Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Factors Such as Parenting Stress and Social Support on the State Anxiety in Parents of Special Needs Children During the COVID-19 Epidemic

Objectives The study aims to investigate the state anxiety of parents of special needs children during the 2019 coronavirus disease (COVID-19) epidemic and the influence of parental stress, social support, and other related variables on the anxiety of parents. Methods Bespoke questionnaires of children’s and parent’s mental and behavioral problems during the epidemic were used in the study. We also used the State Anxiety Inventory (S-AI), the Parenting Stress Index—Short Form-15 (PSI-SF-15), the NEO Five-Factor Inventory (NEO-FFI), and the Multidimensional Scale of Perceived Social Support (MSPSS). The data used in the study were pooled from an online survey of parents of special needs children and analyzed by one-way analysis of variance (ANOVA) and multiple linear regression. Results Overall, 1,451 individuals were included, of which 402 were fathers (27.71%) and 1,049 were mothers (72.29%). ANOVA results showed that educational background, family monthly income, and type of their child’s disability made parents’ state anxiety significantly different. The results of multiple linear regression showed that during the epidemic, social support negatively predicted parents’ state anxiety (B = −0.15, p < 0.001), whereas parenting stress (B = 0.07, p = 0.001) and parental mental and behavioral problems (B = 0.37, p < 0.001) positively predicted parents’ state anxiety. Conclusions During the outbreak of COVID-19, parents of special needs children suffered mental and behavioral problems, together with parenting stress and social support, which influenced their state anxiety. These findings can be used to develop relevant psychological interventions to improve the mental health of vulnerable groups during a pandemic like COVID-19.
First-Principle Insight Into the Effects of Oxygen Vacancies on the Electronic, Photocatalytic, and Optical Properties of Monoclinic BiVO4(001)

In this paper, first-principle calculations were performed to investigate the effects of oxygen (O) vacancies (Ovac) on the crystal structure, electronic distribution, adsorption energies of O2 and H2O and the density of states (DOS) of monoclinic bismuth vanadate (m-BiVO4). Ovac were stable when incorporated into m-BiVO4(001) and increased the adsorption energy of O2. Ovac changed the V3d orbitals of m-BiVO4(001) by adding a new band gap level, causing the redundant electrons of V atoms to become carriers and promoting the separation efficiency of electrons and holes. To verify the first-principle calculations, m-BiVO4 with different Ovac levels was prepared via hydrothermal synthesis. X-ray diffraction (XRD) patterns confirmed the existence of the (001) crystal surface of m-BiVO4. In addition, X-ray photoelectron spectroscopy (XPS) and electron spin resonance (ESR) spectroscopy of m-BiVO4 confirmed the presence of Ovac and demonstrated that, as the Ovac level increased, the number of superoxide radicals (O2-·) and hydroxyl radicals (·OH) produced increased. In addition, m-BiVO4 with a higher Ovac level possessed superior photocatalytic properties to and degraded rhodamine B (RhB) dye nearly 2-fold faster than m-BiVO4 with a lower Ovac level. Finally, the removal rate of RhB increased from 23 to 44%. All experimental results were in good agreement with the first-principle calculated results.
Thiamine-Mediated Cooperation Between Auxotrophic Rhodococcus ruber ZM07 and Escherichia coli K12 Drives Efficient Tetrahydrofuran Degradation

Tetrahydrofuran (THF) is a universal solvent widely used in the synthesis of chemicals and pharmaceuticals. As a refractory organic contaminant, it can only be degraded by a small group of microbes. In this study, a thiamine auxotrophic THF-degrading bacterium, Rhodococcus ruber ZM07, was isolated from an enrichment culture H-1. It was cocultured with Escherichia coli K12 (which cannot degrade THF but can produce thiamine) and/or Escherichia coli K12ΔthiE (which can neither degrade THF nor produce thiamine) with or without exogenous thiamine. This study aims to understand the interaction mechanisms between ZM07 and K12. We found that K12 accounted for 30% of the total when cocultured and transferred with ZM07 in thiamine-free systems; in addition, in the three-strain (ZM07, K12, and K12ΔthiE) cocultured system without thiamine, K12ΔthiE disappeared in the 8th transfer, while K12 could still stably exist (the relative abundance remained at approximately 30%). The growth of K12 was significantly inhibited in the thiamine-rich system. Its proportion was almost below 4% after the fourth transfer in both the two-strain (ZM07 and K12) and three-strain (ZM07, K12, and K12ΔthiE) systems; K12ΔthiE’s percentage was higher than K12’s in the three-strain (ZM07, K12, and K12ΔthiE) cocultured system with exogenous thiamine, and both represented only a small proportion (less than 1% by the fourth transfer). The results of the coculture of K12 and K12ΔthiE in thiamine-free medium indicated that intraspecific competition between them may be one of the main reasons for the extinction of K12ΔthiE in the three-strain (ZM07, K12, and K12ΔthiE) system without exogenous thiamine. Furthermore, we found that ZM07 could cooperate with K12 through extracellular metabolites exchanges without physical contact. This study provides novel insight into how microbes cooperate and compete with one another during THF degradation.
Scaling Invariance of Sports Sex Gap

The controversy over the evolution of sex gap in sports stems from the reported that women’s performance will 1 day overtake men’s in the journal Nature. After debate, the recent studies suggest that the sports sex gap has been stable for a long time, due to insurmountable physiological differences. To find a mathematical model that accurately describes this stable gap, we analyze the best annual records of men and women in 25 events from 1992 to 2017, and find that power-law relationship could be acted as the best choice, with an R-squares as high as 0.999 (p ≤ 0.001). Then, based on the power law model, we use the records of men in 2018 to predict the performance of women in that year and compare them with real records. The results show that the deviation rate of the predicted value is only about 2.08%. As a conclusion, it could be said that there is a constant sex gap in sports, and the records of men and women evolve in parallel. This finding could serve as another quantitative rule in biology.
Investment Planning Methodology for Complex Urban Energy Systems Applied to a Hospital Site

Industrial process integration based on mixed integer linear programming has been used for decades to design and improve industrial processes. The technique has later been extended to solve multi-period and multi-scale problems for the design of urban energy systems. Assistance is indeed required for the elaboration of coordinated investment scheduling strategies to promote renewable and efficient urban energy infrastructure shaping the future energy context for the next decades. Major energy consumers, such as hospital complexes, airports, or educational campuses can act as a driving force for the development of renewable energy cities by attracting profitable large-scale energy networks and infrastructure. The proposed methodology generates optimal alternatives for the replacement, in a long-term perspective, of the various energy supply units and systems considering the evolution of the energy demand and the availability of the energy resources. Energy integration techniques are coupled to a parametric multi-objective optimization routine to select and size the energy equipment with both financial profitability and CO2 emission reduction as objectives. The originality of the developed method lies in the integration of a multi-period mixed integer linear programming formulation to generate long-term investment planning scenarios. The method has been demonstrated on a complex of eight hospitals totaling 466,000 m2 and an operating budget of 1.85 billion USD per year. The energy integration of new centralized and decentralized equipment has been evaluated on a monthly basis over four periods until the year 2035. The results show that among the four scenarios identified, the most optimistic alternative allows to decrease the final energy consumption of about 36%, cut the CO2 emissions by a half, multiply the renewable energy share by a factor 3.5 while reducing the annual total cost by 24%. This scenario considers mainly the integration of a very low temperature district heating with decentralized heat pumps to satisfy the heat requirements below 75°C, as well as heat recovery systems and the refurbishment of about 33% of the building stock.
Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants

Objectives To investigate severe COVID-19 risk by occupational group. Methods Baseline UK Biobank data (2006–10) for England were linked to SARS-CoV-2 test results from Public Health England (16 March to 26 July 2020). Included participants were employed or self-employed at baseline, alive and aged <65 years in 2020. Poisson regression models were adjusted sequentially for baseline demographic, socioeconomic, work-related, health, and lifestyle-related risk factors to assess risk ratios (RRs) for testing positive in hospital or death due to COVID-19 by three occupational classification schemes (including Standard Occupation Classification (SOC) 2000). Results Of 120 075 participants, 271 had severe COVID-19. Relative to non-essential workers, healthcare workers (RR 7.43, 95% CI 5.52 to 10.00), social and education workers (RR 1.84, 95% CI 1.21 to 2.82) and other essential workers (RR 1.60, 95% CI 1.05 to 2.45) had a higher risk of severe COVID-19. Using more detailed groupings, medical support staff (RR 8.70, 95% CI 4.87 to 15.55), social care (RR 2.46, 95% CI 1.47 to 4.14) and transport workers (RR 2.20, 95% CI 1.21 to 4.00) had the highest risk within the broader groups. Compared with white non-essential workers, non-white non-essential workers had a higher risk (RR 3.27, 95% CI 1.90 to 5.62) and non-white essential workers had the highest risk (RR 8.34, 95% CI 5.17 to 13.47). Using SOC 2000 major groups, associate professional and technical occupations, personal service occupations and plant and machine operatives had a higher risk, compared with managers and senior officials. Conclusions Essential workers have a higher risk of severe COVID-19. These findings underscore the need for national and organisational policies and practices that protect and support workers with an elevated risk of severe COVID-19.
Clinical Application of AMH Measurement in Assisted Reproduction

Anti-Müllerian hormone reflects the continuum of the functional ovarian reserve, and as such can predict ovarian response to gonadotropin stimulation and be used to individualize treatment pathways to improve efficacy and safety. However, consistent with other biomarkers and age-based prediction models it has limited ability to predict live birth and should not be used to refuse treatment, but rather to inform counselling and shared decision making. The use of absolute clinical thresholds to stratify patient phenotypes, assess discordance and individualize treatment protocols in non-validated algorithms combined with the lack of standardization of assays may result in inappropriate classification and sub-optimal clinical decision making. We propose that holistic baseline phenotyping, incorporating antral follicle count and other patient characteristics is critical. Treatment decisions driven by validated algorithms that use ovarian reserve biomarkers as continuous measures, reducing the risk of misclassification, are likely to improve overall outcomes for our patients.
A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field

As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates.
A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis

Multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) have been widely applied to the statistical process monitoring (SPM) of chemical processes and their effectiveness for fault detection is well recognized. These methods make use of normal process data to define a tight normal operation region for monitoring. In practice, however, historical process data are often corrupted with faulty data. In this paper, a new process monitoring method is proposed that is composed of three parts: (1) a preanalysis step that first roughly identifies various clusters in a historical data set and then precisely isolates normal and abnormal data clusters by the k-means clustering method; (2) a fault visualization step that visualizes high-dimensional data in 2-D space by performing global Fisher discriminant analysis (FDA), and (3) a new fault diagnosis method based on fault directions in pairwise FDA. A simulation example is used to demonstrate the performance of the proposed fault diagnosis method. An industrial film process is used to illustrate a realistic scenario for data preanalysis, fault visualization, and fault diagnosis. In both examples, the contribution plots method, based on fault directions in pairwise FDA, shows superior capability for fault diagnosis to the contribution plots method based on PCA. © 2005 American Institute of Chemical Engineers AIChE J, 51: 555–571, 2005
Proper Glyphosate Application at Post-anthesis Lowers Grain Moisture Content at Harvest and Reallocates Non-structural Carbohydrates in Maize

Glyphosate (GP)-based herbicides have been widely applied to crops for weed control and pre-harvest desiccation. The objective of this research was to evaluate the effects of pre-harvest GP application on maize or how it physiologically alters this crop. Here, we applied four GP treatment (Control, GP150, GP200, and GP250) on maize lines of Z58 and PH6WC belonging to different maturity groups at grain-filling stages form DAP30 to DAP45. GP application significantly decreased the grain moisture content at harvest by 22–35% for Z58 and by 15–41% for PH6WC. However, the responses of grain weight to glyphosate vary with inbred lines and application time. A high concentration of glyphosate (GP250) reduced the grain weight of Z58 and low concentrations (GP150 and GP200) did not affect, while the grain weight of PH6WC significantly decreased under glyphosate treatment. In summary, our results revealed that timely and appropriate GP application lowers grain moisture content without causing seed yield and quality loss. GP application adversely affected photosynthesis by promoting maturation and leaf senescence. Meanwhile, it also enhanced non-structural carbohydrate (soluble sugars and starch) remobilization from the vegetative organs to the grains. Hence, GP treatment coordinates plant senescence and assimilate remobilization. RNA sequencing revealed that glyphosate regulated the transcript levels of sugar signaling-related genes and induced assimilate repartitioning in grains. This work indicates the practical significance of GP application for maize seed production and harvest, which highlights the contributions of source-sink communication to maize yield in response to external stress or pre-harvest desiccant application.
Expression and Prognostic Characteristics of m6 A RNA Methylation Regulators in Breast Cancer

Purpose N6-methyladenosine (m6A) is the most prevalent modification in mRNA methylation which has a wide effect on biological functions. This study aims to figure out the efficacy of m6A RNA methylation regulator-based biomarkers with prognostic significance in breast cancer. Patients and Methods The 23 RNA methylation regulators were firstly analyzed through ONCOMINE, then relative RNA-seq transcriptome and clinical data of 1,096 breast cancer samples and 112 normal tissue samples were acquired from The Cancer Gene Atlas (TCGA) database. The expressive distinction was also showed by the Gene Expression Omnibus (GEO) database. The gene expression data of m6A RNA regulators in human tissues were acquired from the Genotype-Tissue Expression (GTEx) database. The R v3.5. 1 and other online tools such as STRING, bc-GeneExminer v4.5, Kaplan-Meier Plotter were applied for bioinformatics analysis. Results Results from ONCOMINE, TCGA, and GEO databases showed distinctive expression and clinical correlations of m6A RNA methylation regulators in breast cancer patients. The high expression of YTHDF3, ZC3H13, LRPPRC, and METTL16 indicated poor survival rate in patients with breast cancer, while high expression of RBM15B pointed to a better survival rate. Both univariate and multivariate Cox regression analyses revealed that age and risk scores were related to overall survival (OS). Univariate analysis also delineated that stage, tumor (T) status, lymph node (N) status, and metastasis (M) status were associated with OS. From another perspective, Kaplan-Meier Plotter platform showed that the relatively high expression of YTHDF3 and LRPPRC and the relatively low expression of RBM15B, ZC3H13, and METTL16 in breast cancer patients had worse Relapse-Free Survival (RFS). Breast Cancer Gene-Expression Miner v4.5 showed that LRPPRC level was negatively associated with ER and PR expression, while METTL16, RBM15B, ZC3H13 level was positively linked with ER and PR expression. In HER-2 (+) breast cancer patients, the expression of LRPPRC, METTL16, RBM15B, and ZC3H13 were all lower than the HER-2 (−) group. Conclusion The significant difference in expression levels and prognostic value of m6A RNA methylation regulators were analyzed and validated in this study. This signature revealed the potential therapeutic value of m6A RNA methylation regulators in breast cancer.
Design and Optimization of Quinazoline Derivatives: New Non-nucleoside Inhibitors of Bovine Viral Diarrhea Virus

Bovine viral diarrhea virus (BVDV) belongs to the Pestivirus genus (Flaviviridae). In spite of the availability of vaccines, the virus is still causing substantial financial losses to the livestock industry. In this context, the use of antiviral agents could be an alternative strategy to control and reduce viral infections. The viral RNA-dependent RNA polymerase (RdRp) is essential for the replication of the viral genome and constitutes an attractive target for the identification of antiviral compounds. In a previous work, we have identified potential molecules that dock into an allosteric binding pocket of BVDV RdRp via a structure-based virtual screening approach. One of them, N-(2-morpholinoethyl)-2-phenylquinazolin-4-amine [1, 50% effective concentration (EC50) = 9.7 ± 0.5 μM], was selected to perform different chemical modifications. Among 24 derivatives synthesized, eight of them showed considerable antiviral activity. Molecular modeling of the most active compounds showed that they bind to a pocket located in the fingers and thumb domains in BVDV RdRp, which is different from that identified for other non-nucleoside inhibitors (NNIs) such as thiosemicarbazone (TSC). We selected compound 2-[4-(2-phenylquinazolin-4-yl)piperazin-1-yl]ethanol (1.9; EC50 = 1.7 ± 0.4 μM) for further analysis. Compound 1.9 was found to inhibit the in vitro replication of TSC-resistant BVDV variants, which carry the N264D mutation in the RdRp. In addition, 1.9 presented adequate solubility in different media and a high-stability profile in murine and bovine plasma.
FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human-Robot Cooperative Edutainment

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.