Graphic Interfaces in ADAS

Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6–17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results.

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.Contact:peter@biomatters.com

AIRR-seq data sets are usually large and require specialized analysis methods and software tools. A typical Illumina MiSeq sequencing run generates 20-30 million 2×300bp paired-end sequence reads, which roughly corresponds to 15GB of sequence data to be processed. Other platforms like NextSeq, which is useful in projects where the full V gene is not needed, create about 400million 2×150bp paired-end reads. Because of the size of the data sets, the analysis can be computationally expensive, particularly the early analysis steps like preprocessing and gene annotation that process the majority of the sequence data. A standard desktop PC may take 3-5days of constant processing for a single MiSeq run, so dedicated high-performance computational resources may be required.VDJServer provides free access to high-performance computing (HPC) at the Texas Advanced Computing Center (TACC) through a graphical user interface (Christley et al. Front Immunol 9:976, 2018). VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provides access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene assignment, repertoire characterization, and repertoire comparison. Furthermore, VDJServer has parallelized execution for tools such as IgBLAST, so more compute resources are utilized as the size of the input data grows. Analysis that takes days on a desktop PC might take only a few hours on VDJServer. VDJServer is a free, publicly available, and open-source licensed resource. Here, we describe the workflow for performing immune repertoire analysis on VDJServer's high-performance computing.

The effects of the increase in the average temperature of the Earth's near-surface air and oceans in recent decades are dramatically manifest: desertification, glacier retreat, increased intensity and frequency of hurricanes and extreme weather events. Monitoring the ecosystem is currently the only way we have to assist Governments in making sound decisions concerning the reduction of these dramatic effects and the protection of our environment. Satellite remote sensing data, offering the possibility of covering large spatial area with a high temporal frequency, represents the ideal solution to monitoring, but the huge data volume to process, calibrate and validate by in-situ dataset, cannot be operated effectively by traditional database and computational resources. Grid technology, easily providing powerful computational resources and efficient distributed data management, is an excellent solution for remote sensed data processing and management system. In this paper we present a prototype of a remote sensed data processing system on Grid technology that allows, by a graphical interface, data selection and processing to validate SST measure particularly in costal area.

The GOSIP (Gigabit Optical Serial Interface Protocol) provides communication via optical fibres between multiple kinds of front-end electronics and the KINPEX PCIe receiver board located in the readout host PC. In recent years a stack of device driver software has been developed to utilize this hardware for several scenarios of data acquisition. On top of this driver foundation, several graphical user interfaces (GUIs) have been created. These GUIs are based on the Qt graphics libraries and are designed in a modular way: All common functionalities, like generic I/O with the front-ends, handling of configuration files, and window settings, are treated by a framework class GosipGUI. In the Qt workspace of such GosipGUI frame, specific sub classes may implement additional windows dedicated to operate different GOSIP front-end modules. These readout modules developed by GSI Experiment Electronics department are for instance FEBEX sampling ADCs, TAMEX FPGA-TDCs, or POLAND QFWs. For each kind of front-end the GUIs allow to monitor specific register contents, to set up the working configuration, and to interactively change parameters like sampling thresholds during data acquisition. The latter is extremely useful when qualifying and tuning the front-ends in the electronics lab or detector cave. Moreover, some of these GosipGUI implementations have been equipped with features for mostly automatic testing of ASICs in a prototype mass production. This has been applied for the APFEL-ASIC component of the PANDA experiment currently under construction, and for the FAIR beam diagnostic readout system POLAND.

Chapter Ten Free and Open Source Geospatial Tools for Environmental Modelling and Management

Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility. But the entry barrier remains high for students and early career scientists in computational neuroscience since these simulators typically require programming skills and a complex installation. Here, we describe an installation-free Graphical User Interface (GUI) running in the web browser, which is distinct from the simulation engine running anywhere, on the student’s laptop or on a supercomputer. This architecture provides robustness against technological changes in the software stack and simplifies deployment for self-education and for teachers. Our new open-source tool, NEST Desktop, comprises graphical elements for creating and configuring network models, running simulations, and visualizing and analyzing the results. NEST Desktop allows students to explore important concepts in computational neuroscience without the need to learn a simulator control language before. Our experiences so far highlight that NEST Desktop helps advancing both quality and intensity of teaching in computational neuroscience in regular university courses. We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.

As the issue of data overload is a problem in critical care today, it is of utmost importance to improve acquisition, storage, integration, and presentation of medical data, which appears only feasible with the help of bedside computers. The data originates from four major sources: (1) the bedside medical devices, (2) the local area network (LAN) of the ICU, (3) the hospital information system (HIS) and (4) manual input. All sources differ markedly in quality and quantity of data and in the demands of the interfaces between source of data and patient database. The demands for data acquisition from bedside medical devices, ICU-LAN and HIS concentrate on technical problems, such as computational power, storage capacity, real-time processing, interfacing with different devices and networks and the unmistakable assignment of data to the individual patient. The main problem of manual data acquisition is the definition and configuration of the user interface that must allow the inexperienced user to interact with the computer intuitively. Emphasis must be put on the construction of a pleasant, logical and easy-to-handle graphical user interface (GUI). Short response times will require high graphical processing capacity. Moreover, high computational resources are necessary in the future for additional interfacing devices such as speech recognition and 3D-GUI. Therefore, in an ICU environment the demands for computational power are enormous. These problems are complicated by the urgent need for friendly and easy-to-handle user interfaces. Both facts place ICU bedside computing at the vanguard of present and future workstation development leaving no room for solutions based on traditional concepts of personal computers.(ABSTRACT TRUNCATED AT 250 WORDS)

Summary form only given. This work describes a software prototype developed at Research Center Julich to demonstrate the interoperability between UNICORE (Uniform Interface to Computer Resources) and Globus without changes to any of the systems. By combining UNICORE's workflow oriented approach to job submission with Globus, grid users can gain seamless access to a wide number of Globus enabled systems. We define the following scenario of a job submission from UNICORE to Globus: the user prepares the job via UNICORE's graphical user interface, chooses a Globus site where the job is to be computed and submits it to UNICORE's target system interface (TSI). This is the entity normally interfacing with the local batch system. It is enhanced to communicate with Globus. The TSI translates the job description from the UNICORE specific abstract job object (AJO) into the GRAM Resource Specification Language (RSL) and submits it to the GRAM Gatekeeper. Standard Globus mechanisms are used to monitor the status of the job and transfer the output back to the TSI.

Bottom-up hierarchical and categorical metacomputing for automating composition and deployment of directly computable multiphysics models

The heavy reliance of animal studies on human observers makes them prone to observer-specific biases. To mitigate such shortcomings, much research has been focused on computerizing the characterization of animal behavior. Such automation can lead to more reliable and cost-effective behavior quantifications. Yet, there remain challenges in developing end-to-end solutions that allow users to easily train custom behavioral classifiers with minimal data while maintaining low computational demands. Here we resolve these challenges through a rodent behavior classifier, the real-time rodent behavior classifier using color-based body segmentation (R2C2) algorithm, which uses color-based body segmentation to track rodent body parts and consequently their behaviors. Based on the 'hue, saturation, value' (HSV) color difference in furs or exposed skins, the R2C2 creates simple white-black color boundaries for each body part, which are then used to discern and track body parts in real time to extract movement-based features. We combined wavelet transform-based tracking with HSV color-based body part segmentation to substantially reduce computational requirements while minimizing the number of input features needed for classification. Loading these features into our convolutional neural network algorithm, the R2C2 achieves performance on par with an expert human observer. Furthermore, it can differentiate subtle behavioral patterns associated with autism spectrum disorder in mouse models. As the R2C2 is a complete, lightweight end-to-end pipeline package with a graphical user interface and does not require end-user programming or heavy computation resources, it can be easily adopted in conventional neuroscience laboratories. By enabling effective auto-labeling of fine animal actions, R2C2 will facilitate studies aiming to uncover the neural mechanisms driving behavioral modulations.

cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud.

The analyses of large-scale numerical problems of mining domain such as heat dissipation from rock strata to the ventilation air in large network of underground mine galleries are usually time taking process and require huge computational resources. To overcome this issue, High Performance Computing system (HPC) can be utilized to expedite the numerical analysis process for solving the problems within a reasonable time. In order to utilize HPC, this paper developed parallel finite element codes for solving heat conduction problems of mine galleries. The developed C++ codes of parallel FE are applied in solving 1D and 2D heat conduction problems. For the comparative analysis of 1D and 2D problems, the number of unknown nodes is fixed. It is observed that the computational time reduced when the number of processors increased. On the other hand, the computational time took longer time in 2D problem in comparison to that in 1D. Graphics User Interface (GUI) is also developed in terms of number of nodes of mesh, the boundary temperatures and length of the element. To sum up, the work of this paper reveals that the parallel computing is highly efficient in solving the large scale numerical geo-mining problems.

The development of high-throughput sequencing technologies over the past decade has generated a tidal wave of environmental sequence information from a variety of natural and human engineered ecosystems. The resulting flood of information into public databases and archived sequencing projects has exponentially expanded computational resource requirements rendering most local homology-based search methods inefficient. We recently introduced MetaPathways v1.0, a modular annotation and analysis pipeline for constructing environmental Pathway/Genome Databases (ePGDBs) from environmental sequence information capable of using the Sun Grid engine for external resource partitioning. However, a command-line interface and facile task management introduced user activation barriers with concomitant decrease in fault tolerance. Here we present MetaPathways v2.0 incorporating a graphical user interface (GUI) and refined task management methods. The MetaPathways GUI provides an intuitive display for setup and process monitoring and supports interactive data visualization and sub-setting via a custom Knowledge Engine data structure. A master-worker model is adopted for task management allowing users to scavenge computational results from a number of worker grids in an ad hoc, asynchronous, distributed network that dramatically increases fault tolerance. This model facilitates the use of EC2 instances extending ePGDB construction to the Amazon Elastic Cloud.

Modern biotechnical research is becoming increasingly reliant on computational structural modeling programs to develop novel solutions to scientific questions. Rosetta is one such protein modeling suite that has already demonstrated wide applicability to a number of diverse research projects. Unfortunately, Rosetta is largely a command-line-driven software package which restricts its use among non-computational researchers. Some graphical interfaces for Rosetta exist, but typically are not as sophisticated as commercial software. Here, we present InteractiveROSETTA, a graphical interface for the PyRosetta framework that presents easy-to-use controls for several of the most widely used Rosetta protocols alongside a sophisticated selection system utilizing PyMOL as a visualizer. InteractiveROSETTA is also capable of interacting with remote Rosetta servers, facilitating sophisticated protocols that are not accessible in PyRosetta or which require greater computational resources. InteractiveROSETTA is freely available at https://github.com/schenc3/InteractiveROSETTA/releases and relies upon a separate download of PyRosetta which is available at http://www.pyrosetta.org after obtaining a license (free for academic use).