Scalable Computational Algorithms for Geo-spatial Covid-19 Spread in High Performance Computing

A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknowns to the order of millions. We adopt a parallel scalable solver allowing faster solutions for these high fidelity models. The solver combines domain decomposition and algebraic multigrid preconditioners at multiple levels to achieve the desired strong and weak scalability. As a numerical illustration of this general methodology, a five-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) model of COVID-19 is used to demonstrate the scalability and effectiveness of the proposed solver for a large geographical domain (Southern Ontario). It is possible to predict the infections up to three months for a system size of 92 million (using 1780 processes) within 7 hours saving months of computational effort needed for the conventional solvers

Dynamic relationships between depressive symptoms and insulin resistance over 20 years of adulthood

Background Bidirectional longitudinal relationships between depression and diabetes have been observed, but the dominant direction of their temporal relationships remains controversial. Methods The random-intercept cross-lagged panel model decomposes observed variables into a latent intercept representing the traits, and occasion-specific latent ‘state’ variables. This permits correlations to be assessed between the traits, while longitudinal ‘cross-lagged’ associations and cross-sectional correlations can be assessed between occasion-specific latent variables. We examined dynamic relationships between depressive symptoms and insulin resistance across five visits over 20 years of adulthood in the population-based Coronary Artery Risk Development in Young Adults (CARDIA) study. Possible differences based on population group (Black v. White participants), sex and years of education were tested. Depressive symptoms and insulin resistance were quantified using the Center for Epidemiologic Studies Depression (CES-D) scale and the homeostatic model assessment for insulin resistance (HOMA-IR), respectively. Results Among 4044 participants (baseline mean age 34.9 ± 3.7 years, 53% women, 51% Black participants), HOMA-IR and CES-D traits were weakly correlated (r = 0.081, p = 0.002). Some occasion-specific correlations, but no cross-lagged associations were observed overall. Longitudinal dynamics of these relationships differed by population groups such that HOMA-IR at age 50 was associated with CES-D score at age 55 (β = 0.076, p = 0.038) in White participants only. Longitudinal dynamics were consistent between sexes and based on education. Conclusions The relationship between depressive symptoms and insulin resistance was best characterized by weak correlations between occasion-specific states and enduring traits, with weak evidence that insulin resistance might be temporally associated with subsequent depressive symptoms among White participants later in adulthood

Dynamic relationships between depressive symptoms and insulin resistance over 20 years of adulthood

Background. Bidirectional longitudinal relationships between depression and diabetes have been observed, but the dominant direction of their temporal relationships remains controversial. Methods. The random-intercept cross-lagged panel model decomposes observed variables into a latent intercept representing the traits, and occasion-specific latent ‘state’ variables. This permits correlations to be assessed between the traits, while longitudinal ‘cross-lagged’ associations and cross-sectional correlations can be assessed between occasion-specific latent variables. We examined dynamic relationships between depressive symptoms and insulin resistance across five visits over 20 years of adulthood in the population-based Coronary Artery Risk Development in Young Adults (CARDIA) study. Possible differences based on population group (Black v. White participants), sex and years of education were tested. Depressive symptoms and insulin resistance were quantified using the Center for Epidemiologic Studies Depression (CES-D) scale and the homeostatic model assessment for insulin resistance (HOMA-IR), respectively. Results. Among 4044 participants (baseline mean age 34.9 ± 3.7 years, 53% women, 51% Black participants), HOMA-IR and CES-D traits were weakly correlated (r = 0.081, p = 0.002). Some occasion-specific correlations, but no cross-lagged associations were observed overall. Longitudinal dynamics of these relationships differed by population groups such that HOMAIR at age 50 was associated with CES-D score at age 55 (β = 0.076, p = 0.038) in White participants only. Longitudinal dynamics were consistent between sexes and based on education. Conclusions. The relationship between depressive symptoms and insulin resistance was best characterized by weak correlations between occasion-specific states and enduring traits, with weak evidence that insulin resistance might be temporally associated with subsequent depressive symptoms among White participants later in adulthood.publishedVersio

Scalable computational algorithms for geospatial COVID-19 spread using high performance computing

A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknowns to the order of millions. We adopt a parallel scalable solver that permits faster solutions for these high fidelity models. The solver combines domain decomposition and algebraic multigrid preconditioners at multiple levels to achieve the desired strong and weak scalabilities. As a numerical illustration of this general methodology, a five-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) model of COVID-19 is used to demonstrate the scalability and effectiveness of the proposed solver for a large geographical domain (Southern Ontario). It is possible to predict the infections for a period of three months for a system size of 186 million (using 3200 processes) within 12 hours saving months of computational effort needed for the conventional solvers

Predicting Individual Treatment Response to rTMS for Motor Recovery After Stroke: A Review and the CanStim Perspective

BackgroundRehabilitation is critical for reducing stroke-related disability and improving quality-of-life post-stroke. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation technique used as stand-alone or adjunct treatment to physiotherapy, may be of benefit for motor recovery in subgroups of stroke patients. The Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim) seeks to advance the use of these techniques to improve post-stroke recovery through clinical trials and pre-clinical studies using standardized research protocols. Here, we review existing clinical trials for demographic, clinical, and neurobiological factors which may predict treatment response to identify knowledge gaps which need to be addressed before implementing these parameters for patient stratification in clinical trial protocols.ObjectiveTo provide a review of clinical rTMS trials of stroke recovery identifying factors associated with rTMS response in stroke patients with motor deficits and develop research perspectives for pre-clinical and clinical studies.MethodsA literature search was performed in PubMed, using the Boolean search terms stroke AND repetitive transcranial magnetic stimulation OR rTMS AND motor for studies investigating the use of rTMS for motor recovery in stroke patients at any recovery phase. A total of 1,676 articles were screened by two blinded raters, with 26 papers identified for inclusion in this review.ResultsMultiple possible factors associated with rTMS response were identified, including stroke location, cortical thickness, brain-derived neurotrophic factor (BDNF) genotype, initial stroke severity, and several imaging and clinical factors associated with a relatively preserved functional motor network of the ipsilesional hemisphere. Age, sex, and time post-stroke were generally not related to rTMS response. Factors associated with greater response were identified in studies of both excitatory ipsilesional and inhibitory contralesional rTMS. Heterogeneous study designs and contradictory data exemplify the need for greater protocol standardization and high-quality controlled trials.ConclusionClinical, brain structural and neurobiological factors have been identified as potential predictors for rTMS response in stroke patients with motor impairment. These factors can inform the design of future clinical trials, before being considered for optimization of individual rehabilitation therapy for stroke patients. Pre-clinical models for stroke recovery, specifically developed in a clinical context, may accelerate this process

Best emollients for eczema (BEE) – comparing four types of emollients in children with eczema: protocol for randomised trial and nested qualitative study

Introduction Atopic dermatitis/eczema affects around 20% of children and is characterised by inflamed, dry, itchy skin. Guidelines recommend ‘leave-on’ emollients that are applied directly to the skin to add or trap moisture and used regularly, they can soothe, enhance the skin barrier and may prevent disease ‘flares’. However, the suitability of the many different emollients varies between people and there is little evidence to help prescribers and parents and carers decide which type to try first.Methods and analysis Design: pragmatic, multicentre, individually randomised, parallel group superiority trial of four types of emollient (lotions, creams, gel or ointments).Setting: general practitioner surgeries in England.Participants: children aged over 6 months and less than 12 years with mild-to-severe eczema and no known sensitivity to study emollients.Interventions: study-approved lotion, cream, gel or ointment as the only leave-on emollient for 16 weeks, with directions to apply twice daily and as required. Other treatments, such as topical corticosteroids, used as standard care.Follow-up: 52 weeks.Primary outcome: validated patient-orientated eczema measure measured weekly for 16 weeks.Secondary outcomes: eczema signs (Eczema Area Severity Index) by masked researcher, treatment use, parent satisfaction, adverse events, child and family quality of life (Atopic Dermatitis Quality of Life, Child Health Utility 9D and Dermatitis Family Impact).Sample size: 520 participants (130 per group).Analysis: intention-to-treat using linear mixed models for repeated measures.Nested qualitative study: audio-recording of sample of baseline appointments and up to 60 interviews with participants at 4 and 16 weeks, interviews to be transcribed and analysed thematically.Ethics and dissemination Ethics approval granted by the NHS REC (South West - Central Bristol Research Ethics Committee 17/SW/0089). Findings will be presented at conferences, published in open-access peer-reviewed journals and the study website; and summaries shared with key stakeholders

Expanding the horizon of research into the pathogenesis of the white matter diseases: Proceedings of the 2021 Annual Workshop of the Albert Research Institute for White Matter and Cognition

White matter pathologies are critically involved in the etiology of vascular cognitive impairment–dementia (VCID), Alzheimer’s disease (AD), and Alzheimer’s disease and related diseases (ADRD), and therefore need to be considered a treatable target (Roseborough A, Hachinski V, Whitehead S. White matter degeneration - a treatable target? Roseborough et al. JAMA Neurol [Internet]. 2020 Apr 27;77(7):793–4, [1]. To help address this often-missed area of research, several workshops have been sponsored by the Leo and Anne Albert Charitable Trust since 2015, resulting in the incorporation of “The Albert Research Institute for White Matter and Cognition” in 2020. The first annual “Institute” meeting was held virtually on March 3–4, 2021. The Institute provides a forum and workspace for communication and support of the advancement of white matter science and research to better understand the evolution and prevention of dementia. It serves as a platform for young investigator development, to introduce new data and debate biology mechanisms and new ideas, and to encourage and support new research collaborations and directions to clarify how white matter changes, with other genetic and health risk factors, contribute to cognitive impairment. Similar to previous Albert Trust–sponsored workshops (Barone et al. in J Transl Med 14:1–14, [2]; Sorond et al. in GeroScience 42:81–96, [3]), established expert investigators were identified and invited to present. Opportunities to attend and present were also extended by invitation to talented research fellows and younger scientists. Also, updates on institute-funded research collaborations were provided and discussed. The summary that follows is a synopsis of topics and discussion covered in the workshop

Hormone therapy after the Women's Health Initiative: a qualitative study

BACKGROUND: Publication of results from the Women's Health Initiative study in July 2002 was a landmark event in biomedical science related to postmenopausal women. The purpose of this study was to describe the impact of new hormone therapy recommendations on patients' attitudes and decision-making in a primary care practice. METHODS: A questionnaire including structured and open-ended questions was administered in a family practice office waiting room from August through October 2003. Rationale for taking or not taking hormone therapy was specifically sought. Women 50–70 years old attending for office visits were invited to participate. Data were analyzed qualitatively and with descriptive statistics. Chart review provided medication use rates for the entire practice cohort of which the sample was a subset. RESULTS: Respondents (n = 127) were predominantly white and well educated, and were taking hormone therapy at a higher rate (38%) than the overall rate (26%) for women of the same age range in this practice. Belief patterns about hormone therapy were, in order of frequency, 'use is risky', 'vindication or prior beliefs', 'benefit to me outweighs risk', and 'unaware of new recommendations'. Twenty-eight out of 78 women continued hormones use after July 2002. Of 50 women who initially stopped hormone therapy after July 2002, 12 resumed use. Women who had stopped hormone therapy were a highly symptomatic group. Responses with emotional overtones such as worry, confusion, anger, and grief were common. CONCLUSION: Strategies for decision support about hormone therapy should explicitly take into account women's preferences about symptom relief and the trade-offs among relevant risks. Some women may need emotional support during transitions in hormone therapy use