Assessing the host genetic background effects on type 2 diabetes and obesity development in response to mixed–oral bacteria and high-fat diet using the collaborative cross mouse model

Background: Host genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes(T2 D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross(CC), for dissecting host susceptibility for the development of T2 D and obesity, showing significant variations following high-fat(42% fat) diet(HFD). Here, we aimed to assessing the host genetic background and sex effects on T2 D and obesity development in response to oral-mixed bacterial infection and HFD using the CC lines.Materials and Methods: Study cohort consists of 97 mice from 2 CC lines(both sexes), maintained on either HFD or Standard diet(CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis(Pg) and Fusobacterium nucleatum(Fn) bacteria(control groups without infection). Body weight(BW) and glucose tolerance ability were assessed at the end time point of the experiment.Results: The CC lines varied(P <.05) at their BW gain and glucose tolerance ability(with sex effect) in response to diets and/or infection, showing opposite responses despite sharing the same environmental conditions. The combination of diet and infection enhances BW accumulation for IL1912, while restraints it for IL72. As for glucose tolerance ability, only females(both lines) were deteriorated in response to infection.Conclusions: This study emphasizes the power of the CC mouse population for the characterization of host genetic makeup for defining the susceptibility of the individual to development of obesity and/or impaired glucose tolerance.

A Study on The Multi-Antenna Geometrical Depolarization Channel Modeling

The traditional geometrical depolarization model that single transmitter to single receiver provides a simple method of polarization channel modeling. It can obtain the geometrical depolarization effect of each path if known the antenna configuration, the polarization field radiation pattern and the spatial distribution of scatters. With the development of communication technology, information transmission spectrum is more and more scarce. The original model provides only a single channel polarization state, so the information will be limited that the polarization state carries in the polarization modulation. The research is so significance that how to carries polarization modulation information by using multi-antenna polarization state. However, the present study shows that have no depolarization effect model for multi-antenna systems. In this paper, we propose a multi-antenna geometrical depolarization model. On the basis of a single antenna to calculate the depolarization effect of the model, and through simulation to analysis the main factors that influence the depolarization effect. This article provides a multi-antenna geometrical depolarization channel modeling that can applied to large-scale array antenna, and to some extent increase the speed of information transmission.

Contouring error modeling and simulation of a four-axis motion control system

A layered modeling method is proposed to resolve the problems resulting from the complexity of the error model of a multi-axis motion control system. In this model, a low level layer can be used as a virtual axis by the high level layer. The first advantage of this model is that the complex error model of a four-axis motion control system can be divided into several simple layers and each layer has different coupling strength to match the real control system. The second advantage lies in the fact that the controller in each layer can be designed specifically for a certain purpose. In this research, a three-layered cross coupling scheme in a four-axis motion control system is proposed to compensate the contouring error of the motion control system. Simulation results show that the maximum contouring error is reduced from 0.208 mm to 0.022 mm and the integration of absolute error is reduced from 0.108 mm to 0.015 mm, which are respectively better than 0.027 mm and 0.037 mm by the traditional method. And in the bottom layer the proposed method also has remarkable ability to achieve high contouring accuracy.

On Significance of Heterotic Group Theory in Hybrid Rice Breeding

Heterotic group theory （HGT） has played a major role in supporting hybrid maize breeding for about 100 years. The basic content and studies of HGT, and its application in rice and maize were summarized in this paper. Additionally, difficulties and challenges for hybrid rice breeding in China were analyzed, and necessity and urgency in hybrid rice breeding by using HGT were proposed.

Analysis of dynamic features in intersecting pedestrian flows

This paper focuses on the simulation analysis of stripe formation and dynamic features of intersecting pedestrian flows.The intersecting flows consist of two streams of pedestrians and each pedestrian stream has a desired walking direction.The model adopted in the simulations is the social force model, which can reproduce the self-organization phenomena successfully. Three scenarios of different cross angles are established. The simulations confirm the empirical observations that there is a stripe formation when two streams of pedestrians intersect and the direction of the stripes is perpendicular to the sum of the directional vectors of the two streams. It can be concluded from the numerical simulation results that smaller cross angle results in higher mean speed and lower level of speed fluctuation. Moreover, the detailed pictures of pedestrians＇ moving behavior at intersections are given as well.

Lowering fasting blood glucose with non-dialyzable material of cranberry extract is dependent on host genetic background,sex and diet

Background:Type 2 diabetes(T2D)is a polygenic metabolic disease,character-ized by high fasting blood glucose(FBG).The ability of cranberry(CRN)fruit to regulate glycemia in T2D patients is well known.Here,a cohort of 13 lines of the genetically diverse Collaborative Cross(CC)mouse model was assessed for the effect of non-dialyzable material(NDM)of cranberry extract in lowering fasting blood glucose.Methods:Eight-week-old mice were maintained on either a standard chow diet(con-trol group)or a high-fat diet(HFD)for 12 weeks,followed by injections of intraperi-toneal(IP)NDM(50 mg/kg)per mouse,three times a week for the next 6 weeks.Absolute FBG(mg/dl)was measured bi-weekly and percentage changes in FBG(%FBG)between weeks 0 and 12 were calculated.Results:Statistical analysis showed a significant decrease in FBG between weeks 0 and 12 in male and female mice maintained on CHD.However,a non-significant in-crease in FBG values was observed in male and female mice maintained on HFD dur-ing the same period.Following administration of NDM during the following 6 weeks,the results show a variation in significant levels of FBG lowering between lines,male and female mice and under the different diets.Conclusion:The results suggest that the efficacy of NDM treatment in lowering FGB depends on host genetic background(pharmacogenetics),sex of the mouse(phar-macosex),and diet(pharmacodiet).All these results support the need for follow-up research to better understand and implement a personalized medicine approach/uti-lization of NDM for reducing FBG.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.