The anomalous antiferromagnetic topological phase in pressurized SmB6

Antiferromagnetic materials, whose time-reversal symmetry is broken, can be classified into the Z2 topology if they respect some specific symmetry. Since the theoretical proposal, however, no materials have been found to host the antiferromagnetic topological (AFT) phase to date. Here, for the first time, we demonstrate that the topological Kondo insulator SmB6 can be an AFT system when pressurized to undergo an antiferromagnetic phase transition. In addition to propose the possible candidate for an AFT material, in this work we also illustrate the anomalous topological surface states of the AFT phase which has not been discussed before. Originating from the interplay between the topological properties and the antiferromagnetic surface magnetization, the topological surface states of the AFT phase behave differently as compared with those of a topological insulator. Besides, the AFT insulators are also found promising in the generation of tunable spin currents, which is an important application in spintronics

When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet

Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

GPER-induced signaling is essential for the survival of breast cancer stem cells.

G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs

Effect of Co substitution on magnetic and magnetoresistance effect in La0.67(Ba1-xCox)0.33mno3 system

A series of polycrystalline perovskite manganite of La0.67(Ba1-xCox)0.33MnO3 (x=0.00, 0.30 and 0.50) were prepared by conventional solid-state route. XRD spectrum indicates that single phase rhombohedral perovskite structure had been obtained for x=0.00 sample. When Co is introduced in the Ba site, its structure is distorted from rhombohedral to pseudo-cubic. The SEM images show that the average grain sizes were found to be in 3-8µm (x=0.30) and 2-10µm (x=0.50) with less pore between the grain. For x=0.00, the sample is found in melted condition where no significant clear grain boundary can be found. Pure sample had TC of 343K. However, substitution of Co at Ba site brings down the Curie temperature, TC below 293K. Pure (x=0.0) sample shows Low Field Magnetoresistance (LFMR) effect and the effect weakens when Co is introduced. The highest low-field MR value is -13.0% for sample with x=0.00 in 0.1Tesla applied external magnetic field at 90K and the highest MR value of -22.5% is given by x=0.30 sample at 1Tesla applied magnetic field at 90K. Hence, these indicated that Co will not enhance the extrinsic MR which is due to the grain boundary effect and tend to destroy the LFMR effect

Improvement of n-butanol tolerance in Escherichia coli by membrane-targeted tilapia metallothionein

Background: Though n-butanol has been proposed as a potential transportation biofuel, its toxicity oftencauses oxidative stress in the host microorganism and is considered one of the bottlenecks preventing itsefficient mass production.Results: To relieve the oxidative stress in the host cell, metallothioneins (MTs), which are known as scavengersfor reactive oxygen species (ROS), were engineered in E. coli hosts for both cytosolic and outer-membrane-targeted (osmoregulatory membrane protein OmpC fused) expression. Metallothioneins from human (HMT),mouse (MMT), and tilapia fish (TMT) were tested. The host strain expressing membrane-targeted TMT showed thegreatest ability to reduce oxidative stresses induced by n-butanol, ethanol, furfural, hydroxymethylfurfural, andnickel. The same strain also allowed for an increased growth rate of recombinant E. coli under n-butanol stress.Further experiments indicated that the TMT-fused OmpC protein could not only function in ROS scavenging butalso regulate either glycine betaine (GB) or glucose uptake via osmosis, and the dual functional fusion proteincould contribute in an enhancement of the host microorganism’s growth rate.Conclusions: The abilities of scavenging intracellular or extracellular ROS by these engineering E. coli wereexamined, and TMT show the best ability among three MTs. Additionally, the membrane-targeted fusion protein,OmpC-TMT, improved host tolerance up to 1.5% n-butanol above that of TMT which is only 1%. These resultspresented indicate potential novel approaches for engineering stress tolerant microorganism strains