A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.Comment: Accepted by KDD 201

The role of AMPK/mTOR/S6K1 signaling axis in mediating the physiological process of exercise-induced insulin sensitization in skeletal muscle of C57BL/6 mice

AbstractThe crosstalk between mTORC1/S6K1 signaling and AMPK is emerging as a powerful and highly regulated way to gauge cellular energy and nutrient content. The aim of the current study was to determine the mechanism by which exercise training reverses lipid-induced insulin resistance and the role of AMPK/mTOR/S6K1 signaling axis in mediating this response in skeletal muscle. Our results showed that high-fat feeding resulted in decreased glucose tolerance, which was associated with decreased Akt expression and increased intramuscular triglyceride deposition in the skeletal muscle of C57BL/6 mice. Impairments in lipid metabolism were accompanied by increased total protein and phosphorylation of S6K1, SREBP-1c cleavage, and decreased AMPK phosphorylation. Exercise training reversed these impairments, resulting in improved serum lipid profiles and glucose tolerance. C2C12 myotubes were exposed to palmitate, resulting in an increased insulin-dependent Akt Ser473 phosphorylation, associated with a significant increase in the level of phosphorylation of S6K1 on T389. All these changes were reversed by activation of AMPK. Consistent with this, inhibition of AMPK by compound C induced an enhanced phosphorylation of both S6K1 and Akt, and silencing of S6K1 with siRNA showed no effect on Akt phosphorylation in both the absence and presence of palmitate cultured myotubes. In addition, compound C led to an elevated SREBP-1c cleavage but was blocked by S6K1 siRNA. In summary, exercise training inhibits SREBP-1c cleavage through AMPK/mTOR/S6K1 signaling, resulting in decreased intramyocellular lipid accumulation. Our results provide new insights into the mechanism by which AMPK/mTOR/S6K1 signaling axis mediates the physiological process of exercise-induced insulin sensitization

A study of the HI gas fractions of galaxies at z ~ 1

Due to the fact that HI mass measurements are not available for large galaxy samples at high redshifts, we apply a photometric estimator of the HI-to-stellar mass ratio (M_HI/M_*) calibrated using a local Universe sample of galaxies to a sample of galaxies at z ~ 1 in the DEEP2 survey. We use these HI mass estimates to calculate HI mass functions (HIMFs) and cosmic HI mass densities (Omega_HI), and to examine the correlation between star formation rate and HI gas content, for galaxies at z ~ 1. We have estimated HI gas masses for ~ 7,000 galaxies in the DEEP2 survey with redshifts in the range 0.75 < z < 1.4 and stellar masses M_* > 10^{10} M_solar, using a combination of the rest-frame ultraviolet-optical colour (NUV - r) and stellar mass density (mu_*) as a way to estimate M_HI/M_*. It is found that the high mass end of high-z HI mass function (HIMF) is quite similar to that of the local HIMF. The lower limit of Omega_HI,limit = 2.1 * 10^{-4} h_70^{-1}, obtained by directly integrating the HI mass of galaxies with M_* > 10^{10} M_solar, confirms that massive star-forming galaxies do not dominate the neutral gas at z ~ 1. We study the evolution of the HI mass to stellar mass ratio from z ~ 1 to today and find a steeper relation between HI gas mass fraction and stellar mass at higher redshifts. Specifically, galaxies with M_* = 10^{11} M_solar at z ~ 1 are found to have 3 - 4 times higher neutral gas fractions than local galaxies, while the increase is as high as 4 - 12 times at M_* = 10^{10} M_solar. The quantity M_HI/SFR exhibits very large scatter, and the scatter increases from a factor of 5 - 7 at z = 0 to factors close to a hundred at z = 1. This implies that there is no relation between HI gas and star formation in high redshift galaxies. The HI gas must be linked to cosmological gas accretion processes at high redshifts.Comment: 10 pages, 13 figures, A&A accepte

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure

PO-193 Exercise training decreased lipid accumulation in murine skeletal muscle through Sestrin2-mediated SHIP2-JNK signaling pathway

Objective Obesity is becoming increasingly prevalent and is an important contributor to the worldwide burden of diseases. It is widely accepted that exercise training is beneficial for the prevention and treatment of obesity. However, the underlying mechanism by which exercise training improving&nbsp;skeletal muscle lipid metabolism&nbsp;is still&nbsp;not fully described. Sestrins&nbsp;(Sestrin1-3)&nbsp;are highly conserved stress-inducible protein. Concomitant ablation of Sestrin2 and Sestrin3&nbsp;has been reported to provoke hepatic mTORC1/S6K1 activation and insulin resistance even without nutritional overload and obesity, implicating that Sestrin2 and Sestrin3 have an important homeostatic function in the control of mammalian glucose and lipid metabolism.&nbsp;Our previous results demonstrated that physical exercise increased Sestrin2 expression in murine skeletal muscle, while the role of Sestrin2 in regulating lipid metabolism remains unknown.&nbsp; SH2 domain containing inositol&nbsp;5-phosphatase (SHIP2) acts as a negative regulator of the&nbsp;insulin signaling both in vitro and in vivo. An increased&nbsp;expression of SHIP2 inhibits the insulin-induced Akt&nbsp;activation, glucose uptake, and glycogen synthesis in 3T3-L1 adipocytes, L6 myotubes and tissues of animal models. Alterations of SHIP2 expression and/or enzymatic&nbsp;function appear to have a profound impact on the development of&nbsp;insulin resistance. However, the regulatory function of SHIP2 in lipid metabolism after exercise remains unclear. It has been reported that SHIP2 modulated&nbsp;lipid metabolism&nbsp;through regulating the activity of c-Jun N-terminal kinase (JNK) and Sterol regulatory element-binding protein-1 (SREBP-1). JNK&nbsp;is a subclass of mitogen-activated protein kinase (MAPK) signaling pathway in mammalian cells and plays a crucial role in metabolic changes and inflammation associated with a high-fat diet. Inhibition of JNK reduces lipid deposition and proteins level of fatty acid de novo synthesis in liver cells. It has been reported that&nbsp;Sestrin2 regulated the phosphorylation of JNK, however the underlying mechanism remains unclear.&nbsp;SREBP-1&nbsp;is important in regulating cholesterol biosynthesis and uptake and fatty acid biosynthesis, and SREBP-1 expression produces two different isoforms, SREBP-1a and SREBP-1c. SREBP-1c is responsible for regulating the genes required for de novo lipogenesis and its expression is regulated by insulin. SREBP-1a regulates genes related to lipid and cholesterol production and its activity is regulated by sterol levels in the cell. Altogether, the purpose of this study was to explore the&nbsp;effect and underlying mechanism&nbsp;of Sestrin2 on lipid accumulation after exercise&nbsp;training. Methods Male wild type&nbsp;and SESN2−/− mice&nbsp;were divided into normal chow (NC) and high-fat diet (HFD) groups to create insulin resistance&nbsp;mice model.&nbsp;After 8 weeks the IR model group was then divided into HFD sedentary control&nbsp;and HFD exercise groups (HE). Mice in HE group underwent 6-week treadmill exercise&nbsp;to reveal the effect&nbsp;of&nbsp;exercise&nbsp;training on lipid metabolism in insulin resistance&nbsp;model&nbsp;induced by HFD. We explored&nbsp;the mechanism through which Sestrin2 regulated lipid&nbsp;metabolism in vitro by&nbsp;supplying&nbsp;palmitate, overexpressing&nbsp;or inhibiting&nbsp;SESNs, SHIP2&nbsp;and&nbsp;JNK&nbsp;in myotubes. Results We found that 6-week exercise training decreased body weight, BMI and fat mass in wild type and SESN2-/-&nbsp;mice after high-fat diet&nbsp;(HFD) feeding. And exercise training&nbsp;decreased&nbsp;the level of plasma glucose, serum insulin,&nbsp;triglycerides and free fatty acids in wild type but not in Sestrin2-/-&nbsp;mice. Lipid droplet&nbsp;in skeletal muscle was also decreased&nbsp;in wild type but did not in Sestrin2-/-&nbsp;mice. Moreover, exercise training&nbsp;increased the proteins expression involved in fatty acid oxidation and decreased the proteins which related to fatty acid de novo synthesis. The results of oil red staining and the change of proteins related to fatty acid de novo synthesis and beta oxidation in myotubes treated with palmitate, Ad-SESN2 and siRNA-Sestrin2&nbsp;were consisted with the results in vivo, which suggested that Sestrin2 was a key regulator in lipid metabolism. Exercise training increased Sestrin2 expression and reversed up-regulation of SHIP2 and pJNK induced by HFD in wild type mice but not in Sestrin2-/-&nbsp;mice. In&nbsp;parallel, overexpression of Sestrin2 decreased the level of SHIP2 and pJNK induced by palmitate while Sestrin2 knock down by siRNA-Sestrin2 treatment did not change the expression of SHIP2 and pJNK, which suggested that Sestrin2 modulated SHIP2 and JNK in the state of abnormal lipid metabolism. Inhibition of SHIP2 reduced the activity of JNK, increased lipid accumulation and the proteins of fatty acid synthesis after palmitate treatment and over expression of Sestrin2, which suggest that Sestrin2 modulated lipid metabolism through SHIP2/JNK pathway. Conclusions Sestrin2 plays an important role in improving lipid metabolism after exercise training, and Sestrin2 regulates lipid metabolism by SHIP2-JNK pathway in skeletal muscle