Soil characteristics and microbial responses in post-mine reclamation areas in a typical resource-based city, China

Mining activities worldwide have resulted in soil nutrient loss, which pose risks to crop and environmental health. We investigated the effects of post-mine reclamation activities on soil physicochemical properties and microbial communities based on 16S rRNA sequencing and the further statistical analysis in the coal base in Peixian city, China. The results revealed significant differences in soil microbial relative abundance between reclamation and reference soils. Proteobacteria was the most abundant phyla in all seven mine sites regardless of reclamation age while considerable differences were found in microbial community structure at other levels among different sites. Notebly, Gammaproteobacteria, member of the phylum Proteobacteria, had relatively high abundance in most sites. Furthermore, Kendall’s tau-b correlation heatmap revealed that potentially toxic elements and other physicochemical properties play vital roles in microbial community composition

Comparison of Feature Selection and Classification for MALDI-MS Data

Introduction: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment

Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy

Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment

Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy

A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability

A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability

Effects of duty cycle on properties of Ni–P–Al2O3 nanocomposite deposited layer prepared by pulse-assisted jet electrochemical deposition

In order to improve the surface quality of the nanocomposite deposited layer, the pulse technology was introduced to the jet electrochemical deposition process, and the Ni–P–Al2O3 nanocomposite deposited layer was successfully prepared. By changing the pulse duty cycle to prepare the composite deposited layer and comparing it with the Ni–P–Al2O3 composite deposited layer prepared under the DC condition and the Ni–P deposition layer prepared under the condition of better pulse duty cycle, the influence of pulse duty cycle on its related performance is explored. In this study, SEM, metallurgical microscope, XRD and EDS were used to analyze the surface morphology, section morphology, phase and composition of Ni–P–Al2O3 deposited layers under different experimental conditions. The hardness and elastic-plastic mechanical properties of the nanocomposite deposited layer were evaluated by hardness tester and nanoindentation tester, and the corrosion resistance of the deposited layer was evaluated by electrochemical workstation. The results showed that the Al content in the deposition layer first increases and then decreases with the increase of the pulse duty cycle, and reaches the highest level of 1.77 wt% when the pulse duty cycle is 60%. The surfaces of the deposited layers prepared under this condition were uniformly dense with better corrosion resistance and the highest hardness value reached was 817.4 HV. At the same time, the elastic recovery ratio he/hmax reached the maximum with a value of 0.36, and the resistance to deformation reached the best

Mechanism of YuPingFeng in the Treatment of COPD Based on Network Pharmacology

YuPingFeng (YPF) granules are a classic herbal formula extensively used in clinical practice in China for the treatment of COPD. However, the pathological mechanisms of YPF in COPD remain undefined. In the present research, a network pharmacology-based strategy was implemented to elucidate the underlying multicomponent, multitarget, and multipathway modes of action of YPF against COPD. First, we identified putative YPF targets based on TCMSP databases and constructed a network containing interactions between putative YPF targets and known therapeutic targets of COPD. Next, two topological parameters, “degree” and “closeness,” were calculated to identify target genes in the network. The major hubs were imported to the MetaCore database for pathway enrichment analysis. In total, 23 YPF active ingredients and 83 target genes associated with COPD were identified. Through protein interaction network analysis, 26 genes were identified as major hubs due to their topological importance. GO and KEGG enrichment analysis results revealed YPF to be mainly associated with the response to glucocorticoids and steroid hormones, with apoptotic and HIF-1 signalling pathways being dominant and correlative pathways. The promising utility of YPF in the treatment of COPD has been demonstrated by a network pharmacology approach

Oxygen evolution reaction dynamics monitored by an individual nanosheet-based electronic circuit

Electrocatalysis offers important opportunities for clean fuel production, but uncovering the chemistry at the electrode surface remains a challenge. Here, the authors exploit a single-nanosheet electrode to perform in-situ measurements of water oxidation electrocatalysis and reveal a crucial interaction with oxygen

The Gene Encoding Subunit A of the Vacuolar H+-ATPase From Cotton Plays an Important Role in Conferring Tolerance to Water Deficit

In plant cells, vacuolar H+-ATPases (V-ATPases) are responsible for deacidification of the cytosol and energisation of the secondary transport processes across the tonoplast. A number of V-ATPase subunit genes have been demonstrated to be involved in the regulation of the plant response to water deficit. However, there are no reports on the role of V-ATPase subunit A (VHA-A) in dehydration tolerance of cotton. In this study, cotton GhVHA-A gene was functionally characterized, especially with regard to its role in dehydration stress tolerance. Expression analysis showed that GhVHA-A was differentially expressed in various cotton organs and was induced by dehydration, low temperature, high salinity, and abscisic acid treatment in leaves. We also report that GhVHA-A improve dehydration tolerance in transgenic tobacco and cotton. Virus-induced gene silencing of GhVHA-A decreased the tolerance of cotton plantlets to dehydration stress. Silencing GhVHA-A decreased chlorophyll content and antioxidant enzyme activities and increased malondialdehyde (MDA) content in cotton under dehydration stress. However, transgenic tobacco expressing GhVHA-A exhibited enhanced dehydration resistance, resulting in reduced leaf water loss, higher average root length, and lower MDA levels under dehydration stress. Meanwhile, overexpression of GhVHA-A in tobacco conferred water deficit tolerance by enhancing osmotic adjustment (proline) and the activities of the antioxidant enzymes superoxide dismutase and peroxidase, thereby enhancing reactive oxygen species detoxification. These results suggest that GhVHA-A plays an important role in conferring resistance to dehydration stress. Our results have identified GhVHA-A as a candidate gene for improving dehydration tolerance in plants