Dynamics of social contagions with local trend imitation

Research on social contagion dynamics has not yet including a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability distribution to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edge-based compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final behavior adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final behavior adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theory agrees with the simulation results.Comment: 14 pages, 5 figure

Artificial membrane-like environments for in vitro studies of purified G-protein coupled receptors

AbstractFunctional reconstitution of transmembrane proteins remains a significant barrier to their biochemical, biophysical, and structural characterization. Studies of seven-transmembrane G-protein coupled receptors (GPCRs) in vitro are particularly challenging because, ideally, they require access to the receptor on both sides of the membrane as well as within the plane of the membrane. However, understanding the structure and function of these receptors at the molecular level within a native-like environment will have a large impact both on basic knowledge of cell signaling and on pharmacological research. The goal of this article is to review the main classes of membrane mimics that have been, or could be, used for functional reconstitution of GPCRs. These include the use of micelles, bicelles, lipid vesicles, nanodiscs, lipidic cubic phases, and planar lipid membranes. Each of these approaches is evaluated with respect to its fundamental advantages and limitations and its applications in the field of GPCR research. This article is part of a Special Issue entitled: Membrane protein structure and function

Herbal Medicine for Hot Flushes Induced by Endocrine Therapy in Women with Breast Cancer: A Systematic Review and Meta-Analysis

Objective. This systematic review was conducted to evaluate the clinical effectiveness and safety of herbal medicine (HM) as an alternative management for hot flushes induced by endocrine therapy in breast cancer patients. Methods. Key English and Chinese language databases were searched from inception to July 2015. Randomized Controlled Trials (RCTs) evaluating the effects of HM on hot flushes induced by endocrine therapy in women with breast cancer were retrieved. We conducted data collection and analysis in accordance with the Cochrane Handbook for Systematic Reviews of Interventions. Statistical analysis was performed with the software (Review Manager 5.3). Results. 19 articles were selected from the articles retrieved, and 5 articles met the inclusion criteria for analysis. Some included individual studies showed that HM can relieve hot flushes as well as other menopausal symptoms induced by endocrine therapy among women with breast cancer and improve the quality of life. There are minor side effects related to HM which are well tolerated. Conclusion. Given the small number of included studies and relatively poor methodological quality, there is insufficient evidence to draw positive conclusions regarding the objective benefit of HM. Additional high quality studies are needed with more rigorous methodological approach to answer this question

BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps

Learning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN). In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones. We show that existing state-of-the-art agents do not generalize well. To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially. A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps. The learning process is composed of two phases. In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps. In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions. We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk's generalization ability. Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better. The codes and the datasets are released on our project page https://github.com/Sha-Lab/babywalk.Comment: Accepted by ACL 202