Direct observation of ordered configurations of hydrogen adatoms on graphene

Ordered configurations of hydrogen adatoms on graphene have long been proposed, calculated and searched for. Here we report direct observation of several ordered configurations of H adatoms on graphene by scanning tunneling microscopy. On the top side of the graphene plane, H atoms in the configurations appear to stick to carbon atoms in the same sublattice. A gap larger than 0.6 eV in the local density of states of the configurations was revealed by scanning tunneling spectroscopy measurements. These findings can be well explained by density functional theory calculations based on double sided H configurations. In addition, factors that may influence H ordering are discussed

Quasi-1D graphene superlattices formed on high index surfaces

We report preparation of large area quasi-1D monolayer graphene superlattices on a prototypical high index surface Cu(410)-O and characterization by Raman spectroscopy, Auger electron spectroscopy (AES), low energy electron diffraction (LEED), scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS). The periodically stepped substrate gives a 1D modulation to graphene, forming a superlattice of the same super-periodicity. Consequently the moire pattern is also quasi-1D, with a different periodicity. Scanning tunneling spectroscopy measurements revealed new Dirac points formed at the superlattice Brillouin zone boundary as predicted by theories.Comment: 4 figure

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Transcriptional Regulation of Gene Expression by microRNAs as Endogenous Decoys of Transcription Factors

Background/Aims: MicroRNAs (miRNAs) are known to produce post-transcriptional repression of gene expression. In light of the ability of decoy oligodeocynucleotides (ODNs) to sequestrate transcription factors (TFs) and the similar double-stranded structure between decoy ODNs and miRNAs, we proposed that miRNAs might act as endogenous decoy molecules to produce transcriptional regulation of gene expression. Methods: Quantitative real-time RT-PCR analysis was used to measure the changes of miRNA and mRNA expression. Luciferase reporter gene activity assay was used to investigate the functional interaction between miRNAs and TFs. Electrophoresis mobility shift assay (EMSA) and modified chromatin immunoprecipitation assay (ChIP) were utilized to investigate the physical interactions between miRNAs and TFs. MTT cell viability assay and cellular DNA fragmentation ELISA were used to study apoptotic cell death. Results: We presented here that miRNAs could regulate, either negatively or positively, gene expression at the transcriptional level through its decoy-like actions and this mechanism operates under physiological conditions to produce cellular functions. We identified the putative cis-elements for transcriptional factors NF-κB and NFAT in the mature miR-939 and miR-376a, respectively. We experimentally established the ability of these miRNAs to physically bind their respective target TFs, using EMSA and ChIP methods. We then utilized the luciferase reporter gene assay to characterize the specific regulation of luciferase gene activities by miR-939/pre-miR-939:NF-κB or miR-376a/pre-miR-376a:NFAT interactions. Moreover, miR-939 and miR-376a produced transcriptional regulation of endogenous genes Bcl-xL and FasL/miR-26 that are the transcriptional targets for NF-kB and NFAT, respectively, but are not post-transcriptional targets for these two miRNAs. Finally, interference of these miRNAs with NF-κB and NFAT demonstrated clear phenotypes at the cellular level as manifested by the regulation of neuroblastoma cell death by miR-939 and miR-376a. Conclusion: Our study identified a novel non-canonical mechanism of miRNAs and suggests that when considering the cellular function of miRNAs the decoy-like mechanism for transcriptional regulation (activation or repression) should be taken into account

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation

BackgroundNowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy. ObjectiveThis study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases. MethodsThe relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data. ResultsThe data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%. ConclusionsThe main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM

An effective suggestion method for keyword search of databases

This paper solves the problem of providing high-quality suggestions for userkeyword queries over databases. With the assumption that the returned suggestions areindependent, existing query suggestion methods over databases score candidate suggestions individually and return the top-k best of them. However, the top-k suggestions have high redundancy with respect to the topics. To provide informative suggestions, the returned k suggestions are expected to be diverse, i.e., maximizing the relevance to the user query and the diversity with respect to topics that the user might be interested in simultaneously. In this paper, an objective function considering both factors is defined for evaluating a suggestion set. We show that maximizing the objective function is a submodular function maximization problem subject to n matroid constraints, which is an NP-hard problem