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For agricultural tractors fixed ballast cannot provide high-efficiency traction under time-varying resistance in the field. Combining the characteristics of electric tractors with high-weight battery packs, this paper develops an intelligent ballast control system including a battery position adjustment (BPA) mechanism and an active ballasting control method. A tractor traction performance prediction model was developed to predict traction performance parameters and load-transfer in real time. The movement of the battery pack enables load-transfer based on the BPA. To ensure the lowest sliding rate of optimal tractive efficiency, an active ballasting control method based on a particle swarm optimisation algorithm was proposed that enabled control of the optimal battery position. The results of MATLAB/Simulink simulation indicate that the traction resistance after grading and pre-treatment not only reduced the frequency of change in the resistance signal but also retain the original trend. The results of a hardware in the loop test showed that the average tractive efficiency in the mode of active ballasting mode higher than that of the no ballasting mode (increased by 6.6%) and fixed ballasting mode (increased by 4.7%). The mean wheel slip in active ballasting mode was lower than that of the no ballasting mode (decreased by 10.3%) and the fixed ballasting mode (decreased by 4.9%). In addition, the front axle dynamic load distribution ratio in active ballasting mode was always greater than 0.2, which ensured the operational stability of the tractor. This study provides theoretical support and technical reference for optimising the traction performance of electric tractors. • An intelligent ballast control system with active load-transfer is developed. • The concept of batteries instead of ballasts. • Battery position adjustment mechanism (BPA) is designed. • Active ballasting control method based on traction performance prediction model. • The maximum average tractive efficiency is increased by 6.6%. [ABSTRACT FROM AUTHOR]

Meiotic recombination plays an important role in the process of genetic evolution. Previous researches have shown that the recombination rates provide important information about the mechanism of recombination study. However, at present, most methods ignore the hidden correlation and spatial autocorrelation of the DNA sequence. In this study, we proposed a predictor called iRSpot-DTS to identify hot/cold spots based on the benchmark datasets. We proposed a feature extraction method called dinucleotide-based spatial autocorrelation(DSA) which can incorporate the original DNA properties and spatial information of DNA sequence. Then it used t-SNE method to remove the noise which outperformed PCA. Finally, we used SAE softmax classifier to do classification which is based on networks and can get more hidden information of DNA sequence, our iRSpot-DTS achieved remarkable performance. Jackknife cross validation tests were done on two benchmark datasets. We achieved state-of-the-art results with 96.61% overall accuracy(OA), 93.16% Matthews correlation coefficient (MCC) and over 95% in Sn and Sp which are the best in this state. A predictor called iRSpot-DTS to identify hot/cold spots based on the benchmark datasets. The DSA method can incorporate the original DNA properties and spatial information of DNA sequence. It used t-SNE method to remove the noise and used SAE softmax classifier to do classification which is based on networks. The iRSpot-DTS model achieves the remarkable performance. [ABSTRACT FROM AUTHOR]

Highlights • A feature design model named MACC-PSSM is proposed by integrating Moran autocorrelation and cross correlation with PSSM. • The principal component analysis is successfully applied to predict apoptosis protein subcellular localization. • The MACC–PSSM model achieves the satisfying performance. Abstract The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC–PSSM by integrating Moran autocorrelation and cross correlation with PSSM. Then a 3600-dimensional feature vector is constructed to predict apoptosis protein subcellular localization. Finally, 210 features are selected using principal component analysis (PCA) on the ZW 225 dataset, and support vector machine is adopted as classifier. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets: ZW 225 and CL 317. Our model achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies for datasets ZW 225 and CL 317, which reach 84.9% and 90.5%, respectively. Comparison of our results with other methods demonstrates that MACC–PSSM model can be used as a potential candidate for the accurate prediction of apoptosis protein subcellular localization. [ABSTRACT FROM AUTHOR]

Gram-negative bacterial secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments. Therefore, identification of bacterial secreted proteins becomes a significant process for the research of various diseases and the corresponding drugs. In this paper, we develop a feature design model named ACCP-KL-NMF by fusing PSSM-based auto-cross correlation analysis for features extraction and nonnegative matrix factorization algorithm based on Kullback–Leibler divergence for dimensionality reduction. Hence, a 150-dimensional feature vector is constructed on the training set. Then support vector machine is adopted as the classifier, and the most objective jackknife test is chosen for evaluating the accuracy. The ACCP-KL-NMF model yields the approving performance of the overall accuracy on the test set, and also outperforms the other three existing models. The numerical experimental results show that our model is effective and reliable for identification of Gram-negative bacterial secreted protein types. Moreover, it is anticipated that the proposed model could be beneficial for other biology sequence in future research. [ABSTRACT FROM AUTHOR]

Predicting protein subcellular location with support vector machine has been a popular research area recently because of the dramatic explosion of bioinformation. Though substantial achievements have been obtained, few researchers considered the problem of data imbalance before classification, which will lead to low accuracy for some categories. So in this work, we combined oversampling method with SVM to deal with the protein subcellular localization of unbalanced data sets. To capture valuable information of a protein, a PseAAC (Pseudo Amino Acid Composition) has been extracted from PSSM(Position-Specific Scoring Matrix) as a feature vector, and then be selected by principal component analysis (PCA). Next, samples which are treated by oversampling method to eliminate the imbalance of sample numbers in different classes are fed into support vector machine to predict the protein subcellular location. To evaluate the performance of proposed method, Jackknife tests are performed on three benchmark datasets (ZD98, CL317 and ZW225). Results of SVM experiments with and without oversampling gained by Jackknife tests show that oversampling methods have successfully decrease the imbalance of data sets, and the prediction accuracy of each class in each dataset is higher than 88.9%. With comparison with other protein subcellular localization methods, the method in this work reaches the best performance. The overall accuracies of ZD98, CL317 and ZW225 are 93.2%, 96.00% and 92.15% respectively, which are 2.4%, 8.0% and 8.2% higher than the best methods in the comparison. The excellent overall accuracy gained by the proposed method indicates that the feature representation and selection capture useful information of protein sequence and oversampling methods successfully solve the imbalance of sample numbers in SVM classification. [ABSTRACT FROM AUTHOR]

Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification. [ABSTRACT FROM AUTHOR]

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA. [Display omitted] • A novel model named DapNet-HLA is proposed to predict non-classical HLA binding sites. • Fused word embedding methods are applied to extract features from the datasets. • SENet and Bahdanau attention mechanisms are integrated to optimize and improve the effectiveness of BiLSTM. • Adaptive dual-attention mechanism network based on deep learning is designed to predict non-classical HLA binding sites. [ABSTRACT FROM AUTHOR]

Recent theoretical and experimental researches have revealed that the strained bulk HgTe can be regarded as a three-dimensional topological insulator (TI). Motivated by this, we explore the strain effects on the transport properties of the HgTe surface states, which are modulated by a weak 1D in-plane electrostatic periodic potential in the presence of a perpendicular magnetic field. We analytically derive the zero frequency (dc) diffusion conductivity for the case of quasielastic scattering in the Kubo formalism, and find that, in strong magnetic field regime, the Shubnikov–de Haas oscillations are superimposed on top of the Weiss oscillations due to the electric modulation for null and finite strain. Furthermore, the strain is shown to remove the degeneracy in inversion symmetric Dirac cones on the top and bottom surfaces. This accordingly gives rise to the splitting and mixture of Landau levels, and the asymmetric spectrum of the dc conductivity. These phenomena, not known in a conventional 2D electron gas and even in a strainless TI and graphene, are a consequence of the anomalous spectrum of surface states in a fully stained TI. These results should be valuable for electronic and spintronic applications of TIs, and thus we fully expect to see them in the further experiment. [ABSTRACT FROM AUTHOR]

Green muscardine caused by Metarhizium rileyi affects sericulture, and is typically enzootic and occurs frequently at low incidence. We collected 152 M. rileyi isolates from silkworm cadavers in eight sericulture areas in seven provinces of China, and four strains from other Lepidoptera larvae in Qianshan(QS) County, Anhui province. Nine microsatellite inter-simple sequence repeat (ISSR) primers produced 91 distinct and reproducible bands, revealing a high level (90.11%) of DNA polymorphism. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis divided the populations into four groups, with isolates from Qianshan County forming a single branch. All the 156 M. rileyi isolates were heterogenic and polyphyletic and did not displayed typical regional distribution except strains from Qianshan country. PCA analysis of the nine populations of M. rileyi revealed similar phylogenies among accessions. Genetic differentiation index ( G st ) among eight enzootic populations was 0.3789 and gene flow ( N m ) was 0.4098, suggesting the low gene flow maintained a high degree of differentiation. Gst between the enzootic population of Qianshan County and other seven populations exceeded the threshold of severe differentiation, with moderate differentiation between the remaining seven enzootic populations. Analysis of molecular variance (AMOVA) showed most ISSR variation (61%) among isolates occurred within populations. No significant correlation was observed between geographical and genetic distance. According to cluster analysis based on single enzootic population, every enzootic population showed dominance, namely mainly constituted of strains with high genetic similarity. These data indicated that the green muscardine in each local silkworm population was predominantly caused by a native group of M. rileyi . Furthermore, G st and N m of M. rileyi from silkworm and other Lepidoptera larvae in Qianshan County were 0.1174 and 1.8791, respectively, suggesting strains isolated from different hosts in Qianshan County do not show obvious host specificity. This demonstrated that host transfer may take place in silkworm and other insects. [ABSTRACT FROM AUTHOR]

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases. In this paper, a novel model is designed for identifying 4 mC sites. Firstly, we extract features from original sequences by multi-source feature representation methods, which are mono-nucleotide binary and k -mer frequency, dinucleotide binary and position-specific frequency, ring-function-hydrogen-chemical properties, dinucleotide-based DNA properties and trinucleotide-based DNA properties. Subsequently, gradient boosting decision tree is applied to select the optimal feature set and remove redundant information. Finally, support vector machine is employed to predict 4 mC or non-4mC sites. The accuracies of six datasets reach 0.851, 0.859, 0.801, 0.87, 0.859 and 0.901, respectively, which are superior to previous prediction methods. Therefore, the results show that our predictor is a feasible and effective tool for identifying 4 mC sites. Furthermore, an online web server is established at http://dnan4c.zhanglab.site. [Display omitted] • A predictor is designed to identify DNA N4-methylcytosine sites based on multi-source features and GBDT. • Extract features from original sequences by multi-source feature representation methods. • GBDT is applied to select the optimal feature set and remove redundant information. • An online web server is established at http://dnan4c.zhanglab.site. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by incorporating alternating word frequency and normalized Lempel–Ziv complexity. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on three widely used benchmark datasets, 25PDB, 1189 and 640, respectively. We report 83.6%, 81.8% and 83.6% prediction accuracies for 25PDB, 1189 and 640 benchmarks, respectively. Comparison of our results with other methods shows that the proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets and may at least play an important complementary role to existing methods. [Copyright &y& Elsevier]

Abstract: R3ZnH5 (R=K, Rb, Cs) series have been investigated with respect to the crystal structure, electronic and thermodynamic properties using first-principle methods based on density functional theory with generalized gradient approximation. The optimized structures and atomic coordinates are in good agreement with the experimental data. The strong covalent interactions are obtained between Zn and H atoms in the 18-electron [ZnH4]2− complex, while an ionic interaction is found between [ZnH4]2− and R atom. The formation enthalpies show that the formations of R3ZnH5 hydrides are all exothermic at 298K. The vibration free energies of R3ZnH5 show that the thermodynamic stabilities of R3ZnH5 hydrides decrease with the increasing diameter of R atom. Two possible decomposition reactions of R3ZnH5 series have been suggested in our work. One (reaction one) is that R3ZnH5 hydrides decomposes to elements directly, and the other (reaction two) is that R3ZnH5 hydrides decomposes to RH hydride. The results show that the first decomposition reaction is more favorable one. The spontaneous decomposition reaction of K3ZnH5 hydrides occur upon 465K via reaction one, and 564K via reaction two, respectively. [Copyright &y& Elsevier]

Abstract: A panzootic in praying mantid species Tenodera sinensis and Statilia maculate, caused by Beauveria bassiana, occurred in north, southwest and southeast regions of Anhui Province, eastern China in Autumn, 2009. A 3-d principal component analysis (PCA) of 123 isolates from three sites revealed that the B. bassiana populations were heterogeneous with obvious dominance. Furthermore, the causal source of the panzootic in Anhui was shown to be polyphyletic. The populations were homogenized into homogenous subunits for investigation of genetic structure by inter-simple sequence repeat (ISSR) markers. Variance was greater than 70%, largely due to genetic differences within populations and subpopulations. Genetic distances and genetic differentiation were negatively associated with geographic distances and it was speculated that this was due to the effects of monsoons and topography. Mantid isolates were divided into five pathotypes based on a two-way cluster analysis of genetic distance. Pathotype I consisted of the predominant subpopulations of Huangcangyu and Chashui populations, with a genetic distance of 0.120 and gene flow up to 1.833. This pathotype caused a widespread epizootic in north and southwest Anhui, and Pathotype III caused enzootic at Site A in September and then epizootic in October, while the other three pathotypes caused enzootics at all three investigation sites. The widespread epizootics and isolated enzootics composed the polyphyletic panzootic in Anhui. A strong gene flow between isolates from the two mantid species was identified, resulting in negligible gene differentiation. This indicated a lack of host specificity in mantid isolates of B. bassiana. [Copyright &y& Elsevier]

Abstract: The Cambrian explosion is a grand challenge to science today and involves multidisciplinary study. This event is generally believed as a result of genetic innovations, environmental factors and ecological interactions, even though there are many conflicts on nature and timing of metazoan origins. The crux of the matter is that an entire roadmap of the evolution is missing to discern the biological complexity transition and to evaluate the critical role of the Cambrian explosion in the overall evolutionary context. Here, we calculate the time of the Cambrian explosion by a “C-value clock”; our result quite fits the fossil records. We clarify that the intrinsic reason of genome evolution determined the Cambrian explosion. A general formula for evaluating genome size of different species has been found, by which the genome size evolution can be illustrated. The Cambrian explosion, as a major transition of biological complexity, essentially corresponds to a critical turning point in genome size evolution. [Copyright &y& Elsevier]

Promoter is a region of DNA that determines the transcription of a particular gene. There are several σ factors in the RNA polymerase, which has the function of identifying the promoter and facilitating the binding of the RNA polymerase to the promoter. Owing to the importance of promoter in genome research, it is an urgent task to develop computational tool for effectively identifying promoters and their strength facing the avalanche of DNA sequences discovered in the post-genomic age. In this paper, we develop a model named iPromoter-ET using the k -mer nucleotide composition, binary encoding and dinucleotide property matrix-based distance transformation for features extraction, and extremely randomized trees (extra trees) for feature selection. Its 1st layer is used to identify whether a DNA sequence is of promoter or not, while its 2nd layer is to identify promoter samples as being strong or weak promoter. Support vector machine and the five cross-validation are used to perform identification and assess performance, respectively. The results indicate that our model remarkably outperforms the existing models in both the 1st and 2nd layers for accuracy and stability. We anticipate that our proposed model will become a very effective intelligent tool, or at the least, a complementary tool to the existing modes of identifying promoters and their strength. Moreover, the datasets and codes for iPromoter-ET are freely available at https://github.com/shengli0201/iPromoter-ET. [Display omitted] • A novel identifying model named iPromoter-ET is proposed based on extremely randomized trees for feature selection. • Our iPromoter-ET model achieves remarkably higher accuracy for identifying promoters and their strength. • The performance of iPromoter-ET model outperforms those of existing related models. [ABSTRACT FROM AUTHOR]

Waste classification is regarded as one of the most important strategies for waste management, and its success depends on the active participation of the public. Based on the theory of planned behavior (TPB) and attitude-behavior-condition (A-B-C) theory, this research investigated the key factors affecting residents' domestic waste classification intention (WCI) and waste classification behavior (WCB), and explored the moderating effects of three external factors including infrastructure (INF), government publicity (GP), and incentive measures (IM) on the intention-behavior relationship. The valid data were collected from 584 residents via conducting a face-to-face interview in Chengdu, a pilot city of waste classification in China, and analyzed by using the structural equation model (SEM). Results showed that residents' WCI and WCB had a significant discrepancy with the average values of 4.07 and 2.81, respectively. Attitude (AT), perceived behavior control (PBC) and classification knowledge (CK) were significantly related to residents' WCI, with AT (β = 0.65, P < 0.001) having the greatest direct impact. Meanwhile, CK and INF had a stronger impact on the WCB than WCI. GP indirectly affected WCB through the intermediary effect of CK. In addition, INF and GP had a positively moderating effect on residents' waste classification intention-behavior relationship and can facilitate the intention-behavior conversion. By contrast, IM failed to promote the conversion of residents' classification intention into behavior, and had no direct significant influence on residents' WCB. Finally, some relevant policy suggestions were put forward for the new pilot cities of waste classification in China. • Key factors affecting residents' WCI and WCB were investigated. • Moderating effects of external factors on intention-behavior relation was explored. • Attitude had the greatest direct influence on residents' WCI. • CK and Infrastructure had a stronger impact on WCB than WCI. • Infrastructure and GP can strengthen the intention-behavior conversion. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) refer to functional RNA molecules with a length more than 200 nucleotides and have minimal or no function to encode proteins. In recent years, more studies show that lncRNAs subcellular localization has valuable clues for their biological functions. So it is count for much to identify lncRNAs subcellular localization. In this paper, a novel statistical model named KD-KLNMF is constructed to predict lncRNAs subcellular localization. Firstly, k -mer and dinucleotide-based spatial autocorrelation are incorporated as the feature vector. Then, Synthetic Minority Over-sampling Technique is used to deal with the imbalance dataset. Next, Kullback-Leibler divergence-based nonnegative matrix factorization is applied to select optimal features. And then we utilize support vector machine as the classifier after comparing with other classifiers. Finally, the jackknife test is performed to evaluate the model. The overall accuracies reach 97.24% and 92.86% on training dataset and independent dataset, respectively. The results are better than the previous methods, which indicate that our model will be a useful and feasible tool to identify lncRNAs subcellular localization. The datasets and source code are freely available at https://github.com/HuijuanQiao/KD-KLNMF. Image 1 • A predictor called KD-KLNMF to identify lncRNAs subcellular localization with multiple features and NMF. • Kullback-Leibler divergence-based nonnegative matrix factorization is applied to reduce the dimension of features. • Synthetic Minority Over-sampling Technique (SMOTE) is used to deal with the imbalance dataset. [ABSTRACT FROM AUTHOR]

Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE. [Display omitted] • Multiple features are extracted by using one-hot coding, dipeptide deviation from expected mean and natural vector. • The NeuroPred-ResSE model is developed by integrating residual block and squeeze-excitation attention mechanism. • The performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

The cascading process analysis in the D2Δ 5/2 state of copper monoxide molecule. • Ten unreported vibronic bands are presented. • The molecular constants of excited states for 63CuO and 65CuO are obtained. • Cascading processes are found and analyzed in all observed excited states. The electronic spectrum of the copper monoxide molecule (CuO) was investigated in the range of 430–550 nm using laser induced fluorescence spectroscopy and single vibronic level emission spectra. The CuO molecule was produced by a supersonic jet-cooled discharge assisted laser ablation of Cu target. Twenty two vibronic bands were observed and assigned to the electronic transition progressions from the ground state X2П i to excited states C2П 3/2 , C2П 1/2 , D2Δ 5/2 , E2Δ 5/2 , F2П 3/2 , G2Σ−, H2П 3/2 , and I2П 3/2 , of which ten bands were recorded firstly. The molecular constants of the excited electronic states for 63CuO and 65CuO isotopologues were obtained by a rotational analysis of the spectra. Cascading processes in the electronic excited states were found combining the time-resolved single vibronic level emission spectroscopy. The time-dependent radiative decay curves corresponding to different decay processes were recorded and analyzed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Conductive 1T-HfTe 2 enables to create a new kind of SACs with negatively charged metal center. • Pt@HfTe 2 exhibits excellent catalytic activity for both ORR and OER. • Doped TM atoms act as a 'bridge' to transfer the electrons from the substrate to the reaction intermediates. Advancing efficient catalysts for oxygen reduction reaction (ORR) or oxygen evolution reaction (OER) is imperative for commercializing emerging energy devices. Using density functional theory (DFT) calculations, we propose doping different transition metal (TM) atoms to regulate the electronic structures of the two-dimensional 1T-HfTe 2 monolayer to achieve bifunctional catalysis for the ORR/OER. Due to the small electronegativity of the Hf atom, we found the doped TM atoms can generally form anion centers by accepting abundant charges from the Hf interlayer. At the same time, the highly conductive 1T-HfTe 2 contributes to the charge transfer between the active center and the reaction intermediates, rendering the designed SACs the tunable activity for the reactions. By comparing the theoretical overpotentials of ORR and OER on 15 single-atom catalysts (SACs), Pt-doped system exhibits excellent catalytic activity for both ORR and OER, outperforming the traditional Pt(1 1 1) and RuO 2 (1 1 0) catalysts. Based on the charge transfer mechanism, we clarified that the doped TM atoms act as a 'bridge' to transfer the electrons from the substrate to the reaction intermediates, thereby effectively contributing to the improvement of catalytic activity. In summary, our study shows that, by doping appropriate TM atoms, the intrinsic inert HfTe 2 can be activated toward efficient ORR/OER. This could provide some guidance for the design of new two-dimensional ORR/OER bifunctional catalyst materials. [ABSTRACT FROM AUTHOR]

To solve problems of insufficient load capacity and poor operational efficiency, a new drive system for electric tractors was proposed. It consisted of dual electric motors and a coupled transmission system. The mechanical structure of the drive system of the electric tractor was designed and a prototype was manufactured. The switching rules of the drive system under typical working conditions (ploughing and rotary tillage) were established. Mathematical models of the drive system and the electric tractor were built for designing switching rules rapidly in a personal computer. Then it can be downloaded to the controller for implementing the hardware-in-the-loop (HIL) experiments. Actual data from previous field test of ploughing and rotary tillage were obtained and used as the loads in the HIL experiments. The power performance and operational efficiency of the drive system under these loads were analysed. It showed that the drive system could operate normally in typical working conditions. In terms of efficiency, simulation results showed that the drive system could improve during ploughing and rotary tillage by 9.92% and 12.36% respectively. Results from the HIL test showed that the drive system could improve efficiency of ploughing and rotary tillage by 11.35% and 11.70%, respectively. This research results concluded that the drive system could provide the loading capacity of large loads in typical working conditions, and the operational efficiency is significantly improved. • A new drive system with dual electric motors was designed for the electric tractor. • The smooth switching rules between multiple operation modes were researched. • The HIL test platform with the instrument system and the physical system was built. • The power performance and efficiency of the drive system were verified by the HIL test. [ABSTRACT FROM AUTHOR]

At present, the identification of amyloid becomes more and more essential and meaningful. Because its mis-aggregation may cause some diseases such as Alzheimer's and Parkinson's diseases. This paper focus on the classification of amyloidogenic peptides and a novel feature representation called PhyAve_PSSMDwt is proposed. It includes two parts. One is based on physicochemical properties involving hydrophilicity, hydrophobicity, aggregation tendency, packing density and H-bonding which extracts 15-dimensional features in total. And the other is 60-dimensional features through recursive feature elimination from PSSM by discrete wavelet transform. In this period, sliding window is introduced to reconstruct PSSM so that the evolutionary information of short sequences can still be extracted. At last, the support vector machine is adopted as a classifier. The experimental result on Pep424 dataset shows that PSSM's information makes a great contribution on performance. And compared with other existing methods, our results after cross-validation increase by 3.1%, 3.3%, 0.136 and 0.007 in accuracy, specificity, Matthew's correlation coefficient and AUC value, respectively. It indicates that our method is effective and competitive. • A feature representation called PhyAve_PSSMDwt is applied to identify amyloidogenic peptides. • We introduce sliding windows so that the evolutionary information on PSSM can still be extracted from short sequence. • PhyAve_PSSMDwt model outperforms some other existing methods. [ABSTRACT FROM AUTHOR]

The axial anomaly is investigated based on Fujikawa’s method for chiral tilted Weyl semimetals, in which the Weyl cones with different chiralities tilt to opposite directions. When the electric field (EF) is parallel to the magnetic field (MF), the axial anomaly contributed by electromagnetic fields along tilt direction is reduced less than that along other directions. This results in that the associated effects, such as anomalous Hall effect, chiral magnetic effect and negative magnetoresistivity, are suppressed anisotropically. More interestingly, when the EF is perpendicular to MF, the axial anomaly has a new contribution from the mixed product of tilt vector, EF and MF, which is asymmetry under reversing tilt direction (electron–hole asymmetry) and leads to a current between Dirac cones with different tilt directions and novel magnetoresistivity (MR). Specifically, this contribution can change the properties of MR in some experimental establishments and makes trivial components of MR in un-tilted Weyl semimetal develop non-trivial properties. These changes are consistent with the MR phenomena observed in experiments, such as gate-tunable negative longitudinal MR, Hall MR, extremely large and non-saturating MR. [ABSTRACT FROM AUTHOR]

The fungal pathogen Beauveria bassiana causes serious economic losses in sericulture. Its origin is usually attributed to the release of B. bassiana insecticides against pine caterpillars ( Dendrolimus punctuatus ). In the present study, 488 B. bassiana isolates obtained from silkworm ( Bombyx mori ) collected from 13 Chinese provinces, and 327 B. bassiana isolates obtained from D. punctatus collected from 9 provinces, were analyzed for population genetic structure using the ISSR technique based on genetic distance. A UPGMA dendrogram clustered them into three independent clades: two B. mori clades and one D. punctatus clade. A 3-D principal component analysis further divided them into two completely independent host groups, revealing high host-specificity. This suggested that white muscardine occurring in B. mori populations throughout southern China was not caused by any B. bassiana strain either naturally prevailing in D. punctatus populations or by any strain artificially released as a fungal insecticide against D. punctatus . We further investigated the genetic differentiation coefficient G st and gene flow between B. mori -pathogenic and D. punctatus -pathogenic B. bassiana isolates from across China and from five provinces inhabited by both B. mori and D. punctatus . The G st value across China was computed as 0.410, while the values of the five provinces ranged from 0.508 to 0.689; all above 0.25, which is the threshold for significant genetic differentiation. This suggests that B. bassiana strains isolated from the two different hosts maintained their respective heredity without a convergent homogenization trend, and reduces the possibility that the host range of the caterpillar isolates could expand and enhance their virulence in B. mori . These findings indicate that the use of B. bassiana does not threaten the safety of sericulture. [ABSTRACT FROM AUTHOR]

Abstract: Salt cress (Thellungiella halophila) has become a desirable plant model for molecular analysis of the mechanisms of salt tolerance. Analysis of its physiological action and expressed EST has resulted in better understanding. However, less is known about its genomic features. Here we determined a continuous sequence approximately 83 kb from a salt cress BAC clone, providing the first insight into the genomic feature for this species. The gene density is approximately one gene per 3.6 kb in this sequence. Many types of repetitive sequences are present in this salt cress sequence, including LTR retroelements, DNA transposons and a number of simple sequence repeats. Comparison of sequence similarity indicated that salt cress shares a close relationship with Arabidopsis. Extensive conservation and high-level microcolinearity were uncovered for both genomes. Our study also indicated that genomic DNA alternations (involving chromosome inversion, sequence loss and gene translocation) contributed to the genomic discrepancies between salt cress and Arabidopsis. [Copyright &y& Elsevier]