Environmental Characteristics Research Articles

Assessing structural models of neighborhood and family sociodemographic characteristics and their relations with externalizing psychopathology.

Externalizing psychopathology has been found to have small to moderate associations with neighborhood and family sociodemographic characteristics. However, prior studies may have used suboptimal operationalizations of neighborhood sociodemographic characteristics and externalizing psychopathology, potentially misestimating relations between these constructs. To address these limitations, in the current study we test different measurement models of these constructs and assess the structural relations between them. Using a population-representative sample of 2,195 twins and siblings from the Georgia Twin Study and data from the National Neighborhood Data Archive and 2000 U.S. Census, we assessed the fit of competing measurement models for family sociodemographic, neighborhood sociodemographic, and neighborhood environment characteristics. In structural models, we regressed a general externalizing dimension on different operationalizations of these variables separately and then simultaneously in a final model. Latent variable operationalizations of family sociodemographic, neighborhood sociodemographic, and neighborhood environment characteristics explained no more variance in broad externalizing psychopathology than other operationalizations. In an omnibus model, family sociodemographic characteristics showed a small association with externalizing psychopathology, while neighborhood sociodemographic and environmental characteristics did not. Family sociodemographic characteristics showed small associations with neighborhood sociodemographic and environmental characteristics, and neighborhood sociodemographic characteristics were moderately associated with neighborhood environment. These findings suggest that family sociodemographic characteristics are more associated with the development of broad externalizing psychopathology in youth than neighborhood sociodemographic characteristics and neighborhood environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Human Capital at the Regional Level: Population Forecast Insights into Latvia from a Long-Term Perspective

To remain prosperous and competitive, modern regions must meet the challenge of becoming smart and sustainable, and human capital has a key role in this context. Depopulation increases risks of human capital availability and emigration is a primary reason. Regions that are experiencing a decreased population must develop strategic sustainable regional development policies that consider scientifically based population forecasts. The youth population characterises regional potential for long-term human capital availability. First, children represent a region’s future employees and entrepreneurs. Second, children require availability of educational services at regional level, which promotes regional vitality. Third, the youth population indirectly demonstrates a region’s socio-economic attractiveness for living and working. This study employs the system dynamic method to forecast different population change scenarios at the regional level in Latvia. The results reveal a declining, long-term population tendency for working age and youth populations in Latvian regions. Overall, Latvian regions will remain extremely homogeneous in the long term, with minor differences in the short term. In conclusion, despite regional disparities in terms of quality of life with some leading examples, all regions will experience high emigration loss. Regional social, economic and environmental characteristics demonstrate a need to assess population change trends in a complex manner. This study provides data for understanding short- and long-term perspectives that must be addressed through sustainable regional development policies.   Received: 27 October 2023 / Accepted: 24 March 2024 / Published: 5 May 2024

Enhancement of work place comfort through the use of soft landscape

The work environment has been shown to have a great influence on employees’ physical and mental state, as such employers try as much as possible to provide a comfortable working environment. However, recent studies have shown that the work environment is no longer as comfortable as it used to be due to heat gain from the increasing use of hard landscape features and neglect of soft landscape features in the work environment. This has resulted in stress causing both mild and severe health conditions for employees. Thus, this study aims to analyze the use of soft landscape in enhancing users' comfort with the view of reducing stress and health-related issues in the work environment. The paper adopted a mixed method using observation checklists, structured interviews, questionnaires, and case studies. A total of eighteen office complexes were selected to describe the existing situation as related to most office complexes and to also gain insight into the subject matter as already applied to life situations. Structured interviews and questionnaires were also conducted with occupants of the buildings and professionals to hear their perceptions and expectations of their work environment. Results showed that 20 percent of the offices give proper consideration to the use of soft landscapes in the work environment, and 40 percent give inconsiderable attention to soft landscapes while making use of them in small quantities solely for improving the aesthetical value of the building. In comparison, the remaining 40 percent do not consider landscape use as a means of improving comfort in the work environment. Findings also revealed that 80 percent of office buildings give major consideration to parking space using hard landscapes without a proper blend of soft landscapes after building design which hurts occupant comfort. The research concluded that the incorporation of soft landscape elements both within the building using recesses and protrusions and on the building walls through the use of exoskeleton and climbers while incorporating trees, shrubs, flowers, hedges and water around the building can have these required effects on employee’s comfort.

New insights into biodiversity-disease relationships: the importance of the host community network characterization

Camera-trapping-based social network analysis (SNA) is a valuable tool to characterize communities and identify species with an outstanding role in pathogen maintenance. This study provides new insights into the contentious debate on the effect of biodiversity on disease risk by combining SNA with the assessment of host diversity indicators and pathogen richness in Spain. The apparent species richness detected by camera traps at each study site ranged from 10 to 33 species (mean ± standard error (SE): 20.73 ± 1.94) and their apparent diversity rates (i.e., Shannon index) ranged from 0.57 to 2.55 (mean ± SE: 1.97 ± 0.16). At the community level, vertebrate host diversity had a marginal dilution effect on the disease risk and was negatively correlated to pathogen richness. The exposure to multiple pathogens, as a proxy of disease risk, was negatively associated with apparent host diversity. The disease risk was driven by the interaction of apparent biodiversity with the presence of livestock and with the centrality of the indicator species (i.e., the wild boar). The maximum risk of co-exposure to pathogens was reached when the lowest apparent biodiversity rates coincided with the highest wild boar centrality in the host community or with the presence of livestock, respectively. The highest confluence of pathogens occurred at lower apparent diversity indexes, higher wild boar relative abundances and predominance of agricultural lands. Our results suggest that the diversity-disease relationship is not linear and depends on the environment and host community characteristics, thereby opening avenues for designing new prevention strategies.

Sense of community (SOC) in gated urban neighborhoods: Empirical insights from Accra, Ghana

ABSTRACT Despite gated communities becoming a prominent form of housing in the Global South cities, the extent to which these privatized neighborhoods foster a sense of community remains understudied. Most of the studies on sense of community are based on experiences from the Global North. To address this lacuna, this paper examines the extent to which residents of gated communities develop a sense of community and the different factors that are associated with this sense of community. We conducted regression analysis on cross-sectional survey data from Devtraco and Manet gated communities, to determine factors associated with a sense of community in these gated communities. Our findings demonstrate that residents of gated communities exhibit a stronger sense of community—shaped by different individual, household, and environmental characteristics. Interestingly, our study reveals that males have a higher sense of community compared to females. Age, length of residence, tenure status, household size, and proximity to community features were significant factors associated with sense of community. Our study adds to the extant literature on a sense of community among individuals living in gated communities in the Global South.

Exploring the influence of the learning Metaverse on student satisfaction: an integrated framework of dual-congruity theory and interactive learning environment features

ABSTRACT Employing Metaverse platforms for teaching is an emerging form of computer-assisted education, and such courses often require students to first learn how to operate relevant platforms. Learning to use the platform is a course itself. In this context, this study develops a predictive model to explore students’ satisfaction with learning to operate the Metaverse platforms. We used the dual-congruity theory as the basis for the study, focusing on the features of Metaverse interactive learning environment as the causative factors. A total of 486 respondents who have learned the Metaverse were collected, and partial least squares method was adopted to evaluate the model and test the hypothesis. The study findings reveal that visual appeal, escapism, and perceived Metaverse quality are predictors of self-congruity and perceived diagnosticity, which positively influence students’ learning satisfaction. The study provides recommendations for both education establishments and professionals who develop Metaverse platforms. This study contributes to the academic development of Metaverse education and serves as a guide for better learning effectiveness and satisfaction in information education.

Probing the carbon neutrality drive of environmental-related technologies and energy transition in France and Germany: a novel time–frequency technique

AbstractThis study presents a rare comparative analysis of the factors influencing environmental quality through greenhouse gas (GHG) emissions in the European Union member states’ largest economies, i.e., France and Germany. By considering the unique economic, energy, and environmental characteristics of both countries, the finding unveils a novel perspective in the literature. The research utilizes a recently developed wavelet local multiple correlation (WLMC) technique with quarterly dataset spanning from 1990/Q1 to 2020/Q4. The results demonstrate that environmental-related information and communication technologies innovations, energy transition, and financial development play significant roles in limiting the growth of GHGs emission, particularly in the medium and long term. The wavelet-based Granger causality analysis reveals evidence of feedback causality among the variables in both countries in the medium and long term. Moreover, there are slight differences in the short-term relationships given that the observations are generally similar in later period. Overall, the findings offer a deeper understanding and policy insights regarding the time and frequency dynamics of GHG drivers in France and Germany. Graphical abstract

A Multidisciplinary Perspective on Person-Environment Fit: Relevance, Measurement, and Future Directions

Environments shape people, and at the same time, people are attracted to environments that fit their characteristics because fit facilitates the achievement of people’s desired life outcomes, such as relationship satisfaction, work success, and well-being. In this article, we outline how persons and environments can fit, the relevance of fit and misfit for different life outcomes, and the benefits and pitfalls of different (mis)fit measures. We propose three directions for future research: (a) the use of both subjective and objective (mis)fit measures; (b) the consideration of complex dynamics between person and environment characteristics via pathways through multiple biological, experiential, behavioral, and social layers across the life span; and (c) the integration of insights from different disciplines, including psychology, sociology, neuroscience, and genetics, to move the field forward.

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

ABSTRACTCritical data concerning key developments in global human history now lie submerged on continental shelves where investigations confront significant challenges. Whereas underwater excavations and surveys are expensive and weather dependent and require specialized training and equipment, remote sensing methods can improve chances for success offshore. A refinement in one method, a semi‐automated analysis protocol that can help to identify Pleistocene and Holocene era archaeological deposits in bathymetric LiDAR datasets, is presented here. This method employs contour mapping to identify potential archaeological features in shallow water environments in Apalachee Bay, Florida. This method successfully re‐identified multiple previously recorded archaeological sites in the study region and detected at least four previously undocumented archaeological sites. These results suggest that this procedure can expand on methods to identify and record submerged archaeological deposits in sediment‐starved, shallow‐water environments.

Epidemiological Risk Factors and Modelling Approaches for Risk Assessment of Lumpy Skin Disease Virus Introduction and Spread: Methodological Review and Implications for Risk-Based Surveillance in Australia

Lumpy skin disease (LSD) is a vector-borne infection caused by the poxvirus lumpy skin disease virus (LSDV) and is a serious disease of cattle, water buffalo, and banteng. While the disease has never occurred in Australia, it is regarded as a growing threat to the Australian cattle industry as there is on-going spread of the disease throughout Asia. The development of geospatial decision support tools, such as spatial epidemiological modelling, may assist in assessing areas at greater risk of this threat. To guide the design of disease modelling approaches to support future risk-based surveillance, existing LSDV epidemiological models need to be evaluated. In this study, we performed a literature review to evaluate existing LSDV epidemiological models, identify key risk factors for introduction and spread of LSDV, and consider previously adopted control strategies. The PRISMA guidelines were used to establish the processes for article selection and information extraction, and the PICO process was used to formulate search terms. From studies that met our inclusion criteria, we extracted information on LSDV epidemiological model structure and parameterisation, risk factors for LSDV transmission and spread, and biosecurity control strategies. The literature search retrieved a total of 402 articles from four databases, of which 68 were identified for inclusion in this review following screening. Of the 68 articles reviewed, 47 explored risk factors associated with LSDV transmission and spread, four explored risk factors of LSDV introduction, four explored existing surveillance strategies in LSD-free countries, and 14 presented epidemiological models. Our findings indicate that there are various risk factors for LSDV transmission in LSD endemic countries, including long-distance airborne movement of infected vectors such as stable flies and cattle movement between countries over land borders. Key risk factors for LSDV spread in LSD endemic countries include physical environmental characteristics, weather conditions, and population distributions of livestock and vectors. Our results indicate that while a variety of modelling studies have been conducted, the majority of studies experimentally explored LSD transmission mechanisms in vectors and cattle. Spatial and spatio-temporal models have primarily been developed for LSD endemic countries and focus on the spread of the disease in terms of environmental factors in relation to previous LSD events. There were very few studies on LSD-free countries, and these only focussed on risk of LSD introduction through specific entry pathways. This review did not identify any literature exploring the risk of spread of LSDV following introduction in LSD-free countries or geospatial modelling of the suitability of LSD-free countries for LSDV incursions. In conjunction with the risk parameters and models described in the identified literature, there is need to consider a wide range of risk factors specific to Australia to inform the design of risk-based surveillance for LSD in Australia.

Predictive statistical models for monitoring antimicrobial resistance spread in the environment using Apis mellifera (L. 1758) colonies

The rise of antimicrobial resistance (AMR) is one of the most relevant problems for human and animal health. According to One Health Approach, it is important to regulate the use of antimicrobials and monitor the spread of AMR in the environment as well. Apis mellifera (L. 1758) colonies were used as bioindicators thanks to their physical and behavioural characteristics. During their foraging flights, bees can intercept small particles, including atmospheric particulate matter, etc., and also microorganisms. To date, the antimicrobial surveillance network is limited to the sanitary level but lacks into environmental context. This study aimed to evaluate the use of A. mellifera colonies distributed throughout the Emilia-Romagna region (Italy) as indicators of environmental antimicrobial-resistant bacteria. This was performed by creating a statistical predictive model that establishes correlations between environmental characteristics and the likelihood of isolating specific bacterial genera and antimicrobial-resistant strains. A total of 608 strains were isolated and tested for susceptibility to 19 different antimicrobials. Aztreonam-resistant strains were significantly related to environments with sanitary structures, agricultural areas and wetlands, while urban areas present a higher probability of trimethoprim/sulfamethoxazole-resistant strains isolation. Concerning genera, environments with sanitary structures and wetlands are significantly related to the genera Proteus spp., while the Escherichia spp. strains can be probably isolated in industrial environments. The obtained models showed maximum values of Models Accuracy and robustness (R2) of 55 % and 24 %, respectively. The results indicate the efficacy of utilizing A. mellifera colonies as valuable bioindicators for estimating the prevalence of AMR in environmentally disseminated bacteria. This survey can be considered a good basis for the development of further studies focused on monitoring both sanitary and animal pathology, creating a specific network in the environments of interest.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation index (kNDVI), climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

A multidisciplinary approach to describe population structure of Solea solea in the Mediterranean Sea

Investigating marine species population structure in a multidisciplinary framework can reveal signatures of potential local adaptation and the consequences for management and conservation. In this study we delineate the population structure of common sole (Solea solea) in the Mediterranean Sea using genomic and otolith data, based on single nucleotide polymorphism (SNPs) markers, otolith shape and otolith trace element composition data. We correlated SNPs with environmental and spatial variables to evaluate the impact of the selected features on the actual population structure. Specifically, we used a seascape genetics approach with redundancy (RDA) and genetic-environmental association (GEA) analysis to identify loci potentially involved in local adaptation. Finally, putative functional annotation was investigated to detect genes associated with the detected patterns of neutral and adaptive genetic variation. Results from both genetic and otolith data suggested significant divergence among putative populations of common sole, confirming a clear separation between the Western and Eastern Mediterranean Sea, as well as a distinct genetic cluster corresponding to the Adriatic Sea. Evidence of fine-scale population structure in the Western Mediterranean Sea was observed at outlier loci level and further differentiation in the Adriatic. Longitude and salinity variation accounted for most of the wide and fine spatial structure. The GEA detected significant associated outlier loci potentially involved in local adaptation processes under highly structured differentiation. In the RDA both spatial distribution and environmental features could partially explain the genetic structure. Our study not only indicates that separation among Mediterranean sole population is led primarily by neutral processes because of low connectivity due to spatial segregation and limited dispersal, but it also suggests the presence of local adaptation. These results should be taken into account to support and optimize the assessment of stock units, including a review and possible redefinition of fishery management units.

Analysis of frost layer growth behavior on cryogenic surfaces in ultralow dew point environments by experimental tests and numerical simulations

Maintaining an ultralow dew point is essential for the operation of transonic cryogenic wind tunnels, as it significantly influences aerodynamic characteristics. Even in extremely dry conditions, minute quantities of frost can form on cryogenic surfaces, which compromises data quality. Conducting experiments on frost desublimation in such ultralow dew point environments poses significant challenges due to the demanding experimental conditions. Therefore, this study aimed to explore the desublimation characteristics of cryogenic surfaces in ultralow dew point environments and to address the challenges of experimental investigations under such extreme conditions. Initially, the study experimentally investigated frost formation by assessing the impact of various factors, such as temperature and water vapor content. It was observed that the type of frost formed depended on the water vapor content, and altering the surface temperature influenced the growth region of the frost crystals. The irregular growth of the frost layer on low-temperature surfaces was evident, as temperature markedly affected both the growth rate and the diffusion direction of frost formation. Moreover, the process of trace water frost formation was simulated using a dispersive multiphase model, predicting the frost layer's thickness. The numerical results suggest that the frost layer is micrometers thick, with a minimum thickness of 20 μm. A nonlinear relationship was identified between frost growth and dew point level. This research lays the groundwork for a deeper understanding of desublimation characteristics in ultralow dew point environments and for further investigation of low-temperature heat and mass transfer phenomena.

Timing of seasonal events is correlated with social network position in a wild mammal

Across animal systems, abiotic environmental features, including timing of seasonal events and weather patterns, affect fitness. An individual’s degree of social integration also has fitness consequences, but we lack an understanding of how abiotic features relate to patterns of individual sociality. A deeper understanding of this relationship could be developed from studying systems where these two links with fitness have already been identified. We explored the relationship between individual social behavior and seasonal timing, seasonal length, and weather patterns. We used social network analysis on a sixteen-year dataset of a wild population of hibernating yellow-bellied marmots (Marmota flaviventer). We fit a series of generalized linear mixed models and found that longer growing seasons before winter hibernation and longer winters were associated with increased individual sociality in the following spring. However, later snowmelt was associated with decreased sociality that spring. We found no relationship between individual sociality and various measures of precipitation and temperature. This suggests that seasonal timing and length may be a more important driver of sociality than weather patterns in this system, both as a lag and contemporary effect. Seasonal timing and length may mediate the opportunity or intensity of social interactions. The entwined relationships between the seasonal schedule and weather, and the seemingly contradictory role of winter length and snowmelt, suggests the timing of seasons and its relationship with sociality is complex and further exploration of environment-sociality relationships is required across taxa.Significance statementWhile the adaptive benefits of social behavior are well studied, less is known about how features of the abiotic environment drive variation in individual social behavior. Given increasing stochasticity in the timing of seasonal events and weather patterns, mapping the environment-sociality relationship will provide important insights to the drivers of sociality in the wild. This is particularly salient for species most vulnerable to climate and environmental change, such as seasonal hibernators, like yellow-bellied marmots (Marmota flaviventer). We found that features of seasonal duration were positively associated with increased sociality, whereas the timing of seasonal onset was negatively associated. This work provides empirical evidence towards an important gap in the behavioral ecology literature.

Zoogeographical regions in the Atlantic Forest: patterns and potential drivers

AbstractAimTo delineate present‐day zoogeographical regions of terrestrial vertebrates (frogs, lizards, snakes, birds and non‐volant mammals) in the Atlantic Forest. Within each taxonomic group, we examine the relative importance of abrupt climatic transitions, orographic barriers, past climate change and rivers in shaping zoogeographical boundaries.LocationSouth America's Atlantic Forest.MethodsWe applied a network‐based method to delineate zoogeographical regions, using distribution data (range maps) for 455 species of frogs, 103 lizards, 220 snakes, 917 birds and 202 non‐volant mammals, in 50 × 50 km grid cells. We used hierarchical generalized linear mixed‐effects models to test environmental predictors associated with zoogeographical boundaries. Finally, we intersected the bioregion maps delineated for each group to identify general patterns across all vertebrates.ResultsWe identified four zoogeographical regions for birds and snakes, and five for frogs, lizards and non‐volant mammals. Depending on the group, contemporary and past climate conditions, elevation variation and/or rivers were associated with zoogeographical boundaries. The combined maps indicate that the Atlantic Forest retains four spatially cohesive zoogeographical regions based on present‐day distribution of vertebrates.Main conclusionsCross‐taxon congruence indicates that the geographical and environmental characteristics of the Atlantic Forest have a strong influence on the location of zoogeographical regions for vertebrates. In contrast, transition zones appear to be associated with the spatial distribution of life history traits of each group, potentially explaining the observed differences in the number of bioregions across groups and the position of zoogeographical boundaries. This work paves the way for further research into the evolutionary assembly of the Atlantic Forest's zoogeographical regions and may help inform conservation priorities for maintaining their distinctive faunas.

THE EFFECT OF DRY AND HUMID HEAT ACCLIMATION ON PERFORMANCE IN EACH ENVIRONMENT

BACKGROUND Athletes adopt heat acclimation (HA) to partly restore heat-mediated decrements in performance. However, the influence of environmental characteristics during HA on performance in dry and humid conditions is unclear. Therefore, this study aimed to compare dry and humid HA on performance in each environment. METHODS In a counterbalanced crossover study, ten trained male cyclists (age 43 ± 9 y; body mass 80.8 ± 7.3 kg; maximal oxygen uptake; 59 ± 7 mL.kg-1.min-1) completed a 30-min time-trial in hot-dry (TT-DRY; 42°C, 25% RH) and warm-humid (TT-HUM; 33°C, 80% RH) conditions before and after 8 days of controlled heart rate (HR) HA in either dry (DRY-HA) or humid conditions (HUM-HA). A Bayesian multi-level model was used to determine posterior means, 90% credible intervals, and probability of direction (%). RESULTS The improvement in power output during TT-HUM was similar following DRY-HA (13 W [3, 22]; 99%) and HUMID-HA (11 W [3, 19]; 98%). In TT-DRY, the improvement in power output was 12 W ([1, 22]; 97%) higher following DRY-HA (19 W [10, 28]; >99%) compared to HUM-HA (7 W [-2, 17]; 91%). During TT-HUM, mean rectal temperature changed following HUM-HA (0.03°C [-0.11, 0.17]; 63%) and DRY-HA (-0.14°C [-0.29, 0.02]; 92%), with a difference of 0.16°C ([-0.36, 0.03]; 92%). During TT-DRY, the difference was -0.20°C ([-0.40, 0.01]; 94%) between HUM-HA (-0.22°C [-0.38, -0.06]; 98%) and DRY-HA (-0.03°C [-0.19, 0.13]; 63%). Mean HR during TT-DRY (3 beats·min-1 [-2, 8]; 85%) and TT-HUM (-4 beats·min-1 [-10, 2]; 85%) was similar following DRY-HA and HUM-HA. CONCLUSIONS DRY-HA further improved mean power output during TT-DRY compared to HUM-HA. However, HUM-HA tended to lower rectal temperature more than DRY-HA during TT-DRY. HA condition did not influence HR during either TT. Our data indicate that performance is further improved in hot-dry conditions when acclimating to the same environment.

Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality. To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities. This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention's 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022. GSI images of built environment and cardiometabolic disease prevalence. Census tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features. The study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks. In this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

Why Do Some Lineages Radiate While Others Do Not? Perspectives for Future Research on Adaptive Radiations.

Understanding the processes that drive phenotypic diversification and underpin speciation is key to elucidating how biodiversity has evolved. Although these processes have been studied across a wide array of clades, adaptive radiations (ARs), which are systems with multiple closely related species and broad phenotypic diversity, have been particularly fruitful for teasing apart the factors that drive and constrain diversification. As such, ARs have become popular candidate study systems for determining the extent to which ecological features, including aspects of organisms and the environment, and inter- and intraspecific interactions, led to evolutionary diversification. Despite substantial past empirical and theoretical work, understanding mechanistically how ARs evolve remains a major challenge. Here, we highlight a number of understudied components of the environment and of lineages themselves, which may help further our understanding of speciation and AR. We also outline some substantial remaining challenges to achieving a detailed understanding of adaptation, speciation, and the role of ecology in these processes. These major challenges include identifying factors that have a causative impact in promoting or constraining ARs, gaining a more holistic understanding of features of organisms and their environment that interact resulting in adaptation and speciation, and understanding whether the role of these organismal and environmental features varies throughout the radiation process. We conclude by providing perspectives on how future investigations into the AR process can overcome these challenges, allowing us to glean mechanistic insights into adaptation and speciation.

Evaluation of Heavy Metals Pollution in the Sediments of Diyala River Lower Reaches, Eastern Iraq

Investigating the heavy metals in soil is important to the life of humans and living organisms. Diyala River Lower Reaches was chosen due to the changes in environmental characteristics that took place in recent years. Twelve sediment samples were collected from four different sites. The physical, and chemical properties and the concentrations of nine heavy metals were indicated. The results showed that the average concentrations of arsenic, copper, chromium, cobalt, iron, manganese, nickel, lead, and zinc are 8.5, 45.7, 538.5, 12.2, 5.07, 991.7, 183.5, 16.07, 136.5 ppm, respectively. They reflect contamination with arsenic, chromium, and nickel, while they are free of lead, and zinc contamination, according to the Environmental Protection Agency's (EPA) sediment quality guidelines. The measured contamination indices (the enrichment factor (EF), contamination factor (CF), degree of pollution (Cdeg), geographical accumulation index (Igeo), and pollution load index (PLI)), reflected high contamination factor for arsenic and chromium, and medium for manganese, nickel, lead, and zinc, while low for copper and cobalt. Based on all the results in all sampling sites indicate the presence of heavy element contamination in the sediments of the Diyala River Lower Reaches.