Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs

Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called "connectomics". Connectomics studies the brain as a graph; vertices correspond to neurons (or collections thereof) and edges correspond to structural or functional connections between them. To explore the variability of connectomes---to address both basic science questions regarding the structure of the brain, and medical health questions about psychiatry and neurology---one can study the topological properties of these brain-graphs. We define multivariate glocal graph invariants: these are features of the graph that capture various local and global topological properties of the graphs. We show that the collection of features can collectively be computed via a combination of daisy-chaining, sparse matrix representation and computations, and efficient approximations. Our custom open-source Python package serves as a back-end to a Web-service that we have created to enable researchers to upload graphs, and download the corresponding invariants in a number of different formats. Moreover, we built this package to support distributed processing on multicore machines. This is therefore an enabling technology for network science, lowering the barrier of entry by providing tools to biologists and analysts who otherwise lack these capabilities. As a demonstration, we run our code on 120 brain-graphs, each with approximately 16M vertices and up to 90M edges.Comment: Published as part of 2013 IEEE GlobalSIP conferenc

Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs

Abstract—Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called “connectomics”. Connectomics studies the brain as a graph; vertices correspond to neurons (or collections thereof) and edges correspond to structural or functional connections between them. To explore the variability of connectomes—to address both basic science questions regarding the structure of the brain, and medical health questions about psychiatry and neurology—one can study the topological properties of these brain-graphs. We define multivariate glocal graph invariants: these are features of the graph that capture various local and global topological properties of the graphs. We show that the collection of features can collectively be computed via a combination of daisy-chaining, sparse matrix representation and computations, and efficient approximations. Our custom open-source Python package serves as a back-end to a Web-service that we have created to enable researchers to upload graphs, and download the corresponding invariants in a number of different formats. Moreover, we built this package to support distributed processing on multicore machines. This is therefore an enabling technology for network science, lowering the barrier of entry by providing tools to biologists and analysts who otherwise lack these capabilities. As a demonstration, we run our code on 120 brain-graphs, each with approximately 16M vertices and up to 90M edges. I

Psychological distress among healthcare workers accessing occupational health services during the COVID-19 pandemic in Zimbabwe.

BACKGROUND: Healthcare workers (HCWs) have experienced anxiety and psychological distress during the COVID-19 pandemic. We established and report findings from an occupational health programme for HCWs in Zimbabwe that offered screening for SARS-CoV-2 with integrated screening for comorbidities including common mental disorder (CMD) and referral for counselling. METHODS: Quantitative outcomes were fearfulness about COVID-19, the Shona Symptom Questionnaire (SSQ-14) score (cutpoint 8/14) and the number and proportion of HCWs offered referral for counselling, accepting referral and counselled. We used chi square tests to identify factors associated with fearfulness, and logistic regression was used to model the association of fearfulness with wave, adjusting for variables identified using a DAG. Qualitative data included 18 in-depth interviews, two workshops conducted with HCWs and written feedback from counsellors, analysed concurrently with data collection using thematic analysis. RESULTS: Between 27 July 2020-31 July 2021, spanning three SARS-CoV-2 waves, the occupational health programme was accessed by 3577 HCWs from 22 facilities. The median age was 37 (IQR 30-43) years, 81.9% were women, 41.7% said they felt fearful about COVID-19 and 12.1% had an SSQ-14 score ≥ 8. A total of 501 HCWs were offered referral for counselling, 78.4% accepted and 68.9% had ≥1 counselling session. Adjusting for setting and role, wave 2 was associated with increased fearfulness over wave 1 (OR = 1.26, 95% CI 1.00-1.60). Qualitative data showed high levels of anxiety, psychosomatic symptoms and burnout related to the pandemic. Mental wellbeing was affected by financial insecurity, unmet physical health needs and inability to provide quality care within a fragile health system. CONCLUSIONS: HCWs in Zimbabwe experience a high burden of mental health symptoms, intensified by the COVID-19 pandemic. Sustainable mental health interventions must be multisectoral addressing mental, physical and financial wellbeing

Slow acceptance of universal antiretroviral therapy (ART) among mothers enrolled in IMPAACT PROMISE studies across the globe

CITATION: Stranix‑Chibanda, L., et al. 2019. Slow acceptance of universal antiretroviral therapy (ART) among mothers enrolled in IMPAACT PROMISE studies across the globe. AIDS and Behavior, 23:2522-2531, doi:10.1007/s10461-019-02624-3.The original publication is available at https://link.springer.comThe PROMISE trial enrolled asymptomatic HIV-infected pregnant and postpartum women not eligible for antiretroviral treatment (ART) per local guidelines and randomly assigned proven antiretroviral strategies to assess relative efficacy for perinatal prevention plus maternal/infant safety and maternal health. The START study subsequently demonstrated clear benefit in initiating ART regardless of CD4 count. Active PROMISE participants were informed of results and women not receiving ART were strongly recommended to immediately initiate treatment to optimize their own health. We recorded their decision and the primary reason given for accepting or rejecting the universal ART offer after receiving the START information. One-third of participants did not initiate ART after the initial session, wanting more time to consider. Six sessions were required to attain 95% uptake. The slow uptake of universal ART highlights the need to prepare individuals and sensitize communities regarding the personal and population benefits of the “Treat All” strategy.https://link.springer.com/article/10.1007/s10461-019-02624-3Publisher's versio