques to enhance the accuracy and efficiency of transformer load forecasting in smart grids. The proposed approach involves using BERT for data preprocessing, analysis, and feature extraction on long-term historical load data from power grid transformers. Multiple rounds of training and fine-tuning are then conducted on the BERT architecture using the preprocessed training datasets. Finally, the trained BERT model is used to predict the transformer load, and the predicted results are compared with those obtained based on long short-term memory (LSTM) and actual composite values. The experimental results show that compared with LSTM method, the BERT-based model has higher short-term power load prediction accuracy and feature extraction capability. Moreover, the proposed scheme enables high levels of accuracy, thereby providing valuable support for resource management in power dispatching departments and offering theoretical guidance for carbon reduction initiatives.
Effects of dynamic zero COVID-19 policy on anxiety status and lifestyle changes of pregnant women in rural South China: a survey-based analysis by propensity score matching method

Introduction The coronavirus disease 2019 (COVID-19) pandemic triggered a global public health crisis and has brought an unprecedented impact on pregnant women. The problems faced by pregnant women in the rural areas of China during the epidemic are different from those in urban areas. Although the epidemic situation in China has gradually improved, studying the impact of the previous dynamic zero COVID-19 policy on the anxiety status and lifestyle of pregnant women in rural areas of China, is still necessary. Methods A cross-sectional survey of pregnant women in rural South China was conducted from September 2021 to June 2022.Using questionnaires, sociodemographic characteristics, anxiety status, physical activity, sleep quality, and dietary status of the population were collected. Using the propensity score matching method, the effect of the dynamic zero COVID-19 strategy on the anxiety status and lifestyle of pregnant women was analyzed. Results Among the pregnant women in the policy group (n = 136) and the control group (n = 680), 25.7 and 22.4% had anxiety disorders, 83.1 and 84.7% had low or medium levels of physical activity, and 28.7 and 29.1% had sleep disorders, respectively. However, no significant difference (p > 0.05) was observed between the two groups. Compared with control group, the intake of fruit in the policy group increased significantly (p = 0.019), whereas that of aquatic products and eggs decreased significantly (p = 0.027). Both groups exhibited an unreasonable dietary structure and poor compliance with the Chinese dietary guidelines for pregnant women (p > 0.05). The proportion of pregnant women in the policy group, whose intake of stable food (p = 0.002), soybean, and nuts (p = 0.004) was less than the recommended amount, was significantly higher than that in the control group. Discussion The dynamic zero COVID-19 strategy had little impact on the anxiety status, physical activity, and sleep disorders of pregnant women in the rural areas of South China. However, it affected their intake of certain food groups. Improving corresponding food supply and organized nutritional support should be addressed as a strategic approach to improve the health of pregnant women in rural South China during the pandemic.
Impact of coronavirus disease (COVID-19) on food security: bibliometric analysis and empirical evidence

Impact of coronavirus disease on sustainable growth and food security is dramatically negative. Despite significant number of publications focused on food security and coronavirus disease issues, a deeper analysis of food security damages activated by COVID-19 is necessary. This determines the main task of this research. Theoretical block of the study involves bibliometric analysis of relevant Scopus publications using VOSviewer. Empirical block of the study involves: 1) formation of Food Security Index for 15 European countries based on a combination of Principal Component Analysis, ranking, Fishburn formula and additive convolution; 2) panel data regression modelling aimed at clarifying impact of macroeconomic indicators and healthcare expenditures on food security during 2000–2021 (model 1), 2000–2019 (model 2) and 2020–2021 (model 3). Model 1 demonstrates that 1% increase in current health care expenditures leads to 0.003 units increase in Food Security Index; impact of the dummy variable characterizing COVID-19 pandemic is also positive. Model 2 demonstrates similar results. Model 3 confirms only positive and statistically significant impact of trade and agricultural land on Food Security Index. The results can be useful to government officials for adjusting political measures in food governance, political decision-making and good governance, recovery of sustainable growth in post-pandemic period.
Unveiling the genomic landscape of possible metastatic malignant transformation of teratoma secondary to cisplatin-chemotherapy: a Tempus gene analysis-based case report literature review

In this case report, we describe a patient who developed metastatic liver cancer of unknown primary origin one year following the surgical removal of a retroperitoneal adenocarcinoma. The retroperitoneal adenocarcinoma is considered a malignant transformation of teratoma (MTT), given the patient’s distant history of testicular tumor excised 25 years prior and treated with chemotherapy. Despite no primary tumor being identified, the leading primary hypothesis is that the liver metastasis stemmed from the resected retroperitoneal adenocarcinoma from one year prior. We theorize that the patient’s cisplatin-based chemotherapy 25 years ago may have triggered the MTT, as documented in the existing literature. Using TEMPUS gene testing on both the retroperitoneal adenocarcinoma and the recently discovered liver metastasis, we identified several genes with variants of unknown significance (VUS) that could potentially be linked to cisplatin chemotherapy resistance. While we cannot conclude that this patient definitively underwent MTT, it remains the most plausible explanation. Future research should investigate both the validity of the genes we have uncovered with respect to cisplatin resistance, as well as other genes associated with cisplatin resistance to further understand the pathogenesis of cisplatin resistance for better prediction of treatment response. As the world of medicine shifts towards individualized therapies and precision medicine, reporting and analyzing genetic mutations derived from tumors remains imperative. Our case report aims to contribute to the growing database of defined mutations and underscores the immense potential of genetic analysis in directing personalized treatment options.
Metatranscriptomics unmasks Mollusca virome with a remarkable presence of rhabdovirus in cephalopods

Introduction Mollusks play a significant role in marine ecosystems and have economic value for aquaculture. Sometimes, unexpected and unexplained mortalities among mollusks have been described. The role of potential pathogens such as viruses remains unknown due to the lack of molluscan cell cultures, which is one of the major drawbacks to determining the viral role in such mortalities. Several oceanographic studies have suggested a high abundance of viruses in the oceans. Virus identification and understanding of viral interaction with organisms in marine ecosystems are in their infancy. Metatranscriptomics could become a useful tool to identify viruses using a shotgun approach and the growing number of viral genomes and sequences deposited in public databases. Methods In this work, several bioinformatics approaches were set up to screen Mollusca RNA sequences to find and confirm viral traces in their transcriptomes. This meta-analysis included an extensive search of SRA datasets belonging to mollusks available in the NCBI database, selecting a total of 55 SRA datasets that were further analyzed searching for viral sequences. Results Twenty-two bivalves, 19 cephalopods and 16 gastropods from 16 geographical origins and 17 different tissues were considered. The domain search approach was the most productive method to find viral sequences. This virus search showed that Cephalopoda samples (Idiosepius notoides and Amphioctopus fangsiao) exhibited the highest number of virus identifications. Some of the detected viral sequences were similar or identical to others previously identified. However, 33 putative new viruses were identified and analyzed phylogenetically when the RdRp domain was available. Specifically, Cephalopoda samples showed a considerable number of viruses belonging to the Rhabdoviridae family.
Enjoyment, boredom, and perceived effectiveness of learners in language MOOCs: the mediating effect of self-regulated learning

Self-regulated learning in technology-supported environments has attracted much scholarly attention in recent years. With the rapid expansion of online education, students’ emotions have also been studied extensively in second language acquisition. However, few empirical studies have examined the interrelationship between students’ self-regulated learning and emotions in the emerging field of language MOOCs (LMOOCs). This study bridged this gap by exploring the relationship between foreign language enjoyment (FLE), boredom (FLB), self-regulated learning (SRL), and perceived effectiveness in LMOOC learning. Data were collected among 356 successful learners of a language MOOC in mainland China through a cross-sectional study. The results showed that LMOOC learners had a high level of enjoyment and a moderate level of boredom. A significantly positive relationship was noted between FLE and SRL while a negative relationship was found between FLB and SRL. SRL was confirmed to be the mediator between FLE, FLB, and PE, which partially mediated the effects of FLE on PE and fully mediated the effects of FLB on PE. Perceived effectiveness was predicted by all SRL strategies and time management significantly predicted perceived effectiveness. The results provided pedagogical implications for students to develop positive emotions and effective SRL strategies to achieve better learning outcomes in LMOOC learning.
Line × tester analysis for study of combining ability of quantitative traits in tomato

A study was undertaken to estimate the general and specific combining ability in tomato through line × tester analysis involving 12 lines (females) and 2 testers (males). The combining ability analysis revealed that the variances due to lines and crosses were significant for all the traits studied. The mean squares due to testers and lines × testers were significant for all the traits except fruit weight, plant height and days to maturty respectively. Among parents, the lines BTL-33 and BTL-11 and tester Roma proved the best general combiners for yield and its component traits. The best specific cross-combinations were BTN-46 × Roma, BTL-11 × AC-402 and BTR-49 × Roma. The best cross-combinations did not necessarily involve good general combiners as their parents. Based upon the contribution of lines, testers and their intereactions, it was evident that the variability among the crosses was mainly due to the contribution of lines only.