Huazhong University of Science and Technology

About: Huazhong University of Science and Technology is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Computer science. The organization has 120339 authors who have published 122521 publications receiving 2168040 citations. The organization is also known as: Central China University of Science and Technology.

Quantum Hall effect in black phosphorus two-dimensional electron system

Abstract: The development of new, high-quality functional materials has been at the forefront of condensed-matter research. The recent advent of two-dimensional black phosphorus has greatly enriched the materials base of two-dimensional electron systems (2DESs). Here, we report the observation of the integer quantum Hall effect in a high-quality black phosphorus 2DES. The high quality is achieved by embedding the black phosphorus 2DES in a van der Waals heterostructure close to a graphite back gate; the graphite gate screens the impurity potential in the 2DES and brings the carrier Hall mobility up to 6,000 cm(2) V(-1) s(-1). The exceptional mobility enabled us to observe the quantum Hall effect and to gain important information on the energetics of the spin-split Landau levels in black phosphorus. Our results set the stage for further study on quantum transport and device application in the ultrahigh mobility regime.

Rapid Detection of COVID-19 Coronavirus Using a Reverse Transcriptional Loop-Mediated Isothermal Amplification (RT-LAMP) Diagnostic Platform.

Abstract: The recent outbreak of a novel coronavirus SARS-CoV-2 (also known as 2019-nCoV) threatens global health, given serious cause for concern. SARS-CoV-2 is a human-to-human pathogen that caused fever, severe respiratory disease and pneumonia (known as COVID-19). By press time, more than 70,000 infected people had been confirmed worldwide. SARS-CoV-2 is very similar to the severe acute respiratory syndrome (SARS) coronavirus broke out 17 years ago. However, it has increased transmissibility as compared with the SARS-CoV, e.g. very often infected individuals without any symptoms could still transfer the virus to others. It is thus urgent to develop a rapid, accurate and onsite diagnosis methods in order to effectively identify these early infects, treat them on time and control the disease spreading. Here we developed an isothermal LAMP based method-iLACO (isothermal LAMP based method for COVID-19) to amplify a fragment of the ORF1ab gene using 6 primers. We assured the species-specificity of iLACO by comparing the sequences of 11 related viruses by BLAST (including 7 similar coronaviruses, 2 influenza viruses and 2 normal coronaviruses). The sensitivity is comparable to Taqman based qPCR detection method, detecting synthesized RNA equivalent to 10 copies of 2019-nCoV virus. Reaction time varied from 15-40 minutes, depending on the loading of virus in the collected samples. The accuracy, simplicity and versatility of the new developed method suggests that iLACO assays can be conveniently applied with for 2019-nCoV threat control, even in those cases where specialized molecular biology equipment is not available.

Effect of coronavirus disease 2019 (COVID-19) on maternal, perinatal and neonatal outcome: systematic review.

Abstract: OBJECTIVE: To evaluate the effect of coronavirus disease 2019 (COVID-19) on maternal, perinatal and neonatal outcome by performing a systematic review of available published literature on pregnancies affected by COVID-19. METHODS: We performed a systematic review to evaluate the effect of COVID-19 on pregnancy, perinatal and neonatal outcome. We conducted a comprehensive literature search using PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure Database and Wan Fang Data up to and including 20 April 2020 (studies were identified through PubMed alert after that date). For the search strategy, combinations of the following keywords and medical subject heading (MeSH) terms were used: 'SARS-CoV-2', 'COVID-19', 'coronavirus disease 2019', 'pregnancy', 'gestation', 'maternal', 'mother', 'vertical transmission', 'maternal-fetal transmission', 'intrauterine transmission', 'neonate', 'infant' and 'delivery'. Eligibility criteria included laboratory-confirmed and/or clinically diagnosed COVID-19, patient being pregnant on admission and availability of clinical characteristics, including at least one maternal, perinatal or neonatal outcome. Exclusion criteria were non-peer-reviewed or unpublished reports, unspecified date and location of the study, suspicion of duplicate reporting and unreported maternal or perinatal outcomes. No language restrictions were applied. RESULTS: We identified a high number of relevant case reports and case series, but only 24 studies, including a total of 324 pregnant women with COVID-19, met the eligibility criteria and were included in the systematic review. These comprised nine case series (eight consecutive) and 15 case reports. A total of 20 pregnant patients with laboratory-confirmed COVID-19 were included in the case reports. In the combined data from the eight consecutive case series, including 211 (71.5%) cases of laboratory-confirmed and 84 (28.5%) of clinically diagnosed COVID-19, the maternal age ranged from 20 to 44 years and the gestational age on admission ranged from 5 to 41 weeks. The most common symptoms at presentation were fever, cough, dyspnea/shortness of breath, fatigue and myalgia. The rate of severe pneumonia reported amongst the case series ranged from 0% to 14%, with the majority of the cases requiring admission to the intensive care unit. Almost all cases from the case series had positive computed tomography chest findings. All six and 22 cases that had nucleic-acid testing in vaginal mucus and breast milk samples, respectively, were negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Only four cases of spontaneous miscarriage or termination were reported. In the consecutive case series, 219/295 women had delivered at the time of reporting and 78% of them had Cesarean section. The gestational age at delivery ranged from 28 to 41 weeks. Apgar scores at both 1 and 5 min ranged from 7 to 10. Only eight neonates had birth weight < 2500 g and nearly one-third of neonates were transferred to the neonatal intensive care unit. There was one case of neonatal asphyxia and death. In 155 neonates that had nucleic-acid testing in throat swab, all, except three cases, were negative for SARS-CoV-2. There were no cases of maternal death in the eight consecutive case series. Seven maternal deaths, four intrauterine fetal deaths (one with twin pregnancy) and two neonatal deaths (twin pregnancy) were reported in a non-consecutive case series of nine cases with severe COVID-19. In the case reports, two maternal deaths, one neonatal death and two cases of neonatal SARS-CoV-2 infection were reported. CONCLUSIONS: Despite the increasing number of published studies on COVID-19 in pregnancy, there are insufficient good-quality data to draw unbiased conclusions with regard to the severity of the disease or specific complications of COVID-19 in pregnant women, as well as vertical transmission, perinatal and neonatal complications. In order to answer specific questions in relation to the impact of COVID-19 on pregnant women and their fetuses, through meaningful good-quality research, we urge researchers and investigators to present complete outcome data and reference previously published cases in their publications, and to record such reporting when the data of a case are entered into one or several registries. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Multi-oriented Text Detection with Fully Convolutional Networks

Abstract: In this paper, we propose a novel approach for text detection in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Finally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.