Transcriptome Analysis Reveals the Dynamic and Rapid Transcriptional Reprogramming Involved in Heat Stress and Identification of Heat Response Genes in Rice.

High temperature is one of the most important environmental factors influencing rice growth, development, and yield. Therefore, it is important to understand how rice plants cope with high temperatures. Herein, the heat tolerances of T2 (Jinxibai) and T21 (Taizhongxianxuan2hao) were evaluated at 45 °C, and T21 was found to be sensitive to heat stress at the seedling stage. Analysis of the H2O2 and proline content revealed that the accumulation rate of H2O2 was higher in T21, whereas the accumulation rate of proline was higher in T2 after heat treatment. Meanwhile, transcriptome analysis revealed that several pathways participated in the heat response, including "protein processing in endoplasmic reticulum", "plant hormone signal transduction", and "carbon metabolism". Additionally, our study also revealed that different pathways participate in heat stress responses upon prolonged stress. The pathway of "protein processing in endoplasmic reticulum" plays an important role in stress responses. We found that most genes involved in this pathway were upregulated and peaked at 0.5 or 1 h after heat treatment. Moreover, sixty transcription factors, including the members of the AP2/ERF, NAC, HSF, WRKY, and C2H2 families, were found to participate in the heat stress response. Many of them have also been reported to be involved in biotic or abiotic stresses. In addition, through PPI (protein-protein interactions) analysis, 22 genes were identified as key genes in the response to heat stress. This study improves our understanding of thermotolerance mechanisms in rice, and also lays a foundation for breeding thermotolerant cultivars via molecular breeding.

WeChat-assisted dietary and exercise intervention for prevention of gestational diabetes mellitus in overweight/obese pregnant women: a two-arm randomized clinical trial.

PURPOSE: This study aimed to examine the influence of a WeChat-based dietary and exercise intervention on gestational diabetes mellitus (GDM) prevention in overweight/obese pregnant women in Beijing. METHODS: Overweight/obese pregnant women were recruited in the early stages of pregnancy. After screening by include and exclude standards, eligible women were randomly divided into two groups, intervention and control groups. The control group received a general advice session about pregnancy nutrition and weight management. The intervention group received three face-to-face sessions about personalized dietary and exercise intervention, with the help of WeChat as a monitoring tool to promote treatment plan adherence. At 24-28 weeks of pregnancy, GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Gestational weight gain (GWG), maternal and neonatal outcomes were also collected. RESULTS: This study analyzed 215 participants. At the mid-trimester, 42 (37.8%) women in the control group were diagnosed with GDM (n = 111) versus 25 (24.5%) in the intervention group (n = 104; p < 0.05). The intervention group gained 11.2 ± 4.9 kg during the whole gestation period, with 4.9 ± 3.1 kg-weight increment in the first 25 weeks of pregnancy, versus 13.4 ± 5.0 kg and 6.9 ± 3.2 kg in the first 25 weeks in the control group (between groups: p < 0.001/p = 0.002). Incidence of macrosomia was not significantly lower in the intervention group than in the control group (8/7.9% vs 11/9.9%) (p > 0.05). No significant difference was found in the rate of natural labor and occurrence of perinatal complications (e.g., preterm birth, gestational hypertension, and preeclampsia) between the groups (p > 0.05). CONCLUSIONS: The WeChat-assisted dietary and exercise intervention was effective in reducing the occurrence of GDM and excessive weight gain in overweight/obese pregnant women. Disseminating knowledge of pregnancy and childbirth through social media platforms like WeChat could be an important part of antenatal care.

Screening and optimizing of inhibitors for ammonia-oxidizing bacteria in sediments of malodorous river.

The proper use of selective ammonia-oxidizing archaea (AOA) and/or ammonia-oxidizing bacteria (AOB) inhibitors is critical to distinguish AOA and AOB contribution. In this research, three inhibitors including ampicillin, dicyandiamide (DCD), and allylthiourea (ATU) were examined mainly focusing on inhibiting dosage, adaptability, and effects. The results showed that the optimized inhibitory dosage of ampicillin, DCD, and ATU was separately 1.5 g L-1, 1 mM, and 25 µM. Among the three inhibitors, ATU exhibited the strongest and persistent inhibition effects and resulted in up to 90% inhibition in the AOB-enriched culture. The seemingly weakening inhibiting effects of ATU in the simulated river systems can be attributed to the involved role of AOA, the uneven spatial distribution of ATU, and protection by sediment structure in complex malodorous rivers. The high-throughput pyrosequencing analysis showed the AOB-related genus Nitrosomonas and Nitrosococcus were mostly affected by ATU in the enrichments and the river systems, respectively. The inhibition of ATU was realized mainly by reducing the abundance and activity of AOB. The decrease of the ratio of AOB/AOA amoA gene copy numbers after addition of ATU further confirmed the inhibiting effectiveness of ATU in complex microbial community of malodorous rivers.

Collaborative neurodynamic optimization for solving nonlinear equations.

A distributed optimization method for solving nonlinear equations with constraints is developed in this paper. The multiple constrained nonlinear equations are converted into an optimization problem and we solve it in a distributed manner. Due to the possible presence of nonconvexity, the converted optimization problem might be a nonconvex optimization problem. To this end, we propose a multi-agent system based on an augmented Lagrangian function and prove that it converges to a locally optimal solution to an optimization problem in the presence of nonconvexity. In addition, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. Three numerical examples are elaborated to illustrate the effectiveness of the main results.

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Purpose: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks. Methods: DeepRetina uses the improved Xception65 to extract and learn the characteristics of retinal layers. The Xception65-extracted feature maps are inputted to an atrous spatial pyramid pooling module to obtain multiscale feature information. This information is then recovered to capture clearer retinal layer boundaries in the encoder-decoder module, thus completing retinal layer auto-segmentation of the retinal optical coherence tomography (OCT) images. Results: We validated this method using a retinal OCT image database containing 280 volumes (40 B-scans per volume) to demonstrate its effectiveness. The results showed that the method exhibits excellent performance in terms of the mean intersection over union and sensitivity (Se), which are as high as 90.41 and 92.15%, respectively. The intersection over union and Se values of the nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, outer limiting membrane, photoreceptor inner segment, photoreceptor outer segment, and pigment epithelium layer were found to be above 88%. Conclusions: DeepRetina can automate the segmentation of retinal layers and has great potential for the early diagnosis of fundus retinal diseases. In addition, our approach will provide a segmentation model framework for other types of tissues and cells in clinical practice. Translational Relevance: Automating the segmentation of retinal layers can help effectively diagnose and monitor clinical retinal diseases. In addition, it requires only a small amount of manual segmentation, significantly improving work efficiency.

Inspection of visible components in urine based on deep learning.

PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy. In this study, a method is proposed for the analysis of urinary particles based on deep learning. METHODS: We used seven cellular components (i.e., erythrocytes, leukocytes, epithelial, low-transitional epithelium, casts, crystal, and squamous epithelial cells) in the microscopic imaging of urine as the detection targets. After the extraction of features using Resnet50, feature maps of different sizes are obtained in the last few layers of the feature pyramid net (FPN). The feature maps are then input into the classification subnetwork and regression subnetwork for classification and localization respectively, and detection results are obtained. First, we introduce the basic model (RetinaNet) to detect the cellular components in urinary particles, and the features of the objects can then be extracted more effectively by replacing different basic networks. Lastly, the effects of different weight initialization methods and different anchor scales on the performance of the model are investigated. RESULTS: We obtained the optimal network structure based on the adjustment of the loss functional parameters, thereby achieving the best results in the test set of urinary particles. The experimental data yielded an accuracy of 88.65% with a processing time of only 0.2 s for each image on a GeForce GTX 1080 graphics processing unit (GPU). Our results demonstrate that this method cannot only achieve the speed of the first-stage target detector, but also the accuracy of the two-stage target algorithm in the analysis of urinary particles. CONCLUSION: This study developed new automated analysis urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of urinary particles. Moreover, our approach will be useful for the detection of other cells in the clinical setting.

FecalNet: Automated detection of visible components in human feces using deep learning.

PURPOSE: To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks. METHODS: FecalNet uses the ResNet152 residual network to extract and learn the characteristics of visible components in fecal microscopic images, acquire feature maps in combination with the feature pyramid network, apply the full convolutional network to classify and locate the fecal components, and implement the improved focal loss function to reoptimize the classification results. This allowed the complete automation of the detection and identification of the visible components in feces. RESULTS: We validated this method using a fecal database of 1,122 patients. The results indicated a mean average precision (mAP) of 92.16% and an average recall (AR) of 93.56%. The average precision (AP) and AR of erythrocyte, leukocyte, intestinal mucosal epithelial cells, hookworm eggs, ascarid eggs, and whipworm eggs were 92.82% and 93.38%, 93.99% and 96.11%, 90.71% and 92.41%, 89.95% and 93.88%, 96.90% and 91.21%, and 88.61% and 94.37%, respectively. The average times required by the GPU and the CPU to analyze a fecal microscopic image are approximately 0.14 and 1.02 s, respectively. CONCLUSION: FecalNet can automate the detection and identification of visible components in feces. It also provides a detection and identification framework for detecting several other types of cells in clinical practice.

Gestational diabetes: weight gain during pregnancy and its relationship to pregnancy outcomes.

BACKGROUND: Weight gain during pregnancy reflects the mother's nutritional status. However, it may be affected by nutritional therapy and exercise interventions used to control blood sugar in gestational diabetes mellitus (GDM). This study aimed to evaluate weight gain during gestation and pregnancy outcomes among women with GDM. METHODS: A retrospective study involving 1523 women with GDM was conducted between July 2013 and July 2016. Demographic data, gestational weight gain (GWG), blood glucose, glycated-hemoglobin level, and maternal and fetal outcomes were extracted from medical records. Relationships between GWG and pregnancy outcomes were investigated using multivariate logistic regression. RESULTS: In total, 451 (29.6%) women showed insufficient GWG and 484 (31.8%) showed excessive GWG. Excessive GWG was independently associated with macrosomia (adjusted odds ratio [aOR] 2.20, 95% confidence interval [CI] 1.50-3.52, P < 0.001), large for gestational age (aOR 2.06, 95% CI 1.44-2.93, P < 0.001), small for gestational age (aOR 0.49, 95% CI 0.25-0.97, P = 0.040), neonatal hypoglycemia (aOR 3.80, 95% CI 1.20-12.00, P = 0.023), preterm birth (aOR 0.45, 95% CI 0.21-0.96, P = 0.040), and cesarean delivery (aOR 1.45, 95% CI 1.13-1.87, P = 0.004). Insufficient GWG increased the incidence of preterm birth (aOR 3.53, 95% CI 1.96-6.37, P < 0.001). CONCLUSIONS: Both excessive and insufficient weight gain require attention in women with GDM. Nutritional therapy and exercise interventions to control blood glucose should also be used to control reasonable weight gain during pregnancy to decrease adverse pregnancy outcomes.

Chemoenzymatically synthesized ganglioside GM3 analogues with inhibitory effects on tumor cell growth and migration.

Ganglioside GM3, belonging to glycosphingolipid family, has been known as tumor-associated carbohydrate antigen on several types of tumor. Many studies have revealed that GM3 plays a role in cell proliferation, adhesion and differentiation, which is crucial in the process of cancer development. In the present study, we firstly synthesized novel mannose-containing GM3 analogues by enzymatic hydrolysis and chemical procedures. Then the antiproliferative activity of the novel analogues along with galactose-containing analogues we prepared previously was investigated and the data demonstrated that these analogues exhibited antiproliferative effect on K562 and HCT116 cells. Finally, the influence of these analogues on tumor cell migration was studied on B16, B16-F10 and HCCLM3 cells by wound healing test, because the migration of tumor cells represents one of the relevant factors in assessing the malignancy of cancer. This study could lay the foundation for optimizing leading compounds and provide valuable information for finding new antitumor drugs for cancer therapy.

Rapid probing of photocatalytic activity on titania-based self-cleaning materials using 7-hydroxycoumarin fluorescent probe.

Self-cleaning materials are widely applied, but the available methods for determining their photocatalytic activity are time consuming. A simple analysis method was proposed to evaluate rapidly the photocatalytic activity of self-cleaning materials. This method is based on monitoring of a highly fluorescent product generated by the self-cleaning materials after illumination. Under UV irradiation, holes photo-induced on the surface of self-cleaning materials can oxidize water molecules (or hydroxide ions) adsorbed on the surface to produce hydroxyl radicals, which then quantitatively oxidize coumarin to highly fluorescent 7-hydroxycoumarin. It was observed that the fluorescence intensity of photo-generated 7-hydroxycoumarin at 456 nm (excited at 346 nm) linearly increased with irradiation time, and the fluorescence intensity at a given irradiation time was linearly proportional to the photocatalytic activity of self-cleaning materials. Consequently, the photocatalytic activity of self-cleaning materials was able to be probed simply by using this new method, which requires an analysis time of 40 min, being much less than 250 min required for a dye method.