An ab-initio study of circular photogalvanic effect in chiral multifold semimetals

So far, the circular photogalvanic effect (CPGE) is the only possible quantized signal in Weyl semimetals. With inversion and mirror symmetries broken, Weyl and multifold fermions in band structures with opposite chiralities can stay at different energies and generate a net topological charge. Such kind of net topological charge can present as a quantized signal in the circular polarized light induced injection current. According to current theoretical understanding, RhSi and its counterparts are believed to be the most promising candidate for the experimental observation of the quantized CPGE. However, the real quantized signal was not experimentally observed to date. Since all the previous theoretical studies for the quantized CPGE were based on effective model but not realistic band structures, it should lose some crucial details that influence the quantized signal. The current status motives us to perform a realistic ab-initio study for the CPGE. Our result shows that the quantized value is very easy to be interfered by trivial bands related optic transitions, and an fine tuning of the chemical potential by doping is essential for the observation of quantized CPGE. This work performs the first ab-initio analysis for the quantized CPGE based on realistic electronic band structure and provides an effective way to solve the current problem for given materials.Comment: 7 pages, 5 figure

A hybrid training method for ANNs and its application in multi faults diagnosis of rolling bearing

A hybrid training method with probabilistic adaptive strategy for feedforward artificial neural network was proposed and applied to the problem of multi faults diagnosis of rolling bearing. The traditional training method such as LM shows fast convergence speed, but it’s easy to fall into local minimum. The heuristic method such as DE shows good global continuous optimization ability, but its convergence speed is slow. A hybrid training method of LM and DE is presented, and it overcomes the defects by using the advantages of each other. Probabilistic adaptive strategy which could save the time in some situation is adopted. Finally, this method is applied to the problem of rolling bearing faults diagnosis, and compares to other methods. The results show that, high correct classification rate were achieved by LM, and hybrid training methods still continued to converge while traditional method such as LM stopped the convergence. The probabilistic adaptive strategy strengthened the convergence ability of hybrid method in the latter progress, and achieved higher correct rate

Large anomalous Hall effect in the kagome ferromagnet LiMn6_6Sn6_6

Kagome magnets are believed to have numerous exotic physical properties due to the possible interplay between lattice geometry, electron correlation and band topology. Here, we report the large anomalous Hall effect in the kagome ferromagnet LiMn$_6$Sn$_6$, which has a Curie temperature of 382 K and easy plane along with the kagome lattice. At low temperatures, unsaturated positive magnetoresistance and opposite signs of ordinary Hall coefficient for $\rho_{xz}$ and $\rho_{yx}$ indicate the coexistence of electrons and holes in the system. A large intrinsic anomalous Hall conductivity of 380 $\Omega^{-1}$ cm$^{-1}$, or 0.44 $e^2/h$ per Mn layer, is observed in $\sigma_{xy}^A$. This value is significantly larger than those in other $R$Mn$_6$Sn$_6$ ($R$ = rare earth elements) kagome compounds. Band structure calculations show several band crossings, including a spin-polarized Dirac point at the K point, close to the Fermi energy. The calculated intrinsic Hall conductivity agrees well with the experimental value, and shows a maximum peak near the Fermi energy. We attribute the large anomalous Hall effect in LiMn$_6$Sn$_6$ to the band crossings closely located near the Fermi energy

Research Progress in the Application of Proteomics andMetabolomics in Bee Products

Bee products are gaining increasing popularity among consumers for their high nutritional value and various biological activities. However, adulteration is becoming a prominent problem in the production and sale of bee products, and the mechanisms underlying their biological activities have not been fully elucidated. Proteomics and metabolomics can provide complete and comprehensive descriptions on the overall characteristics of proteins and small-molecular metabolites. In recent years, these two omics approaches have been widely used in the field of bee products, and become a powerful means to solve the problem of adulteration in bee products and elucidate the mechanisms underlying their biological activities. This paper reviews the research progress in the application of proteomics and metabolomics in bee products. Based on an overview of the advantages of proteomics and metabolomics in simultaneous identification of whole components and screening of characteristic markers, the paper also summarizes their applications in the identification of components, discrimination and authentication, and elucidation of mechanisms for biological activities of bee products in detail. In addition, the existing problems are analyzed and the future research directions are proposed. The paper is expected to provide a reference for extensive and in-depth application of omics technologies in the research of bee products

AutoEncoding Tree for City Generation and Applications

City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused on the generation of single objects or indoor scenes, the huge volumes of spatial data in cities pose a challenge to the generative models. Furthermore, few publicly available 3D real-world city datasets also hinder the development of methods for city generation. In this paper, we first collect over 3,000,000 geo-referenced objects for the city of New York, Zurich, Tokyo, Berlin, Boston and several other large cities. Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation. Specifically, we first propose a novel Spatial-Geometric Distance (SGD) metric to measure the similarity between building layouts and then construct a binary tree over the raw geometric data of building based on the SGD metric. Next, we present a tree-structured network whose encoder learns to extract and merge spatial information from bottom-up iteratively. The resulting global representation is reversely decoded for reconstruction or generation. To address the issue of long-dependency as the level of the tree increases, a Long Short-Term Memory (LSTM) Cell is employed as a basic network element of the proposed AETree. Moreover, we introduce a novel metric, Overlapping Area Ratio (OAR), to quantitatively evaluate the generation results. Experiments on the collected dataset demonstrate the effectiveness of the proposed model on 2D and 3D city generation. Furthermore, the latent features learned by AETree can serve downstream urban planning applications

Integration of metabolomics and transcriptomics provides insights into enhanced osteogenesis in Ano5Cys360Tyr knock-in mouse model

IntroductionGnathodiaphyseal dysplasia (GDD; OMIM#166260) is a rare autosomal dominant disorder characterized by diaphyseal sclerosis of tubular bones and cemento-osseous lesions in mandibles. GDD is caused by point mutations in the ANO5 gene. However, the mechanisms underlying GDD have not been disclosed. We previously generated the first knock-in mouse model for GDD expressing a human mutation (p.Cys360Tyr) in ANO5 and homozygous Ano5 knock-in (Ano5KI/KI) mice exhibited representative traits of human GDD especially including enhanced osteogenesis.MethodsMetabolomics and transcriptomics analyses were conducted for wildtype (Ano5+/+) and Ano5KI/KI mature mouse calvarial osteoblasts (mCOBs) grown in osteogenic cultures for 14 days to identify differential intracellular metabolites and genes involved in GDD. Subsequently, related differential genes were validated by qRT-PCR. Cell proliferation was confirmed by CCK8 assay and calcium content in mineral nodules was detected using SEM-EDS.ResultsMetabolomics identified 42 differential metabolites that are primarily involved in amino acid and pyrimidine metabolism, and endocrine and other factor-regulated calcium reabsorption. Concomitantly, transcriptomic analysis revealed 407 differentially expressed genes in Ano5KI/KI osteoblasts compared with wildtype. Gene ontology and pathway analysis indicated that Ano5Cys360Tyr mutation considerably promoted cell cycle progression and perturbed calcium signaling pathway, which were confirmed by validated experiments. qRT-PCR and CCK-8 assays manifested that proliferation of Ano5KI/KI mCOBs was enhanced and the expression of cell cycle regulating genes (Mki67, Ccnb1, and Ccna2) was increased. In addition, SEM-EDS demonstrated that Ano5KI/KI mCOBs developed higher calcium contents in mineral nodules than Ano5+/+ mCOBs, while some calcium-related genes (Cacna1, Slc8a1, and Cyp27b1) were significantly up-regulated. Furthermore, osteocalcin which has been proved to be an osteoblast-derived metabolic hormone was upregulated in Ano5KI/KI osteoblast cultures.DiscussionOur data demonstrated that the Ano5Cys360Tyr mutation could affect the metabolism of osteoblasts, leading to unwonted calcium homeostasis and cellular proliferation that can contribute to the underlying pathogenesis of GDD disorders

Oxidative esterification of acetol with methanol to methyl pyruvate over hydroxyapatite supported gold catalyst: Essential roles of acid-base properties

ABSTRACT(#br)Acetol is a major light oxygenate and readily produced from staged or fast pyrolysis of lignocellulose biomass. Herein we report that acetol can be selectively converted to methyl pyruvate, an important fine chemical, through oxidative esterification over Au-based catalysts. Detailed experimental studies showed that Au on amphoteric supports with appropriate strength and balanced ratio of acid and base sites can facilitate the desired oxidative-esterification pathway without accelerating undesired aldol-condensation or Cannizzaro reactions. In particular, hydroxyapatite (with a Ca/P ratio of 1.62) supported Au achieved 87% selectivity to methyl pyruvate at an acetol conversion of 62%

Thermoelectric Properties of Novel Semimetals: A Case Study of YbMnSb2

The emerging class of topological materials provides a platform to engineer exotic electronic structures for a variety of applications. As complex band structures and Fermi surfaces can directly benefit thermoelectric performance it is important to identify the role of featured topological bands in thermoelectrics particularly when there are coexisting classic regular bands. In this work, the contribution of Dirac bands to thermoelectric performance and their ability to concurrently achieve large thermopower and low resistivity in novel semimetals is investigated. By examining the YbMnSb2 nodal line semimetal as an example, the Dirac bands appear to provide a low resistivity along the direction in which they are highly dispersive. Moreover, because of the regular-band-provided density of states, a large Seebeck coefficient over 160 µV K−1 at 300 K is achieved in both directions, which is very high for a semimetal with high carrier concentration. The combined highly dispersive Dirac and regular bands lead to ten times increase in power factor, reaching a value of 2.1 mW m−1 K−2 at 300 K. The present work highlights the potential of such novel semimetals for unusual electronic transport properties and guides strategies towards high thermoelectric performance. © 2020 The Authors. Advanced Materials published by Wiley-VCH Gmb

Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion: As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost