Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

On the analysis of non-coding roles in open source development

The role of non-coding contributors in Open Source Software (OSS) is poorly understood. Most of current research around OSS development focuses on the coding aspects of the project (e.g., commits, pull requests or code reviews) while ignoring the potential of other types of contributions. Often, due to the assumption that these other contributions are not significant in number and that, in any case, they are handled by the same people that are also part of the “coding team”. This paper aims to investigate whether this is actually the case by analyzing the frequency and diversity of non-coding contributions in OSS development. As a sample of projects for our study we have taken the 100 most popular projects in the ecosystem of NPM, a package manager for JavaScript. Our results validate the importance of dedicated non-coding contributors in OSS and the diversity of OSS communities as, typically, a contributor specializes in a specific subset of roles. We foresee that projects adopting explicit policies to attract and onboard them could see a positive impact in their long-term sustainability providing they also put in place the right governance strategies to facilitate the migration and collaboration among the different roles. As part of this work, we also provide a replicability package to facilitate further quantitative role-based analysis by other researchers.

Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images

Open-source deep neural networks (DNNs) for medical imaging are significant in emergent situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19), since they accelerate the development of high-performance DNN-based systems. However, adversarial attacks are not negligible during open-source development. Since DNNs are used as computer-aided systems for COVID-19 screening from radiography images, we investigated the vulnerability of the COVID-Net model, a representative open-source DNN for COVID-19 detection from chest X-ray images to backdoor attacks that modify DNN models and cause their misclassification when a specific trigger input is added. The results showed that backdoors for both non-targeted attacks, for which DNNs classify inputs into incorrect labels, and targeted attacks, for which DNNs classify inputs into a specific target class, could be established in the COVID-Net model using a small trigger and small fraction of training data. Moreover, the backdoors were effective for models fine-tuned from the backdoored COVID-Net models, although the performance of non-targeted attacks was limited. This indicated that backdoored models could be spread via fine-tuning (thereby becoming a significant security threat). The findings showed that emphasis is required on open-source development and practical applications of DNNs for COVID-19 detection.

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprime's many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

From closed to open. Democratising medical devices using additive manufacture and open source development and distribution systems.

<p><b>Pupils can provide important neurological information that can aid in the diagnosis of a range of conditions, including aneurysms, impending strokes and tumors in the lung (Gale, et al). In a research context, there is increasing interest in studying the intrinsically photosensitive Retinal Ganglion cells (ipRGC), which respond to intense blue light thanks to a photo pigment called melanopsin. Studying these cells could lead to a better understanding of sleep disorders and a range of optic nerve diseases. Although commercially available pupil testing devices do exist, all cost upwards of $10,000, and suffer from either poor portability or limitations in the tests they can perform. Specifically, the ipRGC require a specific intensity of blue light to be activated and measured, which most devices cannot produce. </b></p><p>In recent years, the open source movement has enabled users from around the world to freely collaborate on the development and distribution of their own products. At first, only software could be produced using this approach, however the continued improvement of 3D printing technology has enabled the same model to be applied to physical products as well. From a medical perspective, this is particularly exciting. </p><p>The aim of this research was to produce an inexpensive, open source pupilometer that runs on widely available components, can be distributed online and manufactured using 3D printing technology. In doing so, this thesis asks the question; How can an open source development and distribution model be used in conjunction with online 3D printing services and widely available parts and components to produce an inexpensive and open source pupilometer? </p><p>To answer this, a range of practice based methodologies, including research for design and research through design were used to explore this new potential. The resulting design proposal demonstrates how online file sharing platforms, in conjunction with distributed 3D printing services and online supply chains can be combined to develop new medical devices. The ability to collect pupil data using an open source pupilometer may lead to expanded data collection and diagnostic capabilities from doctors in a number of clinical settings, while a cloud based data collection system taking the form of a smartphone app will create a large biometric database and cooperative online research community. </p>

From closed to open. Democratising medical devices using additive manufacture and open source development and distribution systems.

<p><b>Pupils can provide important neurological information that can aid in the diagnosis of a range of conditions, including aneurysms, impending strokes and tumors in the lung (Gale, et al). In a research context, there is increasing interest in studying the intrinsically photosensitive Retinal Ganglion cells (ipRGC), which respond to intense blue light thanks to a photo pigment called melanopsin. Studying these cells could lead to a better understanding of sleep disorders and a range of optic nerve diseases. Although commercially available pupil testing devices do exist, all cost upwards of $10,000, and suffer from either poor portability or limitations in the tests they can perform. Specifically, the ipRGC require a specific intensity of blue light to be activated and measured, which most devices cannot produce. </b></p><p>In recent years, the open source movement has enabled users from around the world to freely collaborate on the development and distribution of their own products. At first, only software could be produced using this approach, however the continued improvement of 3D printing technology has enabled the same model to be applied to physical products as well. From a medical perspective, this is particularly exciting. </p><p>The aim of this research was to produce an inexpensive, open source pupilometer that runs on widely available components, can be distributed online and manufactured using 3D printing technology. In doing so, this thesis asks the question; How can an open source development and distribution model be used in conjunction with online 3D printing services and widely available parts and components to produce an inexpensive and open source pupilometer? </p><p>To answer this, a range of practice based methodologies, including research for design and research through design were used to explore this new potential. The resulting design proposal demonstrates how online file sharing platforms, in conjunction with distributed 3D printing services and online supply chains can be combined to develop new medical devices. The ability to collect pupil data using an open source pupilometer may lead to expanded data collection and diagnostic capabilities from doctors in a number of clinical settings, while a cloud based data collection system taking the form of a smartphone app will create a large biometric database and cooperative online research community. </p>