Effect of Sciadonic Acid on Hepatic Lipid Metabolism in Obese Mice Induced by A High-fat Diet

Objective: To investigate the potential beneficial effects of sciadonic acid (SA) on improving obesity induced by a high-fat diet in mice. Methods: Forty-eight male C57BL/6 mice were adaptively fed for one week and then randomly divided into the following groups: Control group (C), positive control group (S), model group (M), low-dose sciadonic acid group (LSA), medium-dose sciadonic acid group (MSA), and high-dose sciadonic acid group (HSA). The modeling process lasted for 16 weeks, and the low and high-dose groups were orally administered different doses of SA solution at a fixed time each day. After the modeling period, potential mechanisms of SA in regulating lipid metabolism in obese mice were explored, including aspects such as blood lipid metabolism, hepatic fat metabolism, hepatic oxidative stress, hepatic lipid synthesis, and expression of metabolism-related genes. Results: The high-dose SA intervention in obese mice significantly decreased the levels of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) in serum, while increasing high-density lipoprotein cholesterol (HDL-C) (P<0.05). It inhibited weight gain, reduced epididymal fat accumulation, and improved liver tissue damage. Additionally, SA significantly increased the activities of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in mice (P<0.05), and significantly reduced the production of oxidative end products MDA (P<0.05), alleviated oxidative stress in vivo, and inhibited lipid synthesis by regulating the expression of genes related to lipid metabolism to improve lipid metabolism. Conclusion: SA could improve lipid metabolism disorders in obese mice by suppressing fat accumulation, alleviate oxidative stress, regulate lipid synthesis and metabolism

Application des series de Volterra al'analyse des dispositifs nonlineaires microondes

SIGLECNRS T 59642 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Stair Scheduling for Data Collection in Wireless Sensor Networks

Spatially organized clusters are basic structure for large-scale wireless sensor networks. A cluster is generally composed by a large amount of energy-limited low-tier nodes (LNs), which are managed by a powerful cluster head (CH). The low-tier nodes that are close to the cluster head generally become bottlenecks in data collection applications. Energy efficient scheduling is important for the low-tier sensors to be longevous while guaranteeing reliable communication. In this paper, based on three aspects of performance considerations including network longevity, multihop communication reliability, and sensing system cost minimization, we propose a stair duty-cycle scheduling method for the low-tier sensors. It is designed to make the LNs in the same cluster sleep cooperatively for most of the time and wake up in assigned sequence for multihop communication. Stair scheduling cannot only improve the energy efficiency of the network but also guarantee high communication reliability and low transmission delay. Efficiency of the proposed stair scheduling is verified by analysis and intensive simulations. The results show that the performances of stair scheduling are much better than that of random scheduling algorithms

Spatial Distribution Characteristics of Suitable Planting Areas for <i>Pyrus</i> Species under Climate Change in China

Planting suitability determines the distribution and yield of crops in a given region which can be greatly affected by climate change. In recent years, many studies have shown that carbon dioxide fertilization effects increase the productivity of temperate deciduous fruit trees under a changing climate, but the potential risks to fruit tree planting caused by a reduction in suitable planting areas are rarely reported. In this study, Maxent was first used to investigate the spatial distribution of five Pyrus species in China, and the consistency between the actual production area and the modeled climatically suitable area under the current climatic conditions were determined. In addition, based on Coupled Model Intercomparison Project Phase 6, three climate models were used to simulate the change in suitable area and the migration trend for different species under different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results showed that the suitable area for pear was highly consistent with the actual main production area under current climate conditions. The potential planting areas of P. ussuriensis showed a downward trend under all emission paths from 2020 to 2100; other species showed a trend of increasing first and then decreasing or slowing down and this growth effect was the most obvious in 2020–2040. Except for P. pashia, other species showed a migration trend toward a high latitude, and the trend was more prominent under the high emission path. Our results emphasize the response difference between species to climate change, and the method of consistency analysis between suitable planting area and actual production regions cannot only evaluate the potential planting risk but also provide a reasonable idea for the accuracy test of the modeled results. This work has certain guiding and reference significance for the protection of pear germplasm resources and the prediction of yield

A new method to get initial guess configuration for multi-step sheet metal forming simulations

This study aims to develop a universal, robust, and linear method to obtain an initial guess configuration for the multi-step finite element method (FEM) solver of sheet metal forming. Using the decoupling theory, the deformation at each step in the multi-step FEM solver of the sheet metal forming is decoupled into two independent deformation modes: bending-dominated deformation and stretching-dominated deformation. The configuration of the bending-dominated deformation constrained by the sliding constraint surface is considered as the initial guess configuration for the current step in multi-step FEM solver. To get an accurate initial configuration at each step, the method of Laplace-Beltrami operator (LBO) on a simplicial surface is employed to obtain the initial guess configuration effectively. Several numerical examples are provided for validation and verification of the proposed method through its applications for complicated sheet metal workpieces of finite element simulations. The results show that the proposed method on the simplicial surface for the initial guess configuration within a few iterations to be significantly effective

Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images

Algorithm design and implementation for the detection of large herbivores from low-altitude (200 m–350 m) UAV remote sensing images faces two key problems: (1) the size of a single image from the UAV is too large, and the mainstream algorithm cannot adapt to it, and (2) the number of animals in the image is very small and densely distributed, which makes the model prone to missed detection. This paper proposes the following solutions: For the problem of animal size, we optimized the Faster-RCNN algorithm in terms of three aspects: selecting a HRNet feature extraction network that is more suitable for small target detection, using K-means clustering to obtain the anchor frame size that matches the experimental object, and using NMS to eliminate detection frames that have sizes inconsistent with the size range of the detection target after the algorithm generates the target detection frames. For image size, bisection segmentation was used when training the model, and when using the model to detect the whole image, we propose the use of a new overlapping segmentation detection method. The experimental results obtained for detecting yaks, Tibetan sheep (Tibetana folia), and the Tibetan wild ass in remote sensing images of low-altitude UAV from Maduo County, the source region of the Yellow River, show that the mean average precision (mAP) and average recall (AR) of the optimized Faster-RCNN algorithm are 97.2% and 98.2%, respectively, which are 9.5% and 12.1% higher than the values obtained by the original Faster-RCNN. In addition, the results obtained from applying the new overlap segmentation method to the whole UAV image detection process also show that the new overlap segmentation method can effectively solve the problems of the detection frames not fitting the target, missing detection, and creating false alarms due to bisection segmentation

Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data

Dynamic global vegetation models are useful tools for the simulation of global carbon cycle. However, most models are hampered by the poor availability of global aboveground biomass (AGB) data, which is necessary for the model calibration process. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential AGB, at the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Model results showed that IBIS could reproduce GPP with acceptable accuracy in monthly and annual scales. At the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed AGB for pan-tropical forests. The bias in modeled AGB was mainly caused by the unchanged parameters over the global scale for a specific plant functional type. This study also showed that different meteorological inputs can introduce substantial differences in modeled AGB in the global scale, although this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. (C) 2017 Elsevier B.V. All rights reserved

Camellia oil alleviates DSS-induced colitis in mice by regulating the abundance of intestinal flora and suppressing the NF-κB signaling pathway

Colitis is characterized by colonic inflammation and impaired gut health, and alterations in the gut microbiota may lead to the development of inflammatory bowel disease (IBD). Camellia oil (CO) possesses high nutritional value and offers various health benefits for human metabolic disorders and diseases. However, it is unclear whether CO ameliorates IBD and plays a role in the gut microbiota. This study aimed to investigate the effect of CO on colonic inflammation and gut microbiota using a mice model of dextran sodium sulfate (Dss)-induced ulcerative colitis (UC). Our findings demonstrated that CO inhibited weight loss and colon shortening and improved intestinal barrier function. Additionally, CO treatment reduced oxidative stress and inflammatory responses in Dss-induced colitis. Furthermore, CO significantly inhibited p65 and IκBα phosphorylation and alleviated colonic inflammation. Moreover, we observed that CO treatment increased the abundance of Bacteroides, Lactobacillus, and Odoribacter, while decreasing the abundance of Alistipes, Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-014, uncultured Bacteroidales bacterium, and Prevotellaceae_UCG-001. Additionally, CO treatment promoted the production of SCFAs. These data indicated the promising potential of CO to prevent UC by maintaining gut barrier function, inhibiting the NF-κB signaling pathway, and modulating the gut microbiota

Intelligent Identification and Features Attribution of Saline–Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy

Planting rice in saline–alkali land can effectively improve saline–alkali soil and increase grain yield, but traditional identification methods for saline–alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline–alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline–alkali-tolerant rice varieties and two saline–alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find_peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline–alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline–alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed