Accurate short-term traffic flow prediction plays an important role in the field of modern Intelligent Transportation Systems. Since various uncontrollable factors ( e.g. weather, traffic jams or ...accidents), collected traffic data inevitably contain outliers. This makes it challenge to achieve satisfactory results for traffic flow prediction. Least Squares Twin Support Vector Regression (LSTSVR) has been shown to provide a powerful potential in nonlinear prediction problems. This is especially true when using appropriate heuristic algorithms to determine the parameters of nonlinear LSTSVR. In view of this, a novel LSTSVR model based on the robust <inline-formula> <tex-math notation="LaTeX">\text{L}_{2,\mathrm {p}} </tex-math></inline-formula>-norm (<inline-formula> <tex-math notation="LaTeX">0< p\le 2 </tex-math></inline-formula>) distance is proposed to alleviate the negative effect of traffic data with outliers, called PLSTSVR. An iterative algorithm is designed to solve the optimization problem of PLSTSVR, which has great potential for solving other relevant optimization problems. To search the parameters of constructed PLSTSVR, this paper constructs two traffic flow prediction models based on PLSTSVR and heuristic algorithms (Fruit Fly Optimization Algorithm and Particle Swarm Optimization), called PLSTSVR-FOA and PLSTSVR-PSO. Extensive experiments demonstrate that the constructed models are more effective and robust than other competing models in various experimental settings.
Notch receptors (Notch1-4) play critical roles in tumorigenesis and metastasis of malignant tumors, including breast cancer. Although abnormal Notch activation is related to various tumors, the ...importance of single receptors and their mechanism of activation in distinct breast cancer subtypes are still unclear. Previous studies by our group demonstrated that Notch3 may inhibit the emergence and progression of breast cancer. PTEN is a potent tumor suppressor, and its loss of function is sufficient to promote the occurrence and progression of tumors. Intriguingly, numerous studies have revealed that Notch1 is involved in the regulation of PTEN through its binding to CBF-1, a Notch transcription factor, and the PTEN promoter. In this study, we found that Notch3 and PTEN levels correlated with the luminal phenotype in breast cancer cell lines. Furthermore, we demonstrated that Notch3 transactivated PTEN by binding CSL-binding elements in the PTEN promoter and, at least in part, inhibiting the PTEN downstream AKT-mTOR pathway. Notably, Notch3 knockdown downregulated PTEN and promoted cell proliferation and tumorigenesis. In contrast, overexpression of the Notch3 intracellular domain upregulated PTEN and inhibited cell proliferation and tumorigenesis in vitro and in vivo. Moreover, inhibition or overexpression of PTEN partially reversed the promotion or inhibition of cell proliferation induced by Notch3 alterations. In general, Notch3 expression positively correlated with elevated expression of PTEN, ER, lower Ki-67 index, and incidence of involved node status and predicted better recurrence-free survival in breast cancer patients. Therefore, our findings demonstrate that Notch3 inhibits breast cancer proliferation and suppresses tumorigenesis by transactivating PTEN expression.
Polycomp group (PcG) proteins are members of highly conserved multiprotein complexes, recognized as gene transcriptional repressors during development and shown to play a role in various ...physiological and pathological processes. PcG proteins consist of two Polycomb repressive complexes (PRCs) with different enzymatic activities: Polycomb repressive complexes 1 (PRC1), a ubiquitin ligase, and Polycomb repressive complexes 2 (PRC2), a histone methyltransferase. Traditionally, PRCs have been described to be associated with transcriptional repression of homeotic genes, as well as gene transcription activating effects. Particularly in cancer, PRCs have been found to misregulate gene expression, not only depending on the function of the whole PRCs, but also through their separate subunits. In this review, we focused especially on the recent findings in the transcriptional regulation of PRCs, the oncogenic and tumor-suppressive roles of PcG proteins, and the research progress of inhibitors targeting PRCs.
Abstract
Motivation
RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation ...of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability.
Results
In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies.
Availability and implementation
The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.
For nonsymmetric saddle point problems arising from the steady Navier-Stokes equations, Pan, Ng and Bai presented a deteriorated positive-definite and skew-Hermitian splitting (DPSS) preconditioner ...(Pan et al., 2006) to accelerate the convergence rates of the Krylov subspace iteration methods such as GMRES. In this paper, the unconditional convergence property of the DPSS iteration method is proved and a relaxed DPSS (RDPSS) preconditioner is proposed. The RDPSS preconditioner is much closer to the coefficient matrix than the DPSS preconditioner in certain norm, which straightforwardly results in an RDPSS iteration method. The convergence conditions of the RDPSS iteration are analyzed and the optimal parameter, which minimizes the spectral radius of the RDPSS iteration matrix, is derived. Using the RDPSS preconditioner to accelerate some Krylov subspace methods (like GMRES) is also studied. The eigenproperty of the preconditioned matrix is described and an upper bound of the degree of the minimal polynomial of the preconditioned matrix is obtained. Finally, numerical experiments of a model Navier-Stokes equation are presented to illustrate the efficiency of the RDPSS preconditioner.
This paper introduces a new, two-part scheme for postprocessing single-valued precipitation forecast to create probabilistic quantitative precipitation forecast (PQPF). This scheme, herein referred ...to as the mixed-type nonhomogeneous regression (MNHR), combines the use of logistic regression for estimating rainfall intermittency and nonhomogeneous regression for estimation of additional parameters of the conditional distribution. The performance of MNHR is evaluated relative to operational mixed-type meta-Gaussian distribution (MMGD) and the censored, shifted gamma distribution (CSGD) in postprocessing Global Ensemble Forecast System (GEFS) reforecasts averaged over 25 watersheds in the American River basin in California. The results point to superior performance of MNHR relative to MMGD and CSGD in terms of the skill of postprocessed PQPFs at 24- and 96-h accumulation windows. In addition, it is observed that the performance of CSGD tends to trail behind MNHR and MMGD at least for the 24-h window, though the performance differences tend to narrow at higher forecast amounts and longer lead times. Our analyses suggest that CSGD’s underperformance arises partly from its tendency to inflate the shift parameter estimates, which is pronounced over the study site possibly because of infrequent rainfall occurrence. By contrast, MNHR’s use of logistic regression helps avoid such bias, and its formulation of conditional distribution addresses the lack of skewness of MMGD for higher forecast amounts. Moreover, MHNR-based PQPF exhibits both superior calibration and relatively high sharpness at short lead times and on an unconditional sense, whereas it features lower sharpness relative to the other two suites when conditioned on higher forecast amount. This trade-off between calibration and conditional sharpness warrants further research.
Protein fold recognition is a critical step in protein structure and function prediction, and aims to ascertain the most likely fold type of the query protein. As a typical pattern recognition ...problem, designing a powerful feature extractor and metric function to extract relevant and representative fold-specific features from protein sequences is the key to improving protein fold recognition. In this study, we propose an effective sequence-based approach, called RattnetFold, to identify protein fold types. The basic concept of RattnetFold is to employ a stack convolutional neural network with the attention mechanism that acts as a feature extractor to extract fold-specific features from protein residue-residue contact maps. Moreover, based on the fold-specific features, we leverage metric learning to project fold-specific features into a subspace where similar proteins are closer together and name this approach RattnetFoldPro. Benchmarking experiments illustrate that RattnetFold and RattnetFoldPro enable the convolutional neural networks to efficiently learn the underlying subtle patterns in residue-residue contact maps, thereby improving the performance of protein fold recognition. An online web server of RattnetFold and the benchmark datasets are freely available at http://csbio.njust.edu.cn/bioinf/rattnetfold/.
Display omitted
•A feature extractor termed RattnetFold-FE is proposed, which employs the attention mechanism to capture the hidden patterns in the protein residue-residue contact map.•Inspired by metric learning, a post-processing strategy on the features extracted by RattnetFold is proposed, which can be used to generate more discriminative fold-attention feature and facilitate protein fold recognition.
Currently, many defects have appeared in wind and solar power generation systems. Utilizing the complementary of wind and solar power generation will break the bottleneck of new energy development. ...How to select the site of wind/solar hybrid power station scientifically is a primary problem. First, the framework of the indicator system is obtained from the literature review and refined according to the resources evaluation standards and different perspectives of the various scholars. Then this paper selects seven wind/solar hybrid power stations which have been put into operation as the case study, weights the indicators through the method of order relations, and evaluates these seven regions via ELECTRE-II. The conclusions are consistent with the related research findings and have better correctness, proving the feasibility and effectiveness of the method. It may provide some theoretical basis for the macro-site selection of wind/solar hybrid power station.
As intracellular parasites, viruses depend heavily on host cell structures and their functions to complete their life cycle and produce new viral particles. Viruses utilize or modulate cellular ...translational machinery to achieve efficient replication; the role of ribosome biogenesis and protein synthesis in viral replication particularly highlights the importance of the ribosome quantity and/or quality in controlling viral protein synthesis. Recently reported studies have demonstrated that ribosome biogenesis factors (RBFs) and ribosomal proteins (RPs) act as multifaceted regulators in selective translation of viral transcripts. Here we summarize the recent literature on RBFs and RPs and their association with subcellular redistribution, post-translational modification, enzyme catalysis, and direct interaction with viral proteins. The advances described in this literature establish a rationale for targeting ribosome production and function in the design of the next generation of antiviral agents.
DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, ...several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.
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