Discovering two-dimensional magnetic topological insulators by machine learning

Topological materials with unconventional electronic properties have been investigated intensively for both fundamental and practical interests. Thousands of topological materials have been identified by symmetry-based analysis and ab initio calculations. However, the predicted magnetic topological insulators with genuine full band gaps are rare. Here we employ this database and supervisedly train neural networks to develop a heuristic chemical rule for electronic topology diagnosis. The learned rule is interpretable and diagnoses with a high accuracy whether a material is topological using only its chemical formula and Hubbard $U$ parameter. We next evaluate the model performance in several different regimes of materials. Finally, we integrate machine-learned rule with ab initio calculations to high-throughput screen for magnetic topological insulators in 2D material database. We discover 6 new classes (15 materials) of Chern insulators, among which 4 classes (7 materials) have full band gaps and may motivate for experimental observation. We anticipate the machine-learned rule here can be used as a guiding principle for inverse design and discovery of new topological materials.Comment: 7 pages, 4 figure

Urban nighttime leisure space mapping with nighttime light images and POI data

Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km(2), which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures

An atlas of Caenorhabditis elegans chemoreceptor expression

One goal of modern day neuroscience is the establishment of molecular maps that assign unique features to individual neuron types. Such maps provide important starting points for neuron classification, for functional analysis, and for developmental studies aimed at defining the molecular mechanisms of neuron identity acquisition and neuron identity diversification. In this resource paper, we describe a nervous system-wide map of the potential expression sites of 244 members of the largest gene family in the C. elegans genome, rhodopsin-like (class A) G-protein-coupled receptor (GPCR) chemoreceptors, using classic gfp reporter gene technology. We cover representatives of all sequence families of chemoreceptor GPCRs, some of which were previously entirely uncharacterized. Most reporters are expressed in a very restricted number of cells, often just in single cells. We assign GPCR reporter expression to all but two of the 37 sensory neuron classes of the sex-shared, core nervous system. Some sensory neurons express a very small number of receptors, while others, particularly nociceptive neurons, coexpress several dozen GPCR reporter genes. GPCR reporters are also expressed in a wide range of inter- and motorneurons, as well as non-neuronal cells, suggesting that GPCRs may constitute receptors not just for environmental signals, but also for internal cues. We observe only one notable, frequent association of coexpression patterns, namely in one nociceptive amphid (ASH) and two nociceptive phasmid sensory neurons (PHA, PHB). We identified GPCRs with sexually dimorphic expression and several GPCR reporters that are expressed in a left/right asymmetric manner. We identified a substantial degree of GPCR expression plasticity; particularly in the context of the environmentally-induced dauer diapause stage when one third of all tested GPCRs alter the cellular specificity of their expression within and outside the nervous system. Intriguingly, in a number of cases, the dauer-specific alterations of GPCR reporter expression in specific neuron classes are maintained during postdauer life and in some case new patterns are induced post-dauer, demonstrating that GPCR gene expression may serve as traits of life history. Taken together, our resource provides an entry point for functional studies and also offers a host of molecular markers for studying molecular patterning and plasticity of the nervous system

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study

ObjectiveAcute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm.MethodsWe enrolled AIS patients in our hospital from August 2017 to July 2019, and divided them into the “prolonged LOS” group and the “no prolonged LOS” group. Prolonged LOS was defined as hospitalization for more than 7 days. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data. We compared the predictive capacity of extended LOS in eight different machine learning algorithms. SHapley Additive exPlanations (SHAP) values were used to interpret the outcome, and the most optimal model was assessed by discrimination, calibration, and clinical utility.ResultsProlonged LOS developed in 149 (22.0%) of the 677 eligible patients. In eight machine learning algorithms, prolonged LOS was best predicted by the Gaussian naive Bayes (GNB) model, which had a striking area under the curve (AUC) of 0.878 ± 0.007 in the training set and 0.857 ± 0.039 in the validation set. The variables sorted by the gap values showed that the strongest predictors were pneumonia, dysphagia, thrombectomy, and stroke severity. High net benefits were observed at 0%–76% threshold probabilities, while good agreement was found between the observed and predicted probabilities.ConclusionsThe model using the GNB algorithm proved excellent for predicting prolonged LOS in AIS patients. This simple model of prolonged hospitalization could help adjust policies and better utilize resources

Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak

This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and the high-field-side high-density (HFSHD) front. These phenomena were extensively investigated by using Langmuir probe and Polarimeter-interferometer diagnostics

Identification and verification of a prognostic signature based on a miRNA–mRNA interaction pattern in colon adenocarcinoma

The expression characteristics of non-coding RNA (ncRNA) in colon adenocarcinoma (COAD) are involved in regulating various biological processes. To achieve these functions, ncRNA and a member of the Argonaute protein family form an RNA-induced silencing complex (RISC). The RISC is directed by ncRNA, especially microRNA (miRNA), to bind the target complementary mRNAs and regulate their expression by interfering with mRNA cleavage, degradation, or translation. However, how to identify potential miRNA biomarkers and therapeutic targets remains unclear. Here, we performed differential gene screening based on The Cancer Genome Atlas dataset and annotated meaningful differential genes to enrich related biological processes and regulatory cancer pathways. According to the overlap between the screened differential mRNAs and differential miRNAs, a prognosis model based on a least absolute shrinkage and selection operator-based Cox proportional hazards regression analysis can be established to obtain better prognosis characteristics. To further explore the therapeutic potential of miRNA as a target of mRNA intervention, we conducted an immunohistochemical analysis and evaluated the expression level in the tissue microarray of 100 colorectal cancer patients. The results demonstrated that the expression level of POU4F1, DNASE1L2, and WDR72 in the signature was significantly upregulated in COAD and correlated with poor prognosis. Establishing a prognostic signature based on miRNA target genes will help elucidate the molecular pathogenesis of COAD and provide novel potential targets for RNA therapy

Penyelesaian Tindak Pidana Perjudian yang Dilakukan oleh Anak Menurut UU No.11 Tahun 2012

The title of this legal writing is "The Completion of the Crime of Gambling Carried Out by minors based on the law Number 11 of 2012 on the Juvenile Justice system". This type of research is normative legal research. Normative legal research is a research conducted or focusing on norm of positive law in the form of legislation. Legal issues raised is whether the completion of the crime of gambling by children is in conformity with the law Number 11 of 2012 about the juvenile justice system. The purpose of this research is to determine and analyze the completion of the crime of gambling by children under the law of the juvenile justice system. The result showed that the efforts made to prevent criminal acts of a child is an attempt preventive and repressive efforts. Juvenile justice system is closely related to restorative justice. Regarding the obligation to make a diversion conducted by law enforcement officials, in particular under Article 7 and 96 of the law number 11 of 2012 on the Juvenile Justice System

Modeling and Simulation of Spacecraft Power System Based on Modelica

Spacecraft power system simulation involves the coupling of electrical, thermal and control domains. At present, the modeling and simulation of multi-domain physical system mainly uses the single-domain software to establish a single-domain model, and solves the unified multi-domain modeling and simulation through the interface between the software or using HLA. But it cannot fully support the modeling and simulation of multi-domain physical system, and the model has poor reusability and extensibility. As a multi-domain modeling language, Modelica language supports acausal modelling, unified multi-domain modeling, object-oriented physical modeling and hybrid modeling. So it is widely used in the aerospace area. In this paper, Modelica language is used to establish module library of spacecraft power system on simulation platform MWorks, and the multi-domain simulation model of spacecraft power system is obtained by assembling each sub-model, and the performance of the model is simulated and analyzed so as to achieve the purpose of improving and verifying the model