Platelets Regulate Pulmonary Inflammation and Tissue Destruction in Tuberculosis.

RATIONALE: Platelets may interact with the immune system in tuberculosis (TB) to regulate human inflammatory responses that lead to morbidity and spread of infection. OBJECTIVES: To identify a functional role of platelets in the innate inflammatory and matrix-degrading response in TB. METHODS: Markers of platelet activation were examined in plasma from 50 patients with TB before treatment and 50 control subjects. Twenty-five patients were followed longitudinally. Platelet-monocyte interactions were studied in a coculture model infected with live, virulent Mycobacterium tuberculosis (M.tb) and dissected using qRT-PCR, Luminex multiplex arrays, matrix degradation assays, and colony counts. Immunohistochemistry detected CD41 (cluster of differentiation 41) expression in a pulmonary TB murine model, and secreted platelet factors were measured in BAL fluid from 15 patients with TB and matched control subjects. MEASUREMENTS AND MAIN RESULTS: Five of six platelet-associated mediators were upregulated in plasma of patients with TB compared with control subjects, with concentrations returning to baseline by Day 60 of treatment. Gene expression of the monocyte collagenase MMP-1 (matrix metalloproteinase-1) was upregulated by platelets in M.tb infection. Platelets also enhanced M.tb-induced MMP-1 and -10 secretion, which drove type I collagen degradation. Platelets increased monocyte IL-1 and IL-10 and decreased IL-12 and MDC (monocyte-derived chemokine; also known as CCL-22) secretion, as consistent with an M2 monocyte phenotype. Monocyte killing of intracellular M.tb was decreased. In the lung, platelets were detected in a TB mouse model, and secreted platelet mediators were upregulated in human BAL fluid and correlated with MMP and IL-1β concentrations. CONCLUSIONS: Platelets drive a proinflammatory, tissue-degrading phenotype in TB

Framework for sustained climate assessment in the United States

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society, 100(5), (2019): 897-908, doi:10.1175/BAMS-D-19-0130.1.As states, cities, tribes, and private interests cope with climate damages and seek to increase preparedness and resilience, they will need to navigate myriad choices and options available to them. Making these choices in ways that identify pathways for climate action that support their development objectives will require constructive public dialogue, community participation, and flexible and ongoing access to science- and experience-based knowledge. In 2016, a Federal Advisory Committee (FAC) was convened to recommend how to conduct a sustained National Climate Assessment (NCA) to increase the relevance and usability of assessments for informing action. The FAC was disbanded in 2017, but members and additional experts reconvened to complete the report that is presented here. A key recommendation is establishing a new nonfederal “climate assessment consortium” to increase the role of state/local/tribal government and civil society in assessments. The expanded process would 1) focus on applied problems faced by practitioners, 2) organize sustained partnerships for collaborative learning across similar projects and case studies to identify effective tested practices, and 3) assess and improve knowledge-based methods for project implementation. Specific recommendations include evaluating climate models and data using user-defined metrics; improving benefit–cost assessment and supporting decision-making under uncertainty; and accelerating application of tools and methods such as citizen science, artificial intelligence, indicators, and geospatial analysis. The recommendations are the result of broad consultation and present an ambitious agenda for federal agencies, state/local/tribal jurisdictions, universities and the research sector, professional associations, nongovernmental and community-based organizations, and private-sector firms.This report would not have been possible without the support and participation of numerous organizations and individuals. We thank New York State Governor Andrew M. Cuomo for announcing in his 2018 State of the State agenda that the IAC would be reconvened. The New York State Energy Research and Development Authority (Contract ID 123416), Columbia University’s Earth Institute, and the American Meteorological Society provided essential financial support and much more, including sage advice and moral support from John O’Leary, Shara Mohtadi, Steve Cohen, Alex Halliday, Peter deMenocal, Keith Seitter, Paul Higgins, and Bill Hooke. We thank the attendees of a workshop, generously funded by the Kresge Foundation in November of 2017, that laid a foundation for the idea to establish a civil-society-based assessment consortium. During the course of preparing the report, IAC members consulted with individuals too numerous to list here—state, local, and tribal officials; researchers; experts in nongovernmental and community-based organizations; and professionals in engineering, architecture, public health, adaptation, and other areas. We are so grateful for their time and expertise. We thank the members and staff of the National Academy of Sciences, Engineering, and Medicine’s Committee to Advise the U.S. Global Change Research Program for providing individual comments on preliminary recommendations during several discussions in open sessions of their meetings. The following individuals provided detailed comments on an earlier version of this report, which greatly sharpened our thinking and recommendations: John Balbus, Tom Dietz, Phil Duffy, Baruch Fischhoff, Brenda Hoppe, Melissa Kenney, Linda Mearns, Claudia Nierenberg, Kathleen Segerson, Soroosh Sorooshian, Chris Weaver, and Brian Zuckerman. Mary Black provided insightful copy editing of several versions of the report. We also thank four anonymous reviewers for their effort and care in critiquing and improving the report. It is the dedication, thoughtful feedback, expertise, care, and commitment of all these people and more that not only made this report possible, but allow us all to continue to support smart and insightful actions in a changing climate. We are grateful as authors and as global citizens. Author contributions: RM, SA, KB, MB, AC, JD, PF, KJ, AJ, KK, JK, ML, JM, RP, TR, LS, JS, JW, and DZ were members of the IAC and shared in researching, discussing, drafting, and approving the report. BA, JF, AG, LJ, SJ, PK, RK, AM, RM, JN, WS, JS, PT, GY, and RZ contributed to specific sections of the report

Evaluating knowledge to support climate action: A framework for sustained assessment. report of an independent advisory committee on applied climate assessment.

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Weather Climate and Society 11(3), (2019):465-487, doi: 10.1175/WCAS-D-18-0134.1.As states, cities, tribes, and private interests cope with climate damages and seek to increase preparedness and resilience, they will need to navigate myriad choices and options available to them. Making these choices in ways that identify pathways for climate action that support their development objectives will require constructive public dialogue, community participation, and flexible and ongoing access to science- and experience-based knowledge. In 2016, a Federal Advisory Committee (FAC) was convened to recommend how to conduct a sustained National Climate Assessment (NCA) to increase the relevance and usability of assessments for informing action. The FAC was disbanded in 2017, but members and additional experts reconvened to complete the report that is presented here. A key recommendation is establishing a new nonfederal “climate assessment consortium” to increase the role of state/local/tribal government and civil society in assessments. The expanded process would 1) focus on applied problems faced by practitioners, 2) organize sustained partnerships for collaborative learning across similar projects and case studies to identify effective tested practices, and 3) assess and improve knowledge-based methods for project implementation. Specific recommendations include evaluating climate models and data using user-defined metrics; improving benefit–cost assessment and supporting decision-making under uncertainty; and accelerating application of tools and methods such as citizen science, artificial intelligence, indicators, and geospatial analysis. The recommendations are the result of broad consultation and present an ambitious agenda for federal agencies, state/local/tribal jurisdictions, universities and the research sector, professional associations, nongovernmental and community-based organizations, and private-sector firms.This report would not have been possible without the support and participation of numerous organizations and individuals. We thank New York State Governor Andrew M. Cuomo for announcing in his 2018 State of the State agenda that the IAC would be reconvened. The New York State Energy Research and Development Authority (Contract ID 123416), Columbia University’s Earth Institute, and the American Meteorological Society provided essential financial support and much more, including sage advice and moral support from John O’Leary, Shara Mohtadi, Steve Cohen, Alex Halliday, Peter deMenocal, Keith Seitter, Paul Higgins, and Bill Hooke. We thank the attendees of a workshop, generously funded by the Kresge Foundation in November of 2017, that laid a foundation for the idea to establish a civil-society-based assessment consortium. During the course of preparing the report, IAC members consulted with individuals too numerous to list here—state, local, and tribal officials; researchers; experts in nongovernmental and community-based organizations; and professionals in engineering, architecture, public health, adaptation, and other areas. We are so grateful for their time and expertise. We thank the members and staff of the National Academy of Sciences, Engineering, and Medicine’s Committee to Advise the U.S. Global Change Research Program for providing individual comments on preliminary recommendations during several discussions in open sessions of their meetings. The following individuals provided detailed comments on an earlier version of this report, which greatly sharpened our thinking and recommendations: John Balbus, Tom Dietz, Phil Duffy, Baruch Fischhoff, Brenda Hoppe, Melissa Kenney, Linda Mearns, Claudia Nierenberg, Kathleen Segerson, Soroosh Sorooshian, Chris Weaver, and Brian Zuckerman. Mary Black provided insightful copy editing of several versions of the report. We also thank four anonymous reviewers for their effort and care in critiquing and improving the report. It is the dedication, thoughtful feedback, expertise, care, and commitment of all these people and more that not only made this report possible, but allow us all to continue to support smart and insightful actions in a changing climate. We are grateful as authors and as global citizens. Author contributions: RM, SA, KB, MB, AC, JD, PF, KJ, AJ, KK, JK, ML, JM, RP, TR, LS, JS, JW, and DZ were members of the IAC and shared in researching, discussing, drafting, and approving the report. BA, JF, AG, LJ, SJ, PK, RK, AM, RM, JN, WS, JS, PT, GY, and RZ contributed to specific sections of the report.2020-05-2

Resting state cortico-thalamic-striatal connectivity predicts pesponse to dorsomedial prefrontal rTMS in major depressive disorder

Despite its high toll on society, there has been little recent improvement in treatment efficacy for Major Depressive Disorder (MDD). The identification of biological markers of successful treatment response may allow for more personalized and effective treatment. Here we investigate whether resting state functional connectivity predicted response to treatment with rapid transcranial magnetic stimulation (rTMS) to dorsomedial prefrontal cortex (dmPFC). Twenty five individuals with treatment-refractory MDD underwent a 4-week course of dmPFC-rTMS. Before and after treatment, subjects received resting state functional MRI scans and assessments of depressive symptoms using the Hamilton Depresssion Rating Scale (HAMD17). We found that higher baseline cortico-cortical connectivity (dmPFC-subgenual cingulate and subgenual cingulate to dorsolateral PFC) and lower cortico-thalamic, cortico-striatal and cortico-limbic connectivity were associated with better treatment outcomes. We also investigated how changes in connectivity over the course of treatment related to improvements in HAMD17 scores. We found that successful treatment was associated with increased dmPFC-thalamic connectivity and decreased sgACC-caudate connectivity, Our findings provide insight into which individuals might respond to rTMS treatment and the mechanisms through which these treatments work

Non-motor phenotypic subgroups in adult-onset idiopathic, isolated, focal cervical dystonia

Background: Non-motor symptoms are well established phenotypic components of adult-onset idiopathic, isolated, focal cervical dystonia (AOIFCD). However, improved understanding of their clinical heterogeneity is needed to better target therapeutic intervention. Here, we examine non-motor phenotypic features to identify possible AOIFCD subgroups. Methods: Participants diagnosed with AOIFCD were recruited via specialist neurology clinics (dystonia wales: n = 114, dystonia coalition: n = 183). Non-motor assessment included psychiatric symptoms, pain, sleep disturbance, and quality of life, assessed using self-completed questionnaires or face-to-face assessment. Both cohorts were analyzed independently using Cluster, and Bayesian multiple mixed model phenotype analyses to investigate the relationship between non-motor symptoms and determine evidence of phenotypic subgroups. Results: Independent cluster analysis of the two cohorts suggests two predominant phenotypic subgroups, one consisting of approximately a third of participants in both cohorts, experiencing increased levels of depression, anxiety, sleep impairment, and pain catastrophizing, as well as, decreased quality of life. The Bayesian approach reinforced this with the primary axis, which explained the majority of the variance, in each cohort being associated with psychiatric symptomology, and also sleep impairment and pain catastrophizing in the Dystonia Wales cohort. Conclusions: Non-motor symptoms accompanying AOIFCD parse into two predominant phenotypic sub-groups, with differences in psychiatric symptoms, pain catastrophizing, sleep quality, and quality of life. Improved understanding of these symptom groups will enable better targeted pathophysiological investigation and future therapeutic intervention

Demonstration of surface electron rejection with interleaved germanium detectors for dark matter searches

The following article appeared in Applied Physics Letters 103.16 (2013): 164105 and may be found at http://scitation.aip.org/content/aip/journal/apl/100/26/10.1063/1.4729825The SuperCDMS experiment in the Soudan Underground Laboratory searches for dark matter with a 9-kg array of cryogenic germanium detectors. Symmetric sensors on opposite sides measure both charge and phonons from each particle interaction, providing excellent discrimination between electron and nuclear recoils, and between surface and interior events. Surface event rejection capabilities were tested with two 210 Pb sources producing ∼130 beta decays/hr. In ∼800 live hours, no events leaked into the 8–115 keV signal region, giving upper limit leakage fraction 1.7 × 10−5 at 90% C.L., corresponding to < 0.6 surface event background in the future 200-kg SuperCDMS SNOLAB experiment.This work is supported in part by the National Science Foundation (Grant Nos. AST-9978911, NSF-0847342, PHY-1102795,NSF-1151869, PHY-0542066, PHY-0503729, PHY-0503629, PHY-0503641, PHY-0504224, PHY-0705052,PHY-0801708, PHY-0801712, PHY-0802575, PHY-0847342, PHY-0855299, PHY-0855525, and PHY-1205898), by the Department of Energy (Contract Nos. DE-AC03-76SF00098, DE-FG02-92ER40701, DE-FG02-94ER40823,DE-FG03-90ER40569, DE-FG03-91ER40618, and DESC0004022),by NSERC Canada (Grant Nos. SAPIN 341314 and SAPPJ 386399), and by MULTIDARK CSD2009-00064 and FPA2012-34694. Fermilab is operated by Fermi Research Alliance, LLC under Contract No. De-AC02-07CH11359, while SLAC is operated under Contract No. DE-AC02-76SF00515 with the United States Department of Energy

Connectivity-based parcellation of the human frontal polar cortex

The frontal pole corresponds to Brodmann area (BA) 10, the largest single architectonic area in the human frontal lobe. Generally, BA10 is thought to contain two or three subregions that subserve broad functions such as multitasking, social cognition, attention, and episodic memory. However, there is a substantial debate about the functional and structural heterogeneity of this large frontal region. Previous connectivity-based parcellation studies have identified two or three subregions in the human frontal pole. Here, we used diffusion tensor imaging to assess structural connectivity of BA10 in 35 healthy subjects and delineated subregions based on this connectivity. This allowed us to determine the correspondence of structurally based subregions with the scheme previously defined functionally. Three subregions could be defined in each subject. However, these three subregions were not spatially consistent between subjects. Therefore, we accepted a solution with two subregions that encompassed the lateral and medial frontal pole. We then examined resting-state functional connectivity of the two subregions and found significant differences between their connectivities. The medial cluster was connected to nodes of the default-mode network, which is implicated in internally focused, self-related thought, and social cognition. The lateral cluster was connected to nodes of the executive control network, associated with directed attention and working memory. These findings support the concept that there are two major anatomical subregions of the frontal pole related to differences in functional connectivity

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages