On the Effectiveness of Unit Tests in Test-driven Development

Background: Writing unit tests is one of the primary activities in test-driven development. Yet, the existing reviews report few evidence supporting or refuting the effect of this development approach on test case quality. Lack of ability and skills of developers to produce sufficiently good test cases are also reported as limitations of applying test-driven development in industrial practice. Objective: We investigate the impact of test-driven development on the effectiveness of unit test cases compared to an incremental test last development in an industrial context. Method: We conducted an experiment in an industrial setting with 24 professionals. Professionals followed the two development approaches to implement the tasks. We measure unit test effectiveness in terms of mutation score. We also measure branch and method coverage of test suites to compare our results with the literature. Results: In terms of mutation score, we have found that the test cases written for a test-driven development task have a higher defect detection ability than test cases written for an incremental test-last development task. Subjects wrote test cases that cover more branches on a test-driven development task compared to the other task. However, test cases written for an incremental test-last development task cover more methods than those written for the second task. Conclusion: Our findings are different from previous studies conducted at academic settings. Professionals were able to perform more effective unit testing with test-driven development. Furthermore, we observe that the coverage measure preferred in academic studies reveal different aspects of a development approach. Our results need to be validated in larger industrial contexts.Istanbul Technical University Scientific Research Projects (MGA-2017-40712), and the Academy of Finland (Decision No. 278354)

Challenges and opportunities for ELSI early career researchers

Background: Over the past 25 years, there has been growing recognition of the importance of studying the Ethical, Legal and Social Implications (ELSI) of genetic and genomic research. A large investment into ELSI research from the National Institutes of Health (NIH) Human Genomic Project budget in 1990 stimulated the growth of this emerging field; ELSI research has continued to develop and is starting to emerge as a field in its own right. The evolving subject matter of ELSI research continues to raise new research questions as well as prompt re-evaluation of earlier work and a growing number of scholars working in this area now identify themselves as ELSI scholars rather than with a particular discipline. Main text: Due to the international and interdisciplinary nature of ELSI research, scholars can often find themselves isolated from disciplinary or regionally situated support structures. We conducted a workshop with Early Career Researchers (ECRs) in Oxford, UK, and this paper discusses some of the particular challenges that were highlighted. While ELSI ECRs may face many of the universal challenges faced by ECRs, we argue that a number of challenges are either unique or exacerbated in the case of ELSI ECRs and discuss some of the reasons as to why this may be the case. We identify some of the most pressing issues for ELSI ECRs as: interdisciplinary angst and expertise, isolation from traditional support structures, limited resources and funding opportunities, and uncertainty regarding how research contributions will be measured. We discuss the potential opportunity to use web 2.0 technologies to transform academic support structures and address some of the challenges faced by ELSI ECRs, by helping to facilitate mentoring and support, access to resources and new accreditation metrics. Conclusion: As our field develops it is crucial for the ELSI community to continue looking forward to identify how emerging digital solutions can be used to facilitate the international and interdisciplinary research we perform, and to offer support for those embarking on, progressing through, and transitioning into an ELSI research career

Proteome-based plasma biomarkers for Alzheimer's disease

Alzheimer's disease is a common and devastating disease for which there is no readily available biomarker to aid diagnosis or to monitor disease progression. Biomarkers have been sought in CSF but no previous study has used two-dimensional gel electrophoresis coupled with mass spectrometry to seek biomarkers in peripheral tissue. We performed a case-control study of plasma using this proteomics approach to identify proteins that differ in the disease state relative to aged controls. For discovery-phase proteomics analysis, 50 people with Alzheimer's dementia were recruited through secondary services and 50 normal elderly controls through primary care. For validation purposes a total of 511 subjects with Alzheimer's disease and other neurodegenerative diseases and normal elderly controls were examined. Image analysis of the protein distribution of the gels alone identifies disease cases with 56% sensitivity and 80% specificity. Mass spectrometric analysis of the changes observed in two-dimensional electrophoresis identified a number of proteins previously implicated in the disease pathology, including complement factor H (CFH) precursor and α-2-macroglobulin (α- 2M). Using semi-quantitative immunoblotting, the elevation of CFH and α- 2M was shown to be specific for Alzheimer's disease and to correlate with disease severity although alternative assays would be necessary to improve sensitivity and specificity. These findings suggest that blood may be a rich source for biomarkers of Alzheimer's disease and that CFH, together with other proteins such as α- 2M may be a specific markers of this illness. © 2006 The Author(s).link_to_subscribed_fulltex

A scoping review of health-related stigma outcomes for high-burden diseases in low- and middle-income countries

__Background:__ Stigma is associated with health conditions that drive disease burden in low- and middle-income countries (LMICs), including HIV, tuberculosis, mental health problems, epilepsy, and substance use disorders. However, the literature discussing the relationship between stigma and health outcomes is largely fragmented within disease-specific siloes, thus limiting the identification of common moderators or mechanisms through which stigma potentiates adverse health outcomes as well as the development of broadly relevant stigma mitigation interventions. __Methods:__ We conducted a scoping review to provide a critical overview of the breadth of research on stigma for each of the five aforementioned conditions in LMICs, including their methodological strengths and limitations

Artificial intelligence, systemic risks, and sustainability

Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors

Plasma Biomarkers of Brain Atrophy in Alzheimer's Disease

Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis

p68/DdX5 supports β-Catenin & RNAP II during androgen receptor mediated transcription in prostate cancer

The DEAD box RNA helicase p68 (Ddx5) is an important androgen receptor (AR) transcriptional co-activator in prostate cancer (PCa) and is over-expressed in late stage disease. β-Catenin is a multifunctional protein with important structural and signalling functions which is up-regulated in PCa and similar to p68, interacts with the AR to co-activate expression of AR target genes. Importantly, p68 forms complexes with nuclear β-Catenin and promotes gene transcription in colon cancer indicating a functional interplay between these two proteins in cancer progression. In this study, we explore the relationship of p68 and β-Catenin in PCa to assess their potential co-operation in AR-dependent gene expression, which may be of importance in the development of castrate resistant prostate cancer (CRPCa). We use immunoprecipitation to demonstrate a novel interaction between p68 and β-Catenin in the nucleus of PCa cells, which is androgen dependent in LNCaP cells but androgen independent in a hormone refractory derivative of the same cell line (representative of the CRPCa disease type). Enhanced AR activity is seen in androgen-dependent luciferase reporter assays upon transient co-transfection of p68 and β-Catenin as an additive effect, and p68-depleted Chromatin-Immunoprecipitation (ChIP) showed a decrease in the recruitment of the AR and β-Catenin to androgen responsive promoter regions. In addition, we found p68 immunoprecipitated with the processive and non-processive form of RNA polymerase II (RNAP II) and show p68 recruited to elongating regions of the AR mediated PSA gene, suggesting a role for p68 in facilitating RNAP II transcription of AR mediated genes. These results suggest p68 is important in facilitating β-Catenin and AR transcriptional activity in PCa cells

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes