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Cotton production makes an important contribution to the income of rural residents and the economy in Xinjiang province, which leads other provinces in terms of planted area, total production, and average yield of cotton in China. This study analyzed the competitiveness of cotton production in the study area using the efficiency advantage index (EAI), scale advantage index (SAI), and aggregated advantage index (AAI). Moreover, the factors influencing the productivity of cotton have been investigated by the use of ridge regression and correlation matrix using a dataset for the period 2005 to 2018. The results showed that cotton production had a large comparative advantage in Xinjiang from 2005 to 2018. The average of efficiency advantage index (EAI), scale advantage index (SAI), and aggregated advantage index (AAI) are 1.50, 12.96, and 4.35, respectively. Overall, Xinjiang cotton production has a higher planting scale advantage and productivity. By using ridge regression to calculate the impact of cotton production on agricultural output value in Xinjiang, the results showed that total cotton production, fiscal expenditure on agricultural support, total agricultural machinery power, and fertilizer use had significant positive effects, whereas cotton sown area, average cotton yield, and the proportion of affected area by insects and diseases had negative impact agricultural output value. The study implies the need for a implementing a well-thought and empirically backed plan to support cotton production based on comparative advantage for a specific area, building a cotton production standard system, reducing the cost of cotton production, and building a cotton risk-protection system to protect the interests of cotton farmers and promote the sustainable development of the cotton industry. [ABSTRACT FROM AUTHOR]

ZIF‐8 was used as a precursor and was converted to ZnO by calcining at high temperature to remove the organic functional groups therein. Then ZnO@SnO2 composites were synthesized by hydrothermal method using ZnO as the carrier. All samples were characterized by XRD, SEM, EIS, Transient photocurrent test and Mott‐schottky test. The photodegradation effects of pristine ZnO, SnO2 and ZnO@SnO2 composites on tetracycline (TC) were evaluated and the best results were obtained (ZnO@SnO2=2 : 1), which could remove 91.2 % of TC under visible light irradiation. The effects of pH, ionic strength, and TC concentration were researched in batch experiments. ZnO@SnO2=2 : 1 showed favorable photocatalytic activity and stability, including pH and resistance to ionic strength. The enhanced photocatalytic activity of the ZnO@SnO2=2 : 1 composite could be ascribed to the lower recombination probability of electron‐hole pairs in the heterojunction and the enlarged surface area for TC adsorption during photodegradation. This study provided a reference for heterojunctions with excellent photocatalytic properties. [ABSTRACT FROM AUTHOR]

Simple Summary: Major histocompatibility complex molecules are of significant biological and clinical importance due to their utility in immunotherapy. The prediction of potential MHC binding peptides can estimate a T-cell immune response. The variable length of existing MHC binding peptides creates difficulty for MHC binding prediction algorithms. Thus, we utilized a bilateral and variable long-short term memory neural network to address this specific problem and developed a novel MHC binding prediction tool. As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use. [ABSTRACT FROM AUTHOR]

Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug–target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets. [ABSTRACT FROM AUTHOR]

Lots of biological processes are controlled by gene regulatory networks (GRNs), such as growth and differentiation of cells, occurrence and development of the diseases. Therefore, it is important to persistently concentrate on the research of GRN. The determination of the gene–gene relationships from gene expression data is a complex issue. Since it is difficult to efficiently obtain the regularity behind the gene-gene relationship by only relying on biochemical experimental methods, thus various computational methods have been used to construct GRNs, and some achievements have been made. In this paper, we propose a novel method MMFGRN (for "Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction") to reconstruct the GRN. In order to make full use of the limited datasets and explore the potential regulatory relationships contained in different data types, we construct the MMFGRN model from three perspectives: single time series data model, single steady-data model and time series and steady-data joint model. And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life. [ABSTRACT FROM AUTHOR]

Diseases are usually caused by body's own defects protein or the functional structure of viral proteins. Effective drugs can be combined with these proteins well and remove original functions to achieve the therapeutic effect. The biochemical approaches of drug-target interactions (DTIs) determination is expensive and time-consuming. Therefnal-based methods have been proposed to predict new DTIs. In order to solve the problem of multiple information fusion, we propose a multi-view graph regularized link propagation model (MvGRLP) to predict new DTIs. Multi-view learning could use the complementary and correlated information between different views (features). Compared with existing models, our method achieves comparable and best results on four benchmark datasets. [ABSTRACT FROM AUTHOR]

Relationship of accurate associations between non-coding RNAs and diseases could be of great help in the treatment of human biomedical research. However, the traditional technology is only applied on one type of non-coding RNA or a specific disease, and the experimental method is time-consuming and expensive. More computational tools have been proposed to detect new associations based on known ncRNA and disease information. Due to the ncRNAs (circRNAs, miRNAs and lncRNAs) having a close relationship with the progression of various human diseases, it is critical for developing effective computational predictors for ncRNA–disease association prediction. In this paper, we propose a new computational method of three-matrix factorization with hypergraph regularization terms (HGRTMF) based on central kernel alignment (CKA), for identifying general ncRNA–disease associations. In the process of constructing the similarity matrix, various types of similarity matrices are applicable to circRNAs, miRNAs and lncRNAs. Our method achieves excellent performance on five datasets, involving three types of ncRNAs. In the test, we obtain best area under the curve scores of |$0.9832$|⁠ , |$0.9775$|⁠ , |$0.9023$|⁠ , |$0.8809$| and |$0.9185$| via 5-fold cross-validation and |$0.9832$|⁠ , |$0.9836$|⁠ , |$0.9198$|⁠ , |$0.9459$| and |$0.9275$| via leave-one-out cross-validation on five datasets. Furthermore, our novel method (CKA-HGRTMF) is also able to discover new associations between ncRNAs and diseases accurately. Availability: Codes and data are available: https://github.com/hzwh6910/ncRNA2Disease.git. Contact: fguo@tju.edu.cn [ABSTRACT FROM AUTHOR]

With the emergence of large-scale experimental data on genes and proteins, drug discovery and repositioning will be more difficult in the field of biomedical research. More and more resources are needed for detecting drug–target interactions (DTIs) in the experimental works. The interactions between drugs and targets could been seen as a bipartite network. Many computational methods have been developed to identify DTIs. However, most of them did not integrate multiple information and filter noise or outlier points. In this paper, we develop a fuzzy bipartite local model (FBLM) based on fuzzy least squares support vector machine and multiple kernel learning (MKL) for predicting DTIs. First, multiple kernels are constructed in drug and target spaces, respectively. Then, all corresponding kernels are combined by MKL algorithm in two spaces. Finally, FBLM is employed to identify DTIs. Our proposed approach is tested on four benchmark datasets under three types of cross validation. Comparing with existing outstanding methods, our method is a useful tool for the DTIs prediction. [ABSTRACT FROM AUTHOR]

Highlights • Multi-label protein subcellular localization prediction are focused on this paper. • PP matrix uses the physicochemical properties of proteins • PSSM matrix provides information about the evolution of proteins. • Multi-kernel SVM integrates multiple features to improve prediction accuracy. Abstract Identifying the location of proteins in a cell plays an important role in understanding their functions, such as drug design, therapeutic target discovery and biological research. However, the traditional subcellular localization experiments are time-consuming, laborious and small scale. With the development of next-generation sequencing technology, the number of proteins has grown exponentially, which lays the foundation of the computational method for identifying protein subcellular localization. Although many methods for predicting subcellular localization of proteins have been proposed, most of them are limited to single-location. In this paper, we propose a multi-kernel SVM to predict subcellular localization of both multi-location and single-location proteins. First, we make use of the evolutionary information extracted from position specific scoring matrix (PSSM) and physicochemical properties of proteins, by Chou's general PseAAC and other efficient functions. Then, we propose a multi-kernel support vector machine (SVM) model to identify multi-label protein subcellular localization. As a result, our method has a good performance on predicting subcellular localization of proteins. It achieves an average precision of 0.7065 and 0.6889 on two human datasets, respectively. All results are higher than those achieved by other existing methods. Therefore, we provide an efficient system via a novel perspective to study the protein subcellular localization. [ABSTRACT FROM AUTHOR]

Abstract In medicine research, drug discovery aims to develop a drug to patients who will benefit from it and try to avoid some side effects. However, the tradition experiment is time consuming and expensive. In recent years, computational approaches provide many effective strategies to deal with this issue. In fact, the known associations between drugs and side-effects are less than unknown associations, thus it can be seen as an imbalance classification problem. Although several classification methods have been developed to predict drug-side effect associations, the performance of predictors could also be further improved. In this paper, we propose a novel predictor of drug-side effect associations. First, we construct multiple kernels from drug space and side-effect space, respectively. Then, these corresponding kernels are linear weighted by optimized Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL) algorithm in two different spaces. At last, Kronecker Regularized Least Squares (Kronecker RLS) is employed to fuse drug kernel and side-effect kernel, further identify drug-side effect associations. Compared with many existing methods, our proposed approach achieves better results on three benchmark datasets of drug side-effect associations. The values of Area Under the Precision Recall curve (AUPR) are 0.672, 0.679 and 0.675 on Pauwels's dataset, Mizutani's dataset and Liu's dataset, respectively. The AUPRs are improved by at least 0.012, 0.013 and 0.014 on three different datasets. Experimental results show that our method has outstanding performance among other excellent approaches on identifying drug-side effect associations. [ABSTRACT FROM AUTHOR]

To develop a green and efficient method to synthesize graphene in relative milder conditions is prerequisite for graphene applications. A chemical reducing method has been developed to high efficiently reduce graphene oxide (GO) using Fe2O3 and NH3BH3 as catalyst and reductants, respectively. During the process, environmental and strong reductive nascent hydrogen were generated surrounding the surface of GO sheets by catalyst hydrolysis reaction of NH3BH3 and were used for reduction of GO. The reduction process was studied by ultraviolet absorption spectroscopy, Raman spectroscopy, and Fourier transform infrared spectrum. The structure and morphology of the reduced GO were characterized with scanning electron microscopy and transmission electron microscopy. Compared to metal (Mg/Fe/Zn/Al) particles and acid system which also use nascent hydrogen to reduce GO, this method exhibited higher reduction efficiency (43.6%). Also the reduction was carried out at room temperature condition, which is environmentally friendly. As a supercapacitor electrode, the reversible capacity of reduced graphene oxide was 113.8 F g-1 at 1 A g-1 and the capacitance retention still remained at 90% after 200 cycles. This approach provides a new method to reduce GO with high reduction efficiency by green reductant. [ABSTRACT FROM AUTHOR]

The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3 σ is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor gene. For each 14-3-3 isoform, we have 1000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3 σ isoform. First, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Then, a similarity-based undersampling approach and a SMOTE-like oversampling approach are used to deal with imbalanced distribution of the known peptide motifs. Finally, we consider locally weighted regression to predict affinity values of peptide motifs, which combines the simplicity of linear least squares regression with the flexibility of nonlinear regression. Our method tests on the 1000 peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3 σ isoform, our method has overall Pearson–product–moment correlation coefficient (PCC) and the root mean squared error (RMSE) values of 0.83 and 258.31 for N -terminal sublibrary, and 0.80 and 250.89 for C -terminal sublibrary, respectively. We identify phosphopeptides that preferentially bind to 14-3-3 σ over other isoforms. Several positions on peptide motifs have the same amino acid as experimental substrate specificity of phosphopeptides binding to 14-3-3 σ . Our method is a fast and reliable computational method that can be used in peptide–protein binding identification in proteomics research. [ABSTRACT FROM AUTHOR]

The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3σ is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor genes. For each 14-3-3 isoform, we have 1,000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3σ isoform. First, we propose a sampling criteria to build a predictor for each new peptide sequence. Then, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Finally, we consider elastic net to predict affinity values of peptide motifs, based on ridge regression and least absolute shrinkage and selection operator (LASSO). Our method tests on the 1,000 known peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3σ isoform, our method has overall pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) values of 0.84 and 252.31 for N–terminal sublibrary, and 0.77 and 269.13 for C–terminal sublibrary. We predict affinity values of 16,000 peptide sequences and relative binding ability across six permutated positions similar with experimental values. We identify phosphopeptides that preferentially bind to 14-3-3σ over other isoforms. Several positions on peptide motifs are in the same amino acid category with experimental substrate specificity of phosphopeptides binding to 14-3-3σ. Our method is fast and reliable and is a general computational method that can be used in peptide-protein binding identification in proteomics research. [ABSTRACT FROM AUTHOR]

In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction (⁠|$\sim\! 40$| s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, |$\alpha $| -helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. Contact guofei@csu.edu.cn , jj.tang@siat.ac.cn [ABSTRACT FROM AUTHOR]

Ricin is a toxic protein derived from castor beans and composed of a cytotoxic A chain and a galactose-binding B chain linked by a disulfide bond, which can inhibit protein synthesis and cause cell death. Owing to its high toxicity, ease of preparation, and lack of medical countermeasures, ricin has been listed as both chemical and biological warfare agents. For homeland security or public safety, the unambiguous, sensitive, and rapid methods for identification and quantification of ricin in complicated matrices are of urgent need. Mass spectrometric analysis, which provides specific and sensitive characterization of protein, can be applied to confirm and quantify ricin. Here, we report a liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) method in which ricin was extracted and enriched from serum by immunocapture using anti-ricin monoclonal antibody 3D74 linked to magnetic beads, then digested by trypsin, and analyzed by LC-ESI-MS/MS. Among 19 distinct peptides observed in LC-quadrupole/time of flight-MS (LC-QTOF-MS), two specific and sensitive peptides, T (VGLPINQR) and T (DNCLTSDSNIR), were chosen, and a highly sensitive determination of ricin was established in LC-triple quadrupole-MS (LC-QqQ-MS) operating in multiple reaction monitoring mode. These specific peptides can definitely distinguish ricin from the homologous protein Ricinus communis agglutinin (RCA120), even though the amino acid sequence homology of the A-chain of ricin and RCA120 is up to ca. 93 % and that of B-chain is ca. 85 %. Furthermore, peptide T was preferred in the quantification of ricin because its sensitivity was at least one order of magnitude higher than that of the peptide T. Combined with immunocapture enrichment, this method provided a limit of detection of ca. 2.5 ng/mL and the limit of quantification was ca. 5 ng/mL of ricin in serum, respectively. Both precision and accuracy of this method were determined and the RSD was less than 15 %. This established method was then applied to measure ricin in serum samples collected from rats exposed to ricin at the dosage of 50 μg/kg in an intravenous injection manner. The results showed that ca. 10 ng/mL of the residual ricin in poisoned rats serum could be detected even at 12 h after exposure. [ABSTRACT FROM AUTHOR]

In the study, ZIF-8@BIOI composites were synthesized by the hydrothermal method and then calcined to acquire the ZnO@Bi5O7I composite as a novel composite for the photocatalytic deterioration of the antibiotic tetracycline (TC). The prepared ZnO@Bi5O7I composites were physically and chemically characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer–Emmet–Teller (BET) surface area, UV–Vis diffuse reflectance spectroscopy (DRS), emission fluorescence spectra, transient photocurrent response, electrochemical impedance spectra and Mott–Schottky. Among the composites formed an n–n heterojunction, which increased the separation efficiency of electrons and holes and the efficiency of charge transfer. After the photocatalytic degradation test of TC, it showed that ZnO@Bi5O7I (2:1) had the best photodegradation effect with an 86.2% removal rate, which provides a new approach to the treatment of antibiotics such as TC in wastewater. [ABSTRACT FROM AUTHOR]

Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way. We design a new DCM based on the spectral method and also develop the COGNAC (Comparing Orders of Genes using Novel Algorithms and high-performance Computers) software package. COGNAC uses the new DCM to reduce the phylogenetic tree search space and selects an output tree from the reduced search space based on the MP principle. We test the new DCM using gene order data and inversion distance. The new DCM not only reduces the number of candidate tree topologies but also excludes erroneous tree topologies which can be selected by original MP methods. Initial labeling of internal genomes affects the accuracy of MP methods using gene order data, and the new DCM enables more accurate initial labeling as well. COGNAC demonstrates superior accuracy as a consequence. We compare COGNAC with FastME and the combination of the state of the art DCM (Rec-I-DCM3) and GRAPPA . COGNAC clearly outperforms FastME in accuracy. COGNAC -using the new DCM-also reconstructs a much more accurate tree in significantly shorter time than GRAPPA with Rec-I-DCM3. [ABSTRACT FROM AUTHOR]

Abstract: Bacterial intergenic spacers are non-coding genomic regions enriched with cis-regulatory elements for gene expression. A population genetics approach was used to investigate the evolutionary force shaping the genetic diversity of intergenic spacers among 13 genomes of group A streptococcus (GAS). Analysis of 590 genes and their linked 5′ intergenic spacers showed reduced nucleotide diversity in spacers compared to synonymous nucleotide diversity in protein-coding regions, suggestive of past purifying selection on spacers. Certain spacers showed elevated nucleotide diversity indicative of past homologous recombination with divergent genotypes. In addition, analysis of the difference between mean nucleotide difference and number of segregating sites showed evidence of an excess of rare variants both at nonsynonymous sites in genes and at sites in spacers, which is evidence that there are numerous slightly deleterious variants in GAS populations with potential effects on both protein sequences and gene expression. [Copyright &y& Elsevier]

A p-p heterostructure has been constructed through loading UiO-66 on Bi 2 O 3 as a composite photocatalyst to degrade the antibiotic tetracycline (TC) under visible light. The Bi 2 O 3 @UiO-66 composites were characterized by XRD, SEM, UV, BET, PL and FTIR. The formed p-p heterojunction improves the separation efficiency of electrons and holes, which caused the degradation rate constant of the composite photocatalyst for TC reaching 0.0492 min-1. The UiO-66 is added to improve the TC adsorption capacity and photocatalytic stability of the composite photocatalyst, so that the composite photocatalyst can have a photodegradation effect on TC under different anion and cation environments or a strong acid-base solution environment with the pH value of 1~13. The photoelectrochemical analyses is the evidence of the fast transfer of electron and hole pairs with inhibiting recombination. In this work, UiO-66 was loaded on Bi 2 O 3 as a new composite photocatalyst to degrade tetracycline under visible light. The composite material has excellent photocatalytic efficiency for tetracycline (k ​= ​0,0492min-1) and the optimal degradation concentration of TC solution is 10 mg ​L-1, accompanying with pH ​= ​7, containing Ca2+ or SO 4 2−. The large specific surface area and high stability of UiO-66 improve the photocatalytic stability of the composite material and reduce the recombination of electron holes. The formation of heterojunction effectively improves the separation efficiency of electrons and holes, and improves the efficiency of photocatalysis. [Display omitted] • Composited Bi 2 O 3 and UiO-66 form a p-p heterojunction, which reduces the band gap of electronic transitions and leads to more electron holes to be generated under visible light. • Compared with Bi 2 O 3 and Bi 2 O 3 @UiO-66 (0.5BU, 2BU)，the Bi 2 O 3 @UiO-66 (1BU) has excellent photocatalytic efficiency for tetracycline and the optimal degradation concentration of TC solution is 10 mg⋅L-1, accompanying with pH ​= ​7, containing Ca2+ or SO 4 2−. • UiO-66 loaded on Bi 2 O 3 not only enhance the BET specific surface area but also improves the efficiency of electron-hole separation. [ABSTRACT FROM AUTHOR]

The CSA A23.3 standard for reinforced concrete design provides both an implicit check of deflection control based on minimum member thickness requirements and a direct computation method for deflection. This paper reports on an analytical study that compared maximum span-to-depth ratios from the implicit deflection provisions against ratios determined from direct deflection calculations. Emphasis was placed on the deflection performance of lightly reinforced one-way slabs, including those with high-strength steel reinforcement. The results indicated that maximum span-to-depth ratios should decrease as the span length increases, as the design load increases or as the cracking moment decreases. In contrast to the current implicit provisions, the design strength of the longitudinal reinforcement did not have a significant effect on the minimum slab thickness required to satisfy common deflection criterion. Design aids were proposed, with implications for design presented through a case study of a multispan one-way slab system. Le code de pratique CSA A23.3 concernant le calcul des ouvrages en béton armé fournit une vérification implicite, basée sur le contrôle de la flexion, des exigences minimales de l’épaisseur des membrures et une méthode de calcul directe de la flexion. Le présent article aborde une étude analytique qui comparait les rapports maximaux de travée-profondeur provenant des provisions implicites de la déflexion contre les rapports déterminés par le calcul direct de la flexion. Le rendement en flexion des dalles unidirectionnelles légèrement armées, incluant celles comportant une armature en acier à haute résistance, a été principalement examiné. Les résultats indiquent que les rapports maximaux de travée-profondeur devraient diminuer lorsque la longueur de la travée augmente, la charge de calcul augmente ou lorsque le moment de fissuration diminue. Par rapport aux provisions implicites actuelles, la résistance de calcul des renforcements longitudinaux n’a pas d’effet important sur l’épaisseur minimale de dalle requise pour répondre adéquatement au critère de flexion commun. Des aides à la conception ont été proposées et des conséquences pour la conception sont présentées dans une étude de cas d’un système à travées multiples de dalles unidirectionnelles. [ABSTRACT FROM AUTHOR]

Abstract: Abrin toxin as the target protein, belongs to class II ribosome-inactivating proteins family, has high toxicity to eukaryotic cells. Here, we firstly report the DNA aptamers, isolated by in vitro selection, recognize abrin toxin with high affinity and specificity, and have the advantage of no cross-reaction with structure-similar protein ricin toxin over antibodies. Then, a highly selective and sensitive aptamer-based abrin assay was established using a molecular light switching reagent [Ru(phen)2(dppz)]2+ with a limit of detection of 1nM and a wide linear range from 1 to 400nM with the correlation coefficient of 0.993. This assay can be successfully directly performed not only in physiological buffer but also in more complicated biological matrix, such as diluted serum. [Copyright &y& Elsevier]

We report on our progress in reconstructing phylogenies from gene-order data. We have developed polynomial-time methods for estimating genomic distances that greatly improve the accuracy of trees obtained using the popular neighbor-joining method; we have also further improved the running time of our GRAPPA software suite through a combination of tighter bounding and better use of the bounds. We present new experimental results (that extend those we presented at ISMB’01 and WABI’01) that demonstrate the accuracy and robustness of our distance estimators under a wide range of model conditions. Moreover, using the best of our distance estimators (EDE) in our GRAPPA software suite, along with more sophisticated bounding techniques, produced spectacular improvements in the already huge speedup: whereas our earlier experiments showed a one-million-fold speedup (when run on a 512-processor cluster), our latest experiments demonstrate a speedup of one hundred million. The combination of these various advances enabled us to conduct new phylogenetic analyses of a subset of the Campanulaceae family, confirming various conjectures about the relationships among members of the subset and confirming that inversion can be viewed as the principal mechanism of evolution for their chloroplast genome. We give representative results of the extensive experimentation we conducted on both real and simulated datasets in order to validate and characterize our approaches. [Copyright &y& Elsevier]

In this work, the Bi 2 O 3 @BiOI was first compounded with UiO-66, and the Bi 2 O 3 @BiOI@UiO-66 ternary composite material was prepared by a simple one-pot solvothermal method. Bi 2 O 3 @BiOI@UiO-66 exhibits the higher RhB and tetracycline photocatalytic degradation under visible light irradiation, which can be attributed to the reducing the forbidden bandwidth of the electronic transition and preventing the combination of electrons and holes, the increased surface area of the composite for RhB and tetracycline adsorption accomplished with photodegradation process. • Composited BiOI and Bi 2 O 3 reduces the bandgap of the electronic transition.. • A Bi 2 O 3 @BiOI@UiO-66 material is obtained by adding Bi 2 O 3 and UiO-66 before the synthesis of BiOI. • Enhanced photocatalytic activity and stability has been observed compared to monomers and Bi 2 O 3 @BiOI composites. • Combining with UiO-66 enhances the specific surface area and reduces the combination of electrons and holes. Bi 2 O 3 is a photocatalyst with excellent performance; however, its applications are limited due to its wide bandgap. In this paper, by adding BiOI and the metal–organic framework UiO-66, a Bi 2 O 3 @BiOI@UiO-66 composite material is obtained with high adsorption capacity, in which the bandgap of Bi 2 O 3 is reduced, the recombination of photogenerated electrons and holes is prevented, the photocatalytic efficiency and stability are improved. In visible light degradation experiments, Bi 2 O 3 @BiOI@UiO-66 has obvious degradation effects on Rhodamine B and tetracycline, which are 22.2 and 1.04 times that of pure Bi 2 O 3 , respectively. Bi 2 O 3 @BiOI@UiO-66 demonstrats its potential as photocatalytic degradation material. [ABSTRACT FROM AUTHOR]

Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious experiment via biochemical approaches. Machine learning based methods have been widely used to mine meaningful information of drug research. In this study, we establish a novel computational method to predict DTIs via Dual Laplacian Regularized Least Squares model (DLapRLS) with Hilbert–Schmidt Independence Criterion-based Multiple Kernel Learning (HSIC-MKL). Multiple kernels are built from different information sources (drug and target spaces). Then, above corresponding kernels are integrated by HSIC-MKL. At last, DLapRLS model is trained by Alternating Least Squares Algorithm (ALSA) and employed to predict new DTIs. On four benchmark datasets, the results of our method are comparable and even better than existing models. • DLapRLS employs alternating least squares algorithm to solve the final model. • Heterogeneous information (kernels) is integrated via multiple kernel learning. • For HSIC-MKL, we employ the Laplacian regular term to smooth the weights. [ABSTRACT FROM AUTHOR]

Novel oil-based epoxy acrylate (EA)-like prepolymers were synthesized via the ring-opening reaction of epoxidized plant oils with a new unsaturated carboxyl acid precursor (MAAMA) synthesized by reacting maleic anhydride (MA) with methallyl alcohol (MAA). Since the employed epoxidized oils including epoxidized soybean oil (ESO), epoxidized rubber seed oil (ERSO), and epoxidized wilsoniana seed oil (EWSO) possessed epoxy values of 7.34–4.38%, the obtained epoxy acrylate (EA)-like prepolymers (MMESO, MMERSO, and MMEWSO) indicated a C=C functionality of 7.81–4.40 per triglyceride. Furthermore, effects of the C=C functionality and the addition of hydroxyethyl methacrylate (HEMA) diluent on the ultimate properties of the resulting UV-cured EA-like materials were investigated and compared with those of commercially available acrylated ESO (AESO) resins. As the C=C functionality increased, the storage modulus at 25 °C (E'25), glass transition temperature (Tg), 5% weight–loss temperature (T5), tensile strength and modulus (σ and E), and hardness of the coating for both the pure EA and EA/HEMA resins increased significantly as well. These properties indicated similar trends when comparing the EA materials with 30% of HEMA with those pure EA materials. Specially, although ERSO had a clearly lower epoxy value that ESO, both the UV-cured pure MMERSO and MMERSO/HEMA materials showed much better E'25, Tg, σ, and E than their AESO counterparts, indicating that the MAAMA modification of epoxidized plant oils was much more effective than the modification of acrylic acid to achieve high-performance oil-based epoxy acrylate resins. [ABSTRACT FROM AUTHOR]

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in some specific cell types. Importantly, DGRNS identifies a series of regulatory relationships with high confidence and functional consistency, which have not yet been experimentally confirmed and merit further research. [ABSTRACT FROM AUTHOR]

Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein–protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained |$19$| protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein–protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein–protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of |$6.144$| minutes, average Fnat of best pose of |$0.953$| and average rank of best pose of |$4.158$|⁠. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods. [ABSTRACT FROM AUTHOR]

Plant phenotypic characteristics, especially leaf morphology of leaves, are an important indicator for species identification. However, leaf shape can be extraordinarily complex in some species, such as oaks. The great variation in leaf morphology and difficulty of species identification in oaks have attracted the attention of scientists since Charles Darwin. Recent advances in discrimination technology have provided opportunities to understand leaf morphology variation in oaks. Here, we aimed to compare the accuracy and efficiency of species identification in two closely related deciduous oaks by geometric morphometric method (GMM) and deep learning using preliminary identification of simple sequence repeats (nSSRs) as a prior. A total of 538 Asian deciduous oak trees, 16 Q. aliena and 23 Q. dentata populations, were firstly assigned by nSSRs Bayesian clustering analysis to one of the two species or admixture and this grouping served as a priori identification of these trees. Then we analyzed the shapes of 2328 leaves from the 538 trees in terms of 13 characters (landmarks) by GMM. Finally, we trained and classified 2221 leaf‐scanned images with Xception architecture using deep learning. The two species can be identified by GMM and deep learning using genetic analysis as a priori. Deep learning is the most cost‐efficient method in terms of time‐consuming, while GMM can confirm the admixture individuals' leaf shape. These various methods provide high classification accuracy, highlight the application in plant classification research, and are ready to be applied to other morphology analysis. [ABSTRACT FROM AUTHOR]

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile–Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data. [ABSTRACT FROM AUTHOR]

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers. [ABSTRACT FROM AUTHOR]

Background: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases. [ABSTRACT FROM AUTHOR]

Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology research. Various DNA sequences should have a great variety of representations, and specific functions may focus on corresponding features in the front part of learning model. Currently, however, for multi-class prediction of non-coding DNA regulatory functions, most powerful predictive models do not have appropriate feature extraction and selection approaches for specific functional effects, so that it is difficult to gain a better insight into their internal correlations. Hence, we design a category attention layer and category dense layer in order to select efficient features and distinguish different DNA functions. In this study, we propose a hybrid deep neural network method, called DeepATT, for identifying |$919$| regulatory functions on nearly |$5$| million DNA sequences. Our model has four built-in neural network constructions: convolution layer captures regulatory motifs, recurrent layer captures a regulatory grammar, category attention layer selects corresponding valid features for different functions and category dense layer classifies predictive labels with selected features of regulatory functions. Importantly, we compare our novel method, DeepATT, with existing outstanding prediction tools, DeepSEA and DanQ. DeepATT performs significantly better than other existing tools for identifying DNA functions, at least increasing |$1.6\%$| area under precision recall. Furthermore, we can mine the important correlation among different DNA functions according to the category attention module. Moreover, our novel model can greatly reduce the number of parameters by the mechanism of attention and locally connected, on the basis of ensuring accuracy. [ABSTRACT FROM AUTHOR]

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis. [ABSTRACT FROM AUTHOR]

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact:   jtang@cse.sc.edu , guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn [ABSTRACT FROM AUTHOR]

Background: Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem. Results: In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision. Conclusion: To be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method. [ABSTRACT FROM AUTHOR]

Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/. [ABSTRACT FROM AUTHOR]

Membrane proteins perform a variety of functions vital to the survival of organisms, such as oxidoreductase, transferase or hydrolase. If the type of membrane protein can be detected, the function of protein can be quickly determined. Many existing computational methods not only use the autocorrelation function on the hydrophobicity index of amino acids, but also consider the evolutionary conservatism information of the primary protein sequences. In this study, we employ Average Blocks (AvBlock), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Histogram of Oriented Gradient (HOG) and Pseudo-PSSM (PsePSSM) to extract evolution characteristics from Position-Specific Score Matrix (PSSM). Then, we construct five kernels from above five corresponding feature sets. Finally, we propose a novel Multiple Kernel Support Vector Machine (MKSVM) classifier based on Hilbert Schmidt Independence Criterion (HSIC) to integrate five kernels for identifying membrane proteins. For the performance evaluation, our method is tested on four benchmark datasets of membrane proteins. The comparative results demonstrate that our prediction model achieves the best performance among all existing outstanding approaches. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8. [ABSTRACT FROM AUTHOR]

Motivation Somatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking. Results Using multi-omics data from over 30 000 subjects and 1747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics. Availability and implementation The data underlying this article are available in the online database CoMutDB: http://www.innovebioinfo.com/Database/CoMutDB/Home.php. [ABSTRACT FROM AUTHOR]

Determining drug–drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data. [ABSTRACT FROM AUTHOR]

A biobased diluent, cardanyl acrylate (CA), was synthesized from cardanol and used to modify a castor oil-based polyfunctional polyurethane acrylate (PUA) resin. Firstly, chemical structure of CA was characterized by FT-IR, 1 H NMR, and 13 C NMR. Subsequently, the effect of CA’s content on the biobased content, viscosity, and volumetric shrinkage of the obtained bioresins were studied and compared with the petroleum-based hydroxyethyl acrylate (HEA) diluent. Moreover, ultimate properties of the UV-cured biomaterials such as thermal, mechanical, coating, swelling, and hydrophobic properties were investigated. Finally, UV-curing kinetics of the resulting bioresins were determined by real-time IR. By the addition of CA, the biobased content of the resulting bioresins were improved and the viscosity and volumetric shrinkage were reduced. For example, the obtained bioresin containing 30% of CA possessed a biobased content of 62.8% and volumetric shrinkage of 9.51%, which were clearly better than those of the bioresin with 30% of HEA (50.8% and 21.94%), respectively. Furthermore, many other properties of the UV-cured biomaterials, such as thermal stability, coating’s hardness and adhesion, and hydrophobic properties, were improved by the incorporation of CA. The final C C conversions of the resulting bioresins were also enhanced by the addition of CA. Hence, the cardanol-based diluent showed good potential in the development of UV-curable coatings. [ABSTRACT FROM AUTHOR]

DNA methylation is an important biochemical process, and it has a close connection with many types of cancer. Research about DNA methylation can help us to understand the regulation mechanism and epigenetic reprogramming. Therefore, it becomes very important to recognize the methylation sites in the DNA sequence. In the past several decades, many computational methods—especially machine learning methods—have been developed since the high-throughout sequencing technology became widely used in research and industry. In order to accurately identify whether or not a nucleotide residue is methylated under the specific DNA sequence context, we propose a novel method that overcomes the shortcomings of previous methods for predicting methylation sites. We use k-gram, multivariate mutual information, discrete wavelet transform, and pseudo amino acid composition to extract features, and train a sparse Bayesian learning model to do DNA methylation prediction. Five criteria—area under the receiver operating characteristic curve (AUC), Matthew’s correlation coefficient (MCC), accuracy (ACC), sensitivity (SN), and specificity—are used to evaluate the prediction results of our method. On the benchmark dataset, we could reach 0.8632 on AUC, 0.8017 on ACC, 0.5558 on MCC, and 0.7268 on SN. Additionally, the best results on two scBS-seq profiled mouse embryonic stem cells datasets were 0.8896 and 0.9511 by AUC, respectively. When compared with other outstanding methods, our method surpassed them on the accuracy of prediction. The improvement of AUC by our method compared to other methods was at least 0.0399. For the convenience of other researchers, our code has been uploaded to a file hosting service, and can be downloaded from: https://figshare.com/s/0697b692d802861282d3. [ABSTRACT FROM AUTHOR]

Identification of associations between non-coding RNAs and diseases plays an important role in the study of pathogenesis, which has been a hot topic in recent research. However, traditional methods are time-consuming to detect the associations between non-coding RNAs and diseases. Recently, associations of non-coding RNAs and diseases can be regarded as bipartite network. In this paper, we propose a novel deep multiple kernel learning method, called the multi-layer multi-kernel deep neural network (MLMKDNN). First, many feature matrices are built by multiple features of non-coding RNAs and diseases. Then, these feature matrices are mapped into kernel space and fused by deep neural network. Finally, combine two fused output of MLMKDNN as the predicted values. Three types of non-coding RNAs (miRNA, circRNA and lncRNA) are used to test the performance of MLMKDNN. Compared with other existing methods, our proposed model has high Area Under Precision Recall (AUPR) value on three types of datasets. Experimental results confirm that our method is an effective predictive tool. It provides a framework that can also be applied to the link prediction of other bipartite networks. [ABSTRACT FROM AUTHOR]

BiFeO3 is a photocatalyst with excellent performance. However, its applications are limited due to its wide bandgap. In this paper, MIL-101(Fe)@BiOI composite material is synthesized by hydrothermal method and then calcined at high temperature to obtain BiFeO3@Bi5O7I composite material with high degradation capacity. Among them, an nâ€"n heterojunction is formed, which improves the efficiency of charge transfer, and the recombination of light-generated electrons and holes promotes improved photocatalytic efficiency and stability. The result of photocatalytic degradation of tetracycline under visible light irradiation showed, BiFeO3@Bi5O7I (1:2) has the best photodegradation effect, with a degradation rate of 86.4%, which proves its potential as a photocatalyst. [ABSTRACT FROM AUTHOR]

Retinal diseases can be found timely by observing retinal fundus images. So extracting blood vessels from retinal images is an important part because it is the way to show the changes of vessels. However, most of the previous methods based on deep learning cared more about accuracy and ignored the complexity of the model for segmenting retinal vessels, which makes these methods difficult to apply to medical equipment. Besides, due to the great differences in the width of retinal vessels, some methods cannot well-extract all blood vessels at the same time. Based on above limitations, we propose a new lightweight network, called Res2Unet. It applies a multi-scale strategy to extract blood vessels of different widths and integrates the strategy into the channels to greatly reduce parameters and computation resources. Res2Unet also uses channel-attention mechanism to promote the communication between channels and recalibrate the relationship of channel features. Then, we propose two post-processing methods. One called the local threshold method(LTM) uses a lower local threshold to excavate hidden blood vessels in discontinuous blood vessels of the probability maps. The other named weighted correction method (WCM) combines the probability maps of Unet and Res2Unet to remove false positive and false negative samples. On the DRIVE dataset, the Dice, IOU and AUC of our Res2Unet reach 0.8186, 0.6926 and 0.9772, respectively, which are better than that of Unet with 0.8109, 0.6817 and 0.9751. Importantly, the number of parameters of Res2Unet are about one-third of Unet. It means that Res2Unet has less hardware requirements. [ABSTRACT FROM AUTHOR]

Complex and diverse microbial communities have certain impacts on human health, and specific drugs are needed to treat diseases caused by microbes. However, most of the discovery of associations between microbes and drugs is through biological experiments, which are time-consuming and expensive. Therefore, it is crucial to develop an effective and computational model to detect novel microbe–drug associations. In this study, we propose a model based on Multiple Kernel fusion on Graph Convolutional Network, called MKGCN, for inferring novel microbe–drug associations. Our model is built on the heterogeneous network of microbes and drugs to extract multi-layer features, through Graph Convolutional Network (GCN). Then, we respectively calculate the kernel matrix by embedding features on each layer, and fuse multiple kernel matrices based on the average weighting method. Finally, Dual Laplacian Regularized Least Squares is used to infer new microbe–drug associations by the combined kernel in microbe and drug spaces. Compared with the existing tools for detecting biological bipartite networks, our model has excellent prediction effect on three datasets via three types of cross-validation. Furthermore, we also conduct a case study of the SARS-Cov-2 virus and make a deduction about drugs that may be able to associate with COVID-19. We have proved the accuracy of the prediction results through the existing literature. [Display omitted] • Our model applies the Graph Convolutional Network into Multiple Kernel fusion. • We apply multi-layer GCN to extract different structural information in the graph. • Our method has excellent performance on three microbe–drug association datasets. • We discover some potential drugs being associated with COVID-19. [ABSTRACT FROM AUTHOR]