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[Display omitted] Developing low-power FETs holds significant importance in advancing logic circuits, especially as the feature size of MOSFETs approaches sub-10 nanometers. However, this has been restricted by the thermionic limitation of SS , which is limited to 60 mV per decade at room temperature. Herein, we proposed a strategy that utilizes 2D semiconductors with an isolated-band feature as channels to realize sub-thermionic SS in MOSFETs. Through high-throughput calculations, we established a guiding principle that combines the atomic structure and orbital interaction to identify their sub-thermionic transport potential. This guides us to screen 192 candidates from the 2D material database comprising 1608 systems. Additionally, the physical relationship between the sub-thermionic transport performances and electronic structures is further revealed, which enables us to predict 15 systems with promising device performances for low-power applications with supply voltage below 0.5 V. This work opens a new way for the low-power electronics based on 2D materials and would inspire extensive interests in the experimental exploration of intrinsic steep-slope MOSFETs. [ABSTRACT FROM AUTHOR]

Motivation Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. Results In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs. [ABSTRACT FROM AUTHOR]

Organic small-molecule luminescent materials have high fluorescence quantum efficiency, are easy to purify, and show advantages related to brightness. However, the low mobility of hole carriers hinders their practical applications. To obtain luminescent materials with high fluorescence quantum efficiency, we investigated organic small-molecule thiophene oligomers with electron-donating or electron-accepting substituents at the terminal groups using the density functional theory. Compared with the electron-donating groups (–NH2, –CH3, –C2H5, and –C3H7), the electron-withdrawing groups (–NO2 and –COOH) resulted in unusual electronic and photoluminescent properties. The ultraviolet spectra of the thiophene oligomer derivatives could be modulated in the range of 200–500 nm, making them suitable for application in organic light-emitting devices. In particular, the group with the strongest electron-withdrawing effect (–NO2) resulted in blue light emission in the wavelength range of 400–480 nm, suitable for application in blue chips. Finally, the calculation results show that the donor–acceptor-modified oligomers are worthy of synthesis due to their relatively high electron mobilities. [ABSTRACT FROM AUTHOR]

Motivation 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. Results In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus , respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. Availability and implementation The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG. [ABSTRACT FROM AUTHOR]

RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV. [ABSTRACT FROM AUTHOR]

Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling can lead to color distortion or loss of detail in the output results as the resolution of a feature map or image resolution decreases. Moreover, the over-stacking of convolutional blocks might increase the model complexity. In addition to the challenges of obtaining sufficient training data, overfitting can also arise in deep learning techniques for foggy image processing, which can limit the generalization abilities of the model, posing challenges for its practical applications in real-world scenarios. Considering these issues, this paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We embed the discrete wavelet transform into the Vision Transformer by proposing the WaveletFormer and IWaveletFormer blocks, aiming to alleviate texture detail loss and color distortion in the image due to downsampling. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Such a structure reduces computational expenses and improves the effectiveness of the network. Additionally, we have implemented a feature aggregation module (FAM) to maintain image resolution and enhance the feature extraction capacity of our model, further contributing to its impressive performance in real-world foggy image recovery tasks. Through extensive experiments on real-world fog datasets, we have demonstrated that our WaveletFormerNet achieves superior performance compared to state-of-the-art methods, as shown through quantitative and qualitative evaluations of minor model complexity. Additionally, our satisfactory results on real-world dust removal and application tests showcase the superior generalization ability and improved performance of WaveletFormerNet in computer vision-related applications compared to existing state-of-the-art methods, further confirming our proposed approach's effectiveness and robustness. Our code is available at https://github.com/shengli666666/WaveletFormerNet. [Display omitted] • Combining Vision Transformer and wavelet transform alleviates image detail loss. • Parallel convolution captures frequency information in a lightweight mechanism. • The feature aggregation module enhances the model feature extraction capability. • Experimental results demonstrate WaveletFormerNet's superior dehazing performance. [ABSTRACT FROM AUTHOR]

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]

Conventional silicon-based transistor downscaling is approaching its physical limits, and thus additional novel solutions are urgently desired to address this issue. Herein, we show that 2-D antimonene with heavy effective mass and high density of state (DOS) via strain engineering presents reliable transistor performance with the channel length (${L}_{\text {ch}}$) shrinking below 5 nm. As the biaxial tensile strain increases to 7%, the band switching gives rise to a heavy hole effective mass of $12.6{m}_{{0}}$ and a Van Hoff singularity-like DOS. This unique electronic structure can effectively suppress the tunneling current, resulting in steep subthreshold swings (SSs) and ideal ON-current (${I}_{ \mathrm{ON}}$). Especially, as ${L}_{\text {ch}}$ downscales to 2.2 nm, the OFF-current can be easily reduced to 0.1 $\mu \text{A}/\mu \text{m}$ with SS of 120 mV/dec (310 mV/dec for pristine antimonene) and ${I}_{ \mathrm{ON}}$ exceeds 900 $\mu \text{A}/\mu \text{m}$ , fulfilling the requirements for high-performance applications. Our results provide new insights on extending the scaling limit in energy-efficient gate-controlled devices. [ABSTRACT FROM AUTHOR]

CdS thin films are commonly used as buffer layer in Cu2ZnSnSe4 (CZTSe) solar cells inherited from CuInGaSe2 (CIGS) solar cells. However, in addition to the toxicity problems with Cd in conventional CdS buffer layers, the unfavorable band alignment at the CZTSe/CdS heterojunction confines the further improvement of CZTSe solar cells. ZnSnO (ZTO) is studied in this work as promising buffer layers for CZTSe solar cells. The stoichiometric composition and thickness of the ZTO layer are first optimized by sputtering. Subsequently, the improvement of the performance is demonstrated by sputtering ZTO in Ar gas mixed with H2. The effect of H2 on sputtering ZTO is investigated. The presence of O vacancies in ZTO buffer layer and its effect on carrier transport and device performance are presented. Through optimization, comparable VOC and higher fill factor (FF) are obtained for the CZTSe/ZTO solar cells compared with CZTSe/CdS reference. [ABSTRACT FROM AUTHOR]

In the present study, a functionalized carboxymethyl cellulose-based adsorbent (PEI–PAM–CMC) was prepared via linking polyethyleneimine and polyacrylamide with carboxymethyl cellulose by epichlorohydrin. It possessed a sponge-like structure with a large number of micron-sized pores, which facilitated the mass transfer of Cr(VI). Under neutral conditions, Cr(VI) can be efficiently removed by the protonated PEI–PAM–CMC. The residual levels in the effluent were below 0.5 and 0.05 mg L−1 for 10 and 1 mg L−1 of Cr(VI), respectively. The adsorption data fitted well with the pseudo-second order kinetic model and Langmuir isotherm. Common ions including Cl−, H2PO4−, NO3−, K+, Na+, Ca2+, Mg2+ and Cr3+ had neglectable influence for Cr(VI) adsorption on the PEI–PAM–CMC except SO42−. Meanwhile, PEI–PAM–CMC showed good regeneration and reusability with a 7.5% decrease in the removal rate after ten adsorption/desorption cycles. XPS analysis and DFT calculation revealed that Cr(VI) uptake by the PEI–PAM–CMC was involved in electrostatic attraction and chemical binding. Electrostatics and induction were the main weak interaction between Cr(VI) and the adsorbent, and the major chemical adsorption sites were mainly distributed in the nitrogen groups. This research not only developed an efficient material for Cr(VI) removal but also provided a promising insight into an adsorption mechanism study. [ABSTRACT FROM AUTHOR]

DNA-binding proteins play a vital role in cellular processes. It is an extremely urgent to develop a high-throughput method for efficiently identifying DNA-binding proteins. According to the current research situation, some methods in machine learning and deep learning show excellent computational speed and accuracy, which are worthy of application. In this work, a novel predictor was proposed to predict DNA binding proteins called UMAP-DBP. Firstly, the feature extraction of primary protein sequence was realized based on physicochemical distance transformation, Profile-based auto-cross covariance and General series correlation pseudo amino acid composition. Secondly, uniform manifold approximation and projection (UMAP) and feature importance score methods were used for feature selection; there is a progressive relationship between them. Finally, the Adaboost operation engine with jackknife test were adopted for predicting DNA-binding proteins. For the jackknife test on the BP1075 and BP594, we obtained an overall accuracy of 82.97% and 82.14%, Cohen's kappa (CK) of 0.66 and 0.64, respectively. The results illustrate that a feasible method has been developed for predicting DNA-binding proteins by UMAP and Adaboost. This is the first study in which UMAP has been successfully applied to identify DNA-binding proteins. All the datasets and codes are accessible at https://github.com/Wang-Jinyue/UMAP-DBP. [ABSTRACT FROM AUTHOR]

• Seventeen TaAOX genes were identified from Chinese Spring genome. • Tandem and segmental duplication events were found during the TaAOX genes' evolution. • Most TaAOX genes might play a vital role in the response to various stresses in wheat. • Regulating some TaAOX s' expression may improve wheat adaptability to various stresses. Alternative oxidase (AOX) is an important terminal oxidase involved in the alternative oxidation pathway in plants, which is closely related to various biotic and abiotic stress responses. However, a comprehensive research on AOX gene family of wheat is still lacking. In this study, the members of wheat AOX (TaAOX) family were identified, and their molecular characteristics and gene expression patterns were systematically investigated. Seventeen TaAOX genes were identified from Chinese Spring (CS) genome, which were mapped on 7 chromosomes and mainly clustered on the long arm's distal end of the second homologous groups. Phylogenetic analysis showed that TaAOX genes were classified into four subgroups (Ia, Ib, Ic, and Id), and the Ia subgroup possessed the most members. Tandem duplication and segmental duplication events were found during the evolution of TaAOX genes and they were affected by purifying selection demonstrated by Ka/Ks analysis. The exon numbers of this family gene varied greatly from 1 to 9. Except for Ta3BSAOX14 , all the proteins encoded by the other 16 TaAOX genes contained the amino acid residues of the key active sites in the AOX domain (cd01053). The expression patterns of TaAOX genes in various tissues and under abiotic and biotic stresses were analyzed using public transcriptome data, furthermore, qRT-PCR analysis was performed for some selected TaAOX genes, and the results suggested that most members of this gene family play an important role in response to different stresses in common wheat. Our results provide basic information and valuable reference for further exploring the gene function of TaAOX family by using gene editing, RNAi, VIGS, and other technologies. [ABSTRACT FROM AUTHOR]

A novel method was developed for the sensitive and visual detection of p-phenylenediamine (PPD) via immobilizing the target specie PPD on dialdehyde cellulose membrane (DCM) followed by the reaction with salicylaldehyde. The obtained solid fluorescent membrane (S-PPD-DCM) emitted yellow fluorescence under 365 nm UV light. DCM was not only used as a solid matrix but also played a vital role in the enrichment of PPD. Experimental variables influencing the fluorescence signal were investigated and optimized. Under the optimum conditions, a detection limit of 5.35 μg L−1 was obtained and two linear ranges were observed at 10–100 and 100–1000 μg L−1, respectively. Moreover, the fluorescence of the resultant membrane can still be visualized by naked eye when PPD concentration was 50 μg L−1. The detection of PPD was hardly affected by the coexistence of 1 mg L−1 of o-phenylenediamine, m-phenylenediamine or p henylamine , exhibiting good selectivity. The developed method involved in a two-step Schiff base reaction and enhanced the fluorescence emission via blocking nonradiative intramolecular rotation decay of the excited molecules. It was applied to determine the PPD in spiked hair dye with satisfactory results. Image 1 • A novel method was developed for the sensitive and visual detection of PPD. • DCM as a solid matrix played a vital role in the enrichment of PPD. • The proposed method gave two linear ranges at 0.01–0.1 and 0.1–1 mg L−1, respectively. • The detection limit of PPD was calculated to be 5.35 μg L−1. • Detection mechanism was involved in two-step Schiff base reaction and AIE effect. [ABSTRACT FROM AUTHOR]

DNase I hypersensitive sites (DHSs) are highly sensitive active chromatin regions to DNase I enzymes, which provide the basis for the study of gene transcriptional regulation mechanism and play an important role in the analysis of gene expression regulatory elements. The identification of DHSs has contributed to biomedical research and genome analysis. There are already southern blotting technology and high-throughput sequencing technology to identify DHSs, but these experimental methods are often time-consuming and expensive, thus, novel and powerful computational methods are needed to predict DHSs. It is understood that researchers in related fields have proposed many feasible methods for the identification of DNase I hypersensitive sites. However, the accuracy of these methods is not satisfactory, so it is necessary to use more effective methods to predict DHSs. Therefore, on the basis of previous studies, we design a novel predictor called iDHS-DXG. First of all, we choose three sequence-derived feature representation methods to extract features, including kmer, mismatch and the dinucleotide property matrix based on Moran coefficient. Truncated singular value decomposition is selected for reducing the dimensionality of the benchmark dataset, and the optimal dimension is obtained through the test. Then, synthetic minority over-sampling technique is utilized to balance the positive and negative samples. After that, we introduce extreme gradient boosting ensemble classifier to predict DHSs. Compared with the previous research results, the main performance evaluation metrics of our method have been improved after five-fold cross-validation test. DHSs were identified on two human genome datasets with an accuracy of 90.84% and 91.27% respectively. This result shows that our method is a feasible, effective and competitive tool for the analysis of gene regulatory elements. Our research is helpful for biologists and geneticists to study genome analysis and gene regulation mechanism. Meanwhile, it is also of great significance to the development of human disease and drug design. Furthermore, the datasets and codes of iDHS-DXG can be obtained from the website: http://github.com/Xtian-696/iDHS-DXG/. [ABSTRACT FROM AUTHOR]

Since graphene has been successfully exfoliated, two-dimensional (2D) materials constitute a vibrant research field and open vast perspectives in high-performance applications. Among them, bismuthene and 2D bismuth (Bi) are unique with superior properties to fabricate state-of-the-art energy saving, storage and conversion devices. The largest experimentally determined bulk gap, even larger than those of stanene and antimonene, allows 2D Bi to be the most promising candidate to construct room-temperature topological insulators. Moreover, 2D Bi exhibits cyclability for high-performance sodium-ion batteries, and the enlarged surface together with the good electrochemical activity renders it an efficient electrocatalyst for energy conversion. Also, the air-stability of 2D Bi is better than that of silicene, germanene, phosphorene and arsenene, which could enable more practical applications. This review aims to thoroughly explore the fundamentals of 2D Bi and its improved fabrication methods, in order to further bridge gaps between theoretical predictions and experimental achievements in its energy-related applications. We begin with an introduction of the status of 2D Bi in the 2D-material family, which is followed by descriptions of its intrinsic properties along with various fabrication methods. The vast implications of 2D Bi for high-performance devices can be envisioned to add a new pillar in energy sciences. In addition, in the context of recent pioneering studies on moiré superlattices of other 2D materials, we hope that the improved manipulation techniques of bismuthene, along with its unique properties, might even enable 2D Bi to play an important role in future energy-related twistronics. [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]

The nanoscale electrical properties of self-assembled GeSi quantum rings (QRs) were investigated by conductive scanning probe microscopy at room temperature. The current distribution of individual GeSi QRs measured by conductive atomic force microscopy (CAFM) shows a low conductivity at the central hole as compared to the rim; however, the QRs' composition distribution obtained by selective chemical etching combined with AFM observation reveals that within the QRs' central holes, the Ge content is high, which should lead to a high conductivity instead of a low one as observed. Together with the results obtained by scanning capacitance microscopy (SCM) and electrostatic force microscopy (EFM), it is supposed that the GeSi QRs' electrical properties are mainly determined by the ring-shaped topography, rather than by the complete oxidation of the QRs' central hole or their composition distributions. [ABSTRACT FROM AUTHOR]

Highlights • Effect of wall-effect, and fibre orientation and content on tensile strength of SFRC. • MCST is a multi-axial cube splitting test used to get post-crack properties of SFRC. • An approach for the determination of fibre orientation using MCST is proposed. • MCST method was verified to be a good method of determining the orientation of fibre. Abstract The post-cracking behaviour of steel fibre reinforced concrete (SFRC) are significantly influenced by the distribution of steel fibre in the concrete, while steel fibre distribution is further affected by wall-effect and fibre content. A series of three-point bending tests, cube splitting tests, and Brazilian splitting tests were conducted to analyze the influence of wall-effect and fibre content on the post-cracking tensile strength of SFRC. Furthermore, the influence of wall-effect and fibre content on fibre distribution was determined by conducting inductive tests on cubic SFRC specimens. To directly determine the relationship between fibre content and orientation, and the post-cracking mechanical properties of SFRC, this paper proposes a multi-axial cube splitting test (MCST test). Furthermore, this paper also proposes a method which utilizes the MCST test to measure fibre orientation based on the relationship between post-cracking peak load and toughness, and steel fibre orientation. Therefore, MCST method can be adapted for using with any type of fibre reinforced concrete, while the inductive test can only be used for concrete that is reinforced with metallic fibre. The MCST method is verified by comparing the similarity of the fibre orientations that were obtained using the inductive tests and MCST tests. [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]

Searching for active and low‐cost electrocatalysts for the hydrogen evolution reaction (HER) is crucial to develop sustainable energy, yet it remains a significant challenge. Based on the density functional theory calculations, a new kind of double atom catalysts (HDACs) with hybrid metal and nonmetal center embedded in g‐CN is reported for the HER. It is demonstrated that the introduction of the nonmetal atoms (B, C, Si, P, and S) near the metal sites enables unique charge communication between them, which offers diatomic center a very different catalytic activity than the single atom counterparts. Out of 130 HDACs, Pd‐B, Ti‐C, Ir‐C, Cr‐Si, Mn‐Si, Co‐Si, Rh‐Si, Au‐Si, Ir‐P, Fe‐S, and Ni‐S pairs are identified as the high‐performance electrocatalysts with nearly ideal adsorption strength for proton. Machine learning analysis allows to directly identify the key characteristics that affect the catalytic activity and establish a predictable framework for a fast screen of the unknown chemical space of HDACs. This work paves a new avenue for designing and developing potential HER catalysts. [ABSTRACT FROM AUTHOR]

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA's structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations. [ABSTRACT FROM AUTHOR]

Using electrochemical CO2 reduction reaction (CO2RR) to synthesize value-added hydrocarbons provides a useful solution for environmental issues and energy crisis. However, this process is impeded by the low activity and selectivity of electrocatalysts toward targeted products. Employing density functional theory computations, the graphdiyne and holey graphyne supported single-atom catalysts (SACs, M/GDY and M/HGY) are demonstrated to be the promising candidates for the CO2RR. By taking full elemental diversity of metal sites across the periodic table, 25 catalysts are found to effectively activate CO2 and inhibit competitive hydrogen evolution, and 8 of them show higher activity for CH4 production than Cu(211). Remarkably, changing supports are found to greatly affect limiting potentials and reaction pathways, even leading to an "inert-active" transition for some metal centers. The resulting SACs, including Mn/GDY, Co/HGY, Ru/GDY, and Os/GDY, can achieve high activity with low limiting potentials of " -0.22 to -0.58 V. Machine learning enables to identify the critical role of the polarized charge and magnetic moment of metal atoms in affecting the activity. The built machine learning model also shows an interpretable capability to predict the activity of the other types of SACs, offering a great promise to quick screening of high-performance SACs. [ABSTRACT FROM AUTHOR]

This paper investigated the roles of hydrogen sulfide (H2S) and hydrogen peroxide (H2O2) and the possible relationship between them in regulating the AsA-GSH cycle in wheat leaves under drought stress (DS). Results showed that DS markedly increased the production of H2S and H2O2, the transcript levels and activities of ascorbate peroxidase (APX), glutathione reductase (GR), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase (DHAR); malondialdehyde (MDA) content; and electrolyte leakage (EL). Meanwhile, DS markedly reduced plant height and biomass. Above increases induced by drought stress except MDA content and EL were all suppressed by pretreatments with H2S synthesis inhibitor aminooxyaceticacid (AOA) and H2O2 synthesis inhibitor diphenylene iodonium (DPI). Besides, pretreatments with AOA and DPI further significantly increased MDA content and EL and significantly reduced plant height and biomass under DS. DPI reduced the production of H2O2 and H2S induced by DS. AOA also reduced the production of H2S and H2O2 induced by DS. Pretreatments with NaHS + AOA and H2O2 + DPI reversed above effects of AOA and DPI. Our results suggested that H2S and H2O2 all participated in the up-regulation of AsA-GSH cycle in wheat leaves by DS and possibly affected each other. [ABSTRACT FROM AUTHOR]

Polyethyleneimine functionalized Fe 3 O 4 /steam-exploded rice straw composite (Fe 3 O 4 -PEI-SERS), which combines magnetic separation with adsorption of PEI functionalized biosorbent, was successfully prepared via a simple glutaraldehyde crosslinking method. Its adsorption potential for the removal of Cr(VI) was systematically studied in batch mode. Results showed that Cr(VI) adsorption on Fe 3 O 4 -PEI-SESERS was highly pH-dependent, and the optimum pH was 2.0. The time to reach equilibrium was related to initial Cr(VI) concentration and was 1 and 6 h for 200 and 300 mg/L of Cr(VI), respectively. The adsorption system followed pseudo-second-order kinetic model and Langmuir isotherm. Its maximum adsorption capacity was 280.11, 317.46 and 338.98 mg/g at 25, 35 and 45 °C, respectively. The competitive uptake from coexisting ions (K + , Na + , Cu 2+ , Cl − and NO 3 − ) was insignificant except SO 4 2− . After six adsorption/desorption cycles, the adsorbent retained good adsorption capacity. The Cr(VI) removal involved its partial reduction into Cr(III). Due to the properties of high adsorption capacity, strong magnetic responsiveness, good reusability and Cr(VI) detoxification, the Fe 3 O 4 -PEI-SESERS has a potential application in Cr(VI) removal from wastewater. [ABSTRACT FROM AUTHOR]

The interface transition zone (ITZ) is the weakest area in the concrete three-phase medium, which directly affects concrete strength, stiffness, and durability. Because the ITZ has small-scale characteristics, conventional testing methods cannot be used to study its effect on concrete strength. The objective of this paper is to study the mechanism of ITZ in the process of concrete failure by means of a mesoscopic numerical model, and propose an efficient numerical simulation method for improving the numerical simulation in terms of concrete micromechanics. First, the random irregular aggregates are generated by PFC2D (Particle Flow Code in two-dimensions), and the percentage of aggregates is verified by image processing technology. Next, the contact properties of different media of concrete are defined by using the flat-joint model; based on this, the numerical simulation of Brazilian splitting test (BST) and uniaxial compression test (UCT) is carried out, and the simulation results are compared with the physical test data to verify the model. Finally, after processing the uniaxial compression and tensile test numerical simulation by using the proposed method, it was found that when the strength of ITZ is at least 70% of the strength of cement mortar, concrete can be regarded as a two-phase medium comprising aggregate and cement mortar; thus, ITZ can no longer be considered the weakest area. [ABSTRACT FROM AUTHOR]

This paper investigates a new analog beamforming architecture for massive multiple-input multiple-output (MIMO) systems, where each of the multiple transmit antennas is switched to be on or off to form a beam according to the channel state information at transmitters. This on–off analog beamforming (OABF) scheme has the advantages in terms of both hardware complexities and algorithmic complexities. OABF can completely remove the high-cost, power-consuming, and bulky analog phase shifters that are extensively employed by traditional analog beamforming schemes; it only requires the deployment of low-cost analog switches that are easy to implement. Moreover, we show that the beams formed by such simple antenna on–off switch operations can achieve rather good performances with low-complexity beamforming algorithms. Specifically, we first propose two optimal signal-to-noise ratio maximization algorithms to determine the on–off state of each switch under the per-antenna power constraint and the total power constraint, respectively. After that, we theoretically prove that OABF can achieve the full diversity gain and the full array gain with complexities up to a polynomial order. Numerical results are consistent with our theoretical analysis. We believe that the simple structure of OABF makes massive MIMO much easier to implement in real systems. [ABSTRACT FROM PUBLISHER]

The effect of the steel fibre distribution on concrete is significant, studies regarding the distribution characteristics of steel fibre are of great significance for the application of steel fibre-reinforced concrete (SFRC). Based on different mechanical test results, this paper aims to propose a reliable analysis method for more efficient evaluation of steel fibre distribution by using image processing and the inductive test to do comparison analysis on the steel fibre distribution in different concrete specimens. Firstly, the results of image processing and inductive test of different type of SFRC cubic specimens was analysed to obtain the steel fibre distribution, and a formula for rapid calculation of fibre content was proposed. Then, the influence of steel fibre distribution on the mechanical properties of SFRC was analysed by combining double punch test and three-point bending test results. Finally, the high similarity of the spatial distribution of steel fibre is analysed by the inductive test and image processing, which are reflected by the phase coefficient, and the applicability of these two methods are compared and evaluated. [ABSTRACT FROM AUTHOR]

Phosphorene, an emerging two-dimensional material, has received considerable attention due to its layer-controlled direct bandgap, high carrier mobility, negative Poisson's ratio and unique in-plane anisotropy. As cousins of phosphorene, 2D group-VA arsenene, antimonene and bismuthene have also garnered tremendous interest due to their intriguing structures and fascinating electronic properties. 2D group-VA family members are opening up brand-new opportunities for their multifunctional applications encompassing electronics, optoelectronics, topological spintronics, thermoelectrics, sensors, Li- or Na-batteries. In this review, we extensively explore the latest theoretical and experimental progress made in the fundamental properties, fabrications and applications of 2D group-VA materials, and offer perspectives and challenges for the future of this emerging field. [ABSTRACT FROM AUTHOR]

In this work, magnetic based activated carbon (MPSAC) was prepared via a simple one-step method in the presence of K2CO3 and Fe3O4 and further examined as an adsorbent for the removal of Pb(II). MPSAC possessed a well-developed porosity structure with a high surface area of 1,219 m2/g and contained rich carboxylic functional groups on the surface. It can be separated easily from a suspended system. The pHIEP value of MPSAC was notably lower than the pHPZC, suggesting that the negatively charged surface was largely distributed in the external area. Batch adsorption experiments were carried out by varying the initial pH, contact time, adsorbent dosage, initial Pb(II) concentration, and temperature of solution. Results show that adsorption of Pb(II) on MPSAC was dependent on contact time, solution pH, adsorbent dosage, initial Pb(II) concentration, and temperature, especially solution pH having strong effects at pH 2-4. Adsorption kinetic and equilibrium data were well described by the pseudo-second-order model and Langmuir isotherm with the maximum monolayer adsorption amounts of 146.20, 152.67, and 158.73 mg/g at 293, 303, and 313 K, respectively. Intra-particle diffusion mechanism was partially responsible for the adsorption. The thermodynamic study indicated that the adsorption was a spontaneous and endothermic process. Competitive adsorption showed that MPSAC has a good adsorption selectivity for removal of Pb(II) ion. [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]

Physical layer network coding (PNC) significantly improves the throughput of the two-way relay channel (TWRC). However, most of current work on PNC just focuses on one pair users, and there are limit work on multi-pair PNC, in which the main bottleneck on system performance is the interference caused by the remaining communicating pairs. In this paper, we consider a scenario where multiple pairs communicate with each other through one relay node. For a purpose of comparison, we present the analytical capacities of the multi-pair version of the existing TWRC schemes in closed forms. Considering more practical issues such as the direction distribution of incident signals, we propose a spatial multi-pair physical layer network coding scheme. The novelty includes: in the multiple access phase, the log likelihood ratio combination scheme is proposed, which is robust in bad communication conditions; in the broadcast phase, two adaptive group broadcasting schemes are proposed, which can efficiently overcome the near interference problem. Simulation results validate the superiority of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This work considers Mamdani fuzzy systems constructed from finite input-output data pairs using the generalized fuzzy sets with contradictory, opposite and medium negation (GFScom). Considering that the information available often consists of a set of finite numerical data pairs, a new table look-up scheme for constructing Mamdani fuzzy systems is presented. The designed fuzzy system is proved to be capable of approximating any real continuous function on a compact set to arbitrary degree of accuracy. We use this fuzzy modeling method for the truck back-upper control problem. The effectiveness of the proposed method is demonstrated by a comparison with the traditional table look-up scheme. [ABSTRACT FROM AUTHOR]

Structural class is an important attribute of protein, which plays an important role in both the experiment and theoretical research of protein science. Prediction of protein structural classes has been a challenging task, specifically for low-similarity datasets. In this paper, we develop a feature extraction method PSSS-SOMA-PSSM by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition (PseAAC) based on predicted secondary structure sequence (PSSS) and position-specific scoring matrix (PSSM). We construct a 170-dimensional (170D) feature vector for each protein sequence sample, which contains 10D PSSS features that reflect content, alternating word frequency and novel position information, and contains 160D PSSM features that are calculated by second-order moving average (SOMA) algorithm. The SVM classifier with RBF kernel function and the jackknife test are used to predict and evaluate on 1189, 25PDB and 640 datasets with sequence similarity lower than 40%, 25%, and 25%, respectively. Comparison of our results with other methods shows that the proposed method provides the state-of-the-art performance and a cost-effective alternative to structural classes prediction for low-similarity datasets. [ABSTRACT FROM AUTHOR]

Pulsed DC magnetron sputtering was used to deposit indium‑tin-oxide (ITO) thin films on glass substrates for application in photovoltaic devices as the final window layer. The evolution of crystal phase, morphology, resistivity and transmittance of the ITO films under different thermal annealing procedure ( in - situ annealing versus post-annealing) at relatively low temperatures (up to 300 °C) were studied. Fully crystalline ITO films with low sheet resistance of 24 Ω/□ and high average transmittance (exclude glass substrate) of 90.10% with a Haacke's figure of merit 1.47 × 10 − 2 Ω − 1 were obtained by in - situ annealing at 200 °C. In comparison, the minimum temperature observed to fully crystalize the ITO thin films with competitive crystallinity, electrical and optical properties by post-annealing was 250 °C. The corresponding post-annealed 300 nm thick films showed sheet resistance of 49 Ω/□ and average transmittance (exclude glass substrate) as high as 91.93% with a Haacke's figure of merit 0.882 × 10 − 2 Ω − 1 . The surface morphology of the as-deposited ITO films remained homogeneous after post-annealing whereas large particles (up to 100 nm) were observed on the surface of the films deposited by in - situ annealing. The results indicated that different thermal treatment procedures at lower temperatures produce significant effect on the electrical, optical and other physical properties of the ITO thin films which are critical for the window layer in photovoltaics. [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]

In this paper, we report an electron injection photomultiplication (PM) type inverted organic photodetector (OPD) based on [6,6]-phenyl-C61-butyric acid methyl ester (PC 61 BM), poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)] (PCPDTBT), poly(3-hexylthiophene) (P3HT) and PbS quantum dots (QDs). The effects of PbS QDs added to PCPDTBT: P3HT on the optical and electrical properties of OPDs were investigated. It is found that when the mass ratio of PbS QDs is 5 wt%, the responsivity (R) and external quantum efficiency (EQE) of the device reach 15.3 A/W and 365% at −2 V bias and 430 nm illumination, respectively. The results show that PbS QDs can act as hole traps in the active layer to introduce a large amount of electron injection from the anode circuit, thereby increasing the photocurrent and realizing the PM phenomenon. However, too much doping will cause the trap material to agglomerate and fail, losing the multiplier effect. It provides a theoretical basis and method for high response polyphase inverted PM type OPDs. • An electron injection photomultiplication type inverted organic photodetector was prepared. • PbS QDs can act as hole traps in the active layer to introduce a large amount of electron injection. • When the mass ratio of PbS QDs was 5 wt%, the device performance was the best. • Excessive doping of PbS QDs will result in the disappearance of multiplication effect. [ABSTRACT FROM AUTHOR]

• Combined control method of traction and ballast based on load transfer is proposed. • The ballast system achieves optimal axle load by adjusting the battery's position. • Field ploughing experiments under two control methods re conducted for verification. • The total motor energy consumption was reduced by 7.3% in ploughing. • The combined control method is helpful in improving the uniformity of tillage depth. Agricultural tractors are significant agricultural power machines that must be equipped with different implements according to tillage requirements and operate in a complex field environment. Aiming at the problem that the traditional traction control system could not balance the agronomic requirements of tillage and high-efficiency operation, this study proposed a combined control method of traction and ballast based on load transfer. This method was used for electric tractors with a battery position adjustment (BPA) structure. The tractor-implement system model was built to calculate the dynamic loads of front and rear axles accurately. Then, a multi-objective optimization function for traction performance was constructed, and the multi-objective particle swarm algorithm was employed to solve the optimal battery pack target position. The tractor controller real-time measured the draught force and battery position data. Based on the combined control strategy, the battery pack position and the tillage depth were adjusted to accomplish the cooperative control of the active ballast system and the electro-hydraulic hitch system. Finally, a field test platform for the electric tractor was developed. Ploughing tests under the traction control method and combined control method were performed. The test results showed that the tillage depth uniformity and traction performance under the combined control method were superior to the traction control method. In particular, compared with the traction control method, the tillage depth fluctuation range in the combined control method was reduced by 30.6%, the wheel slip was decreased by 15.1%, the traction efficiency was enhanced by 3.7% and the total motor energy loss was decreased by 4.9%. This study provides a technical reference for improving the economy of energy consumption and the uniformity of tillage depth of tractors in ploughing. [ABSTRACT FROM AUTHOR]

Photon catalysis (PC) is an intriguing quantum optical operation wherein the same number of photons are added to and subsequently subtracted from the relevant optical system. It is an important tool that facilitates photon number manipulation. However, most existing PC methods are suitable only for photonic states residing within few-photon subspace. In addition, the probability of PC decreases exponentially with increase in the number n of the Fock state | n 〉. Thus, this study proposed a high probability PC scheme for optical quantum states with large Fock state population based on nonlinear beam splitters. The results indicate that the fidelity and the probability of PC are both substantially improved. Moreover, in case of detector inefficiency, the scheme of PC with non-linear beam splitter was more robust. Thus, the proposed method provides a useful tool for manipulating optical quantum states with large Fock number state population. [ABSTRACT FROM AUTHOR]

Protein assemblies with high symmetry are widely distributed in nature. Most efforts so far have focused on repurposing these protein assemblies, a strategy that is ultimately limited by the structures available. To overcome this limitation, methods for fabricating novel self-assembling proteins have received intensive interest. Herein, by reengineering the key subunit interfaces of native 24-mer protein cage with octahedral symmetry through amino acid residues insertion, we fabricated a 16-mer lenticular nanocage whose structure is unique among all known protein cages. This newly non-native protein can be used for encapsulation of bioactive compounds and exhibits high uptake efficiency by cancer cells. More importantly, the above strategy could be applied to other naturally occurring protein assemblies with high symmetry, leading to the generation of new proteins with unexplored functions. [ABSTRACT FROM AUTHOR]

Protein assemblies with high symmetry are widely distributed in nature. Most efforts so far have focused on repurposing these protein assemblies, a strategy that is ultimately limited by the structures available. To overcome this limitation, methods for fabricating novel self-assembling proteins have received intensive interest. Herein, by reengineering the key subunit interfaces of native 24-mer protein cage with octahedral symmetry through amino acid residues insertion, we fabricated a 16-mer lenticular nanocage whose structure is unique among all known protein cages. This newly non-native protein can be used for encapsulation of bioactive compounds and exhibits high uptake efficiency by cancer cells. More importantly, the above strategy could be applied to other naturally occurring protein assemblies with high symmetry, leading to the generation of new proteins with unexplored functions. [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]

Recent experiments reveal that the strained bulk HgTe can be regarded as a three-dimensional topological insulator (TI). We further explore the strain effects on magnetotransport in HgTe at magnetic field. We find that the substrate strain associated with the surface index of carriers, can remove the surfaces degeneracy in Landau levels. This accordingly induces the well separated surface quantum Hall plateaus and Shubnikov–de Haas oscillations. These results can be used to generate and detect surface polarization, not only in HgTe but also in a broad class of TIs, which would be very great news for electronic applications of TIs. [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]

Prediction of secreted protein types based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 6800 features are extracted at 17 different gaps; then, 309 features are selected by a filter feature selection method based on the training set. To verify the performance of our method, jackknife and independent dataset tests are performed on the test set and the reported overall accuracies are 93.60% and 100%, respectively. Comparison of our results with the existing method shows that our method provides the favorable performance for secreted protein type prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • The prepared PEI-JE had the same 3D network structure as the JE. • PEI-JE had a maximum adsorption capacity of 268.93 mg/g at 25 ℃. • Cr(VI) removal rate almost kept unchanged after 10 adsorption/desorption cycles. • Cr(VI) removal was involved in electrostatic adsorption, redox and complexation. • The Cr(VI) adsorbed on the surface of PEI-JE was completely reduced to Cr(III). In the present study, a novel PEI-modified Juncus effuses (PEI-JE) adsorbent was prepared based on the natural 3D network structure of JE via a simple and environmental-friendly method with the help of the ethanol/aqueous solution. SEM, EDS, ATR-FTIR and XPS were performed to reveal the physical and chemical structure of the PEI-JE. The Cr(VI) removal by the PEI-JE was investigated by bath adsorption experiments. Results showed that the developed PEI-JE exhibited the same 3D network structure as the JE, which not only was easy to separate from aqueous solution but also can expose more active sites and facilitate mass transfer. The optimal pH for Cr(VI) adsorption on the PEI-JE was 3, and adsorption equilibrium time was related to initial Cr(VI) concentrations. The adsorption kinetic obeyed pseudo-second-order kinetic model, and the adsorption isotherm showed the best fit with Freundlich model. The calculated maximum adsorption capacity was 268.93 mg/g at 25℃. Coexisting ions including Na+, K+, Al3+, H 2 PO 4 −, Cl−, NO 3 – had no remarkable effect on Cr(VI) adsorption, but the presence of SO 4 2−, HCO 3 – and CO 3 2– caused the efficiency losses of 10.9%, 41% and 66%, respectively. After 10 cycles of adsorption/desorption, Cr(VI) removal rate on the PEI-JE almost kept unchanged, demonstrating that the PEI-JE possessed good reusability. Mechanism study suggested that Cr(VI) removal was involved in a three-step reaction, namely electrostatic adsorption, redox and complexation. Film diffusion was the rate-limiting step and chemical adsorption was the main mechanism. The Cr(VI) adsorbed on the PEI-JE surface was completely reduced to Cr(III), demonstrating that PEI-JE is useful for Cr (VI) removal and detoxification. [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]