Atherosclerosis (AS) is a life-threatening vascular disease. RNA N6-methyladenosine (m6A) modification level is dysregulated in multiple pathophysiologic processes including AS. In this text, the roles and molecular mechanisms of m6A writer METTL3 in AS progression were explored in vitro and in vivo. In the present study, cell proliferative, migratory, and tube formation capacities were assessed through CCK-8, Transwell migration, and tube formation assays, respectively. RNA m6A level was examined through a commercial kit. RNA and protein levels of genes were measured through RT-qPCR and western blot assays, respectively. VEGF secretion level was tested through ELISA assay. JAK2 mRNA stability was detected through actinomycin D assay. The relationship of METTL3, IGF2BP1, and JAK2 was investigated through bioinformatics analysis, MeRIP, RIP, RNA pull-down, and luciferase reporter assays. An AS mouse model was established to examine the effect of METTL3 knockdown on AS development in vivo. The angiogenetic activity was examined through chick chorioallantoic membrane assay in vivo. The results showed that METTL3 was highly expressed in ox-LDL-induced dysregulated HUVECs. METTL3 knockdown inhibited cell proliferation, migration, tube formation, and VEGF expression/secretion in ox-LDL-treated HUVECs, hampered AS process in vivo, and prevented in vivo angiogenesis of developing embryos. METTL3 positively regulated JAK2 expression and JAK2/STAT3 pathway in an m6A dependent manner in HUVECs. IGF2BP1 positively regulated JAK2 expression through directly binding to an m6A site within JAK2 mRNA in HUVECs. METTL3 knockdown weakened the interaction of JAK2 and IGF2BP1. METTL3 exerted its functions through JAK2/STAT3 pathway. In conclusion, METTL3 knockdown prevented AS progression by inhibiting JAK2/STAT3 pathway via IGF2BP1.

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Atherosclerosis is the most notable cardiovascular disease, the latter being the main cause of death globally. Endothelial cell dysfunction plays a major role in the pathogenesis of atherosclerosis. However, it is currently unclear which genes are involved between endothelial cell dysfunction and atherosclerosis. This study was aimed at identifying these genes. Based on the GSE83500 dataset, the quantification of endothelial cell function was conducted using single-sample gene set enrichment analysis; the coexpression modules were conducted using weighted correlation network analysis. After building module-trait relationships, tan and yellow modules were regarded as hub modules. 10 hub genes from each hub module were identified by the protein-protein interaction network analysis. The key genes (RAB5A, CTTN, ITGB1, and MMP9) were obtained by comparing the expression differences of the hub gene between atherosclerotic and normal groups from the GSE28829 and GSE43292 datasets, respectively. ROC analysis showed the diagnostic value of key genes. Moreover, the differential expression of key genes in normal and atherosclerotic aortic walls was verified. In vitro, we establish a model of ox-LDL-injured endothelial cells and transfect RAB5A overexpression and shRNA plasmids. The results showed that overexpression of RAB5A ameliorates the proliferation and migration function of ox-LDL-injured endothelial cells, including the ability of tubule formation. It was speculated that the interferon response, Notch signaling pathways, etc. were involved in this function of RAB5A by using gene set variation analysis. With the multiple bioinformatics analysis methods, we detected that yellow and tan modules are related to the abnormal proliferation and migration of endothelial cells associated with atherosclerosis. RAB5A, CTTN, ITGB1, and MMP9 can be used as potential targets for therapy and diagnostic markers. In vitro, overexpression of RAB5A can ameliorate the proliferation and migration function of ox-LDL-injured endothelial cells, and the possible molecules involved in this process were speculated.

Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming.

BackgroundAcute coronary syndrome (ACS) is the most common cause of death in patients with coronary artery disease. The aim of the study was to identify the predictors of both comprehensive clinical risk and severity of coronary lesions by comprehensive use of GRACE and SYNTAX scores in patients with ACS.MethodsClinical data of 225 ACS patients who underwent coronary angiography between 2015 and 2016 were collected. Multiple logistic regression analysis (stepwise) was used to identify the predictors. The predictive ability of predictors and the model were determined using receiver operating characteristics analyses.ResultsMultivariable logistic regression analyses showed that high aspartate aminotransferase (AST) predicted the comprehensive clinical risk with odds ratios (ORs) and 95% confidence intervals (CIs) of 1.011 (1.002–1.021). High total cholesterol (TC) and red blood cell distribution width (RDW) predicted the severity of coronary lesions with ORs and 95% CIs of 1.517 (1.148–2.004) and 1.556 (1.195–2.028), respectively. Low prealbumin predicted both severity of coronary lesions and comprehensive clinical risk of ACS patients with ORs and 95% CIs of 0.743 (0.672–0.821) and 0.836 (0.769–0.909), respectively. The model with a combination of prealbumin and AST had the highest predictive efficacy for comprehensive clinical risk, and the combination of prealbumin, TC, and RDW had the highest predictive efficacy for the severity of coronary lesions. The sensitivity and specificity, and the optimal cut-off values of these four indexes were determined.ConclusionsFour predictors for the comprehensive clinical risk and severity of coronary lesions of ACS were identified, which provided important information for the early diagnosis and appropriate treatment of ACS.