Visual DSD: a design and analysis tool for DNA strand displacement systems

The PyMod project is designed to act as a fully integrated interface between the popular molecular graphics viewer PyMOL, and some of the most frequently used tools for structural bioinformatics, e.g. BLAST, HMMER, Clustal, MUSCLE, PSIPRED, DOPE and MODELLER. Here we report its latest release, PyMod 3, which has been completely renewed with a graphical interface written in PyQt, to make it compatible with the most recent PyMOL versions, and has been extended with a large set of new functionalities compared to its predecessor, i.e. PyMod 2. Starting from the amino acid sequence of a target protein, users can take advantage of PyMod 3 to carry out all the steps of the homology modeling process (i.e. template searching, target-template sequence alignment, model building and quality assessment). Additionally, the integrated tools in PyMod 3 may also be used alone, in order to extend PyMOL with a wide range of capabilities. Sequence similarity searches, multiple sequence/structure alignment building, phylogenetic trees and evolutionary conservation analyses, domain parsing, single/multiple chains and loop modeling can be performed in the PyMod 3/PyMOL environment. A cross-platform PyMod 3 installer package for Windows, Linux and Mac OS X and a complete user guide with tutorials, are available at https://github.com/pymodproject/pymod. Supplementary data are available at Bioinformatics online.

ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics to allow comparison of current runs to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds and historical observations within a specified time period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping. ChronQC facilitates regular monitoring of clinical NGS to enable adherence to high quality clinical standards. ChronQC is freely available on GitHub (https://github.com/nilesh-tawari/ChronQC), Docker (https://hub.docker.com/r/nileshtawari/chronqc/) and the Python Package Index. ChronQC is implemented in Python and runs on all common operating systems (Windows, Linux and Mac OS X). tawari.nilesh@gmail.com or pauline.c.ng@gmail.com. Supplementary data are available at Bioinformatics online.

The Educational ICT (Information and Communication Technology) environment support group of Information Infrastructure Initiative has been managing and operating the educational ICT environment in Kyushu University. We had a PC system that consisted of 598 Windows PCs, printers, and servers. The number of terminals was not enough for 18,000 students. We introduced a new system of 712 iMacs and servers without printers this March 2009. The reasons are as follows: 1) to provide students and teachers with a better educational ICT environment, 2) to increase the number of terminals for students, 3) to decrease the undesirable cost for print service, and 4) to make it possible for users to reset their login password by themselves for their convenience.

In present times, it is not uncommon to have a desktop with two or more monitors. How these operating systems perform in a multiple monitor environment is an interesting topic. In this work, we will evaluate the efficiency of and compare how two popular operating systems, Windows and Mac OS, perform in large and multiple monitor environments. In particular, we will evaluate the performance of menu bars in both operating systems as they serve a near identical purpose and have the same functionality while providing their offerings differently. It is well-known that Mac OS uses a menu bar at the top of the screen (global) and Windows uses a menu bar attached to the top of its respective application (local). The conducted user study shows that the overall performance of Windows menu bar was better than that of the Mac menu bar implementation in the conducted tests.

523 Background: To continue quality timely patient care telemedicine usage increased during the COVID-19 pandemic. A systematic review of the literature supports the use of telemedicine beyond the pandemic. The goal of this quality improvement (QI) project sought to identify barriers and promoters to sustain telemedicine visits for adult oncology/hematology patients seen in the Virtual Advanced Practice Provider (VAPP) clinic. A Telemedicine Toolkit (TT) was developed for providers to enhance and improve the provider/patient experience. Methods: Retrospectivedata retrieved from EMR/VAPP included details from 1,985 patient visits for March-August 2022 to investigate how to optimize the specialty care visit. Over a 10-day period, staff completed pre/posttest surveys to assess engagement and satisfaction with TT. Results: The data points from the statewide EMR and VSee platforms showed the patient median age was 62; age range seen 19-95. More females were seen at 65% (1290) than males at 35% (695); predominant race/ethnicity was white at 47.5% (943). The main diagnosis seen was breast at 28.86% (573), then lung at 7% (127), followed by colon at 6.90% (137). Visit type from high to low: the teaching visit at 44.5% (884), standard follow-up visit (toxicity checks and hospital follow-ups) at 32.9% (645), new genetics visits at 11.5% (228) and follow-up genetic visits at 7% (138), Advance care planning visits (ACP) at 3% (58), urgent care follow-up visit at 1% (24) and the high-risk/preventive care at 0.4% (8). Nearly 250 locations were served predominantly in central Texas. Patient devices used included iPhone/IOS at 48%, Windows OS at 27.5%, Android at 20.5%, and Mac OS at 4%. Conclusions: The VAPP clinic with a robust infrastructure, is now serving the entire state of Texas. The staff noted the TT to be intuitive and helpful. Sustainability of the TT through updates and continued staff education is essential. This project did not find a significant problem with Wi-Fi services and the most common reason for an aborted/canceled visit was the patient did not remember their Apple ID/Google password to be able to download the VSee application. Patient education prior to visits regarding Vsee needs would potentially reduce canceled/aborted visits.

Summary: Protein–protein interaction detection methods are applied on a daily basis by molecular biologists worldwide. After generating a set of potential interactions, biologists face the problem of highlighting the ones that are novel and collecting evidence with respect to literature and annotation. This task can be as tedious as searching for every predicted interaction in several interaction data repositories, or manually screening the scientific literature. To facilitate the task of evidence mining and novelty assessment of protein–protein interactions, we have developed a Cytoscape plugin that automatically mines publication references, database references, interaction detection method descriptions and pathway annotation for a user-supplied network of interactions. The basis for the annotation is ConsensusPathDB—a meta-database that integrates numerous protein–protein, signaling, metabolic and gene regulatory interaction repositories for currently three species: Homo sapiens, Saccharomyces cerevisiae and Mus musculus.Availability: The ConsensusPathDB plugin for Cytoscape (version 2.7.0 or later) can be installed within Cytoscape on a major operating system (Windows, Mac OS, Unix/Linux) with Sun Java 1.5 or later installed through Cytoscape's Plugin manager (category ‘Network and Attribute I/O’). The plugin is freely available for download on the ConsensusPathDB web site (http://cpdb.molgen.mpg.de).Supplementary information: Supplementary data are available at Bioinformatics online.Contact: kamburov@molgen.mpg.de

Comparing transcriptomic data with proteomic data to identify protein-coding sequences is a long-standing challenge in molecular biology, one that is exacerbated by the increasing size of high-throughput datasets. To address this challenge, and thereby to improve the quality of genome annotation and understanding of genome biology, we have developed an integrated suite of programs, called Pinstripe. We demonstrate its application, utility and discovery power using transcriptomic and proteomic data from publicly available datasets. To demonstrate the efficacy of Pinstripe for large-scale analysis, we applied Pinstripe's reverse peptide mapping pipeline to a transcript library including de novo assembled transcriptomes from the human Illumina Body Atlas (IBA2) and GENCODE v10 gene annotations, and the EBI Proteomics Identifications Database (PRIDE) peptide database. This analysis identified 736 canonical open reading frames (ORFs) supported by three or more PRIDE peptide fragments that are positioned outside any known coding DNA sequence (CDS). Because of the unfiltered nature of the PRIDE database and high probability of false discovery, we further refined this list using independent evidence for translation, including the presence of a Kozak sequence or functional domains, synonymous/non-synonymous substitution ratios and ORF length. Using this integrative approach, we observed evidence of translation from a previously unknown let7e primary transcript, the archetypical lncRNA H19, and a homolog of RD3. Reciprocally, by exclusion of transcripts with mapped peptides or significant ORFs (>80 codon), we identify 32 187 loci with RNAs longer than 2000 nt that are unlikely to encode proteins. Pinstripe (pinstripe.matticklab.com) is freely available as source code or a Mono binary. Pinstripe is written in C# and runs under the Mono framework on Linux or Mac OS X, and both under Mono and .Net under Windows. m.dinger@garvan.org.au or j.mattick@garvan.org.au Supplementary data are available at Bioinformatics online.

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) has evolved as a popular technique to study DNA-protein binding or post-translational chromatin/histone modifications at the genomic level. However, the raw microarray intensities generate a massive amount of data, creating a need for efficient analysis algorithms and statistical methods to identify enriched regions. We present a fast, free and powerful, open source R package, rMAT, that allows the identification of regions enriched for transcription factor binding sites in ChIP-chip experiments on Affymetrix tiling arrays. The R-package rMAT is available from the Bioconductor web site at http://bioconductor.org and runs on Linux, MAC OS and MS-Windows. rMAT is distributed under the terms of the Artistic Licence 2.0.

Web pages are constantly getting flooded with dis-tractive content like ads, popups, etc. which take away the focus from the main article of the page. Compared to desktops, smartphones—whose usage for browsing online has been on a steep upward trend—have a smaller screen size thereby compounding the problem. To give the user a good browsing experience on phone and hence increase their engagement with the browser, it is imperative to provide a clutter-free experience to him. Existing techniques do this in two phases—phase one detects if there is an article on a given Web page and phase two proceeds to extract the article in order to display it, removing unwanted elements of the Web page. In this paper, we propose to use a random forest model for article detection in Web pages using DOM and text-based features. With this model, we were able to achieve a precision of 0.91 and a recall of 0.92 which beats the state of the art techniques which are being used by major Android browsers in the market today.

Botnets are one of the most serious threats to Internet security today. Modern botnets have complex infrastructures consisting of multiple components, which can be dynamically installed, updated, and removed at any time during the botnet operation. Tracking botnets is essential for understanding the current threat landscape. However, state-of-the-art analysis approaches have several limitations. Many malware analysis systems like sandboxes have a very limited analysis time-out, and thus only allow limited insights into the long-time behavior of a botnet. In contrast, customized tracking systems are botnet-specific and need to be adopted to each malware family, which requires tedious manual reverse engineering. In this paper, we present BotWatcher, a novel approach for transparent and generic botnet tracking. To this end, we leverage dynamic analysis and memory forensics techniques to execute the initial malware sample and later installed modules in a controlled environment and regularly obtain insights into the state of the analysis system. The key idea behind BotWatcher is that by reasoning about the evolution of system state over time, we can reconstruct a high-level overview of the botnet lifecycle, i.e., the sequence of botnet actions that caused this evolution. Our approach is generic since it relies neither on previous knowledge of the botnet nor on OS-specific features. Transparency is achieved by performing outside-OS monitoring and not installing any analysis tools in the analysis environment. We implemented BotWatcher for Microsoft Windows and Mac OS X (both 32- and 64-bit architectures), and applied it to monitor four botnets targeting Microsoft Windows. To the best of our knowledge, we are the first to present a generic, transparent, and fully automated botnet tracking system.

The rapid development of information technology in the digital world has given birth to various types of activities based on the use of technology in processing value data. The world of education is required to be able to keep up with the rapid and dynamic development of information technology in order to improve knowledge and the quality of human resources so that information technology can be utilized optimally. Processing student value data that contains chronological and updatable information. Microsoft Excel or Microsoft Office Excel is a spreadsheet application program created and distributed by Microsoft Corporation that can be run on Microsoft Windows and Mac OS. Making report cards manually will take a long time and a lot of energy, especially in schools that have a very large number of students. Therefore, here we will discuss the Techniques for Making and Managing Microsoft Excel-Based Report Card Applications. By using the VLOOKUP and HLOOKUP formulas, these two functions are functions in Microsoft Excel that more or less function to search or view data from a table vertically and horizontally. Thus, through this community service program activity, the aim is to provide appropriate basic knowledge to the teaching staff of Bina Bangsa Mandiri Middle School so that in the future the teaching staff in the process of processing student grade data can continue to be developed through the knowledge they have.

In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration). The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the “tsunami” of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past). The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom.

Bill Piel has created a cool tool for creating KMZ files of phylogenies for Google Earth. From the web site: Google Earth (available for Windows and Mac OS X) is opening up all sorts of possibilities for biodiversity informatics (ants being one of the first examples). What is cool about Bill's work is that it departs from simple locality records.As always, after pausing to say "wow", there are all sorts of things that one could think of adding.

This study introduces the NMπ computer program designed for estimation of plant mating system and seed and pollen dispersal kernels. NMπ is a re-implementation of the NM+ program and provides new features such as support for multicore processors, explicit treatment of dioecy, the possibility of incorporating uniparentally cytoplasmic markers, the possibility of assessing assortative mating due to phenotypic similarity and inference about offspring genealogies. The probability model of parentage (the neighbourhood model) accounts for missing data and genotyping errors, which can be estimated along with regular parameters of the mating system. The program has virtually no restrictions with respect to a number of individuals, markers or phenotypic characters. A console version of NMπ can be run under a wide variety of operating systems, including Windows, Linux or Mac OS. For Windows users, a graphical user interface is provided to facilitate operating the software. The program, user manual and example data are available on http://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/.

Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.