Didymin improves UV irradiation resistance in C. elegans

Didymin, a type of flavono-o-glycoside compound naturally present in citrus fruits, has been reported to be an effective anticancer agent. However, its effects on stress resistance are unclear. In this study, we treated Caenorhabditis elegans with didymin at several concentrations. We found that didymin reduced the effects of UV stressor on nematodes by decreasing reactive oxygen species levels and increasing superoxide dismutase (SOD) activity. Furthermore, we found that specific didymin-treated mutant nematodes daf-16(mu86) & daf-2(e1370), daf-16(mu86), akt-1(ok525), akt-2(ok393), and age-1(hx546) were susceptible to UV irradiation, whereas daf-2(e1371) was resistant to UV irradiation. In addition, we found that didymin not only promoted DAF-16 to transfer from cytoplasm to nucleus, but also increased both protein and mRNA expression levels of SOD-3 and HSP-16.2 after UV irradiation. Our results show that didymin affects UV irradiation resistance and it may act on daf-2 to regulate downstream genes through the insulin/IGF-1-like signaling pathway

An Unsupervised Multi-Dimensional Representation Learning Model for Short-Term Electrical Load Forecasting

The intelligent electrical power system is a comprehensive symmetrical system that controls the power supply and power consumption. As a basis for intelligent power supply control, load demand forecasting in power system operation management has attracted considerable research attention in energy management. In this study, we proposed a novel unsupervised multi-dimensional feature learning forecasting model, named MultiDBN-T, based on a deep belief network and transformer encoder to accurately forecast short-term power load demand and implement power generation planning and scheduling. In the model, the first layer (pre-DBN), based on a deep belief network, was designed to perform unsupervised multi-feature extraction feature learning on the data, and strongly coupled features between multiple independent observable variables were obtained. Next, the encoder layer (D-TEncoder), based on multi-head self-attention, was used to learn the coupled features between various locations, times, or time periods in historical data. The feature embedding of the original multivariate data was performed after the hidden variable relationship was determined. Finally, short-term power load forecasting was conducted. Experimental comparison and analysis of various sequence learning algorithms revealed that the forecasting results of MultiDBN-T were the best, and its mean absolute percentage error and root mean square error were improved by more than 40% on average compared with other algorithms. The effectiveness and accuracy of the model were experimentally verified

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future

Membrane-Based Continuous Remover of Trifluoroacetic Acid in Mobile Phase for LC-ESI-MS Analysis of Small Molecules and Proteins

We developed a "continuous" trifluoroacetic acid (TFA) remover based on electrodialysis with bipolar membrane for online coupling of liquid chromatography (LC) and electrospray ionization mass spectrometry (ESI-MS) using TFA containing mobile phase. With the TFA remover as an interface, the TFA anion in the mobile phase was removed based on electrodialysis mechanism, and meanwhile, the anion exchange membrane was self-regenerated by the hydroxide ions produced by the bipolar membrane. So the remover could continuously work without any additional regeneration process. The established LC-TFA remover-MS system has been successfully applied for the qualitative and quantitative analysis of small molecules as well as proteins

School environmental contamination of methicillin-sensitive Staphylococcus aureus as an independent risk factor for nasal colonization in schoolchildren: An observational, cross-sectional study.

ObjectiveWe aim to assess the similarities of proportional, phenotypic, and molecular characteristics between the school environment and schoolchildren on methicillin-sensitive S. aureus (MSSA) isolates.MethodsA cross-sectional study was conducted between March 2016 and August 2016 in eight elementary schools in Guangzhou, China. Nasal swabs from students and environmental swabs from school environments were collected. Univariate and multivariate logistic regression analyses under a multistage stratified cluster cross-sectional survey design were performed to access the prevalence relationship and influencing factors, respectively. Phenotypic and molecular characterizations of MSSA isolates were conducted using the Kirby-Bauer disk diffusion method and polymerase chain reaction assays, respectively.ResultsIn total, 1705 schoolchildren and 1240 environmental samples from 40 classes in eight elementary schools obtained between March and August 2016 were include in this study. The rates of MSSA prevalence among schoolchildren and the environment were 11.44% (195/1705) and 4.60% (57/1240), respectively. The odds ratios and 95% confidence intervals (CIs) on the prevalence of MSSA isolates were 1.11 (95% CI, 1.05-1.29; P = 0.010) and 1.04 (95% CI, 1.01-1.07; P = 0.003) for the school or class environment and students, respectively. Similar phenotypic and molecular characteristics were identified between schoolchildren and the environment. A cause and effect relationship could not be established because the study design was cross-sectional.ConclusionsBecause of the cross-sectional design, we can reveal the association between school environment and schoolchildren on MSSA, but it is not a cause and effect relationship

Association between High-Fat Diet during Pregnancy and Heart Weight of the Offspring: A Multivariate and Mediation Analysis

Maternal nutrition and health status in the peri-pregnancy period are closely related to offspring health. Currently, population studies are unable to provide quantitative relationships and effective measures of peri-pregnancy high-fat diet and offspring myocardial remodeling due to the difficulty of obtaining human samples. This study aimed to establish the mouse models of maternal obesity and high-fat diet supplementation and deprivation during pregnancy. The effects of obesity, periconceptional high-fat diet window, fetal weight, sex, and placental weight on myocardial remodeling in the offspring were measured by single-factor and multiple-factor regression analyses. Moreover, the relationship between perinatal high-fat diet/fetal weight and offspring myocardial remodeling was explored using the mediation analysis model. The multivariate analysis showed that the heart weight to body weight (HW/BW) ratio of the offspring decreased by −1.6525 mg/g for every 1-g increase in fetal weight. The offspring HW/BW increased by 1.1967 mg/g if pregnant women were exposed to a high-fat diet throughout pregnancy. The mediation analysis model of a perinatal high-fat diet for the myocardial remodeling of offspring revealed that fetal weight had a suppression effect on the myocardial weight of offspring, accounting for 60.70%; also, it had a mediating effect on the HW/BW of offspring, accounting for 17.10%. Moreover, subgroup analysis showed an interaction between offspring sex and HW/BW in a maternal high-fat diet during pregnancy. Additionally, a quantitative real-time polymerase chain reaction experiment further proved that a perinatal high-fat diet could change the important indicators of myocardial remodeling in offspring. In conclusion, this study found that a high-fat diet in the periconceptional period influenced factors in offspring myocardial remodeling. Moreover, maternal high-fat diet deprivation attenuated the changes in offspring myocardial remodeling. In addition, the role of fetal weight in mediating maternal high-fat diet-mediated offspring myocardial remodeling was quantified. Our study showed that a sensible and healthy diet during the perinatal period, especially during pregnancy, played a positive role in the health of the offspring

A meta-analysis of the global prevalence rates of Staphylococcus aureus and methicillin-resistant S. aureus contamination of different raw meat products

Previous research has indicated that raw meats are frequently contaminated with Staphylococcus aureus, but data regarding the pooled prevalence rates of S. aureus and methicillin-resistant S. aureus (MRSA) contamination in different types of raw meat products (beef, chicken, and pork) and across different periods, regions, and purchase locations remain inconsistent. We systematically searched the PubMed, EMBASE, Ovid, Web of Science, and HighWire databases to identify studies published up to June 2016. The STROBE guidelines were used to assess the quality of the 39 studies included in this meta-analysis. We observed no significant differences in the pooled prevalence rates of S. aureus and MRSA contamination identified in various raw meat products, with overall pooled prevalence rates of 29.2% (95% confidence interval [CI], 22.8 to 35.9%) and 3.2% (95% CI, 1.8 to 4.9%) identified for the two contaminants, respectively. In the subgroup analyses, the prevalence of S. aureus contamination in chicken products was highest in Asian studies and significantly decreased over time worldwide. In European studies, the prevalence rates of S. aureus contamination in chicken and pork products were lower than those reported on other continents. The pooled prevalence rates of S. aureus contamination in chicken and pork products and MRSA contamination in beef and pork products were significantly higher in samples collected from retail sources than in samples collected from slaughterhouses and processing plants. These results highlight the need for good hygiene during transportation to and manipulation at retail outlets to reduce the risk of transmission of S. aureus and MRSA from meat products to humans