MOTIVATION: Identifying and tracking recombinant strains of SARS-CoV-2 is critical to understanding the evolution of the virus and controlling its spread. But confidently identifying SARS-CoV-2 recombinants from thousands of new genome sequences that are being shared online every day is quite challenging, causing many recombinants to be missed or suffer from weeks of delay in being formally identified while undergoing expert curation. RESULTS: We present RIVET-a software pipeline and visual platform that takes advantage of recent algorithmic advances in recombination inference to comprehensively and sensitively search for potential SARS-CoV-2 recombinants and organize the relevant information in a web interface that would help greatly accelerate the process of identifying and tracking recombinants. AVAILABILITY AND IMPLEMENTATION: RIVET-based web interface displaying the most updated analysis of potential SARS-CoV-2 recombinants is available at https://rivet.ucsd.edu/. RIVET's frontend and backend code is freely available under the MIT license at https://github.com/TurakhiaLab/rivet and the documentation for RIVET is available at https://turakhialab.github.io/rivet/. The inputs necessary for running RIVET's backend workflow for SARS-CoV-2 are available through a public database maintained and updated daily by UCSC (https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/).

A comprehensive data quality assessment is necessary to expand a nursing database that is designed for evaluating the impact of implementing Best Practice Guidelines (BPG) developed by the Registered Nurses' Association of Ontario (RNAO). This case report presents a method to standardize data quality assessments of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database by developing a data quality framework (DQF) and assessing key dimensions of the framework using a data quality index (DQI). The data quality index is a single key performance metric for assessing the quality of the database. The aims of sharing this case report are 2-fold: (1) to promote best practices for assessing data quality by developing and implementing a data quality framework and (2) to demonstrate an unprecedented method of assessing the data quality of a nursing database.

In this paper, we present several novel identifying characteristics of spam-sending bots (or spambots) based on traffic statistics. We use the entropy to measure the distribution skewness for a number of traffic features including packet inter-departure time, email per recipients, rate of change in recipient list and destination domains, and inconsistency in email header information of the outgoing email traffic. We also show how we can measure the deviation in these features from benign emails traffic to decisively detect spambots. Our tool is developed to sit anonymously behind the mail server in a network, capturing SMTP data packets and analyzing the traffic while keeping all of the personal email data private and unrecoverable. Unlike content filtering, our technique is hard to evade and used to detect spam email close to the source. In addition, our technique uses online light weight calculations and can be efficiently deployed in the end-user or ISP devices as well. We evaluated our technique using about 6 million email records of real spambot traffic collected during June 2007 - June 2008. Our evaluation results show that our tool can detect spambots accurately and efficiently even with high traffic volume.

We propose a new framework for doing scientific visualization. The basis for this framework is a combination of particle systems and behavioral animation. Here, particles are not only affected by the field that they are in, but can also exhibit different programmed behaviors. An intuitive delivery system, based on virtual cans of spray paint, is also described to introduce the smart particles into the data set. Hence the name spray rendering. Using this metaphor, different types of spray paint are used to highlight different features in the data set. Spray rendering offers several advantages over existing methods: (1) it generalizes the current techniques of surface, volume and flow visualization under one coherent framework; (2) it works with regular and irregular grids as well as sparse and dense data sets; (3) it allows selective progressive refinement; (4) it is modular, extensible and provides scientists with the flexibility for exploring relationships in their data sets in natural and artistic ways.>