A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.

PM2.5 exposure decreases viability, migration and angiogenesis in human umbilical vein endothelial cells and human microvascular endothelial cells.

Previous studies have confirmed that exposure to particulate matter with a diameter of ≤2.5 µm (PM2.5) is associated with inflammation. PM2.5 decreases cardiac cell viability and increases apoptosis through overproduction of reactive oxygen species (ROS). In the present study, the role of PM2.5 in ECs was investigated in vitro. Human umbilical vein endothelial cells and human microvascular endothelial cells (ECs) were incubated with PM2.5 (100­800 µg/ml) to investigate the effects of PM2.5 on EC viability, migration, tube formation and intracellular levels of ROS. Cell viability and cell apoptosis were determined by MTT assay and flow cytometry analysis. Cell migration was assessed using a Boyden chamber assay, and tube formation was determined by matrigel assay. Tumor necrosis factor­α and interleukin­8 levels were measured by ELISA, and ROS levels were assessed with 2',7'­dichlorofluorescin diacetate. The results indicated that PM2.5 decreases EC viability and increases EC apoptosis in a concentration­dependent manner. PM2.5 also decreased EC tube formation in a dose­dependent manner. The results also demonstrated that PM2.5 suppresses adhesion to EC extracellular matrix proteins. Furthermore, PM2.5 exposure significantly induced ROS generation, indicative of oxidative stress. Finally, it was demonstrated that PM2.5 decreased angiogenesis in vivo. These results suggested that repeated exposure to PM2.5 induces vascular inflammation.

Association of depression with pre-diabetes, undiagnosed diabetes, and previously diagnosed diabetes: a meta-analysis.

We performed a meta-analysis to analyze the associations of depression with pre-diabetes (PreDM), undiagnosed diabetes (UDM), and previously diagnosed diabetes (PDM), and whether the association was affected by important study characteristics. We searched relevant articles published in PubMed and EMBASE up to August, 2015. Studies reporting cross-sectional associations of depression with PreDM, UDM, or PDM compared with normal glucose metabolism (NGM) were included. Odds ratios (ORs) were pooled with random-effect and fixed-effect models. Subgroup analyses by sex, study mean age, different degrees of adjustment, publication year, quality score, and depression assessment scales were also performed. Twenty studies were eligible and included in current analysis. Summary estimates showed that compared with NGM individuals, prevalence of depression was moderately increased in PreDM (random-effect odds ratio (OR) 1.11, 95 % confidence interval (CI) 1.03-1.19) and UDM (OR 1.27, 95 % CI 1.02-1.59), and markedly increased in PDM (OR 1.80, 95 % CI 1.40-2.31). Subgroup analyses showed that the positive association remained only among studies with mean age <60 years old but not among those with mean age ≥60 years old. Summary estimates of ORs with cardiovascular disease adjustment substantially attenuated the association. Our findings suggested that risk of prevalent depression was gradually increased with the deterioration of glucose metabolism among younger age groups but not among older age groups. Comorbid cardiovascular diseases might be an important intermediate factor underlying the association between depression and diabetes.

Discovery of Xuebijing Injection Exhibiting Protective Efficacy on Sepsis by Inhibiting the Expression of HMGB1 in Septic Rat Model Designed by Cecal Ligation and Puncture.

Xuebijing (XBJ) injection is a complex traditional Chinese prescription that has been widely used to treat sepsis in China. However, its underlying mechanisms on sepsis still remain uninvestigated. In this study, 150 male Sprague Dawley rats were randomly divided into a normal control group, cecal ligation and puncture (CLP) group, CLP+XBJ group, and CLP+gibberellic acid group. Each of them contained 3 subgroups of different treatment periods (12, 24, and 48 hours after injection, respectively). The mRNA expression of HMGB1 in liver tissue of the 4 groups was calculated by the semiquantitative reverse-transcription polymerase chain reaction. The level of IL-6, IL-10, and TNF-α was determined by an enzyme-linked immunosorbent assay. Immunohistochemical analysis for HMGB1 showed the effect of XBJ on infiltration of inflammatory cells. The mRNA expression of HMGB1 in liver tissue in CLP+XBJ and CLP+gibberellic acid groups was lower than that in the CLP group. The levels of IL-6, IL-10, and TNF-α were decreased at the each monitored time point. All these results indicated that XBJ exhibits protective efficacy on sepsis by inhibiting the expression of HMGB1.