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The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Salmonella Typhi is a human-restricted pathogen that is transmitted by the faecal–oral route and causative organism of typhoid fever. Using health facility data from 2016 to 2020, this study focuses on modelling the spatial variation in typhoid risk in Ndirande township in Blantyre. To pursue this objective, we developed a marked inhomogeneous Poisson process model that allows us to incorporate both individual-level and environmental risk factors. The results from our analysis indicate that typhoid cases are spatially clustered, with the incidence decreasing by 54% for a unit increase in the water, sanitation, and hygiene (WASH) score. Typhoid intensity was also higher in children aged below 18 years than in adults. However, our results did not show evidence of a strong temporal variation in typhoid incidence. We also discuss the inferential benefits of using point pattern models to characterise the spatial variation in typhoid risk and outline possible extensions of the proposed modelling framework. [ABSTRACT FROM AUTHOR]

It is of great significance for the efficient utilization of water resources and the construction of the ecological environment in China to fully understand the evolution process of the spatial-temporal pattern of evapotranspiration (ET). With the use of the v2.0 and v2.1 ET data sets combined with the Global Land Data Assimilation System and Noah model, this paper selects pixels as the basic research object to analyse the spatial-temporal variation in ET in China during the 71 years from 1948 to 2018. We first applied the TFPW-MK test to study the annual ET trend in China throughout the 71-year period, including the ET trend of each month from January to December and the annual total ET trend. Moreover, we examined the spatial variation in these trends. In addition, we calculated the variation coefficient of the time series of each pixel's ET throughout the 71-year period and the variation coefficient of the spatial distribution of ET in each year to analyse the spatial-temporal fluctuations in ET in the study area. Finally, the Hurst index was adopted to evaluate the future ET trend. Based on these analyses, we observed the following novel spatial-temporal characteristics of ET: from 1948 to 2018, (1) the ET in most regions covered by 89.5% of all pixels in China exhibits an increasing trend. (2) The ET trend in China varies greatly with the change in months, and many regions show the most or least obvious increasing trend (or decreasing trend) at different times. (3) The area with an increasing trend is the largest in May and the smallest in December, and more than half of the pixels in all months of a year reveal an increasing trend. (4) In the northeast, west and south regions of China, the monthly fluctuation in the ET trend is relatively large, which indicates that the ET trend in these regions is greatly affected by the month. (5) The fluctuation in ET in China is larger in the north than it is in the south and larger in the west than it is in the east. The most stable fluctuation occurs in East China. (6) The ET trend of almost all the pixels in the study area remains consistent from 1948 to 2018, and there are large areas with a notable continuity. This results in the spatial variation in ET in the study area increasing. [ABSTRACT FROM AUTHOR]

Cooking and heating using solid fuels, such as dung, wood, agricultural residues, grass, straw, charcoal, and coal, is a main source of household air pollution. This indoor combustion encompasses a diversity of health detrimental pollutants, especially for people from low-income countries like Ethiopia since solid fuels are accessible easily at a lesser cost. Limited studies done showing factors affecting in choosing fuel type and no study, which revealed spatial heterogeneity of solid fuel used based on such nationally representative data. Therefore, this study, aimed at investigating spatial variation and determinants of solid fuel use in Ethiopia. This study was done using the data from the Ethiopian Demographic and Health Survey 2016, a national representative sample (16,650) households were included. Spatial and Multi-level logistic regression analysis was done by considering the DHS data hierarchal nature. Variables in the final model with a p-value < 0.05 were reported as significant predictors of using solid fuel. All analyses were done using ArcGIS V.10.7.1 and STATA V.14 software. The finding of this study revealed that 90.8% (95% CI (87.9%, 91.2%)) of households depend on solid fuel for cooking. Based on the final model ;Male household head (AOR 1.38, 95% CI (1.12–1.71)), age of household head (AOR 1.61, 95% CI (1.20, 2.17)), and 1.49 (OR 1.49, 95% CI (1.12, 1.99)) respectively for the age classes of < 30, and 30–40, education attainment no education (OR 3.14, 95% CI (1.13, 8.71)) and primary education (AOR 2.16, 95% CI (2.78, 5.96), wealth index Poorest (AOR 11.05, 95% CI (5.68, 15.78)), Poorer (OR 5.19, 95% CI (5.43, 13.19)), Middle (OR 3.08, 95% CI (2.44, 8.73)), and Richer (OR 1.30, 95IC (1.07, 13.49)) compared to richest, and not accessibility of electricity (AOR 31.21, 95% CI (35.41, 42.67)), were individual-level factors significantly associated with using solid fuel. Community-level factors like households found at large city (AOR 2.80, 95CI (1.65, 4.77)), small city (AOR 2.58, 95% CI (1.55, 4.32)) town (AOR 4.02, 95% CI (2.46, 6.55)), and countryside (AOR 14.40, 95% CI (6.23, 21.15)) compared households found in capital city, community level media exposure (AOR 6.00, 95% CI (4.61, 7.82)) were statistically predictors in using solid fuel for cooking. This finding revealed that a large proportion of households in Ethiopia heavily depend on biomass, especially wood, for cooking. There was a greater disparity on solid fuel use for cooking in Ethiopia. Implementing major policy interventions should be introduced to reduce solid fuel use for cooking and inequalities in accessing clean fuel in Ethiopia. [ABSTRACT FROM AUTHOR]

With the recent return of large carnivores to forest ecosystems, an important issue for forest owners and managers is how large predators influence the behaviour of their natural prey and, consequently, cervid browsing pressure on forest regeneration. To investigate this issue, we analysed deer pressure on Scots pine and European beech plantations in northern Poland's ecosystems with and without permanent wolf populations. Two characteristics were used to describe deer browsing patterns in plantations: distance from the forest edge (spatial pattern of browsing) and number of saplings browsed (browsing intensity). Beech saplings were more intensively browsed by deer compared to pine saplings. In a forest ecosystem not inhabited by wolves, spatial variation in browsing patterns on small-sized beech plantations was the same between the edge and the center. In contrast, browsing pressure by deer was greater at the edges on large-sized pine plantations. The presence of wolves reduced deer browsing on beech and increased browsing on pine saplings. In addition, deer foraging behaviour changed in large-sized pine plantations, and browsing pressure increased only in the central areas of the plantations. We assume that the presence of wolves in a forest landscape is an important factor that alters browsing pressure on the youngest stands and their spatial pattern, and that this may be a major factor in stand regeneration, especially in small forest patches. [ABSTRACT FROM AUTHOR]

This study aimed to investigate spatial variations in the non-use of modern contraception in Bangladesh and identify associated individual, household, and community-level factors. The analysis utilized data from 16,135 women, extracted from the 2017/18 Bangladesh Demographic and Health Survey. The study's main outcome was the prevalence of non-use of modern contraception (yes or no), while the explanatory variables included factors at the individual, household, and community level. To assess geographical heterogeneity in non-use of modern contraception, Moran's I statistics were applied. Additionally, the Gettis-Ord Gi* was calculated to measure spatial autocorrelation differences across various study locations. The relationship between non-use of modern contraception and location was further explored using a geographically weighted regression model at the cluster level. The results indicated that 42.8% (95% CI 41.6–43.8) of respondents reported non-use of modern contraception in Bangladesh, with significant variation across geographical locations (p < 0.001). Hot spots of high non-use were predominantly identified in the Sylhet, Barishal, and some areas of the Chattogram divisions, while cold spots of low use were concentrated in the Rangpur, Mymensingh, and some areas Rajshahi divisions. Notably, the likelihood of non-use was highest among women and partners with low levels of education. The analysis of other risk factors, such as partner occupation, community-level illiteracy, and poverty, revealed varying effects on non-use of modern contraception across different locations (clusters) within the country. The study's findings underscore the importance of targeted, area-specific policies and programs aimed at promoting knowledge and uptake of modern contraception in Bangladesh. [ABSTRACT FROM AUTHOR]

Microvascular dysfunction is the underlying pathological process in many systemic diseases. However, investigation into its pathogenesis is impeded by the accessibility and complexity of the microvasculature within different organs, particularly for the central nervous system. The retina as an extension of the cerebrum provides a glimpse into the brain through which the microvasculature can be observed. Two major questions remain unanswered: How do the microvessels regulate spatial and temporal delivery to satisfy the varying cellular demands, and how can we quantify blood perfusion in the 3D capillary network? Here, quantitative measurements of red blood cell (RBC) speed in each vessel in the field were made in the in vivo rat retinal capillary network using an ultrafast confocal technique with fluorescently labelled RBCs. Retinal RBC speed and number were found to vary remarkably between microvessels ranging from 215 to 6641 microns per second with significant variations spatially and temporally. Overall, the RBC speed was significantly faster in the microvessels in the superficial retina than in the deep retina (estimated marginal means of 2405 ± 238.2 µm/s, 1641 ± 173.0 µm/s respectively). These observations point to a highly dynamic nature of microvasculature that is specific to its immediate cellular environment and is constantly changing. [ABSTRACT FROM AUTHOR]

Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (Ifluo), effective tissue attenuation coefficient (μT), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability. [ABSTRACT FROM AUTHOR]

In this longitudinal study we compare between and within-strain variation in the home-cage spatial preference of three widely used and commercially available mice strains—C57BL/6NCrl, BALB/cAnNCrl and CRL:CD1(ICR)—starting from the first hour post cage-change until the next cage-change, for three consecutive intervals, to further profile the circadian home-cage behavioural phenotypes. Cage-change can be a stressful moment in the life of laboratory mice, since animals are disturbed during the sleeping hours and must then rapidly re-adapt to a pristine environment, leading to disruptions in normal motor patterns. The novelty of this study resides in characterizing new strain-specific biological phenomena, such as activity along the cage walls and frontality, using the vast data reserves generated by previous experimental data, thus introducing the potential and exploring the applicability of data repurposing to enhance Reduction principle when running in vivo studies. Our results, entirely obtained without the use of new animals, demonstrate that also when referring to space preference within the cage, C57BL/6NCrl has a high variability in the behavioural phenotypes from pre-puberty until early adulthood compared to BALB/cAnNCrl, which is confirmed to be socially disaggregated, and CRL:CD1(ICR) which is conversely highly active and socially aggregated. Our data also suggest that a strain-oriented approach is needed when defining frequency of cage-change as well as maximum allowed animal density, which should be revised, ideally under the EU regulatory framework as well, according to the physiological peculiarities of the strains, and always avoiding the "one size fits all" approach. [ABSTRACT FROM AUTHOR]

Biofuels made from biomass and waste residues will largely contribute to United States' 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds. [ABSTRACT FROM AUTHOR]

With the general rise of computational capacities and the continued efforts to improve computational efficiencies, more and more studies have been utilizing state-of-the-art atmospheric models that enable cloud-resolving simulations over a global domain. Microphysical processes inside clouds, however, are on a scale much smaller than that of a cloud itself, and therefore resolving clouds in a model is not equivalent to resolving cloud microphysical processes. When aerosol–cloud interaction (ACI) is studied, chemistry models enable the prognostic calculations for chemical species, including aerosols, which can perturb cloud microphysics and eventually impact clouds and climate. The large drawback of these models is the high computational cost required for tracking chemical species in space and time that may not be affordable in some studies. As a result, some studies have used non-chemistry models with prescribed cloud droplet number concentrations n c and compared multiple simulations with different n c to assess the impacts of varying aerosol concentrations on clouds. In this study we assess whether the same or similar ACI can be simulated when the aerosol number is increased in a chemistry model and when n c is raised in a non-chemistry model. A case study has been conducted over the Maritime Continent in September 2015 when an extremely large number of aerosols were observed due to fires under a dry condition brought by a strong El Niño. The comparison of the simulations by the chemistry and non-chemistry models shows that the aerosol-driven enhancement of rainfall in the chemistry simulations was not present in the non-chemistry simulations, even with prescribed n c with certain spatial variation based on the chemistry runs. Therefore, simulated ACI may largely differ depending on the manner by which an increase or decrease of aerosols is represented in a model. The result suggests the further need for powerful computational capabilities and the pursuit of a rigorous means to incorporate aerosol species in a non-chemistry model. [ABSTRACT FROM AUTHOR]

Physico-mechanical properties of shale are important parameters in evaluating the stability of potential wellbore and the design of hydraulic fracturing, which are primarily affected by their non-uniform spatial distribution of the microscopic physical–mechanical properties at particle scale. A series of constant strain rate experiments and stress-cycling experiments on shale specimens with different bedding dip angles were conducted to have a comprehensive understanding of the effect of the non-uniform distribution of microscopic failure stress on macroscopic physico-mechanical properties. According to the experimental results and Weibull distribution, we find that bedding dip angle and the dynamic load applying type affect the spatial distributions of microscopic failure stress. The values of crack damage stress (σcd), σcd/σucs (peak stress), εcd (strain at crack damage stress), Poissons' ratio (ν), elastic strain energy (Ue) and dissipated energy (Uirr) of the specimens with more uniform distribution of microscopic failure stress are overall higher, while εucs (peak strain)/εcd and elastic modulus (E) are lower. The dynamic load enables the spatial distributions of microscopic failure stress trend to be more homogeneous prior to the final failure with the increment of σcd/σucs, ν, Ue and Uirr and the decrement of E. [ABSTRACT FROM AUTHOR]

Household food insecurity remains highly prevalent in developing countries (including in Ethiopia) and it has been recognized as a serious public health problem. Several factors such as demographic, economic, social, and clinical factors influence household food insecurity, and these vary geographically. In this work, we investigate the geographical modification of the effect of several factors on chronic food insecurity. The data is from the Ethiopia socioeconomic survey conducted by the Ethiopia Central Statistics Agency (ECSA) in collaboration with the World Bank. Ethiopia socioeconomic survey is a long-term project to collect nationally representative panel survey of over 6500 households. A geo-additive model which accounts the structured and unstructured special effect was adopted to estimate household food insecurity risk factors. The study also revealed significant spatial variations on household food insecurity among administrative zones. Mainly, household living in the Sidama, Gamo Gofa, Shinille, Basketo, Wolyita, Wag Hemira, Liben, Awi, Eastern Tigray and West Harerghe zones, having higher food insecurity than the other zones in Ethiopia. Moreover, the analysis also showed that availability of credit services, proximity to service centers, average years of schooling of members of the household, and household assets are negatively associated with household food insecurity, whereas shocks, age, and dependency ratio increase the odds of a household to be food insecured. The generalized geo-additive mixed-effects model enables simultaneous modeling of spatial correlation, heterogeneity and possible nonlinear effects of covariates. Our study investigated the spatial heterogeneity of household level food insecurity, and its association with shocks, age, dependency ratio, availability of credit services, average years of schooling, and household assets. Our findings have also an important implication for planning as well as in the search for the variables that might account for the residual spatial patterns. [ABSTRACT FROM AUTHOR]

Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, very few studies focused on vegetation fires and the changes during the COVID as compared to pre-pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations in SA/SEA during COVID-2020 and pre-pandemic (2012–2019). Results from the short-term 2020-COVID versus 2019-non-COVID year showed a decline in fire counts varying from − 2.88 to 79.43% in S/SEA. The exceptions in South Asia include Afghanistan and Sri Lanka, with a 152% and 4.9% increase, and Cambodia and Myanmar in Southeast Asia, with an 11.1% and 8.5% increase in fire counts in the 2020-COVID year. The burnt area decline for 2020 compared to 2019 varied from − 0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012–2020 pre-pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from − 0.24 to − 1, although most countries experienced a decrease in fire counts. The associated mean TPM emissions declined from ~ 2.31 Tg (0.73stdev) during 2012–2019 to 2.0 (0.65stdev)Tg in 2020 in South Asia and 6.83 (0.70stdev)Tg during 2012–2019 to 5.71 (0.69 stdev)Tg in 2020 for South East Asian countries. The study highlights variations in fires and emissions useful for fire management and mitigation. [ABSTRACT FROM AUTHOR]

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species' distributions over large spatial scales and for quantifying range dynamics over long temporal scales. [ABSTRACT FROM AUTHOR]

Spatially referenced data arise in many fields, including imaging, ecology, public health, and marketing. Although principled smoothing or interpolation is paramount for many practitioners, regression, too, can be an important (or even the only or most important) goal of a spatial analysis. When doing spatial regression it is crucial to accommodate spatial variation in the response variable that cannot be explained by the spatially patterned explanatory variables included in the model. Failure to model both sources of spatial dependence—regression and extra-regression, if you will—can lead to erroneous inference for the regression coefficients. In this article I highlight an under-appreciated spatial regression model, namely, the spatial Gaussian copula regression model (SGCRM), and describe said model's advantages. Then I develop an intuitive, unified, and computationally efficient approach to inference for the SGCRM. I demonstrate the efficacy of the proposed methodology by way of an extensive simulation study along with analyses of a well-known dataset from disease mapping. [ABSTRACT FROM AUTHOR]

Insectivorous bats consume a diverse array of arthropod prey, with diets varying by bat species, sampling location, and season. North American bat diets remain incompletely described, which is concerning at a time when many bat and insect populations appear to be declining. Understanding the variability in foraging is thus an essential component for effective bat conservation. To comprehensively evaluate local foraging, we assessed the spatial and temporal variability in prey consumed by the little brown bat, Myotis lucifugus, in New Hampshire, USA. We collected bat guano samples from 20 sites over 2 years and analyzed sequence data for 899 of these samples using a molecular metabarcoding approach targeting the cytochrome oxidase I subunit (COI) gene. Some prey items were broadly shared across locations and sampling dates, with the most frequently detected arthropod orders broadly similar to previous morphological and molecular analyses; at least one representative sequence variant was assigned to Coleoptera in 92% of samples, with other frequently detected orders including Diptera (73%), Lepidoptera (65%), Trichoptera (38%), and Ephemeroptera (32%). More specifically, two turf and forest pests were routinely detected: white grubs in the genus Phyllophaga (50%), and the Asiatic Garden beetle, Maladera castanea (36%). Despite the prevalence of a few taxa shared among many samples and distinct seasonal peaks in consumption of specific arthropods, diet composition varied both temporally and spatially. However, species richness did not strongly vary indicating consumption of a broad diversity of taxa throughout the summer. These data characterize little brown bats as flexible foragers adept at consuming a broad array of locally available prey resources. [ABSTRACT FROM AUTHOR]

Multiple human sensory systems exhibit sensitivity to spatial and temporal variations of physical stimuli. Vision has evolved to offer high spatial acuity with limited temporal sensitivity, while audition has developed complementary characteristics. Neural coding in touch has been believed to transition from a spatial to a temporal domain in relation to surface scale, such that coarse features (e.g., a braille cell or corduroy texture) are coded as spatially distributed signals, while fine textures (e.g., fine-grit sandpaper) are encoded by temporal variation. However, the interplay between the two domains is not well understood. We studied tactile encoding with a custom-designed pin array apparatus capable of deforming the fingerpad at 5 to 80 Hz in each of 14 individual locations spaced 2.5 mm apart. Spatial variation of skin indentation was controlled by moving each of the pins at the same frequency and amplitude, but with phase delays distributed across the array. Results indicate that such stimuli enable rendering of shape features at actuation frequencies up to 20 Hz. Even at frequencies > 20 Hz, however, spatial variation of skin indentation continues to play a vital role. In particular, perceived roughness is affected by spatial variation within the fingerpad even at 80 Hz. We provide evidence that perceived roughness is encoded via a summary measure of skin displacement. Relative displacements in neighboring pins of less than 10 µm generate skin stretch, which regulates the roughness percept. [ABSTRACT FROM AUTHOR]

Malaria and anaemia are common diseases that affect children, particularly in Africa. Studies on the risk associated with these diseases and their synergy are scanty. This work aims to study the spatial pattern of malaria and anaemia in Nigeria and adjust for their risk factors using separate models for malaria and anaemia. This study used Bayesian spatial models within the Integrated Nested Laplace Approach (INLA) to establish the relationship between malaria and anaemia. We also adjust for risk factors of malaria and anaemia and map the estimated relative risks of these diseases to identify regions with a relatively high risk of the diseases under consideration. We used data obtained from the Nigeria malaria indicator survey (NMIS) of 2010 and 2015. The spatial variability distribution of both diseases was investigated using the convolution model, Conditional Auto-Regressive (CAR) model, generalized linear mixed model (GLMM) and generalized linear model (GLM) for each year. The convolution and generalized linear mixed models (GLMM) showed the least Deviance Information Criteria (DIC) in 2010 for malaria and anaemia, respectively. The Conditional Auto-Regressive (CAR) and convolution models had the least DIC in 2015 for malaria and anaemia, respectively. This study revealed that children in rural areas had strong and significant odds of malaria and anaemia infection [2010; malaria: AOR = 1.348, 95% CI = (1.117, 1.627), anaemia: AOR = 1.455, 95% CI = (1.201, 1.7623). 2015; malaria: AOR = 1.889, 95% CI = (1.568, 2.277), anaemia: AOR = 1.440, 95% CI = (1.205, 1.719)]. Controlling the prevalence of malaria and anaemia in Nigeria requires the identification of a child's location and proper confrontation of some socio-economic factors which may lead to the reduction of childhood malaria and anaemia infection. [ABSTRACT FROM AUTHOR]

For induced seismicity, the non-stationary, heterogeneous character of subsurface stress perturbations can be a source of spatiotemporal variations in the scaling of event sizes; one of the critical parameters controlling seismic hazard and risk. We demonstrate and test a systematic, statistical, penalized-likelihood approach to analysing both spatial and temporal variations in event size distributions. The methodology used is transferable to the risk analysis of any subsurface operation, especially for small earthquake catalogues. We explore the whole solution space and circumvent conventional, arbitrary choices that require a priori knowledge of these variations. We assess the effect of possible bias in the derivation, e.g., due to tapering of the earthquake-size distribution, correlation between the b-value and the magnitude of completeness and correlation between the b-value and the largest magnitude observed. We analyse the spatiotemporal variations in the earthquake-size distribution of the Groningen induced seismicity catalogue (December 1991–November 16, 2021). We find statistically significant spatial variations without any compelling, statistical evidence of a temporal variation. Furthermore, we find that the largest magnitudes observed are inconsistent with the sampling statistics of an unconstrained earthquake-size distribution. Current risk assessment models likely overestimate the probability of larger magnitude events (M ≥ 3.0) and thus the risk posed. [ABSTRACT FROM AUTHOR]

We examined the spatial variations in 226Ra and 228Ra (activities) concentrations from the surface to a depth of 830 m in the Indian and Southern Oceans from December 2019 to January 2020. 226Ra concentrations at the surface increased sharply from 30° S to 60° S along a ~ 55° E transect (1.4–2.9 mBq/L), exhibiting small vertical variations, while 228Ra decreased southward and became depleted in the Southern Ocean. These distributions indicated the ocean-scale northward lateral transport of 226Ra-rich and 228Ra-depleted currents originating from the Antarctic Circumpolar Current (ACC). 226Ra concentrations indicated that the fractions of the ACC at depths of 0–800 m decreased from 0.95 to 0.14 between 60° S and 30° S. The ACC fractions in the subantarctic western Indian Ocean were higher than those previously reported in the eastern Indian region, indicating preferential transport of the ACC. The fractions obtained were approximately equivalent to those in the western Indian Ocean in the 1970s. This could be attributed to the minimal southward shift of the polar front due to global warming over the last 50 years. [ABSTRACT FROM AUTHOR]

Due to global warming, global and regional extreme precipitation events occur frequently, causing severe drought and flood disasters. This has a significant impact on productivity and human life. Therefore, it is of great significance to study the characteristics of extreme precipitation and its spatiotemporal variation. In this study, we investigate the dominant variation patterns of extreme precipitation (EP), which is characterized by indices, and also analyze its potential causes in the Shandong province of China during 1961–2015 using the daily precipitation data from 123 metrological stations. The results show that there has been a dry trend in the Shandong Province in the past 55 years, that is, with the decrease in precipitation, most of the extreme precipitation index has basically showed a downward trend to varying degrees. In particular, the increase in the number of consecutive dry days (CDD) and the decrease in the number of consecutive wet days (CWD) can better explain the drought in this region. After the 1980s, the extreme precipitation index basically showed an upward trend to varying degrees, indicating that extreme precipitation events have shown an increasing trend in recent years. The spatial distribution of each extreme precipitation index generally increased from north to south. The mutation of each extreme precipitation index occurred in the 1970s and 1990s, and there was a main period of 0.9–2.2 years. In terms of influencing factors, the NINO3 area can be used as the critical sea area for the response of extreme precipitation to SSTAs in the Shandong Province. The research results are helpful to understand the temporal and spatial variations of extreme precipitation and have very important reference value for the prediction of and response to climate change and extreme events in the future. [ABSTRACT FROM AUTHOR]

Many species show spatial variation in body size, often associated with climatic patterns. Studying species with contrasting geographical patterns related to climate might help elucidate the role of different drivers. We analysed changes in the body mass of two sympatric medium-sized carnivores—pine marten (Martes martes) and stone marten (Martes foina)—across Europe over 59 years. The body mass of pine marten increased with decreasing latitude, whereas stone marten body mass varied in a more complex pattern across its geographic range. Over time, the average body mass of pine martens increased by 255 g (24%), while stone marten by 86 g (6%). The greatest increase of body mass along both martens' geographic range was observed in central and southern Europe, where both species occur in sympatry. The body mass increase slowed down over time, especially in allopatric regions. The average pine/stone marten body mass ratio increased from 0.87 in 1960 to 0.99 in 2019, potentially strengthening the competition between them. Thus, a differential response in body size to several drivers over time might have led to an adaptive advantage for pine martens. This highlights the importance of considering different responses among interacting species when studying animal adaptation to climate change. [ABSTRACT FROM AUTHOR]

The subgingival microbiome is one of the most stable microbial ecosystems in the human body. Alterations in the subgingival microbiome have been associated with periodontal disease, but their variations over time and between different subgingival sites in periodontally healthy individuals have not been well described. We performed extensive, longitudinal sampling of the subgingival microbiome from five periodontally healthy individuals to define baseline spatial and temporal variations. A total of 251 subgingival samples from 5 subjects were collected over 6–12 months and deep sequenced. The overall microbial diversity and composition differed significantly between individuals. Within each individual, we observed considerable differences in microbiome composition between different subgingival sites. However, for a given site, the microbiome was remarkably stable over time, and this stability was associated with increased microbial diversity but was inversely correlated with the enrichment of putative periodontal pathogens. In contrast to microbiome composition, the predicted functional metagenome was similar across space and time, suggesting that periodontal health is associated with shared gene functions encoded by different microbiome consortia that are individualized. To our knowledge, this is one of the most detailed longitudinal analysis of the healthy subgingival microbiome to date that examined the longitudinal variability of different subgingival sites within individuals. These results suggest that a single measurement of the healthy subgingival microbiome at a given site can provide long term information of the microbial composition and functional potential, but sampling of each site is necessary to define the composition and community structure at individual subgingival sites. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed novel risks related to the indoor mixing of individuals from different households and challenged policymakers to adequately regulate this behaviour. While in many cases household visits are necessary for the purpose of social care, they have been linked to broadening community transmission of the virus. In this study we propose a novel, privacy-preserving framework for the measurement of household visitation at national and regional scales, making use of passively collected mobility data. We implement this approach in England from January 2020 to May 2021. The measures expose significant spatial and temporal variation in household visitation patterns, impacted by both national and regional lockdown policies, and the rollout of the vaccination programme. The findings point to complex social processes unfolding differently over space and time, likely informed by variations in policy adherence, vaccine relaxation, and regional interventions. [ABSTRACT FROM AUTHOR]

A novel non-linear beamforming method, namely, filtered delay optimally-weighted multiply and sum (F-DowMAS) beamforming is reported for conventional focused beamforming (CFB) technique. The performance of F-DowMAS was compared against delay and sum (DAS), filtered delay multiply and sum (F-DMAS), filtered delay weight multiply and sum (F-DwMAS) and filter delay Euclidian weighted multiply and sum (F-DewMAS) methods. Notably, in the proposed method the optimal adaptive weights are computed for each imaging point to compensate for the effects due to spatial variations in beam pattern in CFB technique. F-DowMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral resolution (LR), axial resolution (AR), contrast ratio (CR) and contrast-to-noise ratio (CNR), estimated from experiments on a commercially available tissue-mimicking phantom. The results demonstrate that F-DowMAS improved the AR by 57.04% and 46.95%, LR by 58.21% and 53.40%, CR by 67.35% and 39.25%, and CNR by 44.04% and 30.57% compared to those obtained using DAS and F-DMAS, respectively. Thus, it can be concluded that the newly proposed F-DowMAS outperforms DAS and F-DMAS. As an aside, we also show that the optimal weighting strategy can be extended to benefit DAS. [ABSTRACT FROM AUTHOR]

We study the effect that disturbances in the ecological landscape exert on the spatial distribution of a population that evolves according to the nonlocal FKPP equation. Using both numerical and analytical techniques, we characterize, as a function of the interaction kernel, the three types of stationary profiles that can develop near abrupt spatial variations in the environmental conditions vital for population growth: sustained oscillations, decaying oscillations and exponential relaxation towards a flat profile. Through the mapping between the features of the induced wrinkles and the shape of the interaction kernel, we discuss how heterogeneities can reveal information that would be hidden in a flat landscape. [ABSTRACT FROM AUTHOR]

Globally, it was estimated that maternal and under-five deaths were high in low-income countries than that of high-income countries. Most studies, however, have focused only on the clinical causes of maternal and under-five deaths, and yet there could be other factors such as ambient particulate matter (PM). The current global estimates indicate that exposure to ambient PM2.5 (with ≤ 2.5 microns aerodynamic diameter) has caused about 7 million deaths and over 100 million disability-adjusted life-years. There are also several health risks that have been linked PM2.5, including mortality, both regionally and globally; however, PM2.5 is a mixture of many compounds from various sources. Globally, there is little evidence of the health effects of various types of PM2.5, which may uniquely contribute to the global burden of disease. Currently, only two studies had estimated the effects of discriminated ambient PM2.5, that is, anthropogenic, biomass and dust, on under-five and maternal mortality using satellite measurements, and this study found a positive association in Africa and Asia. However, the study area was conducted in only one region and may not reflect the spatial variations throughout the world. Therefore, in this study, we discriminated different ambient PM2.5 and estimated the effects on a global scale. Using the generalized linear mixed-effects model (GLMM) with a random-effects model, we found that biomass PM2.5 was associated with an 8.9% (95% confidence interval [CI] 4.1–13.9%) increased risk of under-five deaths, while dust PM2.5 was marginally associated with 9.5% of under-five deaths. Nevertheless, our study found no association between PM2.5 type and global maternal deaths. This result may be because the majority of maternal deaths could be associated with preventable deaths that would require clinical interventions. Identification of the mortality-related types of ambient PM2.5 can enable the development of a focused intervention strategy of placing appropriate preventive measures for reducing the generation of source-specific PM2.5 and subsequently diminishing PM2.5-related mortality. [ABSTRACT FROM AUTHOR]

Aquatic phytoplankton experience large fluctuations in environmental conditions during seasonal succession and across salinity gradients, but the impact of this variation on their diversity is poorly understood. We examined spatio-temporal variation in nano- and microphytoplankton (> 2 µm) community structure using almost two decades of light-microscope based monitoring data. The dataset encompasses 19 stations that span a salinity gradient from 2.8 to 35 along the Swedish coastline. Spatially, both regional and local phytoplankton diversity increased with broad-scale salinity variation. Diatoms dominated at high salinity and the proportion of cyanobacteria increased with decreasing salinity. Temporally, cell abundance peaked in winter-spring at high salinity but in summer at low salinity. This was likely due to large filamentous cyanobacteria blooms that occur in summer in low salinity areas, but which are absent in higher salinities. In contrast, phytoplankton local diversity peaked in spring at low salinity but in fall and winter at high salinity. Whilst differences in seasonal variation in cell abundance were reasonably well-explained by variation in salinity and nutrient availability, variation in local-scale phytoplankton diversity was poorly predicted by environmental variables. Overall, we provide insights into the causes of spatio-temporal variation in coastal phytoplankton community structure while also identifying knowledge gaps. [ABSTRACT FROM AUTHOR]

The prehistory of the Mediterranean region has long been a subject of considerable interest, particularly the links between human groups and regions of origin. We utilize the spatial variation in the δ2H and δ18O values of precipitation (isoscapes) to develop proxies for geographic locations of fauna and humans. Bone collagen hydrogen isotope ratios (δ2H) in cattle (and to a lesser extent, ovicaprids) across the Mediterranean reflect the isotopic differences observed in rainfall (but δ18O values do not). We conclude that δ2H in herbivore bone collagen can be used as a geolocation tracer and for palaeoenvironmental studies such as tracing past isotopic variations in the global hydrological cycle. In contrast, human bone δ2H values are relatively tightly grouped and highly distinct from precipitation δ2H values, likely due to human-specific food practices and environmental modifications. Given the inter-species variability in δ2H, care should be taken in the species selected for study. [ABSTRACT FROM AUTHOR]

The origin, structure, and variability of the Ryukyu Current (RC) have long been debated, mostly due to limited observations. A mooring array, deployed for two years southeast of Miyakojima in the southern portion of the Ryukyu Island chain, has provided, for the first time, data confirming the existence and revealing the characteristics of the RC in that upstream region, including its velocity structure and variability. The observations show a shoreward-intensified current flowing northeastward, with a subsurface core located near the 1,000 m isobath and having a record-long mean speed of up to 19.4 cm s−1 at 500 m depth. Estimated volume transport across the observation section had mean 9.0 Sv (1 Sv = 106 m3 s−1) and standard deviation 8.7 Sv. The RC shows significant barotropic character compared with other similar mid-latitude currents. [ABSTRACT FROM AUTHOR]

This study examined association between selected child health indicators- anaemia, stunting and no/incomplete immunization by inter-linking maternal characteristics at district level and parental characteristics at individual level. A spatial analysis and a binary logit model estimation were employed to draw inferences using the data from the fourth round of National Family Health Survey, 2015–16 of India. Significant spatial clustering of the selected child health outcomes was observed in the country. Mother's educational attainment explained significant district level differential in the selected child health outcomes. At the individual level, parents who are very young, not-educated, socially excluded, belong to poor class were found to be significantly associated with the poor child health outcomes. This study indicates that parental characteristics, such as age, educational attainment and employment substantially determine child health in India, suggesting that an intervention by targeting the households where children are vulnerable is important to improve child health in the country. [ABSTRACT FROM AUTHOR]

Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (Kv) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized Kv estimates from column experiments or grain-size distribution, but Kv may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting Kv spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on Kv variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of Kv and drainage area as a response variable reduces the fuzziness in selecting the "best" PTF. We propose that the PTF can be used in predicting the ranges of Kv values across multiple stream orders. [ABSTRACT FROM AUTHOR]

As a result of advances in sequencing technology, the role of gut microbiota in the mechanism of type-2 diabetes mellitus (T2DM) has been revealed. Studies showing wide distribution of microbiome throughout the human body, even in the blood, have motivated the investigation of the dynamics in gut microbiota across the humans. Particularly, extracellular vesicles (EVs), lipid bilayer structures secreted from the gut microbiota, have recently come into the spotlight because gut microbe-derived EVs affect glucose metabolism by inducing insulin resistance. Recently, intestine hyper-permeability linked to T2DM has also been associated with the interaction between gut microbes and leaky gut epithelium, which increases the uptake of macromolecules like lipopolysaccharide from the membranes of microbes leading to chronic inflammation. In this article, we firstly investigate the co-occurrence of stool microbes and microbe-derived EVs across serum and urine in human subjects (N = 284), showing the dynamics and stability of gut derived EVs. Stool EVs are intermediate, while the bacterial composition in both urine and serum EVs is distinct from the stool microbiome. The co-occurrence of microbes was compared between patients with T2DM (N = 29) and matched in healthy subjects (N = 145). Our results showed significantly higher correlations in patients with T2DM compared to healthy subjects across stool, serum, and urine, which could be interpreted as the dysfunction of intestinal permeability in T2DM. Therefore, the significant correlation of EVs might give insight into the pathophysiological mechanisms of T2DM, as well as the role of EVs as a biomarker in the intestinal permeability of T2DM. [ABSTRACT FROM AUTHOR]

Carbon use efficiency (CUE), one of the most important eco-physiological parameters, represents the capacity of plants to transform carbon into new biomass. Understanding the variations and controls of CUE is crucial for regional carbon assessment. Here, we used 15-years of continuous remote sensing data to examine the variations of CUE across broad geographic and climatic gradients in China. The results showed that the vegetation CUE was averaged to 0.54 ± 0.11 with minor interannual variation. However, the CUE greatly varied with geographic gradients and ecosystem types. Forests have a lower CUE than grasslands and croplands. Evergreen needleleaf forests have a higher CUE than other forest types. Climate factors (mean annual temperature (MAT), precipitation (MAP) and the index of water availability (IWA)) dominantly regulated the spatial variations of CUE. The CUE exhibited a linear decrease with enhanced MAT and MAP and a parabolic response to the IWA. Furthermore, the responses of CUE to environmental change varied with individual ecosystem type. In contrast, precipitation exerted strong control on CUE in grassland, while in forest and cropland, the CUE was mainly controlled by the available water. This study identifies the variations and response of CUE to environmental drivers in China, which will be valuable for the regional assessment of carbon cycling dynamics under future climate change. [ABSTRACT FROM AUTHOR]

In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO2 reduction during the lockdown (March 25–May 3rd, 2020) compared to the pre-lockdown (January 1st–March 24th, 2020) period. Also, a 19% reduction in NO2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO2 reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO2 for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO2 levels decreased exponentially. In contrast, to the decreased NO2 observed for most of the cities, we observed an increase in NO2 for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO2 temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO2, and the results can inform pollution mitigation efforts across different cities of India. [ABSTRACT FROM AUTHOR]

Damaging earthquakes result from the evolution of stress in the brittle upper-crust, but the understanding of the mechanics of faulting cannot be achieved by only studying the large ones, which are rare. Considering a fault as a complex system, microearthquakes allow to set a benchmark in the system evolution. Here, we investigate the possibility to detect when a fault system starts deviating from a predefined benchmark behavior by monitoring the temporal and spatial variability of different micro-and-small magnitude earthquakes properties. We follow the temporal evolution of the apparent stress and of the event-specific residuals of ground shaking. Temporal and spatial clustering properties of microearthquakes are monitored as well. We focus on a fault system located in Southern Italy, where the Mw 6.9 Irpinia earthquake occurred in 1980. Following the temporal evolution of earthquakes parameters and their time-space distribution, we can identify two long-lasting phases in the seismicity patterns that are likely related to high pressure fluids in the shallow crust, which were otherwise impossible to decipher. Monitoring temporal and spatial variability of micro-to-small earthquakes source parameters at near fault observatories can have high potential as tool for providing us with new understanding of how the machine generating large earthquakes works. [ABSTRACT FROM AUTHOR]

Transport fluxes and properties of riverine organic carbon in the tropical monsoon region were the vital parameters in the global riverine organic carbon fluxes budget. The study focused on the riverine organic carbon in the Changhuajiang River (CHJR), locating at the mid-west of the Hainan Island, China. Dissolved organic carbon (DOC) concentrations in the CHJR ranged from 0.22 mg/L to 11.75 mg/L with an average of 1.75 mg/L, which was lower than the average of global rivers and had a significantly temporal and spatial variation. Output flux of riverine DOC was calculated as 0.55 t/km2/y, which could be revised up to 1.03 t/km2/y, considering that the riverine discharge before dam construction. A linear model of riverine DOC flux suitable in CHJR basin was established, which involved the factors, such as soil organic carbon, runoff depth and slope, etc. There was a large variation of POC concentrations in the CHJR where the average POC concentration in the dry season was 2.41 times of the wet season. Riverine POC flux in CHJR basin was calculated as 1.78 t/km2/y, higher than the average of global rivers and far lower than those in other domestic larger rivers. About 8.28 × 103 t POC were exported yearly in CHJR, of which, 7.15 × 103 t originated from terrestrial ecosystem and 1.13 × 103 t stemmed from aquatic ecosystem. Meanwhile, about 87.74% of terrestrial source happened in the wet season and 12.26% in the dry season. This research revealed that the riverine organic carbon mainly stemmed from the surface erosion processes in the drainage basin during the wet season. [ABSTRACT FROM AUTHOR]