Using Computing Containers and Continuous Integration to Improve Numerical Research Reproducibility

Cloud computing has opened new options of collaboration between research teams in the field of high performance computing and numerical research. Running computational workloads in virtual machines became common in recent years. However, the use of computing containers provides many additional advantages besides just proving new possible runtime choice. One of the most important (and often underappreciated) is an option to improve the reproducibility of research results based on complex mathematical modeling. This paper provides an overview of architecture based on computing containers and continuous integration tools we used to achieve reproducible numerical result

A Preliminary Study on Shifting from Virtual Machine to Docker Container for Insilico Drug Discovery in the Cloud

The rapid growth of information technology and internet access has moved many offline activities online. Cloud computing is an easy and inexpensive solution, as supported by virtualization servers that allow easier access to personal computing resources. Unfortunately, current virtualization technology has some major disadvantages that can lead to suboptimal server performance. As a result, some companies have begun to move from virtual machines to containers. While containers are not new technology, their use has increased recently due to the Docker container platform product. Docker’s features can provide easier solutions. In this work, insilico drug discovery applications from molecular modelling to virtual screening were tested to run in Docker. The results are very promising, as Docker beat the virtual machine in most tests and reduced the performance gap that exists when using a virtual machine (VirtualBox). The virtual machine placed third in test performance, after the host itself and Docker

Integration of “omics” Data and Phenotypic Data Within a Unified Extensible Multimodal Framework

Analysis of “omics” data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable “omics” framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling

CI/CD em Dynamics 365 Business Central: Definição de uma abordagem sistemática para implementadores

Práticas contínuas, diga-se integração, entrega e implantação contínuas, são as práticas da área de desenvolvimento de software que permitem que as organizações disponibilizem produtos e funcionalidades com frequência e confiabilidade. A myPartner, sendo uma empresa de consultoria informática com diferentes soluções em vários clientes, tem todo o interesse em explorar estas práticas e como estas podem melhorar a produtividade e qualidade dos seus serviços. Com dezenas de clientes em diversos setores do mercado e realidades de negócio variadas, a myPartner tem a necessidade de manter uma abordagem consistente e sistematizada nos vários projetos implementa. Este documento, realizado no âmbito de uma dissertação de mestrado em Engenharia Informática, tem como objetivo a definição de uma abordagem sistemática para implementação das técnicas e metodologias de Continuous Integration/Continuous Delivery (CI/CD), a aplicar em projetos Dynamics 365 Business Central por empresas de serviços de consultoria informática, como a myPartner. CI/CD permite a automatização de vários processos que, atualmente, são feitos manualmente, aumentando a produtividade e diminuindo o risco de erro humano. Este documento apresenta uma solução para a aplicação destas práticas em projetos de implementação do Business Central, desde a definição dos novos processos de workflow a serem adotados pelas equipas de desenvolvimento, à implementação das várias componentes que constituem a solução.Continuous practices, i.e., continuous integration, delivery, and deployment, are software development practices that enable organizations to make products and features available frequently and reliably. Being a software consulting company with different solutions for multiple clients, myPartner would benefit from exploring these practices and how they can improve the productivity and quality of its services. With dozens of clients in different market sectors and business contexts, myPartner has the need to maintain a consistent and systematic approach in the various projects it implements. This document, carried out within the scope of a master’s thesis in Computer Engineering, aims to define a systematic approach for the implementation of Continuous Integration/Continuous Delivery (CI/CD) techniques and methodologies, to be applied in Dynamics 365 Business Central projects by consulting services companies such as myPartner. CI/CD allows for the automation of several processes that are currently done manually, increasing productivity, and reducing the risk of human error. This document proposes a solution for the application of these practices in Business Central implementation projects, from the definition of new workflow processes to be adopted by the development teams, to the implementation of the various components that make up the solution

Frameworks in medical image analysis with deep neural networks

In recent years, deep neural network based medical image analysis has become quite powerful and achieved similar results performance-wise as experts. Consequently, the integration of these tools into the clinical routine as clinical decision support systems is highly desired. The benefits of automatic image analysis for clinicians are massive, ranging from improved diagnostic as well as treatment quality to increased time-efficiency through automated structured reporting. However, implementations in the literature revealed a significant lack of standardization in pipeline building resulting in low reproducibility, high complexity through extensive knowledge requirements for building state-of-the-art pipelines, and difficulties for application in clinical research. The main objective of this work is the standardization of pipeline building in deep neural network based medical image segmentation and classification. This is why the Python frameworks MIScnn for medical image segmentation and AUCMEDI for medical image classification are proposed which simplify the implementation process through intuitive building blocks eliminating the need for time-consuming and error-prone implementation of common components from scratch. The proposed frameworks include state-of-the-art methodology, follow outstanding open-source principles like extensive documentation as well as stability, offer rapid as well as simple application capabilities for deep learning experts as well as clinical researchers, and provide cutting-edge high-performance competitive with the strongest implementations in the literature. As secondary objectives, this work presents more than a dozen in-house studies as well as discusses various external studies utilizing the proposed frameworks in order to prove the capabilities of standardized medical image analysis. The presented studies demonstrate excellent predictive capabilities in applications ranging from COVID-19 detection in computed tomography scans to the integration into a clinical study workflow for Gleason grading of prostate cancer microscopy sections and advance the state-of-the-art in medical image analysis by simplifying experimentation setups for research. Furthermore, studies for increasing reproducibility in performance assessment of medical image segmentation are presented including an open-source metric library for standardized evaluation and a community guideline on proper metric usage. The proposed contributions in this work improve the knowledge representation of the field, enable rapid as well as high-performing applications, facilitate further research, and strengthen the reproducibility of future studies