Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Origin and Evolution of Drosophila New Genes during Spermatogenesis

Origin of functional new genes is a basic biological process that has significant contribution to organismal diversity. Previous studies in both Drosophila and mammals showed that new genes tend to be expressed in testis, and avoid the X chromosome presumably because meiotic sex chromosome inactivation (MSCI). Here we analyze the published single-cell transcriptome data of Drosophila adult testis and find an enrichment of male germline mitotic genes, but an underrepresentation of meiotic genes on the X chromosome. This can be attributed to an excess of autosomal meiotic genes that were derived from their X-linked mitotic progenitors, which provides direct cell-level evidence for MSCI in Drosophila. We reveal that new genes, particularly those produced by retrotransposition, tend to exhibit an expression shift toward late spermatogenesis compared to their parental copies, probably due to the more intensive sperm competition or sexual conflict. Our results dissect the complex factors including the age, the origination mechanisms and the chromosomal locations that influence the new gene origination and evolution in testis, and identify new gene cases that show divergent cell-level expression pattern from their progenitors for future functional studies.
A BioID-derived proximity interactome for SARS-CoV-2 proteins

The novel coronavirus SARS-CoV-2 is responsible for the ongoing COVID-19 pandemic and has caused a major health and economic burden worldwide. Understanding how SARS-CoV-2 viral proteins behave in host cells can reveal underlying mechanisms of pathogenesis and assist in development of antiviral therapies. Here we use BioID to map the SARS-CoV-2 virus-host interactome using human lung cancer derived A549 cells expressing individual SARS-CoV-2 viral proteins. Functional enrichment analyses revealed previously reported and unreported cellular pathways that are in association with SARS-CoV-2 proteins. We have also established a website to host the proteomic data to allow for public access and continued analysis of host-viral protein associations and whole-cell proteomes of cells expressing the viral-BioID fusion proteins. Collectively, these studies provide a valuable resource to potentially uncover novel SARS-CoV-2 biology and inform development of antivirals.
Influence of Switchgrass TDIF-like Genes on Arabidopsis Vascular Development

As a member of the CLAVATA3 (CLV3)/EMBRYO SURROUNDING REGION (CLE) family, the dodecapeptide tracheary element differentiation inhibitory factor (TDIF) has a major impact on vascular development in plants. However, the influence of polymorphisms in the TDIF peptide motif on activity remains poorly understood. The model plant, Arabidopsis provides a fast and effective tool for assaying the activity of TDIF homologs. Five TDIF homologs from a group of 93 CLE genes in switchgrass (Panicum virgatum), a perennial biomass crop, named PvTDIF-like (PvTDIFL) genes were studied. The expression levels of PvTDIFL1, PvTDIFL3MR3, and PvTDIFL3MR2 were relatively high and all of them were expressed at the highest levels in the rachis of switchgrass. The precursor proteins for PvTDIFL1, PvTDIFL3MR3, and PvTDIFL3MR2 contained one, three, and two TDIFL motifs, respectively. Treatments with exogenous PvTDIFL peptides increased the number of stele cells in the hypocotyls of Arabidopsis seedlings, with the exception of PvTDIFL_4p. Heterologous expression of PvTDIFL1 in Arabidopsis strongly inhibited plant growth, increased cell division in the vascular tissue of the hypocotyl, and disrupted the cellular organization of the hypocotyl. Although heterologous expression of PvTDIFL3MR3 and PvTDIFL3MR2 also affected plant growth and vascular development, PvTDIFL activity was not enhanced by the multiple TDIFL motifs encoded by PvTDIFL3MR3 and PvTDIFL3MR2. These data indicate that in general, PvTDIFLs are functionally similar to Arabidopsis TDIF but that the processing and activities of the PvTDIFL peptides are more complex.
The Potential Benefits of Non-skills Training (Mental Toughness) for Elite Athletes: Coping With the Negative Psychological Effects of the COVID-19 Pandemic

The spread of COVID-19 has had a significant impact on global sport. This is especially true at the elite level, where it has disrupted training and competition. Concomitantly, restrictions have disrupted long-term event planning. Many elite athletes remain unsure when major events will occur and worry about further interruptions. Although some athletes have successfully adapted to the demands of the COVID-19 crisis, many have experienced difficulties adjusting. This has resulted in psychological complications including increased stress, anxiety, and depression. This article critically examines the extent to which non-cognitive skills training, in the form of increased awareness of Mental Toughness, can help elite athletes inoculate against and cope with negative psychological effects arising from the COVID-19 pandemic. Non-cognitive skills encompass intrapersonal (motivations, learning strategies, and self-regulation) and interpersonal (interactions with others) domains not directly affected by intellectual capacity. Previous research indicates that enhancement of these spheres can assist performance and enhance mental well-being. Moreover, it suggests that training in the form of increased awareness of Mental Toughness, can improve the ability to cope with COVID-19 related challenges. In this context, Mental Toughness encompasses a broad set of enabling attributes (i.e., inherent and evolved values, attitudes, emotions, and cognitions). Indeed, academics commonly regard Mental Toughness as a resistance resource that protects against stress. Accordingly, this article advocates the use of the 4/6Cs model of Mental Toughness (i.e., Challenge, Commitment, Control, and Confidence) to counter negative psychological effects arising from COVID-19.
Programmed Death Ligand 2 Gene Polymorphisms Are Associated With Lung Adenocarcinoma Risk in Female Never-Smokers

Objectives Lung cancer in never-smokers is a distinct disease associated with a different genomic landscape, pathogenesis, risk factors, and immune checkpoint inhibitor responses compared to those observed in smokers. This study aimed to identify novel single nucleotide polymorphisms (SNPs) of programmed death-1 (encoded by PDCD1) and its ligands, programmed death ligand 1 (CD274) and 2 (PDCD1LG2), associated with lung cancer risk in never-smoking women. Materials and Methods During September 2002 and July 2012, we enrolled never-smoking female patients with lung adenocarcinoma (LUAD) (n=1153) and healthy women (n=1022) from six tertiary hospitals in Taiwan. SNP data were obtained and analyzed from the genome-wide association study dataset and through an imputation method. The expression quantitative trait loci (eQTL) analysis was performed in both tumor and non-tumor tissues for the correlation between genetic expression and identified SNPs. Results A total of 12 PDCD1LG2 SNPs related to LUAD risk were identified in never-smoking women, including rs2381282, rs4742103, rs4237162, rs4742104, rs12237624, rs78096119, rs6476988, rs7857315, rs10975178, rs7854413, rs56001683, and rs7858319. Among them, six tagged PDCD1LG2 SNPs rs2381282, rs4742103, rs4237162, rs4742104, rs78096119, and rs56001683 were significantly associated with LUAD risk. Specifically, two PDCD1LG2 SNPs, rs12237624 and rs78096119, were associated with previous pulmonary tuberculosis infection in relation to LUAD susceptibility. Through an eQTL assay, we found that rs2381282 (p < 0.001), rs12237624 (p = 0.019), and rs78096119 (p = 0.019) were associated with the expression levels of programed death ligand 2. Conclusions Novel SNPs of programed death ligand 2 associated with lung adenocarcinoma risk were identified. Among them, two SNPs were associated with pulmonary tuberculosis infection in relation to lung adenocarcinoma susceptibility. These SNPs may help to stratify high-risk populations of never-smokers during lung cancer screening.
From STEM to STEAM: An Enactive and Ecological Continuum

STEM and STEAM education promotes the integration between science, technology, engineering, mathematics, and the arts. The latter aims at favoring deep and collaborative learning on students, through curricular integration in K-12 science education. The enactive and ecological psychology approach to education puts attention on the role of the teacher, learning context and socio-cultural environment in shaping lived learning experiences. The approach describes education as a process of embodied cognitive assemblage of guided perception and action. The latter process depends on the interaction of learners with digital and/or analogue learning affordances existing within the socio-technological environment. This article proposes that the scope of an enactive-ecological approach can be extended to the domain of learning science, technology, engineering, arts, and mathematics (STEAM), especially when it comes to understanding deep roots of the learning process. We first present an exhaustive literature review regarding the foundations of both the enactive and the ecological learning theories, along with their differences and key similarities. We then describe the fundamentals and latest research advances of an integrated STEAM pedagogy, followed by the notion of mixed reality (XR) as an emerging educational technology approach, offering an understanding of its current foundations and general disposition on how to understand digital immersion from ecological psychology. Next, we propose a systems theoretical approach to integrate the enactive-ecological approach in STEAM pedagogy, framed in the Santiago school of cognition attending to the interactive dynamics occurring between learners and their interaction with learning affordances existing within their educational medium, establishing that sensorimotor contingencies and attentional anchors are important to restrict sensory variety and stabilize learning concepts. Finally, we consider two empirical studies, one from Chile and the other from New Zealand, in which we demonstrate how the enactive-ecological approach built upon a systems theory perspective can contribute to understanding the roots of STEAM learning and inform its learning design.
Mechanistic Coupling of a Novel in silico Cotyledon Perfusion Model and a Physiologically Based Pharmacokinetic Model to Predict Fetal Acetaminophen Pharmacokinetics at Delivery

Little is known about placental drug transfer and fetal pharmacokinetics despite increasing drug use in pregnant women. While physiologically based pharmacokinetic (PBPK) models can help in some cases to shed light on this knowledge gap, adequate parameterization of placental drug transfer remains challenging. A novel in silico model with seven compartments representing the ex vivo cotyledon perfusion assay was developed and used to describe placental transfer and fetal pharmacokinetics of acetaminophen. Unknown parameters were optimized using observed data. Thereafter, values of relevant model parameters were copied to a maternal-fetal PBPK model and acetaminophen pharmacokinetics were predicted at delivery after oral administration of 1,000 mg. Predictions in the umbilical vein were evaluated with data from two clinical studies. Simulations from the in silico cotyledon perfusion model indicated that acetaminophen accumulates in the trophoblasts; simulated steady state concentrations in the trophoblasts were 4.31-fold higher than those in the perfusate. The whole-body PBPK model predicted umbilical vein concentrations with a mean prediction error of 24.7%. Of the 62 concentration values reported in the clinical studies, 50 values (81%) were predicted within a 2-fold error range. In conclusion, this study presents a novel in silico cotyledon perfusion model that is structurally congruent with the placenta implemented in our maternal-fetal PBPK model. This allows transferring parameters from the former model into our PBPK model for mechanistically exploring whole-body pharmacokinetics and concentration-effect relationships in the placental tissue. Further studies should investigate acetaminophen accumulation and metabolism in the placenta as the former might potentially affect placental prostaglandin synthesis and subsequent fetal exposure.
Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.
Association of Subregional Quantitative Ultra-widefield Fluorescence Angiography Characteristics With the Occurrence of Diabetic Macular Edema and Proliferative Diabetic Retinopathy

Purpose: To explore the relationships of region-specific properties of ultra-widefield fluorescence angiography (UWFFA) images with two adverse outcomes, diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR), and also the severity of diabetic retinopathy (DR). Methods: A cross-sectional observational study was performed to retrospectively analyze UWFFA images of patients with DR. All patients underwent UWFFA and optical coherence tomography examination. Leakage index and microaneurysm (MA) count were measured using Trainable Weka Segmentation, a machine learning algorithm, and ischemic index (ISI) was measured manually. The correlation between UWFFA parameters and severity of DR was analyzed, and receiver operating characteristic curves were used to estimate their diagnostic value for DME and PDR. Results: A total of 108 eyes from 108 patients with DR (mean age of 56.04 ± 8.85 years) were analyzed. As the severity of DR increased, the ISI and leakage index of the panretina and all subregions increased. Panretinal MA count and leakage index were significantly higher in eyes with DME than those without DME (p = 0.044 and 0.001, respectively). Leakage index and ISI were significantly higher in eyes with PDR than those without PDR in both panretinal and subregion-specific measurements (all p < 0.05). Throughout the retina and specifically in the posterior area (PoA), the leakage index had a higher diagnostic value for DME than ISI or MA count (all p < 0.05). The diagnostic value of MA count for PDR was lower than that of ISI and leakage index (all p < 0.05). Conclusion: The ISI, leakage index, and MA count in the PoA and panretina correlated with the severity of DR, especially the posterior parameter. The leakage index was more valuable than ISI and MA count in determining the occurrence of DME. ISI and leakage index were better predictors of PDR.
Systematic Construction and Validation of an RNA-Binding Protein-Associated Prognostic Model for Acute Myeloid Leukemia

Background: The abnormal expression of RNA-binding proteins (RBPs) in various malignant tumors is closely related to the occurrence and development of tumors. However, the role of RBPs in acute myeloid leukemia (AML) is unclear. Methods: We downloaded harmonized RNA-seq count data and clinical data for AML from UCSC Xena, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohorts. R package edgeR was used for differential expression analysis of 337 whole-blood data and 173 AML data. The prognostic value of these RBPs was systematically investigated by using univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO)–Cox regression analysis, and multivariate Cox regression analysis. C-index and calibration diagram were used to judge the accuracy of the model, and decision curve analysis (DCA) was used to judge the net benefit. The biological pathways involved were revealed by gene set enrichment analysis (GSEA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and the protein–protein interaction (PPI) network performed lateral verification on the selected gene set and LASSO results. Results: A prognostic model of 12-RBP signature was established. In addition, the net benefit and prediction accuracy of the prognostic model and the mixed model based on it were significantly higher than that of cytogenetics. It is verified in the TARGET cohort and shows good prediction effect. Both the selection of our gene set and the LASSO results have high credibility. Most of these pathways are involved in the development of the disease, and they also accumulate in leukemia and RNA-related pathways. Conclusion: The prognosis model of the 12-RBP signature found in this study is an optimized biomarker that can effectively stratify the risk of AML patients. Nomogram based on this prognostic model is a reliable method to predict the median survival time of patients. This study expands our current understanding of the role of RBPs in the occurrence of AML and may lay the foundation for future treatment of the disease.
Self-Harm History, Anxiety-Depression, Severity of Disease, and Insight Are Significantly Associated With Suicide Risk in Forensic Psychiatric Inpatients of China

Background: Forensic psychiatric patients have higher suicide risk than the general population. This study aimed to evaluate the extent of suicide risk and to explore the associated factors in forensic psychiatric inpatients in China. Methods: We conducted a cross-sectional study from 1st November, 2018 to 30th January, 2019 in the Forensic Psychiatric Hospital of Hunan Province, China. Patient's information on socio-demographic, clinical, and criminological characteristics was collected. The suicidality subscale of the MINI-International Neuropsychiatric Interview (M.I.N.I.), the Brief Psychiatric Rating Scale (BPRS), and the Severity of Illness of Clinical Global Impressions Scale (CGI-SI) were used to measure present suicide risks, psychiatric symptoms, and the severity of the patient's disease, respectively. Binary logistic regression models were used to examine factors associated with suicide risk. Results: Twenty-one percent (84/408) of the forensic psychiatric inpatients reported suicide risk. Logistic regression analysis suggested that self-harm history (OR:3.47, 95% confidence interval CI: 1.45–8.33), symptoms of anxiety-depression (OR:1.15, 95% CI:1.04–1.27), and more severe mental disorder (OR:1.42, 95% CI:1.08–1.87) were associated with elevated suicide risk, while insight disorder (OR:0.81, 95% CI:0.65–0.99) was related to decreasing suicide risk. Conclusion: The study supplied useful clinical information to recognize high suicide risk in forensic psychiatric inpatients and may aid the development of valuable strategies for preventing and reducing suicide events.
Preliminary Investigations Into the Effect of Exercise-Induced Muscle Damage on Systemic Extracellular Vesicle Release in Trained Younger and Older Men

Background: Exercise-induced muscle damage (EIMD) results in transient muscle inflammation, strength loss, and muscle soreness and may cause subsequent exercise avoidance. Research has recently proven that skeletal muscle can also release extracellular vesicles (EVs) into the circulation following a bout of exercise. However, EV’s potential role, including as a biomarker, in the response to eccentric resistance exercise stimulus remains unclear. Methods: Twelve (younger, n=7, 27.0±1.5years and older, n=5, 63.0±1.0years) healthy, physically active males, undertaking moderate, regular physical activity (3–5 times per week) performed a unilateral high intensity eccentric exercise protocol. Venous plasma was collected for assessment of EVs and creatine kinase (CK) prior to EIMD, immediately after EIMD, and 1–72h post-EIMD, and maximal voluntary isometric contraction (MVIC) and delayed onset muscle soreness (DOMS) were assessed at all time points, except 1 and 2h post-EIMD. Results: A significant effect of both time (p=0.005) and group (p<0.001) was noted for MVIC, with younger participants’ MVIC being higher throughout. Whilst a significant increase was observed in DOMS in the younger group (p=0.014) and in the older group (p=0.034) following EIMD, no significant differences were observed between groups. CK was not different between age groups but was altered following the EIMD (main effect of time p=0.026), with increased CK seen immediately post-, at 1 and 2h post-EIMD. EV count tended to be lower in older participants at rest, relative to younger participants (p=0.056), whilst EV modal size did not differ between younger and older participants pre-EIMD. EIMD did not substantially alter EV modal size or EV count in younger or older participants; however, the alteration in EV concentration (ΔCount) and EV modal size (ΔMode) between post-EIMD and pre-EIMD negatively associated with CK activity. No significant associations were noted between MVIC or DOMS and either ΔCount or ΔMode of EVs at any time point. Conclusion: These findings suggest that profile of EV release, immediately following exercise, may predict later CK release and play a role in the EIMD response. Exercise-induced EV release profiles may therefore serve as an indicator for subsequent muscle damage.