Code Level Research Articles

Young adults, particularly young women, account for an increasingly large share of Dutch mental healthcare expenditure over the period between 2015 and 2021.

There is increasing concern over the mental distress of youth in recent years, which may impact mental healthcare utilisation. Here we aim to examine temporal patterns of mental healthcare expenditures in the Netherlands by age and sex in the period between 2015 and 2021. Comprehensive data from health insurers in the Netherlands at the 3-number postal code level were used for cluster weighted linear regressions to examine temporal patterns of mental healthcare expenditure by age group (18-34 vs 35-65). The same was done for medical specialist and general practitioner costs. Additionally, we examined interactions with gender, by adding the interaction between age, year and sex to the model. Mental healthcare costs for younger adults (18-34) were higher than those for older adults (35-65) at all time points (β = 0.22, 95%-CI = 0.19; 0.25). Furthermore there was an increase in the strength of the association between younger age and mental healthcare costs from β = 0.22 (95%-CI = 0.19; 0.25) in 2015 to β = 0.37 (95%-CI = 0.35; 0.40) in 2021 (p < 0.0001) and this was most evident in women (p < 0.0001). Younger age was associated with lower general practitioner costs at all time points, but this association weakened over time. Younger age was also associated with lower medical specialist costs, which did not weaken over time. Young adults, particularly young women, account for an increasing share of mental healthcare expenditure in the Netherlands. This suggests that mental distress in young people is increasingly met by a response from the medical system. To mitigate this trend a public mental health approach is needed.

6676 Exposure to Neighborhood Violence Rewires Glucocorticoid Receptor Binding and Drives Proliferation and Invasiveness in Lung Tumor Samples

Abstract Disclosure: H. Heath: None. J. Yoo: None. S. Akter: None. A. Jain: None. V. Sharma: None. H. McGee: None. A. Soliman: None. A. Mohamed: None. A.K. Matthews: None. R. Winn: None. Z. Madak Erdogan: None. S. Kim: None. Background: Despite the lower rates and frequency of smoking, Black men experience a higher incidence of lung cancer compared to white men. This health disparity is even more prevalent in Black men from Chicago, suggesting local neighborhood factors impacting tumorigenesis. Preliminary data indicate that, compared to white men, Black men are more likely to reside in neighborhoods with high levels of violence and have elevated levels of hair cortisol. Objective: To understand the link between lung tumorigenesis and exposure to neighborhood violence, our objective is to investigate the chromatin recruitment of the receptor for cortisol, called the glucocorticoid receptor (GR), and to correlate this recruitment with target gene expression levels. Methods: Utilizing CUT &amp; RUN, we identified the gene binding sites of GR using 15 lung tumor and corresponding healthy tissue samples from lung cancer patients living in Chicago. GR recruitment to chromatin was correlated with the neighborhood homicide rate obtained using patients’ zip codes. Spatial transcriptome profiles for these 15 lung tumor samples were obtained from the Space Ranger pipeline and gene expression hot spots of GR target genes were visualized using Loupe Browser (10x Genomics). Results: GR recruitment to regulatory regions of chromatin increases with greater zip code level violence rates. In patients from high violence neighborhoods, tumor samples had higher magnitude of binding compared to normal neighboring tissue. In tumor samples from patients living in high violence compared to low violence neighborhoods, GR binding is seen in genes that are associated with increased tumor aggressiveness. Hot-spot analysis of these genes found strong co-expression of genes associated with proliferation and invasion in samples from high violence but not low violence neighborhoods. Conclusion: Exposure to neighborhood violence may impact tumor biology via increased GR recruitment to genes associated with proliferation and invasion. Presentation: 6/2/2024

Orthopaedic Provider Coding Trends After 2021 Changes to the Current Procedural Terminology Evaluation and Management Coding Guidelines: A Retrospective Cohort Study

Background: The goal of this study was to determine changes in orthopaedic coding practices between December 2020 and January 2021 after training providers on the 2021 Current Procedural Terminology Evaluation and Management (E&amp;M) Centers for Medicare and Medicaid Services guideline changes. Methods: Outpatient encounters in December 2020, January 2021, December 2021, and January 2023 were grouped by provider and E&amp;M code level. The codes used for established patients were 99211, 99212, 99213, 99214, and 99215, ordered from low to high-complexity visits. The codes used for new patients were 99201, 99202, 99203, 99204, and 99205 also in order of complexity from low to high. Results: A statistically significant increase was noted in coding levels for both new and established patients from December 2020 to January 2021 (P &lt; 0.001). For new patients, there was an increase in level 4 codes from 16% to 54% and an increase in level 5 codes from 3% to 6% (P &lt; 0.0001). This notable increase in level 4 and 5 codes was maintained through 2 years (January 2023) for new patients. Conclusions: A statistically significant increase was noted in coding levels for both new and established patients since E&amp;M guideline changes. This change was maintained for 2 years.

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Extreme Heat Events and Emergency Department Visits among Older Adults in California from 2012-2019.

Background and Objectives: Extreme heat events are increasing with climate change impacting human health. This study investigates the impact of extreme heat events on Emergency Department (ED) utilization by older adult patients. Materials and Methods: We conducted a study of all 324 non-federal hospital EDs in California during an 8-year period from data extracted from the California Department of Health Care Access and Information (HCAI). The study utilized a time-stratified case-crossover design to investigate ED visited in patients aged 65 years and older during 1-day and 2-day heat wave events. Extreme heat temperatures were measured and weighted using historical data at the zip code level at the 95th, 97.5th, and 99th percentiles 2012 through 2019. Conditional logistical regression was used to estimate the odds of ED visits during extreme heat events compared to non-extreme heat days. Stratified analyses by age and comorbidity status were conducted. Results: During the study period, 8,744,001 of ED visits among older patients were included in the study analysis. Odds ratios (OR) increased for during 1-day heat events (95th percentile (OR = 1.023, 95%CI: 1.020, 1.027), 97.5th percentile (OR = 1.030, 95%CI: 1.025, 1.035), 99th percentile (OR = 1.039, 95%CI: 1.032, 1.058)) and more so with 2-day heat wave events (95th percentile (OR = 1.031, 95%CI: 1.026, 1.036), 97.5th percentile (OR = 1.039, 95%CI: 1.031, 1.046), 99th percentile (OR = 1.044, 95%CI: 1.032, 1.058)). Older patients with three or more comorbidities had the highest odds of ED visits (OR = 1.085, 95%CI: 1.068, 1.112) at the 99th percentile. Conclusions: Our findings indicate that ED visits increase for older patients during extreme heat events, particularly with event intensity and duration. Older patients with at least one comorbidity were at greater risk.

Abstract B135: Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors

Abstract Background: Disparities in pediatric cancer outcomes by race and ethnicity may be influenced by neighborhood characteristics, particularly residential economic and racial segregation. Prior to a new cancer diagnosis, pediatric patients are vulnerable to structural barriers embedded in their neighborhoods. In particular, residential economic and racial segregation may impede access to initial care for early cancer detection, increasing the likelihood of presenting with distant metastatic disease at diagnosis that can translate to excess morbidity and mortality. This study assesses the association of residential economic and racial segregation as measured by the Index of Concentration at the Extremes (ICE) with the likelihood of presenting with distant metastatic disease (as compared to local or locoregional disease) among children newly diagnosed with cancer. Methods: We studied a cohort of pediatric patients diagnosed with solid tumors at Children’s Healthcare of Atlanta between 2010 and 2016. Patients’ ZIP codes were linked to the ICE, which was constructed at the ZIP code level using American Community Survey 2012-2016 five-year estimates. The ICE was categorized into tertile 1 (most deprived) through tertile 3 (most privileged). Multivariable logistic regression models were used to assess the association between residential economic and racial segregation, defined by tertiles of the ICE, and the likelihood of presenting with distant metastatic disease, as determined by initial cancer staging. All models adjusted for patient- and disease-level covariates, including age, sex at birth, health insurance type, and cancer type. Results: Among 829 patients identified in our analytic sample, 27.6% had distant metastatic disease at diagnosis. Living in the most deprived (ICE tertile 1) versus the most privileged (ICE tertiles 2/3) neighborhoods was significantly associated with a higher likelihood of presenting with distant metastatic disease at diagnosis (adjusted odds ratio [OR] = 1.46; 95% confidence interval [CI] = 1.03 to 2.07; p=0.03). Additionally, non-Hispanic Black patients living in the most deprived neighborhoods were more likely to have distant metastatic disease at diagnosis compared to non-Hispanic White patients living in the most privileged neighborhoods (adjusted OR = 1.83; 95% CI = 1.15 to 2.92; p=0.002). Conclusion: This study provides new insights into how residential economic and racial segregation is associated with metastatic disease at diagnosis among children presenting with a new solid tumor. Our findings highlight the need for geographically targeted interventions to improve community care, resource allocation and investment, and public policies aimed at supporting this vulnerable patient population. Citation Format: Alexandra Cathcart, Sharon M. Castellino, Lu Zhang, Karen Wasilewski-Masker, Andrew L. Hong, Heeju Sohn, Xu Ji. Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B135.

Using neural networks for image analysis in general physiology.

An article with three goals, namely, to (1) provide the set of ideas and information needed to understand, at a basic level, the application of convolutional neural networks (CNNs) to analyze images in biology; (2) trace a path to adopting and adapting, at code level, the applications of machine learning (ML) that are freely available and potentially applicable in biology research; (3) by using as examples the networks described in the recent article by Ríos et al. (2024. https://doi.org/10.1085/jgp.202413595), add logic and clarity to their description.

Generating Java code pairing with ChatGPT

The Large Language Models (LLMs) like ChatGPT 3.5 have created a new era of automatic code generation. However, the existing research primarily focuses on generating simple code based on datasets (such as HumanEval, etc.). Most of approaches pay less attention to complex and practical code generation. Therefore, in this paper, we propose a new approach called “Xd-CodeGen” which can be used to generate large scale Java code. This approach is composed of four phases: requirement analysis, modeling, code generation, and code verification. In the requirement analysis phase, ChatGPT 3.5 is utilized to decompose and restate user requirements. To do so, a knowledge graph is developed to describe entities and their relationship in detail. Further, Propositional Projection Temporal Logic (PPTL) formulas are employed to define the properties of requirements. In the modeling phase, we use knowledge graphs to enhance prompts and generate UML class and activity diagrams for each sub-requirement using ChatGPT 3.5. In the code generation phase, based on established UML models, we make use of prompt engineering and knowledge graph to generate Java code. In the code verification phase, a runtime verification at code level approach is employed to verify generated Java code. Finally, we apply the proposed approach to develop a practical Java web project.

OPTIMIZATION OF CROSS-PLATFORM APPLICATIONS USING THE REACT LIBRARY

In today's world, with the growing use of mobile devices, the importance of creating cross-platform applications that provide high performance and quality of user experience has increased significantly. Improving the creation of such applications using React and React Native opens up wide opportunities for developers due to the possibility of code reuse, high development speed and ease of innovation, and is especially relevant because it concerns not only the technological aspects of development, but also economic efficiency, quality of user experience and the speed of introducing innovations to the software market. Open source and active community communication allow rapid identification and resolution of issues, development of new features and enhancements, and the sharing of knowledge and experience between developers. This creates a positive environment for innovation and growth, promotes rapid technological progress, and improves the quality of end products. The paper analyzes the key aspects and challenges associated with the creation of cross-platform applications, analyzes the main types of optimizations at different levels of development, including the component level, the state and data level, the level of working with APIs and external data, as well as the loading and code level. Special attention is paid to the integration of synchronous and asynchronous rendering to achieve optimal performance and user experience. Highlights the benefits of flexible state management using state managers, as well as the importance of image and media optimization to improve overall website performance. The article notes the role of the developer community in the process of improvement and innovation. Developers who master these technologies and optimize their application can create products that not only meet the requirements of the time, but also form new standards in the field of mobile and web development. Further research and integration of the latest technologies into the development process of cross-platform applications will have a significant impact on the software industry.

Evaluating the Predictive Accuracy of Socioeconomic Metrics on Heart Failure Risk and Outcomes in Maryland.

Introduction Annually, a significant number of Americans are hospitalized due to heart failure (HF), marking it as an important contributor to morbidity and mortality. It also poses a substantial financial burden and leads to considerable losses in productivity. Socioeconomic disparities may intensify the risk of hospital admissions following HF and worsen patient outcomes. Objective This study investigates the predictive accuracy of different socioeconomic metrics on the risk and outcomes of HF in Maryland. Methodology To evaluate the predictive accuracy of various socioeconomic metrics on the risk of HF, we utilized data from the Maryland State Inpatient Database. Our retrospective analysis covered hospital admissions for HF from 2016 to 2020, correlating these with poverty indicators derived from U.S. Census data at the zip code level with socioeconomic metrics like race/ethnicity, insurance, household median income, and neighborhood distress (Distressed Communities Index (DCI)). Multivariate logistic regression models adjusted for confounders and isolated the impact of socioeconomic factors. Result During the study period, a total of 389,220 cases of HF were reported in the Maryland State Inpatient Database (SID). The majority of these patients were White individuals (56.8%) and female (51.1%), with a median age of 73 years (interquartile range (IQR) 62-82 years). The in-hospital mortality rate was 5.1%, while rates of atrial fibrillation, cardiac arrest, and prolonged hospital stay were 34.4%, 0.3%, and 48.4%, respectively. The studied socioeconomic metrics showed varying predictive power for the risk of HF-related admissions and selected outcomes, with the highest predictive accuracy for neighborhood distress on the risk of HF (AUC = 0.53, 95% CI 0.530-0.532), atrial fibrillation (AUC = 0.479, 95% CI 0.477-0.480), cardiac arrest (AUC = 0.511, 95% CI 0.498-0.525), prolonged hospital stays (AUC = 0.531, 95% CI 0.530-0.532), and mortality (AUC = 0.499, 95% CI 0.496-0.502). Conclusions The Distressed Communities Index demonstrates significant predictive power for assessing the risk of hospital admissions following HF and outcomes among individuals with HF, exceeding factors like insurance, race/ethnicity, and household median income.

Evaluation of a Multi-Component Phenotype Algorithm for Systemic Lupus Erythematosus across the PCORnet, OMOP, and i2b2 Common Data Models

The aim of this study was to evaluate the performance of a computable phenotype for systemic lupus erythematosus (SLE) patients when it is ported from a local data warehouse to the i2b2, OMOP, and PCORnet CDMs. We adapted the SLE phenotype to the Northwestern Medicine (NM) Enterprise Data Warehouse (EDW) and NU i2b2, OMOP, and PCORnet instances. Each of the phenotype’s 17 components were determined by a rules-based algorithm built on diagnosis, medication, lab, and procedure codes. We assessed the phenotype over 168 clinician-confirmed SLE patients and 100 healthy controls and calculated agreement between the EDW and CDMs for overall SLE classification, individual SLICC criteria membership, and individual code occurrence level aggregates - count, first and last date, lab values using Cohen’s kappa (Cκ) and intraclass correlation coefficients (ICC). For overall SLE classification, agreement of the OMOP and PCORnet datamarts with the EDW was high (Cκ 0.928, 0.802, respectively) while that of i2b2 was low (Cκ 0.328). For the panel of SLICC criteria, OMOP had high agreement for 14/17 SLICC criteria (Cκ 0.792-1.000). PCORnet had high agreement for 13/17 criteria (Cκ 0.708-1.000). i2b2 had high agreement for 10/17 criteria (Cκ 0.813-1.000) with the exception of lab-based criteria. While agreement between the EDW and CDMs at the level of individual codes was relatively poor, agreement at the levels of overall SLE classification and individual SLICC criteria were reasonably high for OMOP and PCORnet, suggesting that inconsistencies at a micro, code level become less apparent at the macro, phenotype level.

A hypothesis about interrelations of epigenetic factors and transposable elements in memory formation.

The review describes the hypothesis that the drivers of epigenetic regulation in memory formation are transposable elements that influence the expression of specific genes in the brain. The hypothesis is confirmed by research into transposon activation in neuronal stem cells during neuronal differentiation. These changes occur in the hippocampus dentate gyrus, where a pronounced activity of transposons and their insertion near neuron-specific genes have been detected. In experiments on changing the activity of histone acetyltransferase and inhibition of DNA methyltransferase and reverse transcriptase, the involvement of epigenetic factors and retroelements in the mechanisms of memory formation has been shown. Also, a number of studies on different animals have revealed the preservation of long-term memory without the participation of synaptic plasticity. The data obtained suggest that transposons, which are genome sensors highly sensitive to various environmental and internal influences, form memory at the nuclear coding level. Therefore, long-term memory is preserved after elimination of synaptic connections. This is confirmed by the fact that the proteins involved in memory formation, including the transfer of genetic information through synapses between neurons (Arc protein), originate from transposons. Long non-coding RNAs and microRNAs also originate from transposons; their role in memory consolidation has been described. Pathological activation of transposable elements is a likely cause of neurodegenerative diseases with memory impairment. Analysis of the scientific literature allowed us to identify changes in the expression of 40 microRNAs derived from transposons in Alzheimer's disease. For 24 of these microRNAs, the mechanisms of regulation of genes involved in the functioning of the brain have been described. It has been suggested that the microRNAs we identified could become potential tools for regulating transposon activity in the brain in order to improve memory.

Analyzing Fatal Police Shootings: The Roles of Social Vulnerability, Race, and Place in the U.S.

IntroductionSocial vulnerability, race, and place are three important predictors of fatal police shootings. This research offers the first assessment of these factors at the zip code level. MethodsThe 2015-2022 Mapping Police Violence and Washington Post Fatal Force Data (2015-2022) were used and combined with the American Community Survey (2015-2022). The social vulnerability index (SVI) was computed for each zip code by using indicators suggested by CDC, then categorized into low-, medium-, and high-SVI. The analytical file included police officers who fatally shot 6,901 individuals within 32,736 zip codes between 2015-2022. Negative Binomial Regression (NBRG) models were run to estimate the association between number of police shootings and zip code SVI, racial composition, and access to guns using 2015-2022 data. ResultsMoving from low-SVI to high-SVI revealed the number of fatal police shootings increased 8.3 times, with the highest increases in Blacks (20.4 times), and Hispanics (27.1 times). The NBRG showed that moderate-, and high-SVI zip codes experienced higher fatal police shootings by 1.97, and 3.26 times than low-SVI zip codes; zip code racial composition, working age population, number of violent crimes, number of police officers and access to a gun, were other predictors of fatal police shootings. ConclusionsSocial vulnerability and racial composition of a zip code are associated with fatal police shooting, both independently and when considered together. What drives deadly police shootings in the United States is not one single factor, but rather complex interactions between social-vulnerability, race, and place that must be tackled synchronously. Action must be taken to address underlying determinants of disparities in policing.

Impact of Warehouse Expansion on Ambient PM2.5 and Elemental Carbon Levels in Southern California's Disadvantaged Communities: A Two‐Decade Analysis

AbstractOver the past two decades, the surge in warehouse construction near seaports and in economically lower‐cost land areas has intensified product transportation and e‐commerce activities, particularly affecting air quality and health in nearby socially disadvantaged communities. This study, spanning from 2000 to 2019 in Southern California, investigated the relationship between ambient concentrations of PM2.5 and elemental carbon (EC) and the proliferation of warehouses. Utilizing satellite‐driven estimates of annual mean ambient pollution levels at the ZIP code level and linear mixed effect models, positive associations were found between warehouse characteristics such as rentable building area (RBA), number of loading docks (LD), and parking spaces (PS), and increases in PM2.5 and EC concentrations. After adjusting for demographic covariates, an Interquartile Range increase of the RBA, LD, and PS were associated with a 0.16 μg/m³ (95% CI = [0.13, 0.19], p &lt; 0.001), 0.10 μg/m³ (95% CI = [0.08, 0.12], p &lt; 0.001), and 0.21 μg/m³ (95% CI = [0.18, 0.24], p &lt; 0.001) increase in PM2.5, respectively. For EC concentrations, an IQR increase of RBA, LD, and PS were each associated with a 0.021 μg/m³ (95% CI = [0.019, 0.024], p &lt; 0.001), 0.014 μg/m³ (95% CI = [0.012, 0.015], p &lt; 0.001), and 0.021 μg/m³ (95% CI = [0.019, 0.024], p &lt; 0.001) increase. The study also highlighted that disadvantaged populations, including racial/ethnic minorities, individuals with lower education levels, and lower‐income earners, were disproportionately affected by higher pollution levels.

Scalability of CASCADE2001: Gis-Based Flood Risk Screening Tool to Support Watershed Master Planning

Flood risk analysis is the instrument by which floodplain and stormwater utility managers create a sound strategy and adaptation plans to reduce flood potential in their communities. As a result, there is a need to develop a flood risk screening tool to analyze watersheds and find vulnerable areas by leveraging the scientific and technological developments of the last decade to prioritize improvements. Because local municipalities are continuously challenged by the impacts of changes in precipitation and other climatic events, the screening tool needs to be scalable, while providing similar results of spatial inundation extent regardless of the scale. Likewise, the ability to perform this analysis without overwhelming limited modeling budgets is a goal for local governments who may spend large amounts of money on capital in the future to retrofit their stormwater management systems. The present study investigates the scalability of a GIS-based hydrologic-hydraulic model, CASCADE2001, to support development of watershed-based flood protection plans. The comparative analysis of the predicted flood response at three nested levels of scale were applied in south Florida to a 8-digit hydrologic unit code (HUC) level (the Caloosahatchee Watershed), a 12-digit HUC level (the Ninemile Canal Subwatershed within the Caloosahatchee Watershed), and a local municipal level (City of Clewiston, Florida within the Ninemile Canal subwatershed). The findings were that the model was scalable and the infrastructure that mattered with respect to results was driven by the scale.

Distance to offshore wind farms, onshore wind power spillover relationships, and the willingness to pay for farshore, large-scale wind power development

ABSTRACT As demands for clean energy mount to meet international and national climate goals, constant tension and trade-offs regarding where to site large-scale offshore wind farms (OWF) remain. Using data from a choice experiment to elicit preferences for large-scale, 3.6 gigawatts OWFs in Denmark, we find that respondents living far from a proposed or existing offshore wind power projects have a lower willingness to pay (WTP) to locate new OWFs at 12 km, 18 km, or 50 km relative to 8 km from the coast. Furthermore, based on the estimated densities of existing onshore wind turbines at a given respondent’s postal (ZIP) code level, we find evidence of a significant relationship between existing onshore wind power development and preferences for proposed OWF locations.

An automated OpenMP mutation testing framework for performance optimization

Performance optimization continues to be a challenge in modern HPC software. Existing performance optimization techniques, including profiling-based and auto-tuning techniques, fail to indicate program modifications at the source level thus preventing their portability across compilers. This paper describes Muppet, a new approach that identifies program modifications called mutations aimed at improving program performance. Muppet’s mutations help developers reason about performance defects and missed opportunities to improve performance at the source code level. In contrast to compiler techniques that optimize code at intermediate representations (IR), Muppet uses the idea of source-level mutation testing to relax correctness constraints and automatically discover optimization opportunities that otherwise are not feasible using the IR. We demonstrate the Muppet’s concept in the OpenMP programming model. Muppet generates a list of OpenMP mutations that alter the program parallelism in various ways, and is capable of running a variety of optimization algorithms such as delta debugging, Bayesian Optimization and decision tree optimization to find a subset of mutations which, when applied to the original program, cause the most speedup while maintaining program correctness. When Muppet is evaluated against a diverse set of benchmark programs and proxy applications, it is capable of finding sets of mutations that induce speedup in 75.9% of the evaluated programs.

Contextual Deprivation, Race and Ethnicity, and Income in Air Pollution and Cardiovascular Disease

Socioeconomically disadvantaged subpopulations are more vulnerable to fine particulate matter (PM2.5) exposure. However, as prior studies focused on individual-level socioeconomic characteristics, how contextual deprivation modifies the association of PM2.5 exposure with cardiovascular health remains unclear. To assess disparities in PM2.5 exposure association with cardiovascular disease among subpopulations defined by different socioeconomic characteristics. This cohort study used longitudinal data on participants with electronic health records (EHRs) from the All of Us Research Program between calendar years 2016 and 2022. Statistical analysis was performed from September 25, 2023, through February 23, 2024. Satellite-derived 5-year mean PM2.5 exposure at the 3-digit zip code level according to participants' residential address. Incident myocardial infarction (MI) and stroke were obtained from the EHRs. Stratified Cox proportional hazards regression models were used to estimate the hazard ratio (HR) between PM2.5 exposure and incident MI or stroke. We evaluated subpopulations defined by 3 socioeconomic characteristics: contextual deprivation (less deprived, more deprived), annual household income (≥$50 000, <$50 000), and race and ethnicity (non-Hispanic Black, non-Hispanic White). We calculated the ratio of HRs (RHR) to quantify disparities between these subpopulations. A total of 210 554 participants were analyzed (40% age >60 years; 59.4% female; 16.7% Hispanic, 19.4% Non-Hispanic Black, 56.1% Non-Hispanic White, 7.9% other [American Indian, Asian, more than 1 race and ethnicity]), among whom 954 MI and 1407 stroke cases were identified. Higher PM2.5 levels were associated with higher MI and stroke risks. However, disadvantaged groups (more deprived, income <$50 000 per year, Black race) were more vulnerable to high PM2.5 levels. The disparities were most pronounced between groups defined by contextual deprivation. For instance, increasing PM2.5 from 6 to 10 μg/m3, the HR for stroke was 1.13 (95% CI, 0.85-1.51) in the less-deprived vs 2.57 (95% CI, 2.06-3.21) in the more-deprived cohort; 1.46 (95% CI, 1.07-2.01) in the $50 000 or more per year vs 2.27 (95% CI, 1.73-2.97) in the under $50 000 per year cohort; and 1.70 (95% CI, 1.35-2.16) in White individuals vs 2.76 (95% CI, 1.89-4.02) in Black individuals. The RHR was highest for contextual deprivation (2.27; 95% CI, 1.59-3.24), compared with income (1.55; 95% CI, 1.05-2.29) and race and ethnicity (1.62; 95% CI, 1.02-2.58). In this cohort study, while individual race and ethnicity and income remained crucial in the adverse association of PM2.5 with cardiovascular risks, contextual deprivation was a more robust socioeconomic characteristic modifying the association of PM2.5 exposure.

Coverage hetnet based picocell and femtocell for uplink condition around building environment with single knife edge method

The development of HetNet (Heterogeneous Network) radio base stations has experienced many developments. This is indicated by the existence of microcells, macrocells, picocells, femtocells, and so on. In this research, the research is aimed at the propagation of user equipment communication systems in uplink conditions with HetNet picocells and femtocells. UE propagation is on a straight path between the building environment. The communication frequency used is 10 GHz. The communication between Tx and Rx is modeled as a diffraction mechanism, AWGN channel, and atmospheric attenuation. The Single Knife Edge (SKE) method is used to model the mechanism. The propagation channel is faced with AWGN (Additive White Gaussian Noise). The analysis of this research includes the SNR value, Adaptive Modulation and Coding (AMC) level, and percentage of communication coverage area. The AMC is based on the use of MCS (Modulation and Code Scheme). Some of the MCS modulations used QPSK, 16 QAM, and 64 QAM. As research results show that the percentage of communication coverage area obtained was gNB1 80.59 percent, gNB2 65.67%, and selection combining HetNet 95.52%.

Examining Relationships between Perceptions of Air Quality-Objectively Assessed Particulate Matter-And Health-Related Attributions among Midlife and Older Adults from the San Francisco Bay Area, California, USA.

This investigation explored (1) correlations between midlife and older adults' air quality perceptions with objective particulate matter 2.5 (PM2.5) and diesel PM, and (2) correlations between air quality perceptions with health-related attributions among a sample of midlife and older adults (n = 66) living in or around senior affordable public housing sites in California's San Francisco Bay Area. The adapted air quality perception scale was used to measure perceptions of air quality, while health-related attributions were obtained from the vitality plus scale (VPS), with higher values indicating worse perceptions of air quality and poorer responses to health-related attributions, respectively. Self-reported data were linked to zip code level PM2.5 and diesel PM obtained from the CalEnviroScreen 4.0. All correlations were evaluated using Spearman's rank correlations. The mean (SD) age was 70.6 (9.1) years, and 75.7% were female. We observed moderate, positive correlations between both PM2.5 and diesel PM with three domains: perceptions related to protection measures against air quality, emotional/mental perceptions, and sensorial perceptions. We also found evidence of moderate, positive correlations between the domains of physical symptoms, perceptions related to protection measures against air quality, and emotional/mental perceptions with health-related attributions, such as sleep-related items and feelings of restlessness or agitation. Results from this exploratory study suggest that midlife and older adults' perceptions of air quality may be moderately related to both objective air quality data and certain health behaviors and symptoms. Findings underscore the importance of considering individual perceptions as an additional area in public health strategies aimed at protecting midlife and older adults from the impacts of air pollution.