Showing papers by "Nanchang University published in 2020"

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).

Abstract: In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection.

Abstract: In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

Viral dynamics in mild and severe cases of COVID-19.

Abstract: www.thelancet.com/infection Published online March 19, 2020 https://doi.org/10.1016/S1473-3099(20)30232-2 1 day of disease onset at the time of sampling. The DCt values of severe cases remained significantly lower for the first 12 days after onset than those of corresponding mild cases (figure A). We also studied serial samples from 21 mild and ten severe cases (figure B). Mild cases were found to have an early viral clearance, with 90% of these patients repeatedly testing negative on RT-PCR by day 10 post-onset. By contrast, all severe cases still tested positive at or beyond day 10 postonset. Overall, our data indicate that, similar to SARS in 2002–03, Viral dynamics in mild and severe cases of COVID-19

Development and Clinical Application of A Rapid IgM-IgG Combined Antibody Test for SARS-CoV-2 Infection Diagnosis

Abstract: The outbreak of the novel coronavirus disease (COVID-19) quickly spread all over China and to more than 20 other countries. Although the virus (severe acute respiratory syndrome coronavirus [SARS-Cov-2]) nucleic acid real-time polymerase chain reaction (PCR) test has become the standard method for diagnosis of SARS-CoV-2 infection, these real-time PCR test kits have many limitations. In addition, high false-negative rates were reported. There is an urgent need for an accurate and rapid test method to quickly identify a large number of infected patients and asymptomatic carriers to prevent virus transmission and assure timely treatment of patients. We have developed a rapid and simple point-of-care lateral flow immunoassay that can detect immunoglobulin M (IgM) and IgG antibodies simultaneously against SARS-CoV-2 virus in human blood within 15 minutes which can detect patients at different infection stages. With this test kit, we carried out clinical studies to validate its clinical efficacy uses. The clinical detection sensitivity and specificity of this test were measured using blood samples collected from 397 PCR confirmed COVID-19 patients and 128 negative patients at eight different clinical sites. The overall testing sensitivity was 88.66% and specificity was 90.63%. In addition, we evaluated clinical diagnosis results obtained from different types of venous and fingerstick blood samples. The results indicated great detection consistency among samples from fingerstick blood, serum and plasma of venous blood. The IgM-IgG combined assay has better utility and sensitivity compared with a single IgM or IgG test. It can be used for the rapid screening of SARS-CoV-2 carriers, symptomatic or asymptomatic, in hospitals, clinics, and test laboratories.

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)

Abstract: Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and Findings We collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summary To control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.

Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study

Abstract: Objective To assess the prevalence of diabetes and its risk factors. Design Population based, cross sectional study. Setting 31 provinces in mainland China with nationally representative cross sectional data from 2015 to 2017. Participants 75 880 participants aged 18 and older—a nationally representative sample of the mainland Chinese population. Main outcome measures Prevalence of diabetes among adults living in China, and the prevalence by sex, regions, and ethnic groups, estimated by the 2018 American Diabetes Association (ADA) and the World Health Organization diagnostic criteria. Demographic characteristics, lifestyle, and history of disease were recorded by participants on a questionnaire. Anthropometric and clinical assessments were made of serum concentrations of fasting plasma glucose (one measurement), two hour plasma glucose, and glycated haemoglobin (HbA1c). Results The weighted prevalence of total diabetes (n=9772), self-reported diabetes (n=4464), newly diagnosed diabetes (n=5308), and prediabetes (n=27 230) diagnosed by the ADA criteria were 12.8% (95% confidence interval 12.0% to 13.6%), 6.0% (5.4% to 6.7%), 6.8% (6.1% to 7.4%), and 35.2% (33.5% to 37.0%), respectively, among adults living in China. The weighted prevalence of total diabetes was higher among adults aged 50 and older and among men. The prevalence of total diabetes in 31 provinces ranged from 6.2% in Guizhou to 19.9% in Inner Mongolia. Han ethnicity had the highest prevalence of diabetes (12.8%) and Hui ethnicity had the lowest (6.3%) among five investigated ethnicities. The weighted prevalence of total diabetes (n=8385) using the WHO criteria was 11.2% (95% confidence interval 10.5% to 11.9%). Conclusion The prevalence of diabetes has increased slightly from 2007 to 2017 among adults living in China. The findings indicate that diabetes is an important public health problem in China.

Boosting Oxygen Reduction of Single Iron Active Sites via Geometric and Electronic Engineering: Nitrogen and Phosphorus Dual Coordination

Abstract: Atomically dispersed transition metal active sites have emerged as one of the most important fields of study because they display promising performance in catalysis and have the potential to serve as ideal models for fundamental understanding. However, both the preparation and determination of such active sites remain a challenge. The structural engineering of carbon- and nitrogen-coordinated metal sites (M-N-C, M = Fe, Co, Ni, Mn, Cu, etc.) via employing new heteroatoms, e.g., P and S, remains challenging. In this study, carbon nanosheets embedded with nitrogen and phosphorus dual-coordinated iron active sites (denoted as Fe-N/P-C) were developed and determined using cutting edge techniques. Both experimental and theoretical results suggested that the N and P dual-coordinated iron sites were favorable for oxygen intermediate adsorption/desorption, resulting in accelerated reaction kinetics and promising catalytic oxygen reduction activity. This work not only provides efficient way to prepare well-defined single-atom active sites to boost catalytic performance but also paves the way to identify the dual-coordinated single metal atom sites.

Strategies in catalysts and electrolyzer design for electrochemical CO2 reduction toward C2+ products

Abstract: In light of environmental concerns and energy transition, electrochemical CO2 reduction (ECR) to value-added multicarbon (C2+) fuels and chemicals, using renewable electricity, presents an elegant long-term solution to close the carbon cycle with added economic benefits as well. However, electrocatalytic C─C coupling in aqueous electrolytes is still an open challenge due to low selectivity, activity, and stability. Design of catalysts and reactors holds the key to addressing those challenges. We summarize recent progress in how to achieve efficient C─C coupling via ECR, with emphasis on strategies in electrocatalysts and electrocatalytic electrode/reactor design, and their corresponding mechanisms. In addition, current bottlenecks and future opportunities for C2+ product generation is discussed. We aim to provide a detailed review of the state-of-the-art C─C coupling strategies to the community for further development and inspiration in both fundamental understanding and technological applications.

Functional DNA Regulated CRISPR-Cas12a Sensors for Point-of-Care Diagnostics of Non-Nucleic-Acid Targets.

Abstract: Beyond its extraordinary genome editing ability, the CRISPR-Cas systems have opened a new era of biosensing applications due to its high base resolution and isothermal signal amplification. However, the reported CRISPR-Cas sensors are largely only used for the detection of nucleic acids with limited application for non-nucleic-acid targets. To realize the full potential of the CRISPR-Cas sensors and broaden their applications for detection and quantitation of non-nucleic-acid targets, we herein report CRISPR-Cas12a sensors that are regulated by functional DNA (fDNA) molecules such as aptamers and DNAzymes that are selective for small organic molecule and metal ion detection. The sensors are based on the Cas12a-dependent reporter system consisting of Cas12a, CRISPR RNA (crRNA), and its single-stranded DNA substrate labeled with a fluorophore and quencher at each end (ssDNA-FQ), and fDNA molecules that can lock a DNA activator for Cas12a-crRNA, preventing the ssDNA cleavage function of Cas12a in the absence of the fDNA targets. The presence of fDNA targets can trigger the unlocking of the DNA activator, which can then activate the cleavage of ssDNA-FQ by Cas12a, resulting in an increase of the fluorescent signal detectable by commercially available portable fluorimeters. Using this method, ATP and Na+ have been detected quantitatively under ambient temperature (25 °C) using a simple and fast detection workflow (two steps and <15 min), making the fDNA-regulated CRISPR system suitable for field tests or point-of-care diagnostics. Since fDNAs can be obtained to recognize a wide range of targets, the methods demonstrated here can expand this powerful CRISPR-Cas sensor system significantly to many other targets and thus provide a new toolbox to significantly expand the CRISPR-Cas system into many areas of bioanalytical and biomedical applications.

Camrelizumab in patients with previously treated advanced hepatocellular carcinoma: a multicentre, open-label, parallel-group, randomised, phase 2 trial

Abstract: Summary Background Blocking the interaction between PD-1 and its ligands is a promising treatment strategy for advanced hepatocellular carcinoma. This study aimed to assess the antitumour activity and safety of the anti-PD-1 inhibitor camrelizumab in pretreated patients with advanced hepatocellular carcinoma. Methods This is a multicentre, open-label, parallel-group, randomised, phase 2 trial done at 13 study sites in China. Eligible patients were aged 18 years and older with a histological or cytological diagnosis of advanced hepatocellular carcinoma, had progressed on or were intolerant to previous systemic treatment, and had an Eastern Cooperative Oncology Group performance score of 0–1. Patients were randomly assigned (1:1) to receive camrelizumab 3 mg/kg intravenously every 2 or 3 weeks, via a centralised interactive web-response system using block randomisation (block size of four). The primary endpoints were objective response (per blinded independent central review) and 6-month overall survival, in all randomly assigned patients who had at least one dose of study treatment. Safety was analysed in all treated patients. This study is registered with ClinicalTrials.gov , number NCT02989922 , and follow-up is ongoing, but enrolment is closed. Findings Between Nov 15, 2016, and Nov 16, 2017, 303 patients were screened for eligibility, of whom 220 eligible patients were randomly assigned and among whom 217 received camrelizumab (109 patients were given treatment every 2 weeks and 108 every 3 weeks). Median follow-up was 12·5 months (IQR 5·7–15·5). Objective response was reported in 32 (14·7%; 95% CI 10·3–20·2) of 217 patients. The overall survival probability at 6 months was 74·4% (95% CI 68·0–79·7)]. Grade 3 or 4 treatment-related adverse events occurred in 47 (22%) of 217 patients; the most common were increased aspartate aminotransferase (ten [5%]) and decreased neutrophil count (seven [3%]). Two deaths were judged by the investigators to be potentially treatment-related (one due to liver dysfunction and one due to multiple organ failure). Interpretation Camrelizumab showed antitumour activity in pretreated Chinese patients with advanced hepatocellular carcinoma, with manageable toxicities, and might represent a new treatment option for these patients. Funding Jiangsu Hengrui Medicine.

Regenerable and stable sp 2 carbon-conjugated covalent organic frameworks for selective detection and extraction of uranium

Abstract: Uranium is a key element in the nuclear industry, but its unintended leakage has caused health and environmental concerns. Here we report a sp2 carbon-conjugated fluorescent covalent organic framework (COF) named TFPT-BTAN-AO with excellent chemical, thermal and radiation stability is synthesized by integrating triazine-based building blocks with amidoxime-substituted linkers. TFPT-BTAN-AO shows an exceptional UO22+ adsorption capacity of 427 mg g−1 attributable to the abundant selective uranium-binding groups on the highly accessible pore walls of open 1D channels. In addition, it has an ultra-fast response time (2 s) and an ultra-low detection limit of 6.7 nM UO22+ suitable for on-site and real-time monitoring of UO22+, allowing not only extraction but also monitoring the quality of the extracted water. This study demonstrates great potential of fluorescent COFs for radionuclide detection and extraction. By rational designing target ligands, this strategy can be extended to the detection and extraction of other contaminants. Porous materials for uranium capture have been developed in the past, but materials for simultaneous uranium capture and detection are scarce. Here the authors develop a stable covalent organic framework capable of adsorbing and detecting uranyl ions.

Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface

Abstract: This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction

Abstract: The environmental factors of landslide susceptibility are generally uncorrelated or non-linearly correlated, resulting in the limited prediction performances of conventional machine learning methods for landslide susceptibility prediction (LSP). Deep learning methods can exploit low-level features and high-level representations of information from environmental factors. In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP. The FC-SAE consists of four steps: raw feature dropout in input layers, a sparse feature encoder in hidden layers, sparse feature extraction in output layers, and classification and prediction. The Sinan County of Guizhou Province in China, with a total of 23,195 landslide grid cells (306 recorded landslides) and 23,195 randomly selected non-landslide grid cells, was used as study case. The frequency ratio values of 27 environmental factors were taken as the input variables of FC-SAE. All 46,390 landslide and non-landslide grid cells were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide/non-landslide data, the performances of the FC-SAE and two other conventional machine learning methods, support vector machine (SVM) and back-propagation neural network (BPNN), were compared. The results show that the prediction rate and total accuracies of the FC-SAE are 0.854 and 85.2% which are higher than those of the SVM-only (0.827 and 81.56%) and BPNN (0.819 and 80.86%), respectively. In conclusion, the asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.

Silver Single Atom in Carbon Nitride Catalyst for Highly Efficient Photocatalytic Hydrogen Evolution.

Abstract: Single atom catalysts (SACs) with the maximized metal atom efficiency have sparked great attention. However, it is challenging to obtain SACs with high metal loading, high catalytic activity, and good stability. Herein, we demonstrate a new strategy to develop a highly active and stable Ag single atom in carbon nitride (Ag-N2 C2 /CN) catalyst with a unique coordination. The Ag atomic dispersion and Ag-N2 C2 configuration have been identified by aberration-correction high-angle-annular-dark-field scanning transmission electron microscopy (AC-HAADF-STEM) and extended X-ray absorption. Experiments and DFT calculations further verify that Ag-N2 C2 can reduce the H2 evolution barrier, expand the light absorption range, and improve the charge transfer of CN. As a result, the Ag-N2 C2 /CN catalyst exhibits much better H2 evolution activity than the N-coordinated Ag single atom in CN (Ag-N4 /CN), and is even superior to the Pt nanoparticle-loaded CN (PtNP /CN). This work provides a new idea for the design and synthesis of SACs with novel configurations and excellent catalytic activity and durability.

Impact of the COVID-19 Pandemic on the Crude Oil and Stock Markets in the US: A Time-Varying Analysis

Abstract: This research explores the interaction among the COVID-19 pandemic, crude oil market and stock market in the US by utilizing a time-varying parameter vector autoregression (TVP-VAR) model Our results indicate that there is a negative connection between crude oil returns and stock returns Interestingly, contrary to our intuition, we find that the COVID-19 pandemic cannot exert a negative effect but has a statistically significantly positive effect on crude oil returns and stock returns

Ultra-Scalable Spectral Clustering and Ensemble Clustering

Abstract: This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for $K$ K -nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning 10-million-level nonlinearly-separable datasets on a PC with 64 GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669 .

Effectiveness of Entropy Weight Method in Decision-Making

Abstract: Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

Bismuth Oxides with Enhanced Bismuth–Oxygen Structure for Efficient Electrochemical Reduction of Carbon Dioxide to Formate

Abstract: Electrochemical conversion of carbon dioxide (CO2) into high-value chemical products has become a dramatic research area because of the efficient exploitation of carbon resources and simultaneous r...

Regenerable Covalent Organic Frameworks for Photo-enhanced Uranium Adsorption from Seawater.

Abstract: Uranium is a key resource for the development of the nuclear industry, and extracting uranium from the natural seawater is one of the most promising ways to address the shortage of uranium resources Herein, a semiconducting covalent organic framework (named NDA-TN-AO) with excellent photocatalytic and photoelectric activities was synthesized The excellent photocatalytic effect endowed NDA-TN-AO with a high anti-biofouling activity by generating biotoxic reactive oxygen species and promoting photoelectrons to reduce the adsorbed UVI to insoluble UIV , thereby increasing the uranium extraction capacity Owing to the photoinduced effect, the adsorption capacity of NDA-TN-AO to uranium in seawater reaches 607 mg g-1 , which is 133 times of that in dark The NDA-TN-AO with enhanced adsorption capacity is a promising material for extracting uranium from the natural seawater

Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping

Abstract: Commonly used data-driven models for landslide susceptibility prediction (LSP) can be mainly classified as heuristic, general statistical or machine learning models. This study plans to compare the prediction performance of these data-driven models on the landslide susceptibility mapping, thus further to explore the inherently features of these data-driven models. As a result, a more accurate and reliable LSP can be realized through choosing an optimal data-based model. A heuristic model represented by the analytic hierarchy process (AHP), a general statistical model represented by the general linear model (GLM) and information value (IV) model, and machine learning models represented by binary logistic regression (BLR), Multilayer Perceptron (MLP), back-propagation neural network (BPNN), support vector machine (SVM) and C5.0 decision tree (C5.0 DT) are adopted in this study. Shicheng County in China is used as the study area. In total, 369 landslides identified through field investigation are classified as training (70%) and testing datasets (30%). Next, 13 landslide conditioning factors (elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, total surface radiation, population density, Normalized difference vegetation index, distance to river, topographic wetness index and rock types) are acquired from data sources of the free remote sensing images, Digital Elevation Model, field investigation and government reports. The correlations between these conditioning factors and the landslide locations are determined by frequency ratio analysis. Then, the landslide susceptibility indexes (LSIs) calculated by the eight trained models are imported into GIS software to produce landslide susceptibility maps of Shicheng County. Finally, the area under receiver operating characteristic curve (AUC), the calculated LSIs are applied to assess the LSP performance of the present eight models. The testing results show that these eight models generate reasonable LSP results as a whole, further showing that the C5.0 DT is of the highest prediction accuracy with an AUC value of 0.868, followed by the SVM (0.813), BPNN (0.803), MLP (0.792), BLR (0.784), GLM (0.779), IV (0.774) and AHP (0.773). It can be inferred that the machine learning models have higher LSP performance than general statistical and heuristic models due to its high AUC accuracy and reasonable LSIs distribution features, while general statistical model is limited by its linear analysis and heuristic model is limited by subjective weighting process.

Preparation of nickel-iron hydroxides by microorganism corrosion for efficient oxygen evolution

Abstract: Nickel-iron composites are efficient in catalyzing oxygen evolution. Here, we develop a microorganism corrosion approach to construct nickel-iron hydroxides. The anaerobic sulfate-reducing bacteria, using sulfate as the electron acceptor, play a significant role in the formation of iron sulfide decorated nickel-iron hydroxides, which exhibit excellent electrocatalytic performance for oxygen evolution. Experimental and theoretical investigations suggest that the synergistic effect between oxyhydroxides and sulfide species accounts for the high activity. This microorganism corrosion strategy not only provides efficient candidate electrocatalysts but also bridges traditional corrosion engineering and emerging electrochemical energy technologies.