Differential warming at crown scale impacts walnut primary growth onset and secondary growth rate.

Trees are exposed to significant spatio-temporal thermal variations, which can induce intracrown discrepancies in the onset and dynamics of primary and secondary growth. In recent decades, an increase in late winter and early spring temperatures has been observed, potentially accelerating bud break, cambial activation and their potential coordination. Intracrown temperature heterogeneities could lead to asymmetric tree shapes unless there is a compensatory mechanism at the crown level. An original warming experiment was conducted on young Juglans regia trees in a greenhouse. From February to August, the average temperature difference during the day between warmed and control parts was 4°C. The warming treatment advanced the date of budbreak significantly, by up to 14 days. Warming did not alter secondary growth resumption but increased growth rates, leading to higher xylem cell production (twice as many) and to an increase in radial increment (+80% compared to control). Meristems resumptions were asynchronous without coordination in response to temperature. Buds on warmed branches began to swell two weeks prior to cambial division, which was one week earlier than on control branches. A difference in carbon and water remobilisation at the end of bud ecodormancy was noted under warming. Overall, our results argue for a lack of compensatory mechanisms at the crown scale, which may lead to significant changes in tree architecture in response to intra-crown temperature heterogeneities.

Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links.

The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.

Spatial clustering based on geographically weighted multivariate generalized gamma regression.

Geographically Weighted Regression (GWR) is one of the local statistical models that can capture the effects of spatial heterogeneity. This model can be used for both univariate and multivariate responses. However, it should be noted that GWR models require the assumption of error normality. To overcome this problem, we propose a GWR model for generalized gamma distributed responses that can capture the phenomenon of some special continuous distributions. The proposed model is known as Geographically Weighted Multivariate Generalized Gamma Regression (GWMGGR). Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method optimized with the Bernt-Hall-Hall-Haussman (BHHH) algorithm. To determine the significance of the spatial heterogeneity effect, a hypothesis test was conducted using the Maximum Likelihood Ratio Test (MLRT) approach. We made a spatial cluster based on the estimated model parameters for each response using the k-means clustering method to interpret the obtained results. Some highlights of the proposed method are:•A new model for GWR with multivariate generalized gamma distributed responses to overcome the assumption of normally distributed errors.•Goodness of fit test to test the spatial effects in GWMGGR model.•Spatial clustering of districts/cities in Central Java based on three dimensions of educational indicators.

Spatial analysis of the prevalence of abdominal obesity in middle-aged and older adult people in China: exploring the relationship with meteorological factors based on gender differences.

Background: In recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies. Methods: This study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran's I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective. Results: The spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence. Conclusion: Meteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.

Spatial and spatio-temporal statistical implications for measuring structural racism: A review of three widely used residential segregation measures.

Social determinants of health are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning and quality of life outcomes and risks - these social determinants of health often aid in explaining the racial and ethnic health inequities present in the United States (US). The root cause of these social determinants of health has been tied to structural racism, and residential segregation is one such domain of structural racism that allows for the operationalization of the geography of structural racism. This review focuses on three residential segregation measures that are often utilized to capture segregation as a function of race/ethnicity, income, and simultaneously race/ethnicity and income. Empirical findings related to the spatial and spatio-temporal heterogeneity of these residential segregation measures are presented. We also discuss some of the implications of utilizing these three residential segregation measures.

Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression.

BACKGROUND: In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. METHOD: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. RESULTS: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H-H clusters of maternal mortality and DMI were in Baluchistan, and the H-H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. CONCLUSION: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Spatial heterogeneity of per- and polyfluoroalkyl substances caused by glacial melting in Tibetan Lake Nam Co due to global warming.

Per- and polyfluoroalkyl substances (PFASs) in high-latitude polar regions and the Tibetan Plateau have received widespread international attention. Here, we measured 18 PFASs and 11 major isomers in the lake water, sediment, and surrounding runoff of Lake Nam Co in 2020. The concentrations of ultrashort-chain trifluoroacetic acid (TFA) and perfluoropropanoic acid (PFPrA) and major isomers of perfluoooctanoic acid (PFOA) and perfluoooctane sulfonate acid (PFOS) in water bodies in high-latitude polar regions and the Tibetan Plateau are reported for the first time. The results showed that the concentration of ∑PFASs in glacial runoff was approximately 139 % greater than that in nonglacial runoff. The concentrations of ∑PFASs in the lake water and sediment in the southern lake with multiple glacial runoff events were approximately 113 % and 108 % higher, respectively, than those in the northern lake. The concentrations of short-chain perfluorobutanoic acid (PFBA) and ultrashort-chain TFA and PFPrA, which may be indicators of ice and snow melt, exhibited significant spatial heterogeneity. Overall, the spatial heterogeneity of PFAS concentrations in the water, sediment and surrounding runoff of Lake Nam Co may be caused mainly by glacial melting.

Carjacking and homicide in Minneapolis after the police killing of George Floyd: Evidence from an interrupted time series analysis.

There is abundant research showing the disproportionate impacts of violence on health in disadvantaged neighborhoods, making an understanding of recent violent crime trends essential for promoting health equity. Carjackings have been of particular interest in the media, although little research has been undertaken on this violent crime. We use interrupted time series models to examine the impact of the police killing of George Floyd on the spatiotemporal patterns of carjacking in Minneapolis in relation to neighborhood disadvantage. To provide grounding, we compare our results to the well-studied patterns of homicides. Results indicate that carjackings both increased and dispersed spatially after the murder of George Floyd and subsequent social unrest, more so than homicides. Socially disadvantaged neighborhoods experienced the greatest absolute increase while more advantaged neighborhoods saw a greater relative increase. The challenge ahead is to identify policy responses that will effectively curb such violence without resorting to harsh and inequitable policing and sentencing practices.

Fluorescence Spectroscopy With Temperature Functional Tests in the Assessment of Markers of Intracellular Energy Metabolism: Spatial Heterogeneity and Reproducibility of Measurements.

The fluorescence intensities of the cellular respiratory cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD++ (oxidized flavin adenine dinucleotide) reflect energy metabolism in skin and other tissues and can be quantified in vivo by fluorescence spectroscopy (FS). However, the variability of physiological parameters largely determines the reproducibility of measurement results and the reliability of the diagnostic test. In this prospective study, we evaluated the interday reproducibility of NADH and FAD++ fluorescence intensity measurements in the skin of 51 healthy volunteers assessed by the FS at baseline, after local cooling (10°C) and heating of the skin (35°C). Results showed that the fluorescence amplitude of NADH (AFNADH) in forearm skin was the most reproducible of the FS parameters studied. Assessment of AFNADH in the dorsal forearm in combination with a thermal functional test is the most promising method for clinical use for assessing energy metabolism in the skin.

The root microlandscape of arbuscular mycorrhizal fungi.

Understanding the drivers of assemblages of arbuscular mycorrhizal fungi (AMF) is essential to leverage the benefits of AMF for plant growth and health. Arbuscular mycorrhizal fungi are heterogeneously distributed in space even at small scale. We review the role of plant distribution in driving AMF assemblages (the passenger hypothesis), using a transposition of the conceptual framework of landscape ecology. Because rooting systems correspond to habitat patches with limited carrying capacity that differ in quality due to host-preference effects, we suggest considering plant communities as mosaics of AMF microhabitats. We review how predictions from landscape ecology apply to plant community effects on AMF, and the existing evidence that tests these predictions. Although many studies have been conducted on the effect of plant compositional heterogeneity on AMF assemblages, they mostly focused on the effect of plant richness, while only a few investigated the effect of configurational heterogeneity, plant connectivity or plant community temporal dynamics. We propose key predictions and future prospects to fill these gaps. Considering plant communities as landscapes extends the passenger hypothesis by including a spatially explicit dimension and its associated ecological processes and may help understand and manipulate AMF assemblages at small spatial scales.

Multi-scenario prediction and attribution analysis of carbon storage of ecological system in the Huaihe River Basin, China.

Evaluating the impact of large-scale human activities on carbon storage through land use changes is of growing interest in terrestrial ecosystem assessments. The Huaihe River Basin, a vital Chinese grain production area, has undergone marked land use changes amid socio-economic acceleration. Evaluating the impacts of land use change on carbon storage and future carbon sequestration is imperative for regional ecosystem sustainability and Chinese food security, simultaneously, furnishing data support to regional land use planning and decision-making processes. Nonetheless, the mechanisms linking land use changes to carbon storage and the future carbon reservoir responses remain unclear. We utilized a multi-source dataset and representative scenarios, integrating PLUS, InVEST models, and Geodetector to assess land use change impacts on carbon storage in the Huaihe River Basin (2000-2030). The data indicates the following: (1) from 2000 to 2020, cultivated land decreased by 28,344.69 km2, construction land increased by 26,914.56 km2, and other land types changed little. (2) Land use change resulted a carbon loss of 1.17 × 108 t, primarily due to the expansion of construction land. (3) All four simulation scenarios exhibited diminished carbon storage relative to 2020, with the economic development scenario recording the lowest at 4.98 × 109 t and the ecological protection scenario the highest at 5.06 × 109 t. (4) Elevation predominantly drives carbon storage changes, with its interaction with NPP having the greatest impact. The factors synergistically enhance their explanatory power. The research provides a scientific basis for strategies aimed at augmenting regional carbon sequestration and refining low-carbon land management, safeguarding ecosystem stability.

Regional ecological risk assessment through the application of production-living-ecology land use function indicators: a case study of the eco-economic zone of Yellow River Delta.

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures.

Spatial heterogeneity of soil respiration after prescribed burning in Pinus koraiensis forest in China.

Soil respiration (RS) is crucial for releasing carbon dioxide (CO2) from terrestrial ecosystems to atmosphere. Prescribed burning (a common forest management tool), along with its important by-product pyrogenic carbon (PyC), can influence the carbon cycle of forest soil. However, few studies explore RS and PyC spatial correlation after prescribed burning. In this study, we investigated the spatial pattern of RS and its influencing factors by conducting prescribed burnings in a temperate artificial Pinus koraiensis forest. RS was measured 1 day (1 d) pre-prescribed burning, 1 d, 1 year (1 yr) and two years (2 yr) after prescribed burning. Significant decrease in RS were observed 1-2 yr After burning (reductions of 65.2% and 41.7% respectively). The spatial autocorrelation range of RS decreased pre-burning (2.72m), then increased post-burning (1 d: 2.44m; 1 yr: 40.14m; 2 yr: 9.8m), indicating a more homogeneous distribution of patch reduction. Pyrogenic carbon (PyC) in the soil gradually decreased in the short term after burning with reductions of 19%, 52%, and 49% (1d., 1 yr And 2 yr After the fire, respectively). However, PyC and RS exhibited a strong spatial positive correlation from 1 d.- 1 yr post-burning. The spatial regression model of dissolved organic carbon (DOC) on RS demonstrated significant positive spatial correlation in all measurements (pre- and post-burning). Microbial carbon to soil nitrogen ratio (MCN) notably influenced RS pre-burning and 1-2 yr post-burning. RS also showed significant spatial correlation in cross-variance with NH4+-N and NO3--N post-burning. The renewal of the PyC positively influenced RS, subsequently affecting its spatial distribution in 1d.- 1yr. Introducing PyC into RS studies helps enhances understanding of prescribed fire effects on forest soil carbon (C) pools, and provides valuable information regarding regional or ecosystem C cycling, facilitating a more accurate prediction of post-burning changes in forest soil C pools.

Bioprinted, spatially defined breast tumor microenvironment models of intratumoral heterogeneity and drug resistance.

Cellular, extracellular matrix (ECM), and spatial heterogeneity of tumor microenvironments (TMEs) regulate disease progression and treatment efficacy. Developing in vitro models that recapitulate the TME promises to accelerate studies of tumor biology and identify new targets for therapy. Here, we used extrusion-based, multi-nozzle 3D bioprinting to spatially pattern triple-negative MDA-MB-231 breast cancer cells, endothelial cells (ECs), and human mammary cancer-associated fibroblasts (HMCAFs) with biomimetic ECM inks. Bioprinted models captured key features of the spatial architecture of human breast tumors, including varying-sized dense regions of cancer cells and surrounding microvessel-rich stroma. Angiogenesis and ECM stiffening occurred in the stromal area but not the cancer cell-rich (CCR) regions, mimicking pathological changes in patient samples. Transcriptomic analyses revealed upregulation of angiogenesis-related and ECM remodeling-related signatures in the stroma region and identified potential ligand-receptor (LR) mediators of these processes. Breast cancer cells in distinct parts of the bioprinted TME showed differing sensitivities to chemotherapy, highlighting environmentally mediated drug resistance. In summary, our 3D-bioprinted tumor model will act as a platform to discover integrated functions of the TME in cancer biology and therapy.

Modeling the spatial relationship between landscape services and vulnerability assessment.

Even though many studies have been thus far devoted to landscape services (LS) or vulnerability assessment (VA) alone, the relationship between these two concepts has been less investigated. The current study attempts to model the spatial relationship between LS and VA in the Tarhan area (west of Iran). For this purpose, a combination of the analytic hierarchy process (AHP), remote sensing (RS), and geographic information system (GIS) techniques are applied to assess vulnerability. Variogram models and geostatistical simulations are used for mapping and evaluating landscape services. Moreover, the geographically weighted regression (GWR) is operated to predict the relationship between LS and VA. The results indicate that landform and socioeconomic variables are also two main factors shaping variations in VA. As well, the levels of services provided in the possible landscape are affected by the vulnerability. The assessment accordingly establishes that natural and cultural values have significant effects on services, in terms of their spatial distribution and nature. Exploring the relationship between LS and VA correspondingly depicts that VA has a direct influence on the capacity and entity of LS provision (adjusted R2 = 0.67). The outcomes of the study provide a foundation for nature management and conservation, although it is less able to analyze the trade-off between ecosystem degradation and future development trends. The nexus of LS and future ecological processes in an integrated system can be thus the subject of further research.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021.

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.

Slow recovery rates and spatial aggregation of Triatoma infestans populations in an area with high pyrethroid resistance in the Argentine Chaco.

BACKGROUND: The emergence of pyrethroid resistance has threatened the elimination of Triatoma infestans from the Gran Chaco ecoregion. We investigated the status and spatial distribution of house infestation with T. infestans and its main determinants in Castelli, a municipality of the Argentine Chaco with record levels of triatomine pyrethroid resistance, persistent infestation over 2005-2014, and limited or no control actions over 2015-2020. METHODS: We conducted a 2-year longitudinal survey to assess triatomine infestation by timed manual searches in a well-defined rural section of Castelli including 14 villages and 234 inhabited houses in 2018 (baseline) and 2020, collected housing and sociodemographic data by on-site inspection and a tailored questionnaire, and synthetized these data into three indices generated by multiple correspondence analysis. RESULTS: The overall prevalence of house infestation in 2018 (33.8%) and 2020 (31.6%) virtually matched the historical estimates for the period 2005-2014 (33.7%) under recurrent pyrethroid sprays. While mean peridomestic infestation remained the same (26.4-26.7%) between 2018 and 2020, domestic infestation slightly decreased from 12.2 to 8.3%. Key triatomine habitats were storerooms, domiciles, kitchens, and structures occupied by chickens. Local spatial analysis showed significant aggregation of infestation and bug abundance in five villages, four of which had very high pyrethroid resistance approximately over 2010-2013, suggesting persistent infestations over space-time. House bug abundance within the hotspots consistently exceeded the estimates recorded in other villages. Multiple regression analysis revealed that the presence and relative abundance of T. infestans in domiciles were strongly and negatively associated with indices for household preventive practices (pesticide use) and housing quality. Questionnaire-derived information showed extensive use of pyrethroids associated with livestock raising and concomitant spillover treatment of dogs and (peri) domestic premises. CONCLUSIONS: Triatoma infestans populations in an area with high pyrethroid resistance showed slow recovery and propagation rates despite limited or marginal control actions over a 5-year period. Consistent with these patterns, independent experiments confirmed the lower fitness of pyrethroid-resistant triatomines in Castelli compared with susceptible conspecifics. Targeting hotspots and pyrethroid-resistant foci with appropriate house modification measures and judicious application of alternative insecticides with adequate toxicity profiles are needed to suppress resistant triatomine populations and prevent their eventual regional spread.

Spatial and Seasonal Changes in Microbial Community of Hynobius amjiensis Breeding Pools in a Sphagnum-Dominated Peatland.

Peatlands deliver a variety of beneficial ecosystem services, particularly serving as habitats for a diverse array of species. Hynobius amjiensis is a critically endangered amphibian initially discovered in a Sphagnum-dominated peatland in Anji, China. The unique habitat requirements of H. amjiensis make it highly vulnerable to environmental changes. Here, we investigated the different breeding pools of H. amjiensis in the Sphagnum-dominated peatland (the type locality) for a one-year period to evaluate the interactions among the egg sacs present, water quality, and microbial communities (16S and 18S rRNA gene amplicon). The numbers of egg sacs were higher in the breeding pools located at the marginal area than those at the core area of the peatland. Similarly, the α-diversity of bacteria, fungi, and protists were lower in the core region compared to those at the edge of the peatland, perhaps due to water eutrophication. The microbial communities and water quality differed significantly among breeding pools and sampling months. The simpler microbial networks of the breeding pools in the core wetland may impact the numbers and health of the egg sacs. This study contributes to a better understanding of the effect of water quality on biodiversity in peatlands, and it can also guide regulations for wetland conservation and the protection of endangered species.

A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy.

BACKGROUND: Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis. MATERIALS AND METHODS: In this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression. RESULTS: Among the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively). CONCLUSION: Multiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.