Search

Rhizosphere microbial communities are to be as critical factors for plant growth and vitality, and their adaptive differentiation strategies have received increasing amounts of attention but are poorly understood. In this study, we obtained bacterial and fungal amplicon sequences from the rhizosphere and bulk soils of various ecosystems to investigate the potential mechanisms of microbial adaptation to the rhizosphere environment. Our focus encompasses three aspects: niche preference, functional profiles, and cross-kingdom co-occurrence patterns. Our findings revealed a correlation between niche similarity and nucleotide distance, suggesting that niche adaptation explains nucleotide variation among some closely related amplicon sequence variants (ASVs). Furthermore, biological macromolecule metabolism and communication among abundant bacteria increase in the rhizosphere conditions, suggesting that bacterial function is trait-mediated in terms of fitness in new habitats. Additionally, our analysis of cross-kingdom networks revealed that fungi act as intermediaries that facilitate connections between bacteria, indicating that microbes can modify their cooperative relationships to adapt. Overall, the evidence for rhizosphere microbial community adaptation, via differences in gene and functional and co-occurrence patterns, elucidates the adaptive benefits of genetic and functional flexibility of the rhizosphere microbiota through niche shifts. [ABSTRACT FROM AUTHOR]

Background: To analyze vault effects of crystalline lens rise (CLR) and anterior chamber parameters (recorded by Pentacam) in highly myopic patients receiving implantable collamer lenses (ICLs), which may avoid subsequent complications such as glaucoma and cataract caused by the abnormal vault. Methods: We collected clinical data of 137 patients with highly myopic vision, who were all subsequent recipients of V4c ICLs between June 2020 and January 2021. Horizontal ciliary sulcus-to-sulcus diameter (hSTS) and CLR were measured by ultrasonic biomicroscopy (UBM), and a Pentacam anterior segment analyzer was used to measure horizontal white-to-white diameter (hWTW), anterior chamber depth (ACD), anterior chamber angle (ACA), anterior chamber volume (ACV), CLR, and postoperative vault (Year 1 and Month 1). The lens thickness (LT) was determined by optical biometry (IOL Master instrument). The predictive model was generated through multiple linear regression analyses of influential factors, such as hSTS, CLR, hWTW, ACD, ACA, ACV, ICL size, and LT. The predictive performance of the multivariate model on vault after ICL was assessed using the receiver operating characteristic (ROC) curve with area under the curve (AUC) as well as the point of tangency. Results: Average CLR assessed by UBM was lower than the average value obtained by Pentacam (0.561 vs. 0.683). Bland-Altman analysis showed a good consistency in the two measurement methods and substantial correlation (r = 0.316; P = 0.000). The ROC curve of Model 1 (postoperative Year 1) displayed an AUC of 0.847 (95% confidence interval [CI]: 74.19–95.27), with optimal threshold of 0.581 (sensitivity, 0.857; specificity, 0.724). In addition, respective values for Model 2 (postoperative Month 1) were 0.783 (95% CI: 64.94–91.64) and 0.522 (sensitivity, 0.917; specificity, 0.605). Conclusion: CLR and anterior chamber parameters are important determinants of postoperative vault after ICL placement. The multivariate regression model we constructed may serve in large part as a predictive gauge, effectively avoid postoperative complication. [ABSTRACT FROM AUTHOR]

Motivation Numerous high-accuracy drug–target affinity (DTA) prediction models, whose performance is heavily reliant on the drug and target feature information, are developed at the expense of complexity and interpretability. Feature extraction and optimization constitute a critical step that significantly influences the enhancement of model performance, robustness, and interpretability. Many existing studies aim to comprehensively characterize drugs and targets by extracting features from multiple perspectives; however, this approach has drawbacks: (i) an abundance of redundant or noisy features; and (ii) the feature sets often suffer from high dimensionality. Results In this study, to obtain a model with high accuracy and strong interpretability, we utilize various traditional and cutting-edge feature selection and dimensionality reduction techniques to process self-associated features and adjacent associated features. These optimized features are then fed into learning to rank to achieve efficient DTA prediction. Extensive experimental results on two commonly used datasets indicate that, among various feature optimization methods, the regression tree-based feature selection method is most beneficial for constructing models with good performance and strong robustness. Then, by utilizing Shapley Additive Explanations values and the incremental feature selection approach, we obtain that the high-quality feature subset consists of the top 150D features and the top 20D features have a breakthrough impact on the DTA prediction. In conclusion, our study thoroughly validates the importance of feature optimization in DTA prediction and serves as inspiration for constructing high-performance and high-interpretable models. Availability and implementation https://github.com/RUXIAOQING964914140/FS_DTA. [ABSTRACT FROM AUTHOR]

Due to the underdeveloped nature of organized labor, it is possible to view the 'individual' and 'collective' components of labor legislation in China as separate and severable. This article aims to challenge such thinking by arguing that collective labor law and collective bargaining practices in China have profoundly shaped the law of employment contracts and individual employment relations. To this end, analyzing the laws surrounding individual employment contracts should not proceed without considering collective labor law. This article investigates, in the first three decades following the establishment of the People's Republic of China in 1949, the significance of collective rights to the underdevelopment of legal rules of employment rights and the emergence of the socialist social contract. This article also examines, after the economic reform of 1978, the various ways collective bargaining contributed to the transformation from the socialist social contract to the standard contract of employment and from an underdeveloped to a comprehensive framework of employment legislation. Finally, in the post-economic-reform decades, the analysis suggests that collective bargaining encourages the empowerment of trade unions with legislative and administrative efforts and facilitates the incorporation of terms and conditions improvement into individual employment contracts. [ABSTRACT FROM AUTHOR]

Furthermore, in terms of protein identification, Xu et al. concentrated on the study of antioxidant protein identification; they proposed a machine learning method, SeqSVM, to predict antioxidant proteins through extracted sequence features [[10]]. The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases. Seven papers focus on describing protein function prediction or protein identification, which include the prediction of signal peptides in proteins, protein hydroxylation site prediction, protein-protein interaction (PPI) prediction, and protein identification. [Extracted from the article]

Background: Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. Results: In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. Conclusions: DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at . [ABSTRACT FROM AUTHOR]

Aiming at the melt splashing behavior in the smelting process of an oxygen-enriched side-blowing furnace, the volume of fluid model and the realizable k − ε turbulence model are coupled and simulated. The effects of different operating parameters (injection velocity, immersion depth, liquid level) on splash height are explored, and the simulation results are verified by water model experiments. The results show that the bubbles with residual kinetic energy escape to the slag surface and cause slag splashing. The slag splashing height gradually increases with the increase in injection velocity, and the time-averaged splashing height reaches 1.01 m when the injection speed is 160 m/s. Increasing the immersion depth of the lance, and the slag splashing height gradually decreases. When the immersion depth is 0.12 m, the time-averaged splashing height is 0.85 m. Increasing the liquid level is beneficial to reduce the splash height, when the liquid level is 2.7 m, the splash height reduces to 0.77 m. With the increase in the liquid level, the slag splashing height gradually decreases, and the time-averaged splashing height is 0.77 m when the initial liquid level is 2.7 m. [ABSTRACT FROM AUTHOR]

A novel reaching law‐based sliding mode controller is proposed for a permanent magnet synchronous motor (PMSM) speed regulation system with uncertainties and unknown load torque in this paper. The proposed reaching law is the improvement of the traditional power rate reaching law (PRL) by using a simple tuning function of the sliding variable. The tuning function is designed such that the reaching speed is fast when the system states are far away from the sliding surface, and vice versa. Theoretical analysis shows that the reaching time of the proposed reaching law is always shorter than that of the traditional PRL with the same gains. Moreover, unlike the traditional PRL and some existing auto‐tuning PRLs, the proposed reaching law can provide globally bounded reaching time independently on the initial conditions, and the reaching time can be effectively reduced by tuning the reaching law gains. Based on this novel reaching law, a disturbance observer is designed to estimate the total disturbance, and then based on the estimated disturbance and the novel reaching law, a sliding mode speed controller is designed for the robust control of PMSM speed regulation system. Simulations and experiments are carried out to demonstrate the superiority of the proposed control method. [ABSTRACT FROM AUTHOR]

Motivation Multiple sequence alignment (MSA) is a fundamental problem in bioinformatics. The quality of alignment will affect downstream analysis. MAFFT has adopted the Fast Fourier Transform method for searching the homologous segments and using them as anchors to divide the sequences, then making alignment only on segments, which can save time and memory without overly reducing the sequence alignment quality. MAFFT becomes slow when the dataset is large. Results We made a software, WMSA, which uses the divide-and-conquer method to split the sequences into clusters, aligns those clusters into profiles with the center star strategy and then makes a progressive profile–profile alignment. The alignment is conducted by the compiled algorithms of MAFFT, K-Band with multithread parallelism. Our method can balance time, space and quality and performs better than MAFFT in test experiments on highly conserved datasets. Availability and implementation Source code is freely available at https://github.com/malabz/WMSA/ , which is implemented in C/C++ and supported on Linux, and datasets are available at https://github.com/malabz/WMSA-dataset. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Bonding dissimilar elements to provide synergistic effects is an effective way to improve the performance of metal catalysts. However, as the properties become more dissimilar, achieving synergistic effects effectively becomes more difficult due to phase separation. Here we describe a comprehensive study on how subnanoscale alloying is always effective for inter‐elemental synergy. Thirty‐six combinations of both bimetallic subnanoparticles (SNPs) and nanoparticles (NPs) were studied systematically using atomic‐resolution imaging and catalyst benchmarking based on the hydrogen evolution reaction (HER). Results revealed that SNPs always produce greater synergistic effects than NPs, the greatest synergistic effect was found for the combination of Pt and Zr. The atomic‐scale miscibility and the associated modulation of electronic states at the subnanoscale were much different from those at the nanoscale, which was observed by annular‐dark‐field scanning transmission electron microscopy (ADF‐STEM) and X‐ray photoelectron spectroscopy (XPS), respectively. [ABSTRACT FROM AUTHOR]

Bonding dissimilar elements to provide synergistic effects is an effective way to improve the performance of metal catalysts. However, as the properties become more dissimilar, achieving synergistic effects effectively becomes more difficult due to phase separation. Here we describe a comprehensive study on how subnanoscale alloying is always effective for inter‐elemental synergy. Thirty‐six combinations of both bimetallic subnanoparticles (SNPs) and nanoparticles (NPs) were studied systematically using atomic‐resolution imaging and catalyst benchmarking based on the hydrogen evolution reaction (HER). Results revealed that SNPs always produce greater synergistic effects than NPs, the greatest synergistic effect was found for the combination of Pt and Zr. The atomic‐scale miscibility and the associated modulation of electronic states at the subnanoscale were much different from those at the nanoscale, which was observed by annular‐dark‐field scanning transmission electron microscopy (ADF‐STEM) and X‐ray photoelectron spectroscopy (XPS), respectively. [ABSTRACT FROM AUTHOR]

• A novel method was proposed to identify RNA m2G sites using hybrid features. • The over-sample method SMOTE was adopted to deal with the problem of data imbalance. • After using MRMD to select features, the performance of the model is improved. • The RFhy-m2G is superior to other methods, which can effective identify m2G sites. N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors. [ABSTRACT FROM AUTHOR]

Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming, and may have adverse side effects on normal cells. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction scheme called iAFPs-Mv-BiTCN to predict antifungal peptides correctly. The training peptides are encoded using word embedding methods such as skip-gram and attention mechanism-based bidirectional encoder representation using transformer. Additionally, transform-based evolutionary features are generated using the Pseduo position-specific scoring matrix using discrete wavelet transform (PsePSSM-DWT). The fused vector of word embedding and evolutionary descriptors is formed to compensate for the limitations of single encoding methods. A Shapley Additive exPlanations (SHAP) based global interpolation approach is applied to reduce training costs by choosing the optimal feature set. The selected feature set is trained using a bi-directional temporal convolutional network (BiTCN). The proposed iAFPs-Mv-BiTCN model achieved a predictive accuracy of 98.15 % and an AUC of 0.99 using training samples. In the case of the independent samples, our model obtained an accuracy of 94.11 % and an AUC of 0.98. Our iAFPs-Mv-BiTCN model outperformed existing models with a ~4 % and ~5 % higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed iAFPs-Mv-BiTCN model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia. • A Bidirectional Temporal Convolutional Networks-based computational model is developed for the Prediction of Antifungal peptides. • A Transform evolutionary matrix, self-attention based transformer, and fasttext-based word embedding are employed to numerically represent the peptide samples. • The SHAP interpolation-based feature selection is applied to select optimal features from the Hybrid vector • The proposed iAFPs-Mv-BiTCN model achieved the highest predictive results using training and independent datasets than existing computational models. [ABSTRACT FROM AUTHOR]

Based on the discrete element method (DEM) and GPU parallel computing, a particle heat transfer model is developed to simulate the heat transfer in a pebble bed of the high-temperature gas-cooled reactor (HTGR). The model is implemented based on a previously developed GPU-DEM program by our team and uses the mesh-based neighbor searching algorithm for the heat transfer calculation. This model couples the conduction and radiative heat transfer between the pebbles and incorporates neural networks and empirical fittings to calculate the radiation view factors, which can improve computational efficiency. The effective thermal conductivity of different models and experimental data are used to verify the accuracy of the model, and the influence of different radiation heat transfer models on the results is also compared. The results show that the effective thermal conductivity derived from the current model is comparable to the classical models at different temperatures, and the numerical simulation results based on the current model are in good agreement with the corresponding experimental data. Additionally, the model achieves a single-core speedup ratio of 126–395 times with GPU acceleration, significantly enhancing computational efficiency. In conclusion, the current model has been effectively verified for accuracy and computational efficiency, and it demonstrates great potential in dealing with large-scale pebble flow and heat transfer challenges in HTGRs. • A Voronoi-tessellation-free new heat transfer model is proposed for pebble beds. • View factors are calculated by neural networks to couple conduction and radiation. • GPU parallel computing is employed at a speedup ratio of 126–395 times of CPU. • An alternate-read-write method and unified Memory Access technology are used. • The model accuracy is validated and discussed by comparing it with experiments. [ABSTRACT FROM AUTHOR]

Motivation With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for searching the etiopathogenesis and treatment of diseases. Nevertheless, it is inefficient to learn new associations only through biotechnology. Results Consequently, we present a computational method, GMNN2CD, which employs a graph Markov neural network (GMNN) algorithm to predict unknown circRNA–disease associations. First, used verified associations, we calculate semantic similarity and Gaussian interactive profile kernel similarity (GIPs) of the disease and the GIPs of circRNA and then merge them to form a unified descriptor. After that, GMNN2CD uses a fusion feature variational map autoencoder to learn deep features and uses a label propagation map autoencoder to propagate tags based on known associations. Based on variational inference, GMNN alternate training enhances the ability of GMNN2CD to obtain high-efficiency high-dimensional features from low-dimensional representations. Finally, 5-fold cross-validation of five benchmark datasets shows that GMNN2CD is superior to the state-of-the-art methods. Furthermore, case studies have shown that GMNN2CD can detect potential associations. Availability and implementation The source code and data are available at https://github.com/nmt315320/GMNN2CD.git. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git. Author summary: More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is fundamental to many biological applications. But most classical MSA algorithms are difficult to handle large-scale multiple sequences, especially long sequences. Therefore, some recent aligners adopt an efficient divide-and-conquer strategy to divide long sequences into several short sub-sequences. Selecting the common segments (i.e. anchors) for division of sequences is very critical as it directly affects the accuracy and time cost. So, we proposed a novel algorithm, FMAlign, to improve the performance of multiple nucleotide sequence alignment. We use FM-index to extract long common segments at a low cost rather than using a space-consuming hash table. Moreover, after finding the longer optimal common segments, the sequences are divided by the longer common segments. FMAlign has been tested on virus and bacteria genome and human mitochondrial genome datasets, and compared with existing MSA methods such as MAFFT, HAlign and FAME. The experiments show that our method outperforms the existing methods in terms of running time, and has a high accuracy on long sequence sets. All the results demonstrate that our method is applicable to the large-scale nucleotide sequences in terms of sequence length and sequence number. The source code and related data are accessible in https://github.com/iliuh/FMAlign. [ABSTRACT FROM AUTHOR]

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF. [ABSTRACT FROM AUTHOR]

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools is the high-dimensional, high-sparsity, high-noise and large-scale characteristics of single-cell RNA-seq data. Deep learning technologies match the characteristics of single-cell RNA-seq data perfectly and offer unprecedented promise. Here, we give a systematic review for most popular single-cell RNA-seq analysis methods and tools based on deep learning models, involving the procedures of data preprocessing (quality control, normalization, data correction, dimensionality reduction and data visualization) and clustering task for downstream analysis. We further evaluate the deep model-based analysis methods of data correction and clustering quantitatively on 11 gold standard datasets. Moreover, we discuss the data preferences of these methods and their limitations, and give some suggestions and guidance for users to select appropriate methods and tools. [ABSTRACT FROM AUTHOR]

Predicting the capability of decay-heat removal for a pebble bed of the high-temperature gas-cooled reactor is a cutting-edge issue in the research of generation IV advanced reactors. In this research, numerical simulations of heat transfer in the pebble bed reactor have been conducted by developing two kinds of models, a particle-scale model and a porous media model. The aim is to estimate the effect thermal conductivity of the pebble bed and predict the internal temperature field. The former model relies on the pebble–pebble conduction and radiation at a scale as fine as every fuel pebble. The latter one is an overall model at the scale mainly on the whole feature and capability of the pebble bed reactor. These two models have generated consistent results in an intermediate case considering the effective thermal conductivity for radiation and conduction of the pebbles. Based on this consistency, the pebble bed's overall features of the internal field of the temperature field in pressurized helium have been well predicted. The simulation results validate the pretty good capability of the pebble bed reactor on decay-heat removal. The maximum temperature of the reactor core is limited to about 350 °C at the 7 MPa pressurized helium. This means the safety of the HTR-PM reactor could be reasonably guaranteed. • A new SPH kernel by combining a polynomial and a reciprocal term is proposed. • Validated by dam-break, the kernel is used for natural convection in reactor cores. • Comparing to existing kernels shows the differences and (dis)-advantages of them. • Various temperature differences, channel numbers/lengths are simulated. • A mapping method is used to transfer particles' velocity/temperature to Eulerian. [ABSTRACT FROM AUTHOR]

In this study, we investigated the effect of the preparation process i.e., the process informatics (PI) parameters of sensor elements on the responses of Pt-, Pd-, and Au-loaded SnO 2 sensors. These responses were predicted by an artificial neural network (ANN) using a dataset comprising 441 data points that had been fabricated and evaluated under many parameters in our previous studies. We reported an optimal data preprocessing method based on the relational expression between the sensor response of a semiconductor sensor and the concentration of a target gas and the effect of each PI parameter based on predicted sensor responses under untested conditions. • Prediction of the effects of process informatics on SnO 2 -type sensors. • Using artificial neural networks for the prediction. • Preparing a dataset comprising 441 data points from our previous studies. • Optimization of data preprocessing based on the theory of semiconductor sensors. [ABSTRACT FROM AUTHOR]

Reverse transcription polymerase chain reaction (RT-PCR), the primary test for COVID-19, requires complicated sample collection and several hours to obtain results. Breath test for exhaled volatile organic compounds (VOCs) has gained substantial research attention as a simple non-invasive and fast screening method. However, a unique VOC fingerprint as a potential prognostic biomarker is still unavailable. Accordingly, this study prepared simulated VOC gases to test the classification performance of a sensing system. The simulated VOC gases were selected according to the actual composition of the exhaled breath of patients with acute respiratory diseases, including COVID-19. Two sets of metal oxide sensor arrays, comprising eight commercial sensors and eight Advanced Institute of Science and Technology (AIST) laboratory-made sensors, were prepared to test their sensing ability for the simulated gases. The principal component analysis (PCA) results revealed that the AIST sensors had better sensing ability than the commercial sensors. Moreover, the recursive feature elimination cross-validation (RFECV) of random forest (RF) further confirmed the superiority of the AIST sensors over the commercial sensors. An artificial neural network (ANN) with excellent prediction performance for gas concentration was developed. This study provides a promising method for rapidly screening respiratory diseases. • Development of sensor arrays for breath analysis for screening COVID-19. • Selecting nine target VOCs based on reports from exhaled breaths of patients. • Testing two sensor arrays with machine learning to discriminate VOCs. • Important to combine various sensor-response principles of semiconductors. [ABSTRACT FROM AUTHOR]

Background: Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging.Methods: In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm.Results: Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA .Conclusions: We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I. [ABSTRACT FROM AUTHOR]

How best to utilize the microbial taxonomic abundances in regard to the prediction and explanation of human diseases remains appealing and challenging, and the relative nature of microbiome data necessitates a proper feature selection method to resolve the compositional problem. In this study, we developed an all-in-one platform to address a series of issues in microbiome-based human disease prediction and taxonomic biomarkers discovery. We prioritize the interpretation, runtime and classification accuracy of the distal discriminative balances analysis (DBA-distal) method in selecting a set of distal discriminative balances, and develop DisBalance, a comprehensive platform, to integrate and streamline the workflows of disease model building, disease risk prediction and disease-related biomarker discovery for microbiome-based binary classifications. DisBalance allows the de novo model-building and disease risk prediction in a very fast and convenient way. To facilitate the model-driven and knowledge-driven discoveries, DisBalance dedicates multiple strategies for the mining of microbial biomarkers. The independent validation of the models constructed by the DisBalance pipeline is performed on seven microbiome datasets from the original article of DBA-distal. The implementation of the DisBalance platform is demonstrated by a complete analysis of a shotgun metagenomic dataset of Ulcerative Colitis (UC). As a free and open-source, DisBlance can be accessed at http://lab.malab.cn/soft/DisBalance. The source code and demo data for Disbalance are available at https://github.com/yangfenglong/DisBalance. [ABSTRACT FROM AUTHOR]

The compositionality of the microbiome data is well-known but often neglected. The compositional transformation pertains to the supervised learning of microbiome data and is a critical step that decides the performance and reliability of the disease classifiers. We value the excellent performance of the distal discriminative balance analysis (DBA) method, which selects distal balances of pairs and trios of bacteria, in addressing the classification of high-dimensional microbiome data. By applying this method to the species-level abundances of all the disease phenotypes in the GMrepo database, we build a balance-based model repository for the classification of human gut microbiome–related diseases. The model repository supports the prediction of disease risks for new sample(s). More importantly, we highlight the concept of balance-disease associations rather than the conventional microbe-disease associations and develop the human Gut Balance-Disease Association Database (GBDAD). Each predictable balance for each disease model indicates a potential biomarker-disease relationship and can be interpreted as a bacteria ratio positively or negatively correlated with the disease. Furthermore, by linking the balance-disease associations to the evidenced microbe-disease associations in MicroPhenoDB, we surprisingly found that most species-disease associations inferred from the shotgun metagenomic datasets can be validated by external evidence beyond MicroPhenoDB. The balance-based species-disease association inference will accelerate the generation of new microbe-disease association hypotheses in gastrointestinal microecology research and clinical trials. The model repository and the GBDAD database are deployed on the GutBalance server, which supports interactive visualization and systematic interrogation of the disease models, disease-related balances and disease-related species of interest. [ABSTRACT FROM AUTHOR]

In viruses, posttranslational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for a better understanding of the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus–human interactions using comprehensive viral PTM datasets. To fill this gap, we developed the first comprehensive viral posttranslational modification database (VPTMdb) for collecting systematic information of PTMs in human viruses and infected host cells. The VPTMdb contains 1240 unique viral PTM sites with 8 modification types from 43 viruses (818 experimentally verified PTM sites manually extracted from 150 publications and 422 PTMs extracted from SwissProt) as well as 13 650 infected cells' PTMs extracted from seven global proteomics experiments in six human viruses. The investigation of viral PTM sequences motifs showed that most viral PTMs have the consensus motifs with human proteins in phosphorylation and five cellular kinase families phosphorylate more than 10 viral species. The analysis of protein disordered regions presented that more than 50% glycosylation sites of double-strand DNA viruses are in the disordered regions, whereas single-strand RNA and retroviruses prefer ordered regions. Domain–domain interaction analysis indicating potential roles of viral PTMs play in infections. The findings should make an important contribution to the field of virus–human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. VPTMdb online web server (http://vptmdb.com:8787/VPTMdb/) was implemented for users to download viral PTM data and predict phosphorylation sites of interest. [ABSTRACT FROM AUTHOR]

Identifying whether a pentatricopeptide repeat (PPR) exists in an amino acid is a significant task in the field of bioinformatics. To address this problem, an identification method that combines an optimal feature set selection framework and machine learning algorithms is proposed to recognize the PPR coding genes and proteins in the sequence of amino acid. The original 188-dimensional (D) features are obtained using a feature extraction method, which is successively optimised through a covariance analysis, max-relevant-max-distance processing, and principal component analysis to reduce it to an optimal feature set that has fewer but more expressive features. Four machine learning methods are then used to serve as the classifiers for the identification task. The final number of feature data dimensions is reduced from 188 to only 10, and according to the experimental results from support vector machine methods, the loss of the AUC and the F 1 values are only 3.26% and 10.1%, respectively. Moreover, after applying the J48, random forest, and naïve Bayes methods as classifiers, it was also found that the optimal feature set with 10 dimensions has an almost equivalent performance for a 10-fold validation test. Identifying whether a pentatricopeptide repeat (PPR) exists in an amino acid is a significant task in the field of bioinformatics. To address this problem, an identification method that combines an optimal feature set selection framework and machine learning algorithms is proposed to recognise the PPR coding genes and proteins in the sequence of an amino acid. The original 188-dimensional (D) features are obtained using a feature extraction method, which is successively optimised through a covariance analysis, max-relevant-max-distance processing, and a principal component analysis to reduce it to an optimal feature set that has fewer but more expressive features. Four machine learning methods are then used to serve as the classifiers for the identification task. The final number of feature data dimensions is reduced from 188 to only 10, and according to the experimental results from support vector machine methods, the loss of the AUC and F 1 the value are only 3.26% and 10.1%, respectively. Moreover, after applying the J48, random forest, and naïve Bayes methods as classifiers, it was also found that the optimal feature set with 10 dimensions has an almost equivalent performance for a 10-fold validation test Image, graphical abstract [ABSTRACT FROM AUTHOR]

CRISPR-based base editors (BEs) are powerful tools for precise nucleotide substitution in a wide range of organisms, but spatiotemporal control of base editing remains a daunting challenge. Herein, we develop a photoactivatable base editor (Mag-ABE) for spatiotemporally controlled genome editing in vivo for the first time. The base editing activity of Mag-ABE can be activated by blue light for spatiotemporal regulation of both EGFP reporter gene and various endogenous genes editing. Meanwhile, the Mag-ABE prefers to edit A4 and A5 positions rather than to edit A6 position, showing the potential to decrease bystander editing of traditional adenine base editors. After integration with upconversion nanoparticles as a light transducer, the Mag-ABE is further applied for near-infrared (NIR) light-activated base editing of liver in transgenic reporter mice successfully. This study opens a promising way to improve the operability, safety, and precision of base editing. [Display omitted] [ABSTRACT FROM AUTHOR]

The flow, stacking, and mixing of pebbles in the High-Temperature Gas-cooled Reactor (HTGR) will affect the power distribution of the core and thus affect the economy and safety of the nuclear reactor. Simulations of pebble loading and mixing in HTR-PM based on GPU-DEM have been studied with particle numbers ranging from 230,000 to 420,000. The effects of four loading methods on pebble mixing are compared and analyzed. Mean position, segregation index (SI), Lacey's mixing index (PSMI), mixing entropy (ME), and porosity are used for quantitative analysis. In addition, an alternative approximate method is proposed to calculate the particle number fraction, which can help solve the problem that the particle number fraction is related to the mesh size. The final result shows that different physical parameters, such as mass and Young's modulus, will induce slight stratification during pebble mixing. At the same time, the simulation results with different loading methods have different mixing degrees. The reduced model mixes better than the single-pebble-loading method, but the latter is closer to engineering practice. • Loading & mixing of two kinds of pebbles in a real-scale bed are simulated • An in-house GPU-DEM program has been developed to simulate 420,000 particles • Mixing degrees are analyzed by mean position, SI, PSMI, ME, and porosity • Effect of mass and Young's modulus on the mixing of pebbles is explored. • A new method is proposed to calculate number fractions to solve the mesh effects. [ABSTRACT FROM AUTHOR]

In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method. [ABSTRACT FROM AUTHOR]

Motivation Methylation and transcription factors (TFs) are part of the mechanisms regulating gene expression. However, the numerous mechanisms regulating the interactions between methylation and TFs remain unknown. We employ machine-learning techniques to discover the characteristics of TFs that bind to methylation sites. Results The classical machine-learning analysis process focuses on improving the performance of the analysis method. Conversely, we focus on the functional properties of the TF sequences. We obtain the principal properties of TFs, namely, the basic polar and hydrophobic Ile amino acids affecting the interaction between TFs and methylated DNA. The recall of the positive instances is 0.878 when their basic polar value is >0.1743. Both basic polar and hydrophobic Ile amino acids distinguish 74% of TFs bound to methylation sites. Therefore, we infer that basic polar amino acids affect the interactions of TFs with methylation sites. Based on our results, the role of the hydrophobic Ile residue is consistent with that described in previous studies, and the basic polar amino acids may also be a key factor modulating the interactions between TFs and methylation. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning-based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high-quality generic prediction and informative physicochemical properties (IPPs) identification simultaneously. Results In this work, P hysicochemical P roperty-based T herapeutic P eptide P redictor (PPTPP), a Random Forest-based prediction method was presented to address this issue. A novel feature encoding and learning scheme were initiated to produce and rank physicochemical property-related features. Besides being capable of predicting multiple therapeutics peptides with high comparability to established predictors, the presented method is also able to identify peptides' informative IPP. Results presented in this work not only illustrated the soundness of its working capacity but also demonstrated its potential for investigating other therapeutic peptides. Availability and implementation https://github.com/YPZ858/PPTPP. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/. [ABSTRACT FROM AUTHOR]

Due to the concerted efforts to utilize the microbial features to improve disease prediction capabilities, automated machine learning (AutoML) systems aiming to get rid of the tediousness in manually performing ML tasks are in great demand. Here we developed mAML, an ML model-building pipeline, which can automatically and rapidly generate optimized and interpretable models for personalized microbiome-based classification tasks in a reproducible way. The pipeline is deployed on a web-based platform, while the server is user-friendly and flexible and has been designed to be scalable according to the specific requirements. This pipeline exhibits high performance for 13 benchmark datasets including both binary and multi-class classification tasks. In addition, to facilitate the application of mAML and expand the human disease-related microbiome learning repository, we developed GMrepo ML repository (GMrepo Microbiome Learning repository) from the GMrepo database. The repository involves 120 microbiome-based classification tasks for 85 human-disease phenotypes referring to 12 429 metagenomic samples and 38 643 amplicon samples. The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments. Database URL : http://lab.malab.cn/soft/mAML [ABSTRACT FROM AUTHOR]

Background: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. Results: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. Conclusion: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface. [ABSTRACT FROM AUTHOR]

Background: Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. Methods: In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. Results: The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. Conclusion: Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia. [ABSTRACT FROM AUTHOR]

Motivation N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed. Results In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail. Availability and implementation The web-server 4mCPred is accessible at http://server.malab.cn/4mCPred/index.jsp. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract Food waste (FW) is environmentally unfriendly and decays easily under ambient conditions. Vanadium (V) and chromium (Cr) contamination in soils has become an increasing concern due to risks to human health and environmental conservation. Volatile fatty acids (VFAs) derived from FW was applied as soil washing agent to treat V and Cr-contaminated soil collected from a former V smelter site in this work. The Community Bureau of Reference (BCR) three-step sequential extraction procedure was used to identify geochemical fractions of V and Cr influencing their mobility and biological toxicity. Optimal parameters of a single washing procedure were determined to be a 4 h contact time, liquid–solid ratio of 10:1, VFAs concentration of 30 g/L, and reaction temperature of 25 °C, considering for typical soil remediation projects and complete anaerobic fermentation of FW. Under the optimal conditions, butyric acid fermentation VFAs attained removal rates of 57.09 and 23.55% for extractable fractions of V and Cr, respectively. Simultaneously, a multi-washing process under a constant liquid–solid ratio using fresh and recycled VFAs was conducted, which led to an improvement on the total removal efficiency of toxic metals. The washing procedure could reach the pollution thresholds for several plants, such as of S. viridis, K. scoparia, M. sativa, and E. indica. This strategy enhances the utilization of VFAs derived from food waste, has a positive effect on V and Cr-contaminated soil remediation, wastewater control of soil washing and FW disposal. Graphical abstract Image 1 Highlights • VFAs derived from food waste can be applied to treat V and Cr-contaminated soil. • Removal efficiency of extractable fractions for V reached 57.09%. • Residual fractions proportion of 94.72% for Cr led to much lower removal efficiency. • VFAs solution was reused during multi-washing procedures. [ABSTRACT FROM AUTHOR]

Highlights • Photocatalytic reactor with floated glass balls was designed for photodegradation. • Glass balls were coated with Bi 2 O 3 /TiO 2 film and constantly rotated by air pumping. • Bi 2 O 3 /TiO 2 film was prepared by sol-gel route and improve visible light absorption. • Constant rotation of glass balls improved efficient contact and light utilization. • Efficient photoactivity and high durability were achieved for organics removal. Abstract A novel photocatalytic reactor was designed for the photo-degradation of organic pollutants with floated glass balls on the solution surface, which were constantly rotated by the air pumping and coated with Bi 2 O 3 /TiO 2 film. The uniform and stable Bi 2 O 3 /TiO 2 film was prepared by the facile sol-gel method at the optimal conditions of Bi Bi 2 O 3 content (1.0 mol%) and the three coating times with the film thickness of 210 nm. It was uniformly distributed and stably combined with the surface of the glass ball. Modification of Bi 2 O 3 significantly improved the visible light absorption of TiO 2 film and inhibited the photo-induced charges, promoting the photo-degradation activity. More importantly, the air pumping at the bottom of the reactor impelled the constant rotation of floated glass balls. It could realize the uniform aqueous membrane on the catalyst film to improve the efficient contact between catalyst and organics and to utilize the irradiation light efficiently, inhibiting the light shield effect of dyeing wastewater and facilitating the photo-degradation. Moreover, the Bi 2 O 3 /TiO 2 photocatalytic film in this photocatalytic reactor exhibited high durability and prevented the leakage of catalyst, providing a potential for the practical applications in the future. [ABSTRACT FROM AUTHOR]

It is known that redox reaction can take place among the solutions of potassium ferrocyanide (K4[Fe(CN)6]), glucose (C6H 1 2 O6) and glucose oxidase (Glucose Oxidase, GOD). In this work, the method of electrochemical biosensor detection based on screen printed electrode was used to observe the redox reaction among these solutions. The relationship between redox reaction and parameters was studied by examining the effects of concentration and scanning speed of three solutions. [ABSTRACT FROM AUTHOR]

This study developed a GPU-based DEM (GPU-DEM) model for the pebble flow in the packed bed. The details of the GPU-DEM scheme have been illustrated. The uniform mesh neighbor-search method, unified memory addressing (UMA), and special boundary conditions were incorporated in the GPU-DEM model. Cases of the direct impact of two identical particles, the angle of repose of particles drained from a lifted hopper, and the circulating hopper to mimic a pebble flow in High-Temperature Gas-cooled Reactor (HTGR) reactors were carried out to systematically verify the applicability, computational efficiency, and accuracy of the GPU-DEM model. Related results, like the unloading speed, porosity, and velocity distribution, obtained by the GPU-DEM model have been validated too. In addition, the effects of single-precision floating-point number computation on GPU were discussed. The results demonstrated that the GPU-DEM can achieve 18– 20 times acceleration while maintaining accuracy, even when single-precision floating-point numbers were used for calculation. Therefore, it is a powerful tool to explore the particle flows in chemical engineering applications. [Display omitted] • A GPU-based DEM model and scheme for pebble flows in the packed bed are developed. • Uniform mesh search, unified memory addressing (UMA), and BCs were incorporated. • Applicability, computational efficiency, and accuracy of the GPU-DEM were analyzed. • Unloading speed, porosity, and velocity distribution were obtained for validation too. • With equivalent accuracy, GPU-DEM can achieve 18– 20 times as fast as the CPU-DEM. [ABSTRACT FROM AUTHOR]

• Neural tangent kernel is used to construct the similarity matrices. • Correntropy-loss function is introduced into matrix factorization. • An efficient iterative algorithm is employed to optimize the model. Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance. [ABSTRACT FROM AUTHOR]

Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches. [ABSTRACT FROM AUTHOR]

In online learning, the contribution of old samples to a model decreases as time passes, and old samples gradually become invalid. Although the Online Sequential Extreme Learning Machine (OS-ELM) can avoid the repetitive training of old samples, invalid samples are still used, which goes against improving the accuracy of an OS-ELM model. The Online Sequence Extreme Learning Machine with Forgetting Mechanism (FOS-ELM) timely discards invalid samples, but it does not consider the differences among valid samples and then has the limitation on boosting the accuracy and generalization. To solve this issue, the Memory Degradation Based OS-ELM (MDOS-ELM) is proposed in this paper. The MDOS-ELM adjusts the weights of the old and new samples in real time by a self-adaptive memory factor, and simultaneously discards invalid samples. The self-adaptive memory factor is determined by two elements. One is the similarity between the new and old samples, and the other is the prediction errors of the current training samples on the previous model. The performance of the proposed MDOS-ELM is validated on both regression and classification datasets which include an artificial dataset and twenty-two real-world dataset. The results demonstrate that the MDOS-ELM model outperforms the OS-ELM and the FOS-ELM models on the accuracy and generalization. [ABSTRACT FROM AUTHOR]

Background: Tobacco use is a major concern to the Military Health System of the Department of Defense (DoD). The 2011 DoD Health Related Behavior Survey reported that 24.5% of active duty personnel are current smokers, which is higher than the national estimate of 20.6% for the civilian population. Overall, it is estimated that tobacco use costs the DoD $1.6 billion a year through related medical care, increased hospitalization, and lost days of work, among others.Methods: This study evaluated future health outcomes of Tricare Prime beneficiaries aged 18-64 yr (N = 3.2 million, including active duty and retired military members and their dependents) and the potential economic impact of initiatives that DoD may take to further its effort to transform the military into a tobacco-free environment. Our analysis simulated the future smoking status, risk of developing 25 smoking-related diseases, and associated medical costs for each individual using a Markov Chain Monte Carlo microsimulation model. Data sources included Tricare administrative data, national data such as Centers for Disease Control and Prevention mortality data and National Cancer Institute's cancer registry data, as well as relative risks of diseases obtained from a literature review.Findings: We found that the prevalence of active smoking among the Tricare Prime population will decrease from about 24% in 2015 to 18% in 2020 under a status quo scenario. However, if a comprehensive tobacco control initiative that includes a 5% price increase, a tighter clean air policy, and an intensified media campaign were to be implemented between 2016 and 2020, the prevalence of smoking could further decrease to 16%. The near 2 percentage points reduction in smoking prevalence represents an additional 81,240 quitters and translates to a total lifetime medical cost savings (in 2016 present value) of $968 million, with 39% ($382 million) attributable to Tricare savings.Discussion: A comprehensive tobacco control policy within the DoD could significantly decrease the prevalence and lifetime medical cost of tobacco use. If the smoking prevalence among Prime beneficiaries could reach the Healthy People 2020 goal of 12%, through additional measures, the lifetime savings could mount to $2.08 billion. To achieve future savings, DoD needs to pay close attention to program design and implementation issues of any additional tobacco control initiatives. [ABSTRACT FROM AUTHOR]

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition. Author summary: Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios. [ABSTRACT FROM AUTHOR]