Antidiabetic Effect of an Active Components Group from Ilex kudingcha and Its Chemical Composition

The leaves of Ilex kudingcha are used as an ethnomedicine in the treatment of symptoms related with diabetes mellitus and obesity throughout the centuries in China. The present study investigated the antidiabetic activities of an active components group (ACG) obtained from Ilex kudingcha in alloxan-induced type 2 diabetic mice. ACG significantly reduced the elevated levels of serum glycaemic and lipids in type 2 diabetic mice. 3-Hydroxy-3-methylglutaryl coenzyme A reductase and glucokinase were upregulated significantly, while fatty acid synthetase, glucose-6-phosphatase catalytic enzyme was downregulated in diabetic mice after treatment of ACG. These findings clearly provided evidences regarding the antidiabetic potentials of ACG from Ilex kudingcha. Using LC-DAD/HR-ESI-TOF-MS, six major components were identified in ACG. They are three dicaffeoylquinic acids that have been reported previously, and three new triterpenoid saponins, which were the first time to be identified in Ilex kudingcha. It is reasonable to assume that antidiabetic activity of Ilex kudingcha against hyperglycemia resulted from these six major components. Also, synergistic effects among their compounds may exist in the antidiabetic activity of Ilex kudingcha

Fine-grained Emotion Role Detection Based on Retweet Information

User behaviors in online social networks convey not only literal information but also one’s emotion attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this paper, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model, named as MERM, to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%

The influence of advertisement boards, street and source layouts on CO dispersion and building intake fraction in three-dimensional urban-like models

Heavy traffic flows commonly result in large vehicular pollutant exposure in near-road buildings. Street layouts and pollutant source settings are key factors. Advertisement boards are sometimes adopted for business purpose, but their impacts on pollutant dispersion and exposure are still unclear. Thus, this paper numerically investigates the influence of aspect ratios (building height/street width, H/Ws=1 or 2; *Revised Manuscript with No Changes Marked Click here to view linked References H=30m), source locations and advertisement-board settings on the flow, carbon monoxide (CO) dispersion and exposure within three-dimensional urban-like models under the parallel approaching wind to the main streets. Personal intake fraction (P_iF) represents the fraction of total vehicular emissions inhaled averagely by each person of a population. Spatial mean P_iF is named as and that for the entire building as building intake fraction (B). With span-wise CO source fixed in target secondary streets of No 2 or 13 (S2 or S13), is particularly large in target streets. B decreases exponentially toward downstream from the target street and S13 cases attain greater B and larger exponential decreasing rates. Cases with H/Ws=2 experiences more limited upward dispersion and subsequently smaller (0.155-0.339ppm) of entire target street than cases with H/Ws=1 (0.375-0.731ppm). For cases with stream-wise CO source along the main street (Smain), B first rises quickly toward downstream, then adjusts to equilibrium values (0.051-0.063ppm). Finally, with span-wise source, vertical and double-layer advertisement boards produce stronger upward CO transportation and greater B than lateral and single-layer types, while with Smain source, the double-layer and lateral types produce larger B and shorter exposure adjustment distance

FlyNet 2.0: Drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified

ROD-revenue: seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data

International audienceRecent years witness the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers' side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability-to quantitatively explain the desired relationship. Finally we evaluate our model by predicting drivers' revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers' revenue by offering useful guidance

SIDIS-RC EvGen: a Monte-Carlo event generator of semi-inclusive deep inelastic scattering with the lowest-order QED radiative corrections

SIDIS-RC EvGen is a C++ standalone Monte-Carlo event generator for studies of semi-inclusive deep inelastic scattering (SIDIS) processes at medium to high lepton beam energies. In particular, the generator contains binary and library components for generating SIDS events and calculating cross sections for unpolarized or longitudinally polarized beam and unpolarized, longitudinally or transversely polarized target. The structure of the generator incorporates transverse momentum-dependent parton distribution and fragmentation functions, whereby we obtain multi-dimensional binned simulation results, which will facilitate the extraction of important information about the three-dimensional nucleon structure from SIDIS measurements. In order to build this software, we have used recent elaborate QED calculations of the lowest-order radiative effects, applied to the leading order Born cross section in SIDIS. In this paper, we provide details on the theoretical formalism as well as the construction and operation of SIDIS-RC EvGen, e.g., how we handle the event generation process and perform multi-dimensional integration. We also provide example programs, flowcharts, and numerical results on azimuthal transverse single-spin asymmetries.Comment: 53 pages, 10 figures, and 3 listing