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This research was undertaken to determine expression patterns of vacuolar processing enzyme (VPE) genes in four apple (Malus domestica (Suckow) Borkh) cultivars 'Royal Gala (RG)', 'Golden Delicious (GD)', 'Ralls Janet (RJ)' and 'Fuji (FJ)' during ripening. The results showed that GD and RG had higher respiration rate, ethylene production, content of malondialdehyde (MDA), relative electrical conductivity (REC) and VPE activity than RJ and FJ during ripening. A total of 17 MdVPEs were found to be expressed in four cultivars. These MdVPEs exhibited different expression patterns during ripening of four cultivars. Generally, MdVPEβ1, MdVPEγ2, MdVPEγ4 and MdVPEδ7 might be closely associated with ripening of all cultivars. MdVPEα6 might have a role in the early period of ripening of ACS1–2/-2 (1-Aminocyclopropane-1-carboxylate synthase) homozygous cultivars (RG, RJ and FJ). Our data here would be helpful in understanding the associations between MdVPEs and storage properties of different apple cultivars. [ABSTRACT FROM AUTHOR]

Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time., Author(s): Taiguo Li (corresponding author) [1]; Tiance Zhang [1]; Yingzhi Zhang [1]; Liben Yang [1] 1. Introduction The World Health Organization (WHO) released “Road safety” in 2017 and emphasized the [...]

Introduction: Endometrial cancer (EC) is a prevalent malignancy affecting the female population, with an increasing incidence among younger age groups. DNA methylation, a common epigenetic modification, is well‐established to play a key role in cancer progression. We suspected whether DNA methylation could be used as biomarkers for EC prognosis. Methods: In the present study, we analyzed bulk RNA‐sequencing data from 544 EC patients and DNA methylation data from 430 EC patients in the TCGA‐UCEC cohort. We applied weighted correlation network analysis to select a key gene set associated with panoptosis. We conducted correlation analysis between transcriptomic data of the selected key genes and DNA methylation data to identify valuable DNA methylation sites. These sites were further screened by Cox regression and least absolute shrinkage and selection operator analysis. Immune microenvironment differences between high‐risk and low‐risk groups were assessed using single‐sample gene set enrichment analysi, xCell and MCPcounter algorithms. Results: Our results identified five DNA methylation sites (cg03906681, cg04549977, cg06029846, cg10043253 and cg15658376) with significant prognostic value in EC. We constructed a prognostic model using these sites, demonstrating satisfactory predictive performance. The low‐risk group showed higher immune cell infiltration. Notably, methylation of site cg03906681 was negatively related to CD8 T cell infiltration, whereas cg04549977 exhibited positive correlations with immune infiltration, particularly in macrophages, activated B cells, dendritic cells and myeloid‐derived suppressor cells. PD0325901_1060 was strongly correlated with risk scores, indicating a potential therapeutic response for high‐risk EC patients. Conclusion: We have developed a robust DNA methylation‐based prognostic model for EC, which holds promise for improving prognosis prediction and personalized treatment approaches. These findings may contribute to better management of EC patients, particularly in identifying those at higher risk who may benefit from tailored interventions. [ABSTRACT FROM AUTHOR]