Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes Diagnosis through Machine Learning: Investigating Algorithms and Data Augmentation for Class Imbalanced BRFSS Dataset

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. Our study focuses on predicting whether a person is at risk of diabetes, considering the individual's health and socio-economic conditions while mitigating the challenges posed by imbalanced data. To minimize the impact of imbalance data, we employed several data augmentation techniques such as oversampling (SMOTE-N), undersampling (ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms. Our study sheds light on the significance of carefully utilizing data augmentation techniques, without any data leakage, in enhancing the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to ML prediction, enabling them to make data-informed strategies.
Hypermedia-based software architecture enables Test-Driven Development

Abstract Objectives Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud. Materials and methods Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test “templates” that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort. Results After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage. Discussion Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser’s application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly. Conclusion Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.
Pathogenic signal peptide variants in the human genome

Abstract Secreted and membrane proteins represent a third of all cellular proteins and contain N-terminal signal peptides that are required for protein targeting to endoplasmic reticulum (ER). Mutations in signal peptides affect protein targeting, translocation, processing, and stability, and are associated with human diseases. However, only a few of them have been identified or characterized. In this report, we identified pathogenic signal peptide variants across the human genome using bioinformatic analyses and predicted the molecular mechanisms of their pathology. We recovered more than 65 thousand signal peptide mutations, over 11 thousand we classified as pathogenic, and proposed framework for distinction of their molecular mechanisms. The pathogenic mutations affect over 3.3 thousand genes coding for secreted and membrane proteins. Most pathogenic mutations alter the signal peptide hydrophobic core, a critical recognition region for the signal recognition particle, potentially activating the Regulation of Aberrant Protein Production (RAPP) quality control and specific mRNA degradation. The remaining pathogenic variants (about 25%) alter either the N-terminal region or signal peptidase processing site that can result in translocation deficiencies at the ER membrane or inhibit protein processing. This work provides a conceptual framework for the identification of mutations across the genome and their connection with human disease.
Study on the status of exploitation and utilization of water resources in harbin and the sustainable development strategy

An introduction of the characteristic of water resources distribution and current status of exploitation and utilization is given in this paper,it's concluded that the water resources shortcoming has been becoming the control factor that limits the development of economics.The exploitation and utilization is not reasonable in a scientific view,and the control of water resources pollution is not mature.It is necessary to carry out water resources sustainable development based on the above analysis,the concrete implementation methods are proposed.
Changes in economic activity and mental distress among young adults during the COVID-19 pandemic: Differences between the first and second infection waves in the UK

Background While infection rates, lockdown policies, and labor market conditions substantially varied across COVID-19 waves, the majority of evidence on young adults’ mental health remains focused on initial responses in early 2020. The variability of the relationship between economic activity and mental health over time therefore remains poorly understood in this age group. Methods Using linear mixed models, we investigated the relationship between current activity and changes in activity and mental distress (GHQ-12) among 1,390 young adults aged 16–34 via the UK Household Longitudinal Study COVID-19 survey. The association was explored in the first (from April to July 2020) and second (from September 2020 to March 2021) infection waves. Current activity was defined as “not working”, “working <17.5 hours/week”, “17.5–35 hours/week”, and “> = 35 hours/week”. Changes in activity were derived from current and pre-pandemic working hours and divided into four categories: “working with no reduced hours”, “working fewer hours”, “no longer working”, and “did not work before the pandemic”. Results During the first wave, no association reached statistical significance. During the second wave: 1) compared to “currently not working”, working 35 or more hours was associated with decreased distress (b = -1.54; 95%CI -2.39, -0.69) and working less than 17.5 hours was not (b = -0.62; 95%CI -1.66, 0.41); 2) compared to “working with no reduced hours compared with before the outbreak”, no longer working was associated with increased distress (b = 1.58, 95%CI 0.61, 2.55) and working with reduced hours was not (b = 0.47, 95%CI -0.24, 1.17). Conclusion Above the mental health inequalities experienced at the start of the pandemic, full-time work–even with variation in work hours–continued to be a protective factor against mental distress among young adults during the second wave in the UK. Stable, full-time work can better support this age group’s mental well-being over time.
Extended 14C Data Base and Revised CALIB 3.0 14C Age Calibration Program

The age calibration program, CALIB (Stuiver & Reimer 1986), first made available in 1986 and subsequently modified in 1987 (revision 2.0 and 2.1), has been amended anew. The 1993 program (revision 3.0) incorporates further refinements and a new calibration data set covering nearly 22,000 cal yr (≈18,400 14C yr). The new data, and corrections to the previously used data set, derive from a 6-yr (1986–1992) time-scale calibration effort of several laboratories.
Classification of mathematical test questions using machine learning on datasets of learning management system questions

Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen’s Kappa and Matthew’s correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level.
The role of perceived autonomy support and fear of failure: A weekly diary study on work-related rumination

Grounded in self-determination theory, the present study examined the weekly fluctuations in different forms of work-related rumination depending on perceived autonomy support and fear of failure at the workplace. Work-related rumination has three dimensions, affective rumination (negative emotions or affect), problem-solving pondering (thinking over the actions to handle the problems), and psychological detachment (mentally distancing oneself from work during nonwork time). In total, 111 employees (Mage = 34.88, SD = 10.43) from various occupations were followed over the course of three weeks via weekly measurements, resulting in 333 matched observations. Multilevel random coefficient modeling showed that on the weeks when employees reported higher levels of perceived autonomy support from the leader, they engaged in affective rumination and problem-solving pondering less. However, weekly fluctuations in psychological detachment from work was not associated with perceived autonomy support. Moreover, on the weeks when employees experienced high fear of failure, they reported less psychological detachment from work during nonwork time. Lastly, within-and and between-person fear of failure moderated the negative link between perceived autonomy support and affective rumination. Findings showed that perceived autonomy support is a protective factor for employees high in both state and trait fear of failure in decreasing affective rumination. Directions for future research and implication for practice were discussed.
Black American women’s attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety

Abstract Objectives This study aimed to understand Black American women’s attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety. Methods A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identified as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as, attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety. Results The findings of the study (N = 395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the Southern region of the United States had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions. Discussion Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety. Conclusion Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase likelihood of adoption and engagement with digital mental health interventions for management of anxiety.
Up-regulation of plasma miRNA-21 and miRNA-422a in postmenopausal osteoporosis

Background Many researchers focused on diverse miRNAs in the progression of osteoporosis in postmenopausal women. This study aimed to evaluate the association between plasma miRNA-21-5p and miRNA-422a with osteoporosis in postmenopausal women. Methods This cross-sectional comparative study was performed on 126 randomly selected postmenopausal women aged 50–65, including 65 osteoporotic and 61 normal-bone mineral density (BMD) women. miRNA-21 and miRNA-422a were identified using qRT-PCR in these women. BMD was evaluated by the dual-energy X-ray absorptiometry method. A binary logistic regression model adjusted for confounders was used to evaluate the associations between plasma miRNAs’ expression levels and osteoporosis. The Area Under Curve (AUC) was calculated to differentiate low BMD in the postmenopausal period using Receiver-Operator Characteristic (ROC) curves. Results miRNA-21 and miRNA-422a were significantly up-regulated in osteoporotic compared to non-osteoporotic postmenopausal women. The expression levels of miRNA-21 and miRNA-422a indicated a significant reverse correlation with both lumbar spine and femoral neck density. After adjusting the confounders, the likelihood of osteoporosis in the postmenopausal women with under the median plasma levels of miRNA-21 (OR = 0.025; 95% CI: 0.003 to 0.198, p<0.001) and miRNA-422a (OR = 0.037; 95% CI: 0.007 to 0.211, p<0.001) was significantly less than the women with the levels above the median. There were significant inverse correlations between miRNA-21 (p<0.001, r = -0.511) and miRNA-422a (p<0.001, r = -0.682) with BMD-lumbar spine as well as an inverse correlation between miRNA-21(p<0.001, r = -0.374) and miRNA-422a (p<0.001, r = -0.602) with BMD-femoral neck. The AUC (95%CI) for miRNA-21 and miRNA-422a was 0.84 (0.77 to 0.91) and 0.98 (0.96 to 0.99), respectively. ROC analysis illustrated that sensitivity and specificity values were 83.1% and 74%, respectively, for miRNA-21 at the cut-off point of 3.38. Also, at the cut-off point of 2.86, a sensitivity of 94% as well as a specificity of 89% was determined for miRNA-422a. Conclusions This study indicated that the odds of osteoporosis in postmenopausal women increased with the higher expression of plasma miRNA-21 and miRNA-422a.
Activity‐aggression behavioural syndromes exist in males but not in females of the field cricket Teleogryllus emma

Abstract Previous studies on sex differences in behaviour have largely focused on differences in average behaviours between sexes. However, males and females can diverge not only in average behaviours but also in the direction of behavioural correlations at the individual level (i.e. behavioural syndromes). Behavioural syndromes, with their potential to constrain the independent evolution of behaviours, may play a role in shaping sex‐specific responses to selection and contributing to the development of sex differences in behaviour. Despite the pivotal role of behavioural syndromes in the evolution of sexual dimorphism in behaviour, robust empirical evidence of sex differences in behavioural syndromes based on repeated measurements of behaviours is scarce. In this study, we conducted repeated measurements of activity and aggression in male and female field crickets Teleogryllus emma, providing evidence of sex differences in the existence of behavioural syndromes. Males exhibited a significantly positive behavioural syndrome between activity and aggression, whereas females, in contrast, did not show any aggressive behaviour, resulting in the absence of such a syndrome. The sex differences in the existence of the activity‐aggression behavioural syndromes in this species could be attributed to differences in selection. Selection favouring more active and aggressive males may have shaped a positive activity‐aggression behavioural syndrome in males, whereas the absence of selection favouring female aggression may have resulted in the absence of aggression and the related behavioural syndrome in females. However, given the plasticity of behaviour with changes in age or the environment, further research is needed to explore how sex differences in the existence of activity‐aggression behavioural syndromes change across contexts. Furthermore, understanding the genetic underpinning of sex differences in a behavioural syndrome would be pivotal to assess the role of behavioural syndromes in the evolution of sexual dimorphism in behaviours.
Driving on the Wrong Side of the Day: Synchrony and Gender Effects

The purpose of the current study was to investigate the impact of circadian cycles, gender, and propensity for cognitive errors on driving performance. Participants (14 women, 6 men), classified as “Evening Types,” performed a low-fidelity driving task during either their peak (evening) or off-peak (morning) times. Although neither penalty nor damage scores varied significantly by time of day, a gender by time of day interaction was observed for damage scores. Specifically, women driving during the day (off-peak) had accrued significantly more damage to their vehicles than did women driving in the evening (peak). However, damage scores for men were not significantly impacted by the time of day in which they drove. Women also received significantly higher penalty scores regardless of time of day. Participants' overall propensity for committing cognitive failures was significantly related to penalty scores (r = .50), but not to damage scores (r = .39). The results help identify individual differences in risk factors for driving.
Preclinical models for Type 1 Diabetes Mellitus - A practical approach for research

Numerous preclinical models have been developed to advance biomedical research in type 1 diabetes mellitus (T1DM). They are essential for improving our knowledge of T1DM development and progression, allowing researchers to identify potential therapeutic targets and evaluate the effectiveness of new medications. A deeper comprehension of these models themselves is critical not only to determine the optimal strategies for their utilization but also to fully unlock their potential applications in both basic and translational research. Here, we will comprehensively summarize and discuss the applications, advantages, and limitations of the commonly used animal models for human T1DM and also overview the up-to-date human tissue bioengineering models for the investigation of T1DM. By combining these models with a better understanding of the pathophysiology of T1DM, we can enhance our insights into disease initiation and development, ultimately leading to improved therapeutic responses and outcomes.
Potential Lifshitz transition at optimal substitution in nematic pnictide Ba1−xSrxNi2As2

BaNi2As2 is a structural analog of the pnictide superconductor BaFe2As2, which, like the iron-based superconductors, hosts a variety of ordered phases including charge density waves (CDWs), electronic nematicity, and superconductivity. Upon isovalent Sr substitution on the Ba site, the charge and nematic orders are suppressed, followed by a sixfold enhancement of the superconducting transition temperature (Tc). To understand the mechanisms responsible for enhancement of Tc, we present high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements of the Ba1−xSrxNi2As2 series, which agree well with our density functional theory (DFT) calculations throughout the substitution range. Analysis of our ARPES-validated DFT results indicates a Lifshitz transition and reasonably nested electron and hole Fermi pockets near optimal substitution where Tc is maximum. These nested pockets host Ni dxz/dyz orbital compositions, which we associate with the enhancement of nematic fluctuations, revealing unexpected connections to the iron-pnictide superconductors. This gives credence to a scenario in which nematic fluctuations drive an enhanced Tc.