PM2.5 exposure disparities persist despite strict vehicle emissions controls in California.

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from Californias aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity

Cardiovascular disease and the role of oral bacteria

In terms of the pathogenesis of cardiovascular disease (CVD) the focus has traditionally been on dyslipidemia. Over the decades our understanding of the pathogenesis of CVD has increased, and infections, including those caused by oral bacteria, are more likely involved in CVD progression than previously thought. While many studies have now shown an association between periodontal disease and CVD, the mechanisms underpinning this relationship remain unclear. This review gives a brief overview of the host-bacterial interactions in periodontal disease and virulence factors of oral bacteria before discussing the proposed mechanisms by which oral bacterial may facilitate the progression of CVD

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Code to support: PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

<div> </div> <p>This Github repository contains the Python code for the paper titled "PM<sub>2.5</sub> exposure disparities persist despite strict vehicle emissions controls in California", currently in review at Science Advances. For more information on the analysis, including methodological details, please see the paper: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6584780166c1381729bcf0b0" rel="nofollow">link</a>.</p> <p>Below are links to the key tools and datasets used in this paper:</p> <ul> <li>ECHO-AIR: <a href="https://echo-air-model.github.io/" rel="nofollow">https://echo-air-model.github.io/</a></li> <li>California InMAP Source-Receptor Matrix: <a href="../records/7548607" rel="nofollow">https://zenodo.org/record/2589760</a>. (Accessed Mar. 28, 2024)</li> <li>Year-2010 race-ethnicity data at Census block level: <a href="https://www.nhgis.org/" rel="nofollow">https://www.nhgis.org/</a>.</li> </ul> <p>This repository contains scripts for reproducing the main text figures. The scripts are organized into two folders, as described below.</p> <div> <h2>Code Description</h2> </div> <div> <h3>ECHO-AIR Output Processing</h3> </div> <p>Our estimates of PM<sub>2.5</sub> concentration are modeled using <a href="https://echo-air-model.github.io/" rel="nofollow">ECHO-AIR</a>, a novel open source pipeline based on the InMAP Source-Receptor Matrix. All of the relevant data outputs from ECHO-AIR are included in our data repository.</p> <p>To simplify the scripts used to reproduce the main text figures, we have first included <a href="https://github.com/lkoolik/mobile_retrospective/tree/main/01_ECHO_AIR_Output_Processing">two pre-processing scripts</a> for summarizing these gridded concentration estimates.</p> <ol> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/01_ECHO_AIR_Output_Processing/a_pre_process_by_race_ethnicity.py"><code>a_pre_process_by_race_ethnicity.py</code></a>: summarizes the population-weighted mean exposure concentration and exposure disparity for each racial-ethnic group for each vehicle type.</li> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/01_ECHO_AIR_Output_Processing/b_pre_process_by_policy.py"><code>b_pre_process_by_policy.py</code></a>: summarizes the population-weighted mean exposure concentration and exposure disparity from each vehicle type for each year for AB617 community residents in aggregate, SB535 community residents in aggregate, AB617 community residents by community, and regional boundaries defined in Koolik et al. (2024)</li> </ol> <div> <h3>Main Text Figures</h3> </div> <p>The four figures from the main text of Koolik et al. (2024) are fully reproducible via <a href="https://github.com/lkoolik/mobile_retrospective/tree/main/02_Main_Text_Figures">four scripts</a> included in this repository.</p> <ol> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/02_Main_Text_Figures/figure01_pwm_and_re.py"><code>figure01_pwm_and_re.py</code></a>: creates the line plots of population-weighted mean exposure and relative disparity in exposure by each demographic group</li> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/02_Main_Text_Figures/figure02_exposure_distributions.py"><code>figure02_exposure_distributions.py</code></a>: creates the distribution of population by race-ethnicity at each decile of exposure to the full vehicle fleet in 2000 and 2019</li> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/02_Main_Text_Figures/figure03_disparity_by_source.py"><code>figure03_disparity_by_source.py</code></a>: creates the three-panel figure that breaks down absolute and relative disparity by vehicle fleet for Hispanic Californians</li> <li><a href="https://github.com/lkoolik/mobile_retrospective/blob/main/02_Main_Text_Figures/figure04_spatial_heterogeneity.py"><code>figure04_spatial_heterogeneity.py</code></a>: creates the map of contributions to disparity by vehicle type for the full state, three geographic regions, the two aggregate categories of environmental justice communities, and each of California's AB617 communities</li> </ol> <div> <h3>Requirements</h3> </div> <p>All six scripts included in this repository are written in Python 3.10.2. For reproducibility, we have tried to minimize the number of libraries used to generate these figures. Each library (and corresponding version) is listed below.</p> <ul> <li>cmcrameri (v1.7)</li> <li>geopandas (v0.10.2)</li> <li>matplotlib (v3.5.1)</li> <li>numpy (v1.22.3)</li> <li>pandas (v1.4.2)</li> <li>seaborn (v0.11.2)</li> </ul&gt