High-Valence Mo Doping and Oxygen Vacancy Engineering to Promote Morphological Evolution and Oxygen Evolution Reaction Activity.

The rational design of high-efficiency, low-cost electrocatalysts for electrochemical water oxidation in alkaline media remains a huge challenge. Herein, combined strategies of metal doping and vacancy engineering are employed to develop unique Mo-doped cobalt oxide nanosheet arrays. The Mo dopants exist in the form of high-valence Mo6+, and the doping amount has a significant effect on the structure morphology, which transforms from 1D nanowires/nanobelts to 2D nanosheets and finally 3D nanoflowers. In addition, the introduction of vast oxygen vacancies helps to modulate the electronic states and increase the electronic conductivity. The optimal catalyst MoCoO-3 exhibits greatly increased active sites and enhanced reaction kinetics. It gives a dramatically lower overpotential at 50 mA cm-2 (288 mV), much smaller than that of the undoped counterpart (418 mV) and comparable to those of the recently reported electrocatalysts. Density functional theory results further verify that the increased electronic conductivity and optimized adsorption energy toward oxygen evolution reaction intermediates are mainly responsible for the enhanced catalytic activity. Moreover, the assembled two-electrode electrolyzer (MoCoO-3||Pt/C) exhibits superior performance with the cell potential decreased by 233 mV to reach a current density of 50 mA cm-2 with respect to the benchmark counterpart catalysts (RuO2||Pt/C). This work might contribute to the rational design of effective, low-cost electrocatalyst materials by combining multiple strategies.

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction.

The influence of earthquake disasters on human social life is positively related to the magnitude and intensity of the earthquake, and effectively avoiding casualties and property losses can be attributed to the accurate prediction of earthquakes. In this study, an electromagnetic sensor is investigated to assess earthquakes in advance by collecting earthquake signals. At present, the mainstream earthquake magnitude prediction comprises two methods. On the one hand, most geophysicists or data analysis experts extract a series of basic features from earthquake precursor signals for seismic classification. On the other hand, the obtained data related to earth activities by seismograph or space satellite are directly used in classification networks. This article proposes a CNN and designs a 3D feature-map which can be used to solve the problem of earthquake magnitude classification by combining the advantages of shallow features and high-dimensional information. In addition, noise simulation technology and SMOTE oversampling technology are applied to overcome the problem of seismic data imbalance. The signals collected by electromagnetic sensors are used to evaluate the method proposed in this article. The results show that the method proposed in this paper can classify earthquake magnitudes well.

Study on nonlinear constitutive model of skin under compression load over a wide range of strain rates.

PURPOSE: In wound ballistics, skin has obvious blocking effect in the biological target penetration of projectiles. An analytical description of skin mechanical properties under compression can set the basis for the numerical simulation and the evaluation of blocking effect. METHODS: In this study, an improved three-parameter solid visco-elastic model was proposed to describe the skin creep phenomenon. And then combined with Maxwell and Ogden model, a new nonlinear skin constitutive model, consisting of hyper-elastic unit, creep unit and relaxation unit in parallel, was established. Here, we examine the material properties of freshly harvested porcine skin in compression at strain rates from 0.01/s to 4000/s. RESULTS: The model is verified by comparison with the experimental results by our test and that in the literature at different strain rates. CONCLUSIONS: It shows that calculated results of the constitutive model agree well with the experiment data at extremely low to high strain rates, which is useful for the description of the heterogeneous, nonlinear viscoelastic, relaxation and creep mechanical response of skin under compression.

Achieving Universal Wearing of Face Masks During the COVID-19 Pandemic: A Practical Solution from Shanghai, China.

BACKGROUND: Face masks are basic protective equipment for preventing respiratory infectious diseases. The measures to properly dispose of and allocate face masks during the early stage of an epidemic caused by respiratory infectious diseases deserve worldwide attention. METHODS: A qualitative research approach was used to document the practice of a citywide face mask-wearing strategy of the Shanghai Municipal Government (called the "Shanghai solution" in this article). Based on data from government work documents, an online face mask-allocation and sales system was built to offer real-time updates of face-mask appointments and sales information in all designated pharmacies and neighborhood committees in Shanghai. RESULTS: In the Shanghai solution, a total of 24.8 million residents in 6,031 committees were covered, in order to achieve universal wearing of face masks during the COVID-19 crisis. Up to 110 million face masks were dispensed to residents in six rounds of face-mask allocation during February to April. This practical experience in Shanghai solved two key problems (insufficient face-mask capacity, protection of vulnerable population) with the supply of face masks by addressing four essential characteristics: overall coordination, on-demand distribution, efficient distribution, and technical support. CONCLUSION: The practice of the citywide face mask-supply strategy of Shanghai could provide several pointers for management of a shortage of emergency materials, dispatch, and transport to other countries during the pandemic.

LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases.

Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut.net/lncrnadisease/.

A potential prognostic lncRNA signature for predicting survival in patients with bladder urothelial carcinoma.

Increasing evidence has highlighted the critical roles of long non-coding RNA (lncRNA) in cancer development and progression. However, the prognostic power of expression-based lncRNA signature for predicting overall survival in patients with Bladder Urothelial Carcinoma (BLCA) has not been investigated. Here, we performed a comprehensive analysis for lncRNA expression profiles and corresponding clinical information of 234 BLCA patients from The Cancer Genome Atlas (TCGA). We established a set of four-lncRNAs that were significantly associated with BLCA patients' survival. Using the prognostic four-lncRNA signature, we successfully classified the BLCA patients into high-risk and low-risk groups, and the prognostic power of the four-lncRNA signature was further validated in the testing dataset and entire dataset. Multivariate Cox regression and stratified analyses demonstrated that the prognostic power of the four-lncRNA signature was independent of other clinical variables. Functional enrichment analyses suggested the four prognostic lncRNAs may be involved in known BLCA-related biological processes and pathways. Our results demonstrated that the four-lncRNA signature could be novel independent biomarkers for predicting survival in patients with BLCA.

[Characteristics and Risks of Heavy Metals Content in Surface Sediment of Tidal Flat Areas in Eastern China].

To understand the heavy metals content such as As, Cd, Cr, Cu, Hg, Pb, and Zn, as well as their distribution features in surface sediments of tidal flat areas of Eastern China, mathematical statistics, correlation analysis, factor analysis and other statistical methods are used. This study collected 1906 surface sediment samples of As, Cd, Cr, Cu, Hg, Pb, Zn were collected as part of a systematic research effort. These results show that the coastal tidal flats zone of Eastern China contains surface sediment samples with heavy metal concentrations (As, Cd, Cr, Cu, Hg, Pb, and Zn) with is not high but has increased due to anthropogenic sources. The total content of heavy metals in Jiangsu province is low, however, in Shanghai and Zhejiang provinces relatively high heavy metal contents are present. In addition to natural causes, the heavy metal content in coastal sediment in Shanghai and Zhejiang is significantly affected by human activities, while Jiangsu coastal surface sediments has light impacts from human activities. Heavy metal contamination in coastal areas Shanghai, Zhejiang, and Fujian provinces is particularly serious including Hg, Cd, and Cu contamination. The results of an evaluation using the Marine Pollution Sediment Index and Mean Sediment Quality Guidelines show that in Eastern China, the overall sediment pollutant ecological risk is low for Jiangsu and Fujian provinces, however, the ecological risk for the tidal flats of Shanghai and Zhejiang provincial beaches is relatively high due to heavy metal concentrations.