Vascular contributions to cognitive impairment and dementia: Research consortia that focus on etiology and treatable targets to lessen the burden of dementia worldwide

The research into vascular contributions to cognitive impairment and dementia (VCID) aims to understand the importance of cerebrovascular biology in cognitive decline. Prevention and treatment of VCID is poised to have major impact on dementia-related disease burden and is thus a critical emerging objective in dementia research. This article presents VCID consortia focused on multidisciplinary approaches to identify key pathologic targets and develop diagnostic tools with the goal of bridging the divide between basic research and clinical trials. Members of these multi-institute, multidisciplinary consortia provide a prospective on the history and emerging science of VCID and how VCID consortia can address some of the more complex questions in VCID and drive the field forward. These consortia, and others like them, are uniquely suited to tackle some of the most difficult obstacles in translating research to the clinic

Inhibitor of growth protein 3 epigenetically silences endogenous retroviral elements and prevents innate immune activation

Endogenous retroviruses (ERVs) are subject to transcriptional repression in adult tissues, in part to prevent autoimmune responses. However, little is known about the epigenetic silencing of ERV expression. Here, we describe a new role for inhibitor of growth family member 3 (ING3), to add to an emerging group of ERV transcriptional regulators. Our results show that ING3 binds to several ERV promoters (for instance MER21C) and establishes an EZH2-mediated H3K27 trimethylation modification. Loss of ING3 leads to decreases of H3K27 trimethylation enrichment at ERVs, induction of MDA5-MAVS-interferon signaling, and functional inhibition of several virus infections. These data demonstrate an important new function of ING3 in ERV silencing and contributing to innate immune regulation in somatic cells

miRNA contributions to pediatric-onset multiple sclerosis inferred from GWAS.

ObjectiveOnset of multiple sclerosis (MS) occurs in childhood for approximately 5% of cases (pediatric MS, or ped-MS). Epigenetic influences are strongly implicated in MS pathogenesis in adults, including the contribution from microRNAs (miRNAs), small noncoding RNAs that affect gene expression by binding target gene mRNAs. Few studies have specifically examined miRNAs in ped-MS, but individuals developing MS at an early age may carry a relatively high burden of genetic risk factors, and miRNA dysregulation may therefore play a larger role in the development of ped-MS than in adult-onset MS. This study aimed to look for evidence of miRNA involvement in ped-MS pathogenesis.MethodsGWAS results from 486 ped-MS cases and 1362 controls from the U.S. Pediatric MS Network and Kaiser Permanente Northern California membership were investigated for miRNA-specific signals. First, enrichment of miRNA-target gene network signals was evaluated using MIGWAS software. Second, SNPs in miRNA genes and in target gene binding sites (miR-SNPs) were tested for association with ped-MS, and pathway analysis was performed on associated target genes.ResultsMIGWAS analysis showed that miRNA-target gene signals were enriched in GWAS (P = 0.038) and identified 39 candidate biomarker miRNA-target gene pairs, including immune and neuronal signaling genes. The miR-SNP analysis implicated dysregulation of miRNA binding to target genes in five pathways, mainly involved in immune signaling.InterpretationEvidence from GWAS suggests that miRNAs play a role in ped-MS pathogenesis by affecting immune signaling and other pathways. Candidate biomarker miRNA-target gene pairs should be further studied for diagnostic, prognostic, and/or therapeutic utility

Peer Reporting and the Perception of Fairness

Economic motives are not the only reasons for committing a (small) crime. People consider social norms and perceptions of fairness before judging a situation and acting upon it. If someone takes a bundle of printing paper from the office for private use at home, then a colleague who sees this can take action by talking to the offender or someone else (peer reporting). We investigate how fairness perception influences the decision to act upon incorrect behavior or not

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

A model for collisions in granular gases

We propose a model for collisions between particles of a granular material and calculate the restitution coefficients for the normal and tangential motion as functions of the impact velocity from considerations of dissipative viscoelastic collisions. Existing models of impact with dissipation as well as the classical Hertz impact theory are included in the present model as special cases. We find that the type of collision (smooth, reflecting or sticky) is determined by the impact velocity and by the surface properties of the colliding grains. We observe a rather nontrivial dependence of the tangential restitution coefficient on the impact velocity.Comment: 11 pages, 2 figure

Digital intervention increases influenza vaccination rates for people with diabetes in a decentralized randomized trial

People with diabetes (PWD) have an increased risk of developing influenza-related complications, including pneumonia, abnormal glycemic events, and hospitalization. Annual influenza vaccination is recommended for PWD, but vaccination rates are suboptimal. The study aimed to increase influenza vaccination rate in people with self-reported diabetes. This study was a prospective, 1:1 randomized controlled trial of a 6-month Digital Diabetes Intervention in U.S. adults with diabetes. The intervention group received monthly messages through an online health platform. The control group received no intervention. Difference in self-reported vaccination rates was tested using multivariable logistic regression controlling for demographics and comorbidities. The study was registered at clinicaltrials.gov: NCT03870997. A total of 10,429 participants reported influenza vaccination status (5158 intervention, mean age (±SD) = 46.8 (11.1), 78.5% female; 5271 control, Mean age (±SD) = 46.7 (11.2), 79.4% female). After a 6-month intervention, 64.2% of the intervention arm reported influenza vaccination, vers us 61.1% in the control arm (diff = 3.1, RR = 1.05, 95% CI [1.02, 1.08], p = 0.0013, number needed to treat = 33 to obtain 1 additional vaccination). Completion of one or more intervention messages was associated with up to an 8% increase in vaccination rate (OR 1.27, 95% CI [1.17, 1.38], p < 0.0001). The intervention improved influenza vaccination rates in PWD, suggesting that leveraging new technology to deliver knowledge and information can improve influenza vaccination rates in high-risk populations to reduce public health burden of influenza. Rapid cycle innovation could maximize the effects of these digital interventions in the future with other populations and vaccines