With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

SUMMARY: The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis. AVAILABILITY AND IMPLEMENTATION: Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.

Trials involving genomic-driven treatment selection require the coordination of many teams interacting with a great variety of information. The need of better informatics support to manage this complex set of operations motivated the creation of OpenGeneMed. OpenGeneMed is a stand-alone and customizable version of GeneMed (Zhao et al. GeneMed: an informatics hub for the coordination of next-generation sequencing studies that support precision oncology clinical trials. Cancer Inform 2015;14(Suppl 2):45), a web-based interface developed for the National Cancer Institute Molecular Profiling-based Assignment of Cancer Therapy (NCI-MPACT) clinical trial coordinated by the NIH. OpenGeneMed streamlines clinical trial management and it can be used by clinicians, lab personnel, statisticians and researchers as a communication hub. It automates the annotation of genomic variants identified by sequencing tumor DNA, classifies the actionable mutations according to customizable rules and facilitates quality control in reviewing variants. The system generates summarized reports with detected genomic alterations that a treatment review team can use for treatment assignment. OpenGeneMed allows collaboration to happen seamlessly along the clinical pipeline; it helps reduce errors made transferring data between groups and facilitates clear documentation along the pipeline. OpenGeneMed is distributed as a stand-alone virtual machine, ready for deployment and use from a web browser; its code is customizable to address specific needs of different clinical trials and research teams. Examples on how to change the code are provided in the technical documentation distributed with the virtual machine. In summary, OpenGeneMed offers an initial set of features inspired by our experience with GeneMed, a system that has been proven to be efficient and successful for coordinating the application of next-generation sequencing in the NCI-MPACT trial.

SUMMARY: Non-negative matrix factorization (NMF) is an increasingly used algorithm for the analysis of complex high-dimensional data. BRB-ArrayTools is a widely used software system for the analysis of gene expression data with almost 9000 registered users in over 65 countries. We have developed a NMF analysis plug-in in BRB-ArrayTools for unsupervised sample clustering of microarray gene expression data. Our analysis tool also incorporates an algorithm for Semi-NMF which can handle both positive and negative elements for log-ratio data. Output includes a heat map of sample clusters and differentially expressed genes with extensive biological annotation. For comparison, output also includes the results of K-means clustering. AVAILABILITY: The NMF analysis plug-in is freely available in BRB-ArrayTools for non-commercial users. BRB-ArrayTools can be downloaded at http://linus.nci.nih.gov/BRB-ArrayTools.html. The algorithms used for NMF and Semi-NMF are available at ftp://linus.nci.nih.gov/pub/NMF.

SUMMARY: A Gene Set Expression Comparison kit is developed as a module of BRB-ArrayTools for discovering biologically meaningful patterns in gene expression data. The kit consists of gene sets of transcription factor (TF) targets, gene sets containing genes whose protein products share the same protein domain and gene sets of microRNA targets. Using this module of BRB-ArrayTools, researchers can efficiently analyze pre-defined sets of gene whose expression is correlated with a categorical quantitative phenotype or patient survival. AVAILABILITY: Gene Set Expression Comparison kit is freely available as a module of BRB-ArrayTools for non-commercial users. BRB-ArrayTools is available at http://linus.nci.nih.gov/BRB-ArrayTools.html.

In this paper, the long-term variability and response to climate change of runoff of main rivers in Gansu province are analyzed by meteorological and hydrological data. The results show that the fundamental features of runoff in seasonal change are non-uniform distribution of annual runoff, concentrated runoff in flood period, deficit runoff lower in spring and winter and drought at the end of spring and early summer. In the past 40 years, the annual runoff of Shulehe river in west section of Hexi area has obvious trend to increase, the annual runoff of Heihe river in middle section has slight trend to increase and the increase becomes salient in spring; The runoff of Shiyanghe river, Huanghe River and Jialingjiang river all tend to decline, and the southeastern Jialingjiang river, Jinghe river and Weihe river has the most distinct declining tendency. The variable features of runoff has close correlation to climate change.

BACKGROUND: Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples. RESULTS: We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data. CONCLUSIONS: The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.

MOTIVATION: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development. RESULTS: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. SUPPLEMENTARY INFORMATION: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf

Presents the World Wide Web-based tool TEST (T-cell Epitope Search Tool) for searching T-cell epitopes based on experimental data from positional scanning synthesized combinatorial libraries (PS-SCL). Implemented with a strong statistical analysis package, a relational database and Java applets, the TEST tool first generates a scoring matrix and then uses the matrix to scan and score all possible peptides in the user-specified protein database. Laboratory information management, such as data storage, data retrieval and data comparison, is also provided. The TEST tool has been applied to a variety of T-cell clones having known or unknown specificities with reliable predictions. The system should prove useful for the identification of target antigens in infectious diseases, autoimmune disorders and tumors, and also for the development of vaccines.