An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study.

BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs. OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities. METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model. RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the "NWO Navigate Stroke" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes. CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.

Spatial transcriptome reveals the region-specific genes and pathways regulated by Satb2 in neocortical development.

BACKGROUND: It is known that the neurodevelopmental disorder associated gene, Satb2, plays important roles in determining the upper layer neuron specification. However, it is not well known how this gene regulates other neocortical regions during the development. It is also lack of comprehensive delineation of its spatially regulatory pathways in neocortical development. RESULTS: In this work, we utilized spatial transcriptomics and immuno-staining to systematically investigate the region-specific gene regulation of Satb2 by comparing the Satb2+/+ and Satb2-/- mice at embryonic stages, including the ventricle zone (VZ) or subventricle zone (SVZ), intermediate zone (IZ) and cortical plate (CP) respectively. The staining result reveals that these three regions become moderately or significantly thinner in the Satb2-/- mice. In the cellular level, the cell number increases in the VZ/SVZ, whereas the cell number decreases in the CP. The spatial transcriptomics data show that many important genes and relevant pathways are dysregulated in Satb2-/- mice in a region-specific manner. In the VZ/SVZ, the key genes involved in neural precursor cell proliferation, including the intermediate progenitor marker Tbr2 and the lactate production related gene Ldha, are up-regulated in Satb2-/- mice. In the IZ, the key genes in regulating neuronal differentiation and migration, such as Rnd2, exhibit ectopic expressions in the Satb2-/- mice. In the CP, the lineage-specific genes, Tbr1 and Bcl11b, are abnormally expressed. The neuropeptide related gene Npy is down-regulated in Satb2-/- mice. Finally, we validated the abnormal expressions of key regulators by using immunofluorescence or qPCR. CONCLUSIONS: In summary, our work provides insights on the region-specific genes and pathways which are regulated by Satb2 in neocortical development.

Experimental study on the influence of non-penetrating crack spatial distribution on brittle fracture process.

To gain insights into the spatial distribution of non-penetrating cracks during the rock fracture process, a comprehensive uniaxial compression test is conducted on cubic gypsum specimens (100 mm × 100 mm × 100 mm) containing two non-penetrating cracks. The two pre-formed cracks are rectangular, with dimensions of 25 mm length, 2 mm width, and depths of 80 mm and 35 mm on adjacent sides of the specimen. The depth of the 80 mm crack can be adjusted from 0° to 150° in increments of 30°, while the other is fixed at a 45° angle. The results show that the spatial distribution of non-penetrating cracks can significantly influence the strength of the specimen. Initially, the strength of the specimen exhibits an upward trend and subsequently declines as the pre-crack inclination angle of the main rupture plane increases, ultimately reaching its pinnacle at 90°. The total percentage of tensile cracks in specimens with different inclinations are found to be 57%, 57%, 63%, 77%, 68%, and 61%, respectively. This change aligns seamlessly with the fluctuation in specimen strength as influenced by the angle of inclination. Non-penetrating cracks can also induce spalling on the specimen surface and give rise to anti-wing cracks, thereby exacerbating the spalling on the specimen surface. The inclinations of non-penetrating cracks can inevitably exert a certain influence on the propagation of neighboring non-penetrating cracks. Additionally, the macro-scale shear fracture of the specimen often occurs on the side of the non-penetrating crack that is deeper. The curved tensile fracture surface formed by the extension of the non-penetrating crack bears resemblance to the non-penetrating region in its ability to somewhat restrain the propagation of new cracks. Even under uniaxial compression, the spalling surface of the specimen containing spatial non-penetrating cracks frequently exhibits fracture characteristics belonging to I-III mode fracture, while its interior may display characteristics belonging to I-II-III mode fracture. These findings hold significant implications for comprehending and elucidating the genuine fracture process and three-dimensional fracture theory of rocks.

The Prominence of Action Sequences and Behavioral Similarity in the Morris Water Task.

The question of What is learned when navigating to a place is reinforced has been the subject of considerable debate. Prevailing views emphasize cognitive structures (e.g., maps) or associative learning, which has shaped measurement in spatial navigation tasks (e.g., the Morris water task [MWT]) toward selection of coarse measures that do not capture precise behaviors of individual animals. We analyzed the navigation paths of 15 rats (60 trials each) in the MWT at high temporal resolution (30Hz) and utilized dynamic time warping to quantify the similarity of paths within and between animals. Paths were largely direct, yet suboptimal, and included changes in speed and trajectory that were established early in training and unique to each animal. Individual rats executed similar paths from the same release point from trial to trial, which were distinct from paths executed by other rats as well as paths performed by the same rat from other release points. These observations suggest that rats learn to execute similar path sequences from trial to trial for each release point in the MWT. Occasional spontaneous deviations from the established, unique behavioral sequence, resulted in profound disruption in navigation accuracy. We discuss the potential implications of sequence navigation behaviors for understanding relations between behavior and spatial neural signals such as place cells, grid cells, and head direction cells. Supplementary Information: The online version contains supplementary material available at 10.1007/s40614-024-00402-8.

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.

Treating gaps and biases in biodiversity data as a missing data problem.

Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large-scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long-term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow.

Robust All-Day Frostphobic Surfaces.

The application of solar-thermal surfaces for antifrosting and defrosting has emerged as a passive and environmentally friendly approach to mitigate the negative consequences of frost formation, such as structural damage and reduced heat transfer efficiency. However, achieving robust all-day frostphobicity solely through interfacial modification and solar-thermal effects is challenging in practical applications: The thick frost that accumulates at night strongly scatters solar radiation, rendering the solar-thermal coatings ineffective during the daytime. Additionally, these nanostructured coatings are susceptible to wear and tear when exposed to the outdoors for extended periods of time. To address these challenges, we present an innovative frostphobic surface that incorporates V-grooved structures with superhydrophobic solar-thermal layers (VSSs). The out-of-plane gradient structures facilitate spatially regulated vapor diffusion, an enhanced photothermal effect, and robust water repellency. These features not only prevent frost from covering the entire surface overnight, enabling effective solar-thermal defrosting during the daytime, but also protect the surface from deterioration. The combined merits ensure robust all-day frostphobicity and exceptional durability, making the VSS surface promising for practical applications and extending the lifespan in extreme environments.

Multi-scale beta-diversity patterns in testate amoeba communities: species turnover and nestedness along a latitudinal gradient.

The relationship between species diversity and spatial scale is a central topic in spatial community ecology. Latitudinal gradient is among the core mechanisms driving biodiversity distribution on most scales. Patterns of ß-diversity along latitudinal gradient have been well studied for aboveground terrestrial and marine communities, whereas soil organisms remain poorly investigated in this regard. The West Siberian Plain is a good model to address diversity scale-dependence since the latitudinal gradient does not overlap with other possible factors such as elevational or maritime. Here, we collected 111 samples following hierarchical sampling (sub-zones, ecosystem types, microhabitat and replicate samples) and performed multi-scale partitioning of ß-diversity of testate amoeba assemblages as a model of study. We found that among-ecosystem ß-diversity is a leading scale in testate amoeba assemblages variation. Rare species determine ß-diversity at all scale levels especially in the northern regions, where rare taxa almost exclusively accounted for the diversity at the ecosystem level. ß-Diversity is generally dominated by the turnover component at all scales in lower latitudes, whereas nestedness prevailed at among-ecosystem scale in higher latitudes. These findings indicate that microbial assemblages in northern latitudes are spatially homogeneous and constrained by historical drivers at larger scales, whereas in southern regions, it is dominated by the turnover component both at the microhabitat and ecosystem scales and therefore determined by recent vegetation and environmental heterogeneity. Overall, we have provided the evidence for the existence of negative latitudinal gradient for among-ecosystem ß-diversity but not for among-microhabitat and among-sample ß-diversity for terrestrial testate amoeba communities.

A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology.

Educators are witnessing the unfolding of the era of artificial intelligence, raising the question of how to transfer the benefits of yesterday's pedagogy to the future of education. An interactive digital mind map of infectious diseases was developed for second-year medical students (n = 865). Analysis of user engagement showed global distribution with 498 QR scans on a single day. Student responses (n = 79, 9.1% response rate) indicated positive feedback on the resources of Extremely Satisfied (range 65-75%, n = 59-51). The findings of the study support further expansion of MedMicroMaps to cross-platform interfaces with adaptations for diverse audiences within allied health fields. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-024-02047-3.

Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.

Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or smooth (continuous) transition to an undesired steady state. Theories predict that changes in the structure of the underlying spatial patterns precede such transitions. While there has been a large body of research on identifying early warning signals of critical transitions, the problem of forecasting the type of transitions (sudden versus smooth) remains an open challenge. We address this gap by developing an advanced machine learning (ML) toolkit that serves as an early warning indicator of spatio-temporal critical transitions, Spatial Early Warning Signal Network (S-EWSNet). ML models typically resemble a black box and do not allow envisioning what the model learns in discerning the labels. Here, instead of naively relying upon the deep learning model, we let the deep neural network learn the latent features characteristic of transitions via an optimal sampling strategy (OSS) of spatial patterns. The S-EWSNet is trained on data from a stochastic cellular automata model deploying the OSS, providing an early warning indicator of transitions while detecting its type in simulated and empirical samples.

Spatio-temporal variation and influencing factors of aero-sports tourism internet attention in Shaanxi Province, China.

Despite China's building into a leading sporting nation and sport-tourism integration high-quality development strategy in the 20th National Congress of the Communist Party of China, existing tourism studies seldom concern sport-tourism integration, especially their spatial hot spots and evolutional trend based on geospatial big data. This study aims to probe into the spatiality and the underlying mechanism of sport tourism through internet attention data in 2015, 2018, and 2021 by social network analysis, with a specific focus on aero-sports tourism. Shaanxi Province is chosen as the study site given its advantages of rich aero-sports tourism resources and various aero-sports modes (e.g., sky diving, paragliding, etc.). The results are concluded: (1) At the provincial scale, the aero-sports tourism internet attention shows a pattern of "strong in the middle and weak in the north and south". (2) At the regional scale, the sub-group clusters within the three specific regions (Shanbei, Guanzhong, and Shannan) of Shaanxi Province turn to be inter-regional clusters. Guanzhong region, especially with Xi'an as the core, is dominant in connecting its peripheral area. Since 2016, the radiation effects of the Guanzhong Region have shown a homogeneous trend of yearly growth and effect strengthening, yet become loosely connected with a heterogeneous trend in 2021 due to the COVID-19 epidemic. (3) At the city scale, the core area of aero-sports tourism internet attention expanded from Xi'an and Xianyang to Yulin and Baoji from 2015 to 2021, resulting from urban economic strength and aviation flight camp club development. (4) The number of general aviation manufacturers, tourist attendance, and tourism revenue significantly affect aero-sports tourism internet attention.

Algorithm of spatial-temporal simulation for environment-strain interactions in strain-strain consortia based on resource competition mechanism.

Interaction simulation for co-culture systems is important for optimizing culture conditions and improving yields. For industrial production, the environment significantly affects the spatial-temporal microbial interactions. However, the current research on polymicrobial interactions mainly focuses on interaction patterns among strains, and neglects the environment influence. Based on the resource competition relationship between two strains, this research set up the modules of cellular physicochemical properties, nutrient uptake and metabolite release, cellular survival, cell swimming and substrate diffusion, and investigated the spatial-temporal strain-environment interactions through module coupling and data mining. Furthermore, in an Escherichia coli-Saccharomyces cerevisiae consortium, the total net reproduction rate decreased as glucose was consumed. E. coli gradually dominated favorable positions due to its higher glucose utilization capacity, reaching 100 % abundance with a competitive strength of 0.86 for glucose. Conversely, S. cerevisiae decreased to 0 % abundance with a competitive strength of 0.14. The simulation results of environment influence on strain competitiveness showed that inoculation ratio and dissolved oxygen strongly influenced strain competitiveness. Specifically, strain competitiveness increased with higher inoculation ratio, whereas E. coli competitiveness increased as dissolved oxygen increased, in contrast to S. cerevisiae. On the other hand, substrate diffusion condition, micronutrients and toxins had minimal influence on strain competitiveness. This method offers a straightforward procedure without featured downscaling and provides novel insights into polymicrobial interaction simulation.

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 impact on inflation of Java Island prediction using Autoregressive Integrated Moving Average (ARIMA) and Generalized Space-Time ARIMA (GSTARIMA).

Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island's inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.

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.

Nationwide Investigation on Organophosphate Flame Retardants in Tea from China: Migration from Packaging Materials and Implications for Global Risk Assessment.

In this study, we measured 15 common organophosphate flame retardants (OPFRs) in six categories of tea samples across China. OPFRs were found in all the tea samples, with the total concentrations of OPFRs (∑OPFRs) at 3.44-432 ng/g [geometric mean (GM): 17.6 ng/g]. Triphenyl phosphate (TPhP) was the dominant OPFR, accounting for 39.0-76.2% of ∑OPFRs across all tea categories. The potential factors influencing the residual OPFRs in tea were thoroughly examined, including the agricultural environment, fermentation, and packaging of teas. Tea packaging materials (TPMs) were then identified as the primary sources of OPFRs in teas. The migration test revealed that OPFRs with lower molecular weights and log Kow values exhibited a higher propensity for facilitating the migration of OPFRs from TPMs to teas. The estimated daily intakes of OPFRs from teas were relatively higher for the general populations in Mauritania, Gambia, Togo, Morocco, and Senegal (3.18-9.79 ng/kg bw/day) than China (3.12 ng/kg bw/day). The health risks arising from OPFRs in Chinese teas were minor. This study established a baseline concentration and demonstrated the contamination sources of OPFRs in Chinese tea for the first time, with an emphasis on enhancing the hygiene standards for TPMs.

Modeling tuberculosis transmission flow in China, 2010-2012.

BACKGROUND: China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. METHODS: 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. RESULTS: Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. CONCLUSIONS: Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Epidemiological profile of malaria in a rural community in the Amazon, Mato Grosso State, Brazil, 2011.

BACKGROUND: More than 95% of malaria transmission in Brazil occurs in the Legal Amazon Region, which in 2010 recorded around 333,429 cases reported in the Epidemiological Surveillance Information System-Malaria (Sivep_malaria), presenting an annual parasitic incidence (IPA) of 13.1 cases/1000 inhabitants. METHODS: This was a descriptive study that measured the community prevalence of Plasmodium infection and its relationship with land use in Três Fronteiras District, Colniza Municipality, Mato Grosso State. Data were collected during household visits in July 2011, with blood collection from finger pricks for the preparation of thick smear slides, and completion of a standardized case notification form. A georeferenced database was analysed, with land use evaluated as categorical variables. A kernel density map was built to show the density of cases and their location. RESULTS: Of the 621 respondents, 68(11%) had Plasmodium infection: 39 (57.4%) with Plasmodium vivax, 27(39.7%) with Plasmodium falciparum and two (2.9%) with mixed infections. Among infected individuals, 49 (72.1%) were men. Cases of malaria were distributed over the district, with greater occurrence of cases per household in open areas close to the mining company and artisanal mining sites. The was a greater density of cases located in the gold mining region. CONCLUSION: Transmission of malaria in Três Fronteiras District has a heterogeneous distribution. Individuals residing in mining and timber extraction sites have increased occurrence of Plasmodium infection.

Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh.

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 µS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %-97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951-2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.