Exogenous High-Mobility Group Box 1 Protein Injection Improves Cardiac Function after Myocardial Infarction: Involvement of Wnt Signaling Activation

Exogenous high-mobility group box 1 protein (HMGB1) injection could prevent left ventricular remodeling and enhance left ventricular function during myocardial infarction (MI). However, the mechanism remains unclear. This paper was to investigate in the mechanism of cardioprotection of HMGB1 during MI in rats. Anesthetized male rats were treated once with HMGB1 (200 ng) 4 h after MI and then executed after 7 and 28 days, respectively. Cardiac function, collagen deposition, and dishevelled-1 and β-catenin protein expression were measured. After MI 7 days or 28 days, the left ventricular ejection fraction (LVEF) was significantly decreased compared to that of sham-operated control group (P < 0.05). However, the LVEF HMGB1-treated groups were significantly higher compared to those of the MI group in both 7 days and 28 days (P < 0.05). The collagen volume fraction was significantly reduced in the HMGB1-treated group in infarcted border zone. HMGB1 could activate the expression of dishevelled-1 and β-catenin proteins (P < 0.05). Our study suggested that exogenous high-mobility group box 1 protein injection improves cardiac function after MI, which may be involved in Wnt/β-catenin signaling activation

Critical Roles of microRNA-141-3p and CHD8 in Hypoxia/Reoxygenation-Induced Cardiomyocyte Apoptosis

Background: Cardiovascular diseases are currently the leading cause of death in humans. The high mortality of cardiac diseases is associated with myocardial ischemia and reperfusion (I/R). Recent studies have reported that microRNAs (miRNAs) play important roles in cell apoptosis. However, it is not known yet whether miR-141-3p contributes to the regulation of cardiomyocyte apoptosis. It has been well established that in vitro hypoxia/reoxygenation (H/R) model can follow in vivo myocardial I/R injury. This study aimed to investigate the effects of miR-141-3p and CHD8 on cardiomyocyte apoptosis following H/R. Results: We found that H/R remarkably reduces the expression of miR-141-3p but enhances CHD8 expression both in mRNA and protein in H9c2 cardiomyocytes. We also found either overexpression of miR-141-3p by transfection of miR-141-3p mimics or inhibition of CHD8 by transfection of small interfering RNA (siRNA) significantly decrease cardiomyocyte apoptosis induced by H/R. Moreover, miR-141-3p interacts with CHD8. Furthermore, miR-141-3p and CHD8 reduce the expression of p21. Conclusion: MiR-141-3p and CHD8 play critical roles in cardiomyocyte apoptosis induced by H/R. These studies suggest that miR-141-3p and CHD8 mediated cardiomyocyte apoptosis may offer a novel therapeutic strategy against myocardial I/R injury-induced cardiovascular diseases

A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers

Leveraging artificial intelligence to identify high-risk patients for postoperative sore throat: An observational study

Postoperative sore throat (POST) is a prevalent complication after general anesthesia and targeting high-risk patients helps in its prevention. This study developed and validated a machine learning model to predict POST. A total number of 834 patients who underwent general anesthesia with endotracheal intubation were included in this study. Data from a cohort of 685 patients was used for model development and validation, while a cohort of 149 patients served for external validation. The prediction performance of random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost) models was compared using comprehensive performance metrics. The Local Interpretable Model-Agnostic Explanations (LIME) methods elucidated the best-performing model. POST incidences across training, validation, and testing cohorts were 41.7%, 38.4%, and 36.2%, respectively. Five predictors were age, sex, endotracheal tube cuff pressure, endotracheal tube insertion depth, and the time interval between extubation and the first drinking of water after extubation. After incorporating these variables, the NN model demonstrated superior generalization capabilities in predicting POST when compared to the XGBoost and RF models in external validation, achieving an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI 0.74–0.89) and a precision–recall curve (AUPRC) of 0.77 (95% CI 0.66–0.86). The model also showed good calibration and clinical usage values. The NN model outperforms the XGBoost and RF models in predicting POST, with potential applications in the healthcare industry for reducing the incidence of this common postoperative complication

Endophytic Colletotrichum species from Dendrobium spp. in China and Northern Thailand

Species of Colletotrichum are commonly found in many plant hosts as pathogens, endophytes and occasionally saprobes. Twenty-two Colletotrichum strains were isolated from three Dendrobium species – D. cariniferum, D. catenatum and D. harveyanum, as well as three unidentified species. The taxa were identified using morphological characterisation and phylogenetic analyses of ITS, GAPDH, ACT and ß–tubulin sequence data. This is the first time to identify endophytic fungi from Dendrobium orchids using the above method. The known species, Colletotrichum boninense, C. camelliae-japonicae, C. fructicola, C. jiangxiense and C. orchidophilum were identified as fungal endophytes of Dendrobium spp., along with the new species, C. cariniferi, C. chiangraiense, C. doitungense, C. parallelophorum and C. watphraense, which are introduced in this paper. One strain is recorded as an unidentified species. Corn meal agar is recommended as a good sporulation medium for Colletotrichum species. This is the first report of fungal endophytes associated with Dendrobium cariniferum and D. harveyanum. Colletotrichum camelliae-japonicae, C. jiangxiense, and C. orchidophilum are new host records for Thailand

MEI Kodierung der frühesten Notation in linienlosen Neumen

Das Optical Neume Recognition Project (ONRP) hat die digitale Kodierung von musikalischen Notationszeichen aus dem Jahr um 1000 zum Ziel – ein ambitioniertes Vorhaben, das die Projektmitglieder veranlasste, verschiedenste methodische Ansätze zu evaluieren. Die Optical Music Recognition-Software soll eine linienlose Notation aus einem der ältesten erhaltenen Quellen mit Notationszeichen, dem Antiphonar Hartker aus der Benediktinerabtei St. Gallen (Schweiz), welches heute in zwei Bänden in der Stiftsbibliothek in St. Gallen aufbewahrt wird, erfassen. Aufgrund der handgeschriebenen, linienlosen Notation stellt dieser Gregorianische Gesang den Forscher vor viele Herausforderungen. Das Werk umfasst über 300 verschiedene Neumenzeichen und ihre Notation, die mit Hilfe der Music Encoding Initiative (MEI) erfasst und beschrieben werden sollen. Der folgende Artikel beschreibt den Prozess der Adaptierung, um die MEI auf die Notation von Neumen ohne Notenlinien anzuwenden. Beschrieben werden Eigenschaften der Neumennotation, um zu verdeutlichen, wo die Herausforderungen dieser Arbeit liegen sowie die Funktionsweise des Classifiers, einer Art digitalen Neumenwörterbuchs

The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding

Grapevine is one of the most economically important crops worldwide. However, the previous versions of the grapevine reference genome consisted of thousands of fragments with missing centromeres and telomeres, which limited the accessibility of the repetitive sequences, the centromeric and telomeric regions, and the inheritance of important agronomic traits in these regions. Here, we assembled a telomere-to-telomere (T2T) gap-free reference genome for the pinot noir cultivar (PN40024) using the PacBio HiFi long reads. The T2T reference genome (PN_T2T) was 69 Mb longer with 9026 more genes identified than the 12X.v2 version (Canaguier et al., 2017). We annotated 67% repetitive sequences, 19 centromeres and 36 telomeres, and incorporated gene annotations of previous versions into the PN_T2T. We detected a total of 377 gene clusters, which showed associations with complex traits, such as aroma and disease resistance. Even though the PN40024 sample had been selfed for nine generations, we still found nine genomic hotspots of heterozygous sites associated with biological processes, such as the oxidation-reduction process and protein phosphorylation. The fully annotated complete reference genome, therefore, provides important resources for grapevine genetics and breeding.This work was supported by the National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas) to Yongfeng Zhou, the National Key Research and Development Program of China(grant2019YFA0906200), the Agricultural Science and Technology Innovation Program (CAAS-ZDRW202101), the Shenzhen Science and Technology Program (grant KQTD2016113010482651), the BMBF funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI). We thank Bianca Frommer, Marie Lahaye, David Navarro-Payá, Marcela K. Tello-Ruiz and Kapeel Chougule for their help in analyzing the RNA-Seq data and in running the gene annotation pipeline. This study is also based upon work from COST Action CA17111 INTEGRAPE and form COST Innovators Grant IG17111 GRAPEDIA, supported by COST (European Cooperation in Science and Technology).ViticultureT2Tgap-fregene clustercentromeretelomerePublishe