Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

The data assimilation method to improve sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied on this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area get improved respectively by ~57.9% and ~55.5%; the sea fog of the other case completely mis-forecasted by 3DVAR is produced successfully by EnKF. These improvements of EnKF relative to 3DVAR are benefited from its flow-dependent background error, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement on the initial moisture conditions for sea fog forecast.

This research investigates the fatigue crack propagation on the lower surface of rocket deflector troughs of offshore rocket launch platforms. Initially, a numerical model of the offshore rocket launch platform is established using ABAQUS based on the Extended Finite Element Method (XFEM). Subsequently, two variable parameters—namely, the initial crack length and initial tilt angle—are introduced. This research systematically analyzes the impact of these parameters on fatigue crack propagation patterns in both the maximum stress and maximum deformation regions of the deflector channels under the combined conditions of high temperature and impact. Finally, the research indicates that the propagation length of surface cracks in the deflector trough exhibits a trend of initial increase followed by a subsequent decrease with the increase in the preset inclination angle. Notably, the stable propagation rate of the crack in the region of maximum deformation surpasses that observed in the region of maximum stress. Through meticulous comparative analysis, it is evident that temperature loading significantly fosters the initiation and propagation of cracks, particularly in the upper region of the deflector channel's lower surface.

Background: Jerusalem artichoke (Helianthus tuberosus L.) is tolerant to salinity stress and has high economic value. The salt tolerance mechanisms of Jerusalem artichoke are still unclear. Especially in the early stage of Jerusalem artichoke exposure to salt stress, the plant physiology, biochemistry and gene transcription are likely to undergo large changes. Elucidating these changes may be of great significance to understanding the salt tolerance mechanisms of it. Results: We obtained high-quality transcriptome from leaves and roots of Jerusalem artichoke exposed to salinity (300 mM NaCl) for 0 h, 6 h, 12 h, 24 h and 48 h, with 150,129 unigenes and 9023 DEGs (Differentially Expressed Genes). The RNA-seq data were clustered into time-dependent groups (nine clusters each in leaves and roots); gene functions were distributed evenly among the groups convergence. KEGG enrichment analysis showed the genes related to plant hormone signal transduction were enriched in almost all treatment comparisons. Under salt stress, genes belongs to PYL (abscisic acid receptor PYR / PYL family), PP2C (Type 2C protein phosphatases), GH3 (Gretchen Hagen3), ETR (ethylene receptor), EIN2/3 (ethylene-insensitive protein 2/3), JAZ (Genes such as jasmonate ZIM-domain gene) and MYC2 (Transcription factor MYC2) had extremely similar expression patterns. The results of qPCR of 12 randomly selected genes confirmed the accuracy of RNA-seq. Conclusions: Under the impact of high salinity (300mM) environment, Jerusalem artichoke in the seedling stage was difficult to survive for a long time, and the phenotype was severe in the short term. Based on the expression of genes on the time scale, we found that the distribution of gene functions in time is relatively even. Upregulation of the phytohormone signal transduction had a crucial role in the response of Jerusalem artichoke seedlings to salt stress, the genes of abscisic acid, auxin, ethylene, and jasmonic acid had the most obvious change pattern.

Ovarian cancer is the most frequent cause of deaths in gynecologic malignancies. Many possible mechanisms have been proposed via RNAseq and DNAseq technique recently. However, the driving factors are still obscure. The possible reasons are attributed to the incomplete human reference. This study integrated the canonical mapping-based and mapping-free protocols to extract reliable variations and novel events. We eventually obtained 450 reliable SNVs from the WES data and novel events from the RNAseq data, including 154 SNVs, 462 intron events, two repeats and six splice events. We identified six differentially expressed genes and six contigs that are significantly related to survival prognosis. The recurrent SNVs in significantly differentially expressed genes can be validated in an independent cohort of 20 Chinese ovarian cancer patients.

Omicron variants have higher infectivity, more immune escape but lower risk of severe clinical outcomes than the ancestral variants. However, people are still wondering whether SARS-CoV-2 may become lethal again or whether some highly-pathogenic strain may emerge in the future. Omicron is more selective proliferation in the upper respiratory tract (URT). The temperature of human URT is 33–34°C, and the virus strains other than Omicron cannot replicate effectively at this temperature. While Omicron adapted to the low temperature environment, so its distribution mainly depends on the expression levels of ACE2 in cells. Similar distribution switch from lungs to URT has been observed for the 1918 H1N1 influenza virus, who vanished after 1921 and has been turned to be the seasonal human influenza A viruses. However, some relatively high-pathogenic SARS-CoV-2 strains and influenza-virus strains may still emerge in the future. Dynamic changes in the viral virulence should be monitored constantly.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

To understand the molecular mechanism underlying yellowing in pineapples during ripening, coupled with alterations in fruit quality, comprehensive metabolome and transcriptome investigations were carried out. These investigations were conducted using the pulp samples collected at three distinct stages of maturity: young fruit (YF), mature fruit (MF), and fully mature fruit (FMF). This study revealed a noteworthy increase in the levels of total phenols and flavones, coupled with a concurrent decline in lignin and total acid content, as the fruit transitioned from YF to FMF. Furthermore, the analysis yielded 167 differentially accumulated metabolites (DAMs) and 2194 differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes enrichment analysis based on DAMs and DEGs revealed that the biosynthesis of plant secondary metabolites, particularly the flavonol, flavonoid, and phenypropanoid pathways, plays a pivotal role in fruit yellowing. Additionally, a comprehensive regulatory network encompassing genes that contribute to metabolisms of flavones, flavonols, lignin, and organic acids was proposed. This network sheds light on the intricate processes that underlies fruit yellowing and quality alterations. These findings enhance our understanding of the regulatory pathways governing pineapple ripening and offer valuable scientific insight into the molecular breeding of pineapples.

(1) Background: Heart failure (HF) is the final stage of multiple cardiac diseases, which has now become a severe public health problem worldwide. β-Adrenergic receptor (β-AR) overactivation is a major pathological factor associated with multiple cardiac diseases and mediates cardiac fibrosis and inflammation. Previous study has demonstrated that Bruton's tyrosine kinase (BTK) mediated cardiac fibrosis by TGF-β related signal pathways, indicating that BTK was a potential drug target for cardiac fibrosis. Zanubrutinib, a second-generation BTK inhibitor, has shown anti-fibrosis effects in previous research. However, it is unclear whether Zanubrutinib can alleviate cardiac fibrosis induced by β-AR overactivation;(2) Methods: In vivo: Male C57BL/6J mice were treated with or without the β-AR agonist isoproterenol (ISO) to establish cardiac fibrosis animal model. Here, results showed that BTK inhibitor Zanubrutinib (ZB) had a great effect on cardiac fibrosis and inflammation induced by β-AR. In vitro: Results showed that ZB alleviated β-AR-induced cardiac fibroblast activation and macrophage proinflammatory cytokine production. Further mechanism studies demonstrated that ZB inhibited β-AR-induced cardiac fibrosis and inflammation by BTK、STAT3、NF-κB and PI3K/Akt signal pathways both in vivo and in vitro; (4) Conclusions: our research provides evidence that ZB ameliorates β-AR-induced cardiac fibrosis and inflammation.