Molecular Characterization of a Debilitation-Associated Partitivirus Infecting the Pathogenic Fungus Aspergillus flavus

The opportunistic human pathogenic fungus Aspergillus flavus is known to be infected with mycoviruses. In this study, we report a novel mycovirus A. flavus partitivirus 1 (AfPV1) that was originally isolated from the abnormal colonial morphology isolate LD-3-8 of A. flavus. AfPV1 has spherical virus-like particles about 40 nm in diameter, and three double-stranded RNA (dsRNA) segments (dsRNA1, 2, and 3 with lengths of 1.7, 1.4, and 1.1 kbp, respectively) were packaged in the virions. dsRNA1, dsRNA2, and dsRNA3 each contained a single open reading frame and potentially encoded 62, 42, and 32 kDa proteins, respectively. The dsRNA1 encoded protein shows similarity to the RNA-dependent RNA polymerase (RdRp) of partitiviruses, and the dsRNA2 product has no significant similarity to any other capsid protein (CP) in the GenBank databases, beside some homology with the hypothetical “capsid” protein of a few partitiviruses. The dsRNA3 encodes a protein with no similarity to any protein in the GenBank database. SDS-PAGE and polypeptide mass fingerprint-mass spectrum (PMF-MS) analyses indicated that the CP of the AfPV1 was encoded by dsRNA2. Phylogenetic analysis showed that the AfPV1 and relative viruses were found in an unclassified group inside the Partitiviridae family. AfPV1 seems to result in debilitation symptoms, but had no significant effects to murine pathogenicity. These findings provide new insights into the partitiviruses taxonomy and the interactions between viruses and A. flavus

Genetic mapping reveals a candidate gene CmoFL1 controlling fruit length in pumpkin

Fruit length (FL) is an important economical trait that affects fruit yield and appearance. Pumpkin (Cucurbita moschata Duch) contains a wealth genetic variation in fruit length. However, the natural variation underlying differences in pumpkin fruit length remains unclear. In this study, we constructed a F2 segregate population using KG1 producing long fruit and MBF producing short fruit as parents to identify the candidate gene for fruit length. By bulked segregant analysis (BSA-seq) and Kompetitive Allele-Specific PCR (KASP) approach of fine mapping, we obtained a 50.77 kb candidate region on chromosome 14 associated with the fruit length. Then, based on sequence variation, gene expression and promoter activity analyses, we identified a candidate gene (CmoFL1) encoding E3 ubiquitin ligase in this region may account for the variation of fruit length. One SNP variation in promoter of CmoFL1 changed the GT1CONSENSUS, and DUAL-LUC assay revealed that this variation significantly affected the promoter activity of CmoFL1. RNA-seq analysis indicated that CmoFL1 might associated with the cell division process and negatively regulate fruit length. Collectively, our work identifies an important allelic affecting fruit length, and provides a target gene manipulating fruit length in future pumpkin breeding

MicroRNA-125a Regulates Cell Proliferation Via Directly Targeting E2F2 in Osteosarcoma

Background/Aims: Increasing evidence has shown that miR-125a plays important role in human cancer progression. However, little is known about the function of miR-125a in osteosarcoma. Methods: The expression of miR-125a in osteosarcoma tissues and cell lines were examined by qRT-PCR. The biological role of miR-125a in osteosarcoma cell proliferation was examined in vitro. The targets of miR-125a were identified by a dual-luciferase reporter assay. Results: The results showed that the expression of miR-125a expression is significantly lower in osteosarcoma tissues and cell lines. Survival curves showed that the survival of patients in high miR-125a expression was significantly longer than that of patients with low miR-125a expression, and multivariate analysis suggested that miR-125a is an independent prognostic factor for osteosarcoma patients. In addition, it was found in this study that miR-125a can inhibit the growth of osteosarcoma cells. The dual-luciferase reporter assay demonstrated that E2F2 is a novel target gene for miR-125a. In addition, in a recovery experiment, it was shown that miR-125a inhibits the biological function of osteosarcoma cells by inhibiting the expression of E2F2. Conclusion: Our results suggest that miR-125a acts as a tumor suppressor via regulation of E2F2 expression in osteosarcoma progression, and miR-125a may represent a novel therapeutic target for the treatment of osteosarcoma

Fluctuation in residual strain and dissipated energy of saturated sandstone under tiered cyclic loading.

To study the influence of cyclic stress on the nonlinear behavior of saturated sandstone, the residual strain properties and energy dissipation characteristics of the sandstone under tiered cyclic loading were experimentally investigated. The axial/radial residual deformation and energy dissipation characteristics of sandstone at different cyclic stress stages were analyzed in detail. By combining the mathematical statistics, fluctuation coefficients of the residual strain and energy dissipation, and correlation coefficients of axial/radial residual strain and energy dissipation were defined to describe the process. It was determined that these newly defined physical variables were closely related to the elastic-plastic state (or instability failure state) of the rock

The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis

Abstract Background This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. Method From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. Results Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. Conclusion This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students

In vivo volumetric imaging of calcium and glutamate activity at synapses with high spatiotemporal resolution.

Studying neuronal activity at synapses requires high spatiotemporal resolution. For high spatial resolution in vivo imaging at depth, adaptive optics (AO) is required to correct sample-induced aberrations. To improve temporal resolution, Bessel focus has been combined with two-photon fluorescence microscopy (2PFM) for fast volumetric imaging at subcellular lateral resolution. To achieve both high-spatial and high-temporal resolution at depth, we develop an efficient AO method that corrects the distorted wavefront of Bessel focus at the objective focal plane and recovers diffraction-limited imaging performance. Applying AO Bessel focus scanning 2PFM to volumetric imaging of zebrafish larval and mouse brains down to 500 µm depth, we demonstrate substantial improvements in the sensitivity and resolution of structural and functional measurements of synapses in vivo. This enables volumetric measurements of synaptic calcium and glutamate activity at high accuracy, including the simultaneous recording of glutamate activity of apical and basal dendritic spines in the mouse cortex

Retinal microvascular and neuronal pathologies probed in vivo by adaptive optical two-photon fluorescence microscopy

The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. Optimizing the design of a two-photon fluorescence microscope (2PFM) and sample preparation procedure, we found that adaptive optics (AO), by measuring and correcting ocular aberrations, is essential for resolving putative synaptic structures and achieving three-dimensional cellular resolution in the mouse retina in vivo. Applying AO-2PFM to longitudinal retinal imaging in transgenic models of retinal pathology, we characterized microvascular lesions with sub-capillary details in a proliferative vascular retinopathy model, and found Lidocaine to effectively suppress retinal ganglion cell hyperactivity in a retinal degeneration model. Tracking structural and functional changes at high-resolution longitudinally, AO-2PFM enables microscopic investigations of retinal pathology and pharmacology for disease diagnosis and treatment in vivo

DataSheet1_Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.CSV

Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.</p

DataSheet2_Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.CSV

Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.</p