Population Model Research Articles

A new perspective on the stellar mass-metallicity relation of quiescent galaxies from the LEGA-C survey

We investigated the stellar mass-metallicity relation (MZR) using a sample of 637 quiescent galaxies with 10.4 ≤ log(M*/M⊙) < 11.7 selected from the LEGA-C survey at 0.6 ≤ z ≤ 1. We derived mass-weighted stellar metallicities using full-spectral fitting. We find that while lower-mass galaxies are both metal-rich and metal-poor, there are no metal-poor galaxies at high masses, and that metallicity is bounded at low values by a mass-dependent lower limit. This lower limit increases with mass, empirically defining a MEtallicity-Mass Exclusion (MEME) zone. We find that the spectral index MgFe ≡ √Mgb × Fe4383, a proxy for the stellar metallicity, also shows a mass-dependent lower limit resembling the MEME relation. Crucially, MgFe is independent of stellar population models and fitting methods. By constructing the metallicity enrichment histories, we find that, after the first gigayear, the star formation history of galaxies has a mild impact on the observed metallicity distribution. Finally, from the average formation times, we find that galaxies populate differently the metallicity-mass plane at different cosmic times, and that the MEME limit is recovered by galaxies that formed at z ≥ 3. Our work suggests that the stellar metallicity of quiescent galaxies is bounded by a lower limit which increases with the stellar mass. On the other hand, low-mass galaxies can have metallicities as high as galaxies ∼1 dex more massive. This suggests that, at log(M*/M⊙)≥10.4, rather than lower-mass galaxies being systematically less metallic, the observed MZR might be a consequence of the lack of massive metal-poor galaxies.

Effectiveness of nutrition interventions targeting university-level student populations across the League of Arab States: a systematic scoping review.

The university setting is an ideal setting to implement nutrition education interventions. Because the transition to college life often overlaps with young adults' increase in independence in food choice and nutrition related behavior, this is an optimal time to target this population and several models exist to best utilize the college setting to support nutrition education interventions. This review aimed to examine the effectiveness of nutrition interventions in university student populations across the League of Arab States. A scoping review following the PRISMA-ScR guidelines was conducted across 14 databases for relevant publications published through May 2023 to find studies conducted across Arab countries. Quality assessment was conducted using the Effective Public Health Practice Project tool. Seven primary studies were included in this review. Although the interventions and outcome measures were disparate, there were positive impacts of the interventions, including improved nutrition knowledge and nutrition habits, knowledge of nutrient sources, and knowledge about the relationship between diet and disease. Recommendations to maximize the efficacy of nutrition interventions conducted across the League of Arab States include purposeful design of the intervention based on a theoretical framework, the use of a control group in the research design, and follow-up measures to examine post-intervention effects. Consideration should also be given to intervention duration and availability in college-students' schedules. Adaptation of intervention materials, measures and delivery methodologies should emphasize local foods, food culture and dietary practices.

Fecundity without nectar is insufficient for the persistence of a blue butterfly.

Organisms with complex life cycles undergo ecological transitions between life stages, often resulting in stage-specific resource use. The relative contribution of each stage-specific resource to vital rates influences population dynamics and subsequently whether habitats can support viable populations. In lepidopterans, survival to reproduction requires sufficient resources for immature life stages, but the extent to which resources for adults are critical to population persistence is variable. We studied Boisduval's blue butterflies (Icaricia icarioides), in a greenhouse experiment, to quantify the effect of the adult diet, nectar, on vital rates. Butterflies fed ad libitum produced 3.4 times more eggs, on average, over their lifetime and lived 6 more days relative to those which only had access to water. We used these experimental data to parameterize a population model to test if vital rates with and without nectar result in viable population growth rates. Despite individual females laying 68 eggs without nectar, we found that Boisduval's blue butterfly populations will not persist without the improved fecundity associated with nectar resources (λ<1). In this species, although amino acids in the adult diet contributed to various improvements in fecundity, these improvements did not translate to improvements in population growth rates. Incorporating our experimental vital rates into a population model indicates that the relative abundance and quality of nectar can alter at what threshold other resource(s) are limiting the population.

A Systematic Review and Narrative Synthesis of Factors Affecting Pre-exposure Prophylaxis Willingness Among Black Women for HIV Prevention.

Pre-exposure prophylaxis (PrEP) can significantly reduce human immunodeficiency virus (HIV) transmission among Black women in the United States (U.S.), a group disproportionately affected by HIV. However, PrEP uptake in this HIV-vulnerable population is low. This review analyzes the factors influencing Black women's PrEP willingness using the Behavioral Model for Vulnerable Populations (BMVP). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison, Outcome, Study Design (PICOS) framework, we conducted a systematic literature search and selected 24 peer-reviewed studies on PrEP willingness. Narrative synthesis revealed a heterogeneous landscape of the determinants affecting PrEP willingness among Black women, categorized into three main domains. Predisposing demographic and social factors included younger age, unmarried status, higher education, sexual or gender minority identity, trust in healthcare providers, and perceived HIV risk. Predisposing behavioral factors included condomless sex, multiple partners, and engagement in sex work. Socioeconomic status, health insurance, healthcare access, support systems, and structural challenges were identified as enabling factors influencing Black women's PrEP willingness. Finally, the perceived need domain and health-related factors influencing the perceived need for PrEP included a history of sexually transmitted infections (STI), intimate partner violence (IPV), and birth control interference. This study emphasizes the complexity of the barriers and facilitators of PrEP uptake and, thus, the need for tailored interventions and health strategies to promote its use. Addressing the interconnected individual, interpersonal, and structural determinants of PrEP access is crucial for improving PrEP willingness and thereby advancing health equity in this population.

Evaluating HbA1c-to-average glucose conversion with patient-specific kinetic models for diverse populations.

The discrepancy between estimated glycemia from HbA1c values and actual average glucose (AG) levels has significant implications for treatment decisions and patient understanding. Factors contributing to the gap include red blood cell (RBC) lifespan and glucose uptake into the RBC. Personalized models have been proposed to enhance AG prediction accuracy by considering interpersonal variation. This study contributes to our understanding of personalized models for estimating AG from HbA1c. Utilizing data from seven studies (340 participants), including Hispanic/Latino populations with or at risk of non-insulin-treated type 2 diabetes (T2D), we examined kinetic features across cohorts. Additionally, the study simulated scenarios to understand data requirements for improving accuracy. Personalized approaches improved agreement between AG estimations and CGM-AG, particularly with four or more weeks of training CGM data. A multiple linear regression model using kinetic parameters and added clinical features was shown to improve the accuracy of personalized models further. As CGM usage extends beyond type 1 diabetes, there is growing interest in leveraging CGM data for clinical decision-making. Patient-specific models offer a valuable tool for managing glycemic status in patients with discordant HbA1c and AG values.

Simulation modelling demonstrates differential performance of connectivity methods in their ability to predict genetic diversity in complex landscapes

ContextThere have been few evaluations of how well different connectivity modelling methods are able to predict the spatial genetic structure and genetic diversity of populations residing in complex landscapes. Given the wide application of connectivity modelling tools in applied conservation planning, it is crucial to broadly evaluate how these models perform across resistance, movement, and population structure conditions in predicting genetic diversity patterns. Such evaluations are critical to provide rigorous, biologically based guidance for conservation and management applications. ObjectivesOur goal was to investigate how the predictions of three connectivity models were related to spatial patterns of genetic diversity complex landscapes, considering factors such as population structure, resistance, genetic drift, genetic disequilibrium, and organism movement abilities. MethodsWe evaluated the performance of several connectivity methods across seven a priori landscape resistance surfaces to provide a broad assessment of their performance. We used CDPOP, an individual-based, spatially explicit population and genetic simulation model, to simulate genetic diversity across these resistance surfaces. This provided a pool of genetic diversity patterns that were the response factor in our simulation experiment. We then simulated landscape connectivity with several popular connectivity methods, including resistant kernels, Circuitscape, and Pathwalker, and evaluated how well they were able to predict spatial patterns of genetic diversity. ResultsResistant kernel outperformed other connectivity methods in predicting landscape patterns of genetic diversity. The strongest relationships occurred when the population process has created spatial structure but has not yet led to significant genetic diversity loss due to drift. The time lag disequilibrium was relatively short. Long simulation times resulted in severe reduction in prediction ability due to drift. ConclusionsResistant kernel predictions were much more strongly related to spatial patterns of genetic diversity than were predictions produced by Circuitscape and Pathwalker, across a large combination of population structures. Strong relationships exist between functional connectivity and genetic diversity, with clearer and stronger associations seen in spatial patterns of allelic richness compared to heterozygosity or spatial effective population size. Our results confirm the strong relationship between genetic diversity and population connectivity, and suggest that computationally efficient incidence function algorithms, such as resistant kernel methods, are well suited to predicting functional connectivity.

JWST Observations of Starbursts: Massive Star Clusters in the Central Starburst of M82

We present a near-infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses >104 M ⊙. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of ≈1 pc and stellar masses up to 106 M ⊙. By comparing the color–color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A V ∼ 3−24 mag, corresponding to A 2.5μm ∼ 0.3−2.1 mag. There is still appreciable dust extinction toward these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is β = 1.9 ± 0.2, in excellent agreement with studies of star clusters in other galaxies.

Prevalence of cardiomyopathy and cardiac mortality in a colony of non-purebred cats in New Zealand

ABSTRACT Aims To evaluate the prevalence of subclinical cardiomyopathy and cardiac mortality in a research colony of non-purebred cats, established as a model of the wider cat population in New Zealand. Methods All apparently healthy, compliant, non-pregnant, non-neonatal cats in the colony at the Centre for Feline Nutrition (Massey University, Palmerston North, NZ) underwent physical examination and echocardiography using a 4.4–6.2-MHz probe by a board-certified veterinary cardiologist. Cardiac phenotype was classified following current guidelines. Hypertrophic cardiomyopathy (HCM) phenotype was defined as an end-diastolic left ventricular wall thickness ≥ 6 mm. Colony mortality data from February 2012 to February 2022 was reviewed to determine cardiac mortality. Results Cats (n = 132; 65 females and 67 males) included in the study had a median age of 4.1 (IQR 3.0–8.0) years. Thirty-two (24%) cats had a heart murmur, and three (2%) cats had an arrhythmia. Echocardiography revealed heart disease in 24 (18.2%) cats, including 23 with an HCM phenotype and one with a restrictive cardiomyopathy phenotype. Of the cats with the HCM phenotype, 3/23 had systemic hypertension or hyperthyroidism or both, and these cats were excluded from the final diagnosis of HCM (20/132; 15.2 (95% CI = 9.5–22.4)%). Between 2012 and 2022, 168 colony cats died, with 132 undergoing post-mortem examination. Heart disease was considered the cause of death in 7/132 (5.3%; 95% CI = 2.2–10.6%) cats; five had HCM, one a congenital heart defect, and one myocarditis. The overall prevalence of death related to HCM in the colony during this period was 3.8% (95% CI = 1.2–8.6%). Three cats with HCM and the cat with a congenital heart defect died unexpectedly without prior clinical signs, while congestive heart failure was observed prior to death in two cats with HCM and the cat with myocarditis. Additionally, 30/132 (22.7%) cats had cardiac abnormalities but died for non-cardiac reasons. Conclusions Subclinical cardiomyopathy, specifically HCM, was common in cats in the colony. Given that the colony originated as a convenience selection of non-purebred cats in New Zealand, the true prevalence of HCM in the wider New Zealand population is likely to fall within the 95% CI (9.5–22%). The proportion of deaths of colony cats due to HCM was lower (3.8%) supporting the conclusion that subclinical cardiomyopathy may not progress to clinical disease causing death. Clinical relevance Veterinarians should be aware of the high prevalence of subclinical HCM when treating cats. Abbreviations CAM: Systolic anterior motion of the chordae tendineae; CFN: Centre for Feline Nutrition; HCM: Hypertrophic cardiomyopathy; LA/Ao: Left atrial to aortic ratio; LV FS: Left ventricular fractional shortening; LVIDd: Left ventricular internal diameters in end-diastole; LVIDs: Left ventricular internal diameter in end-systole; LVWT: Max Maximum left ventricular wall thickness; SAM: Systolic anterior motion of the mitral valve; 2D: Two-dimensional

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include the effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that have already been identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total source-frame mass M > 70 M ⊙) binaries covering eccentricities up to 0.3 at 15 Hz emitted gravitational-wave frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place a conservative upper limit for the merger rate density of high-mass binaries with eccentricities 0 < e ≤ 0.3 at 16.9 Gpc−3 yr−1 at the 90% confidence level.

Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build’Health™ framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build’Health™ concept.

Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.

Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables-the levels of data, spatial scales, idiographic vs population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmark analyses using a locally collected functional magnetic resonance imaging dataset (N = 124) involving an experimental thermal pain task. The results demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.

Characterization of Bipolaris bicolor germination: effects of a physical factor on fungal adaptability.

Studies on physiological responses to stimuli from physical factors are essential for understanding the dynamics of the microorganisms and higly important for the management of plant diseases. Besides, the development of an epidemiological model for pathogen populations requires studying their physiological responses to physical stimuli. The objective of this study was to evaluate the germination dynamics of spores from six isolates of Bipolaris bicolor under effects of light at 25°C. Suspensions of 1.6 × 105 conidia mL- 1 from the B. bicolor isolates were inoculated onto Petri dishes containing agar-water culture medium and incubated in a BOD chamber under two physical conditions: (a) constant darkness and (b) constant light for five hours. The study was conducted in a completely randomized design, with a 6 × 2 factorial arrangement (six B. bicolor isolates and two physical conditions) and five replications. The length of the germ tube was measured hourly. The constant darkness resulted in higher mean germ tube growth for the pathogen; however, differences in the final germination percentage were found among the isolates. The isolate F-24-02 exhibited the highest germination adaptability to constant darkness, presenting the longest germ tube length.

Quantifying maternal reproductive output of chondrichthyan fishes

For the live-bearing and egg-laying class of chondrichthyan fishes a three parameter logistic ‘maternity’ function with a variable upper asymptote, PMax, can be used to predict the average probability of a female giving birth or laying eggs in a season. Although fundamental to calculating the reproductive capacity of a population, few studies report maternity functions, with maturity functions often used as a proxy. Applying logistic models to simulated and empirical data showed that it was feasible to estimate PMax from maternal data and that accuracy, bias, and confidence interval coverage often improved compared to when a fixed value was used. However, sample sizes of 100–200 maternal females were typically required for accurate estimation of PMax. While maturity parameters could be estimated with greater accuracy, substituting them for maternity parameters overestimated lifetime reproductive output. Greater use of maternity functions has the potential to improve calculation of reproductive output in quantitative populations models. In addition to improvements in parameter estimation, this method involves fewer assumptions and enables statistical inferences to be made on frequency of reproduction.

FamLink2 – A comprehensive tool for likelihood computations in pedigrees analyses involving linked DNA markers accounting for genotype uncertainties

There is an increasing demand for software that can handle an arbitrary number of linked markers in forensic genetics; primarily with application to inference of relationships and direct matching but also in applications such as ancestry inference and mixture interpretation. With the emergence of sequencing technologies, denser sets of SNP markers are generated and analyzed. Additionally, sequence data of low quality and quantity DNA generate uncertainty about the underlying true genotype. We provide an efficient implementation of a general model for pedigree likelihood computations with genetic marker data using a three-layered approach. The top and first layer is the population model where allele frequencies and population substructure are accounted for. The second layer is the inheritance model which efficiently handles linked markers using an IBD model. The third and bottom layer is the observational level where we model the likelihood of the true genotype given underlying reads as well as parameters for errors. We exemplify the utility of our implementation as well as provide validation according to guidelines established by the ISFG using a combination of two published SNP panels. We demonstrate that computations are feasible for panels encompassing 10,000 markers and we argue that, due to the properties of the underlying algorithm, extending the number of markers will result in a linear increase in computation time. In addition we study the impact of parameters used in our model and suggest some guidelines pertaining to their values. The results demonstrate that a probabilistic model for low coverage sequence read data is needed instead of relying on an a threshold based genotype and applying our general model for inference of relationships on a real case can be superior, i.e. higher information content, to other methods relying on either fixed genotypes with low quality sequence data or simple pair wise relationship tests. In summary, the implementation, FamLink2 (freely available at https://famlink.se), can jointly handle genetic linkage, genotype uncertainty and population substructure for an arbitrary pedigree with data for any number of individuals. Whereas the current study will focus on calculations disregarding mutations, FamLink2 has the ability to model mutations for certain built-in pedigrees.

A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.

Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostication. There is however a need to verify the validity of these ML models in diverse patient populations. We aim to create machine learning models for prognostication purposes in the Qatari population. By incorporating inpatient variables into model development, we aim to leverage more information. 1501 consecutive patients with acute sICH admitted to Hamad General Hospital(HGH) between 2013 and 2023 were included. We trained, evaluated, and compared several ML models to predict 90-day mortality and functional outcomes. For our dataset, we randomly selected 80% patients for model training and 20% for validation and used k-fold cross validation to train our models. The ML workflow included imbalanced class correction and dimensionality reduction in order to evaluate the effect of each. Evaluation metrics such as sensitivity, specificity, F-1 score were calculated for each prognostic model. Mean age was 50.8(SD 13.1) years and 1257(83.7%) were male. Median ICH volume was 7.5ml(IQR 12.6). 222(14.8%) died while 897(59.7%) achieved good functional outcome at 90 days. For 90-day mortality, random forest(RF) achieved highest AUC(0.906) whereas for 90-day functional outcomes, logistic regression(LR) achieved highest AUC(0.888). Ensembling provided similar results to the best performing models, namely RF and LR, obtaining an AUC of 0.904 for mortality and 0.883 for functional outcomes. Random Forest achieved the highest AUC for 90-day mortality, and LR achieved the highest AUC for 90-day functional outcomes. Comparing ML models, there is minimal difference between their performance. By creating an ensemble of our best performing individual models we maintained maximum accuracy and decreased variance of functional outcome and mortality prediction when compared with individual models.

The TMRCA of general genealogies in populations of variable size.

We study the time to the most recent common ancestor of a sample of finite size in a wide class of genealogical models for populations with variable size. This is made possible by recently developed results on inhomogeneous phase-type random variables, allowing us to obtain the density and the moments of the TMRCA of time-dependent coalescent processes in terms of matrix formulas. We also provide matrix simplifications permitting a more straightforward calculation. With these results, the TMRCA provides an explicative variable to distinguish different evolutionary scenarios.

Abstract PR012: Social and Structural Influences of Preventive Cancer Screenings for Native Hawaiian and Pacific Islander Adults in Hawaiʻi

Abstract Background: Native Hawaiians and Pacific Islanders (NHPI) are hindered by sociocultural, geographic, and environmental factors that contribute to cancer health disparities, often masked by aggregated data with Asian Americans. An average of 7,393 Hawai‘i residents are diagnosed with invasive cancer yearly, and rates are disproportionate ethnically; . Native Hawaiians and Micronesians suffer higher mortality rates for breast cancer (24.9 and 21.6 per 100,000) than White women (17.0). This study aims to investigate social drivers of health behaviors among NHPI in Hawaiʻi by examining social factors that influence cancer screenings through mixed methods. Methods: This mixed methods study is a follow-up to a cross-sectional survey administered in 2018 to 3 Pacific Islander ethnic subgroups: Native Hawaiians, Chuukese, and Marshallese in Hawaiʻi using Respondent Driven Sampling within public housing (N=1,010 NHPI). Disparate cancer screening practices were uncovered within these groups when compared to Hawaiʻi state data. Logistic regression was used to analyze predictors of colon, breast, and cervical cancer screenings. The Gelberg Behavioral Model for Vulnerable Populations and socio-ecological model served as the theoretical models. To triangulate the findings, 16 semi-structured interviews with adults living within the communities first surveyed were conducted in 2023 to uncover any additional drivers of health behaviors that the regression missed. Results: Quantitative findings include cultural/linguistic misunderstandings between NHPI and healthcare providers. Participants who indicated difficulty understanding a provider’s instructions because of language were 51% less likely to be screened for cervical cancer (p = .04). Individuals who hold neutral acculturation to both their ethnic and American culture are less likely to be screened for cancer. Those whose responses fell in a neutral category were 40% less likely than those with a high ethnic identity to have had a colon cancer screening (p = .02). Qualitative findings demonstrate differing views among Pacific Islander groups including competition for resources between groups within public housing. All of the interviewees highlight the role of God and faith in decision making. Informational exchanges take place mainly within the family and cohabitants. From triangulating the findings, there is a lack of strong social ties/networks that could facilitate health-based resources and knowledge. Families are not discussing self-care or family histories of disease, which are instrumental in the early detection of cancers and missed within the regression. Conclusion: This study provides insight into the social drivers of cancer screenings for NHPI communities in Hawaiʻi. Novel insights for the fields of medical sociology and public health are highlighted by influential social processes to NHPI cancer screening behaviors. Recommendations include further research and programs that focus on and foster social dynamics of these communities in relation to cancer screenings. Citation Format: Mark L. Willingham Jr., Kevin Cassel, Wei Zhang, Tressa P. Diaz, Angela Sy, Ian Pagano, Munirih R. Ta'afaki, Angelina G. Mummert. Social and Structural Influences of Preventive Cancer Screenings for Native Hawaiian and Pacific Islander Adults in Hawaiʻi [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 PR012.

A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data.

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.

Existence and uniqueness result for reaction-diffusion model of diffusive population dynamics

The present work investigates a convolution type nonlinear integrodifferential equation with diffusion. This type of equations represents not only pure mathematical interest, but also is widely used in various applications, especially in wide range of problems on population dynamics arising in biology. The existence of a parametric family of travelling wave solutions as well as the uniqueness of the solution in certain class of bounded continuous functions on R \mathbb {R} is proved. The study investigates also some important properties as well as asymptotic behaviour of constructed solutions. These results are then used to derive a new uniform estimate for the deviation between successive iterations, which provides us with a strong tool to control the number of iterations on our way of computing the desired numerical approximation of the exact solution. Finally, we apply our theoretical results to two well-known population problems modelled by delayed reaction-diffusion equation: Diffusion model for spatial-temporal spread of epidemics and stage structured population model.

Modeling and dynamical analysis of an ecological population with the Allee effect

Modeling and dynamical analysis of an ecological population with the Allee effect