Spatial Aggregates Research Articles

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined by a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates.

Structure and process in primary health care for children and spatial distribution of infant mortality.

To identify the spatial patterns of the quality of the structure of primary health care services and the teams' work process and their effects on infant mortality in Brazil. An ecological study of spatial aggregates, using the 5,570 municipalities in Brazil as the unit of analysis. Secondary databases from the Programa Nacional de Melhoria do Acesso e Qualidade da Atenção Básica (PMAQ-AB - National Program for Improving Access and Quality of Primary Care), the Mortality Information System (SIM), and the Live Birth Information System (SINASC) were used. In 2018, the infant mortality rate was the outcome of the study, and the exposure variables were the proportion of basic health units (BHU) with adequate structure and work processes. Global and local Moran's indices were used to evaluate the degree of dependence and spatial autocorrelation. Spatial linear regression was used for data analysis. In 2018, in Brazil, the infant mortality rate was 12.4/1,000 live births, ranging from 10.6/1,000 and 11.2/1,000 in the South and Southeast, respectively, to 14.1/1,000 and 14.5/1,000 in the Northeast and North regions, respectively. The proportion of teams with an adequate work process (β = -3.13) and the proportion of basic health units with an adequate structure (β = -0.34) were associated with a reduction in the infant mortality rate. Spatial autocorrelation was observed between smoothed mean infant mortality rates and indicators of the structure of primary health care services and the team's work process, with higher values in the North and Northeast of Brazil. There is a relationship between the structure of primary health care services and the teams' work process with the infant mortality rate. In this sense, investment in the qualification of health care within the scope of primary health care can have an impact on reducing the infant mortality rate and improving child health care.

Impact of the improvement of living conditions on tuberculosis mortality in Brazil: an ecological study.

The risk of death due to tuberculosis (TB) in Brazil is high and strongly related to living conditions (LC). However, epidemiological studies investigating changes in LC and their impact on TB are lacking. To evaluate the impact of LC on TB mortality in Brazil. This ecological study, using panel data on spatial and temporal aggregates, was conducted in 1,614 municipalities between 2002 and 2015. Data were collected from the Mortality Information System and the Brazilian Institute of Geography and Statistics. The proxy variable used for LC was the Urban Health Index (UHI). Negative binomial regression models were used to estimate the effect of the UHI on TB mortality rate. Attributable risk (AR) was used as an impact measure. From 2002 to 2015, TB mortality rate decreased by 23.5%, and LC improved. The continuous model analysis resulted in an RR = 0.89 (95%CI = 0.82-0.96), so the AR was -12.3%. The categorized model showed an effect of 0.92 (95%CI = 0.83-0.95) in municipalities with intermediate LC and of 0.83 (95%CI = 0.82-0.91) in those with low LC, representing an AR for TB mortality of -8.7% and -20.5%, respectively. Improved LC impacted TB mortality, even when adjusted for other determinants. This impact was greater in the strata of low-LC municipalities.

Quantification and visualization of US racial geography using the National Racial Geography Dataset 2020.

Racial geography studies the spatial distributions of multiracial populations. Technical challenges arise from the fact that US Census data, upon which all US-based studies rely, is only available in the form of spatial aggregates at a few levels of granularity. This negatively affects spatial analysis and, consequently, the quantification of racial segregation, especially on a smaller length scale. A recent methodology called the Racial Landscape (RL) stochastically disaggregates racial data at the level of census block aggregates into a grid of monoracial cells. RL-transformed racial data makes possible pattern-based, zoneless analysis, and visualization of racial geography. Here, we introduce the National Racial Geography Dataset 2020 (NRGD2020)-a collection of RL-based grids calculated from the 2020 census data and covering the entire conterminous US. It includes a virtual image layer for a bird's-eye-like view visualization of the spatial distribution of racial sub-populations, numerical grids for calculating racial diversity and segregation within user-defined regions, and precalculated maps of racial diversity and segregation on various length scales. NRGD2020 aims to facilitate and extend spatial analyses of racial geography and to make it more interpretable by tightly integrating quantitative analysis with visualization (mapping).

Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.

Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT). Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF's potential as a biomarker of DME. Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea. Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications.

Uncertainty of spatial averages and totals of natural resource maps

AbstractGlobal, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and, hence, it is good practice to report estimates of the associated map uncertainty so that users can evaluate their fitness for use.We explain why quantification of uncertainty of spatial aggregates is more complex than uncertainty quantification at point support because it must account for spatial autocorrelation of the map errors. Unfortunately, this is not done in a number of recent high‐profile studies. We describe how spatial autocorrelation of map errors can be accounted for with block kriging, a method that requires geostatistical expertise. Next, we propose a new, model‐based approach that avoids the numerical complexity of block kriging and is feasible for large‐scale studies where maps are typically made using machine learning. Our approach relies on Monte Carlo integration to derive the uncertainty of the spatial average or total from point support prediction errors. We account for spatial autocorrelation of the map error by geostatistical modelling of the standardized map error.We show that the uncertainty strongly depends on the spatial autocorrelation of the map errors. In a first case study, we used block kriging to show that the uncertainty of the predicted topsoil organic carbon in France decreases when the support increases. In a second case study, we estimated the uncertainty of spatial aggregates of a machine learning map of the above‐ground biomass in Western Africa using Monte Carlo integration. We found that this uncertainty was small because of the weak spatial autocorrelation of the standardized map errors.We present a tool to get realistic estimates of the uncertainty of spatial averages and totals of natural resource maps. The method presented in this paper is essential for parties that need to evaluate whether differences in aggregated environmental variables or natural resources between regions or over time are statistically significant.

Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling

Although most models for rainfall extremes focus on pointwise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To capture the joint behaviour of precipitation aggregates evaluated at different spatial scales, parsimonious and effective models must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at different scales and intensities, e.g., convective and frontal, with extremes of aggregates for typical catchment sizes arising from extremes of only one of these processes, rather than a combination of them. High-intensity convective events cause extreme spatial aggregates at small scales but the contribution of lower-intensity large-scale fronts is likely to increase as the area aggregated increases. Thus, to capture small to large scale spatial aggregates within a single approach requires a model that can accurately capture the extremal properties of both convective and frontal events. Previous extreme value methods have ignored this mixture structure; we propose a spatial extreme value model which is a mixture of two components with different marginal and dependence models that are able to capture the extremal behaviour of convective and frontal rainfall and more faithfully reproduces spatial aggregates for a wide range of scales. Modelling extremes of the frontal component raises new challenges due to it exhibiting strong long-range extremal spatial dependence. Our modelling approach is applied to fine-scale, high-dimensional, gridded precipitation data. We show that accounting for the mixture structure improves the joint inference on extremes of spatial aggregates over regions of different sizes.

Modelling extremes of spatial aggregates of precipitation using conditional methods

Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other imposing highly restrictive assumptions. To overcome these issues, we propose a model for high-resolution precipitation data from which we can simulate realistic fields and explore the behaviour of spatial aggregates. Recent developments have seen spatial extensions of the Heffernan and Tawn (J. R. Stat. Soc. Ser. B. Stat. Methodol. 66 (2004) 497–546) model for conditional multivariate extremes which can handle a wide range of dependence structures. Our contribution is twofold: extensions and improvements of this approach and its model inference for high-dimensional data and a novel framework for deriving aggregates addressing edge effects and subregions without rain. We apply our modelling approach to gridded East Anglia, UK precipitation data. Return-level curves for spatial aggregates over different regions of various sizes are estimated and shown to fit very well to the data.

Spatio-temporal clustering of amyotrophic lateral sclerosis in France: A population-based study.

To assess spatial aggregates of amyotrophic lateral sclerosis (ALS) incident cases, using a solid geo-epidemiological statistical method, in France. This population-based study (2003-2011) investigated 47.1 million person-years of follow-up (PYFU). Case ascertainment of incident ALS cases was based on multiple sources (ALS referral centers, hospital centres and health insurance data). Neurologists confirmed all ALS diagnoses. Exhaustiveness was estimated through capture-recapture. Aggregates were investigated in four steps: (a) geographical modelling (standardized incidence ratio (SIR) calculation), (b) analysis of the spatial distribution of incidence (Phothoff-Winttinghill's test, Global Moran's Index, Kulldorf's spatial scan statistic, Local Moran's Index), (c) classification of the level of certainty of spatial aggregates (i.e. definite cluster; probable over-incidence area; possible over-incidence area) and (d) evaluation of the robustness of the results. The standardized incidence of ALS was 2.46/100,000 PYFU (95% CI 2.31-2.63, European population as reference) based on 1199 incident cases. We identified 13 areas of spatial aggregates: one cluster (stable in robustness analysis), five probable over-incidence areas (2 stable in robustness analysis) and seven possible over-incidence areas (including 4 stable areas in robustness analysis). A cluster was identified in the Rhône-Alpes region: 100 observed vs 54.07 expected cases for 2,411,514 PYFU, SIR: 1.85 (95% CI 1.50-2.25). We report here one of the largest investigations of incidence and spatial aggregation of ALS ever performed in a western country. Using a solid methodology framework for case ascertainment and cluster analysis, we identified 13 areas that warrant further investigation.

Approximate Bayesian inference for analysis of spatiotemporal flood frequency data

Extreme floods cause casualties and widespread damage to property and vital civil infrastructure. Predictions of extreme floods, within gauged and ungauged catchments, is crucial to mitigate these disasters. In this paper a Bayesian framework is proposed for predicting extreme floods, using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are involved and a time trend is present. Other challenges involve balancing model complexity and parsimony, using an appropriate model selection procedure and making inference based on a reliable and computationally efficient approach. We here propose a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models, which include catchment descriptors as covariates and spatially correlated model components, are proposed for the four parameters at the latent level. To achieve computational efficiency with large datasets and richly parametrized models, we exploit a highly accurate and fast approximate Bayesian inference approach which can also be used to efficiently select models separately for each of the four regression models at the latent level. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom. The framework performed well in terms of flood predictions for both ungauged catchments and future observations at gauged catchments. The results show that the spatial model components for the transformed location and scale parameters as well as the time trend are all important, and none of these should be ignored. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5% per decade with range 0.1% to 2.8% and reveal a spatial structure across the United Kingdom. When the interest lies in estimating return levels for spatial aggregates, we further develop a novel copula-based postprocessing approach of posterior predictive samples in order to mitigate the effect of the conditional independence assumption at the data level, and we demonstrate that our approach indeed provides accurate results.

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy.

Computational methodology for drug delivery to the inner ear using magnetic nanoparticle aggregates

Background and objective: The main goal of the proposed study is to improve the efficiency of the ear treatment via targeted drug delivery to the inner ear, i.e. the cochlea. Although pharmacotherapy has been proposed as a solution to prevent damage or restore functionality to hair cells, the main challenge in such treatments is ensuring adequate drug delivery to the cells. To this end, we present a methodology for the evaluation of the magnetic forces needed to move magnetic particle nanorobots (abbreviated as MNP) and their aggregates through the cochlea round window membrane (RWM).Methods: The FEM - Lagrangian-Eulerian approach (Abaqus software) was used to determine the specific parameters of movement of the nanoparticles crossing the RWM. This method results in a high consistency of FEM simulations and in-vivo experimental results in regards to the required magnetic force during the movement of spherical nanoparticles with a given viscosity ηave. Based on the analysis of the experimental studies found in subject literature, the sizes of the MNPs and their aggregates able to cross RWM with and without the application of magnetic force FM have been determined.Results: The present work accounts for both the experimental and theoretical aspects of these investigations. Presented research confirms the definite usability of the Lagrange-Euler method for the precise determination of the required magnetic force value FM to control the accelerated motion of MNP aggregates of complex shapes through RWM. It is possible to determine the predominant parameters with a precision of less than 5% for single-layer aggregates and spatial aggregates crossing the RWM. It can be concluded that the MNPs and their aggregates should not be larger than 500–750 nm to cross the RWM with high velocities of penetration close to 800 nm/s for magnetic forces of hundreds 10−14 Newtons.Conclusions: The proposed Lagrangian-Eulerian approach is capable of accurately predicting the movement parameters of MNP aggregates of irregular shape that are close to the experimental test cases. The presented method can serve as a supplementary tool for the design of drug delivery systems to the inner ear using MNPs.

Cross-platform- and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States

Spatial aggregates of survey and web search data make it possible to identify the heterogeneous well-being effects of social media platforms. This study reports evidence from different sources of longitudinal data that suggests that the well-being effects of social media differ across platforms and population groups. The well-being effects of frequent social media visits are consistently positive for Facebook but negative for Instagram. Group-level analyses suggest that the positive well-being effects are experienced mainly by white, high-income populations at both the individual and the county level, while the adverse effects of Instagram use are observed on younger and Black populations. The findings are corroborated when geocoded web search data from Google is used and when self-reports from surveys are used in place of region-level aggregates. Greater Instagram use in regions is also linked to higher depression diagnoses across most sociodemographic groups.

The triple epidemics of arboviruses in Feira de Santana, Brazilian Northeast: Epidemiological characteristics and diffusion patterns

Arboviruses are diseases of worldwide importance in the field of communicable diseases. In Brazil, the reemergence of dengue and the emergence of chikungunya and Zika since 2014 have led to epidemic waves of great magnitude and rapid spread. However, their diffusion patterns vary and change over time. This study analyzes the spatial diffusion of the simultaneous circulation of three arboviruses transmitted by the same vector in a large urban space over two epidemic waves in consecutive years. An ecological study of spatial and temporal aggregates on the occurrence of dengue, chikungunya, and Zika, from 2014 to 2019, in Feira de Santana, Bahia State, was carried out using data of cases reported to the national surveillance system. Four different methods were used to analyze the spatial diffusion: Kernel Estimation with sequential maps, cumulative nearest-neighbor ratios (NNI) over time, spatial correlograms and local autocorrelation changes (LISA) over time. From 2014–2019, there were 21,723 confirmed cases of arboviruses. The highest incidences were among women (496.9, 220.2, and 91.0 cases/100,000 women for dengue, chikungunya and Zika respectively). By age group, the highest incidences were from ages 10–19 years old (609.3 dengue cases/100,000), from 60 and more (306.7 chikungunya cases/100,000), and from 0-9 years old (124.1 Zika cases/100,000 inhabitants). The temporal distribution demonstrated two epidemic waves of simultaneous circulation in 2014 and 2015. Kernel maps indicate that arboviruses spread to neighboring areas near the first hotspots, suggesting an expansion diffusion pattern. The NNI, spatial correlograms and LISA changes results suggest expansion patterns for the three arboviruses in all periods. The spatial diffusion pattern of dengue, Zika, and chikungunya in the 2014–2015 epidemics in Feira de Santana was expansion. These findings are useful to guide prevention measures and reduce occurrence in other areas.

Human Parsing With Pyramidical Gather-Excite Context

Human parsing, especially in the wild, has attracted a lot of attention due to its great potential in many real-world applications. The Pyramid Spatial Parsing (PSP) module has shown superior performances in scene and human parsing tasks. However, the basic AvgPool operation in PSP equally aggregates spatial clues of a local region, and thus mixes up influences of different human parts presented in this region. It results in failures in capturing useful contexts relevant to parsing different parts. To address this problem, a suitable mechanism to collect spatial clues aligning with different human parts is proposed in this paper. We employ a Gather-Excite (GE) operation, a replacement of the AvgPool-Upsample operation in a pyramidical structure, to accurately reflect relevant human parts of various scales. The GE operation contains two steps: the gather operation that adaptively aggregates spatial clues to relevant human parts, and the excite operation that generates new feature maps with the gathered contextual information. This results in a novel Pyramidical Gather-Excite Context (PGEC) module to solve the multi-scale problem and parse person at various scales. The PGEC module is composed of multiple GE operations with different spatial extents and aggregates local and global spatial clues for better modeling multi-scale contextual information in parallel. Moreover, we integrate the PGEC module with fine-grained details, edge preserving module and deep supervision to formulate a novel PGEC Network (PGECNet) for human parsing. The proposed PGECNet has achieved state-of-the-art performance on four single-person human parsing datasets ( i.e. , LIP, PPSS, ATR and Fashion Clothing) and two multi-person human parsing datasets ( i.e. , PASCAL-Person-Part and CIHP). The experimental results show that the proposed PGEC is superior to the PSP and ASPP modules especially in single-human parsing task. The source code is publicly available at https://github.com/31sy/PGECNet .

МЕТОДЫ ПРОГНОЗИРОВАНИЯ ДЕНЕЖНЫХ ПОТОКОВ НЕГОСУДАРСТВЕННЫХ КОРПОРАТИВНЫХ СТРУКТУР

Global changes in the operating conditions of non-state corporate structures, caused by the forced vacation in the spring of 2020, caused a sharp reduction in cash receipts and sharpened the attention of financiers to determine the future values of receipts, Disposals and cash balances. The author of the article has proved what types of economic and professional interests of the main three groups of users of financial and economic information-owners, top managers and financiers- are associated with the interest in obtaining high-quality information about the future values of cash flows of non-state corporate structures. The article explains why it is appropriate to use the term forecasting rather than planning in relation to predicting the future value of indicators that characterize cash flows. In addition, referring to the opinion of well-known foreign scientists and financial practitioners, the author provides arguments in favor of the fact that forecasting cash flows, rather than profit, is of paramount importance in modern conditions. The author’s definition of the cash flow forecast is given, revealing its content in the following projections: as a result of the process, as a document, as a set of methodological techniques and as a source of information for management decisions. The author conducted a study of theoretically developed and applied in practice methods of forecasting cash flows. In the results, the author systematized by application areas, and briefly described the mechanism for applying the main methods of forecasting cash flows that can be used for modern corporate structures. In particular, the use of the situational analysis method, expert assessment method, and the method of processing spatial, temporal, and spatio-temporal aggregates, as well as methods based on their combination and common in modern financial practice, such as forecasting cash flows based on proportional dependencies, was considered; budgeting of cash flows based on the technology of budgeting and cash flow design based on simulating a set of planned business operations on the used versions of accounting programs. In conclusion, the author concludes that there is no ideal method for forecasting cash flows and the need to take into account the information interests of users, the complexity of application, the availability of high-quality information and compliance with the management culture of the corporate structure in which forecasting is performed.

Identifying building typologies and their spatial patterns in the metropolitan areas of Marseille and Osaka

Buildings are an essential component of urban form. Urban morphologists know that buildings can be classified by type, but types are specific to given cultural areas. In a transnational context, detailed expert knowledge is not always available, hence the need for identifying typologies of buildings inductively from large urban databases exists. This paper presents the application of a Bayesian Network clustering protocol to buildings and the study of the spatial aggregates of the obtained family types for two metropolitan areas located in countries with marked cultural and societal differences: Osaka-Kobe in Japan and Marseille-Provence in France. Six indicators of building characteristics are calculated and used to perform the clustering: Footprint Surface, Elongation, Convexity, Number of Adjoining Neighbors, Height and Specialization. Cluster results are first extracted, detailed and analyzed and then, building type prevalence is studied at the metropolitan scale using local indicators of network-constrained clusters (ILINCS). The building families obtained through clustering show that these two coastal metropolitan areas are made up of apparently similar “ingredients” (very similar typologies are found at the relatively coarse level of detail of our study), but with different weights and spatial organization. This approach is appropriate for the automated processing of large building datasets and the results are a good entry point to study the link between building families, urban development periods and urban functions.

RESEARCH OF EFFECT OF DPEI-PROCESSING OF SHIITAKE FRUIT BODY ON PHYSICAL AND MECHANICAL PROPERTIES OF MUSHROOM SUSPENSION

The use of discrete-pulse energy input (DPEI) mechanisms in various industries has become a reliable tool for the intensification of heat and mass transfer processes in various technological lines and reduction of specific energy consumption. The study of structural transformations in heterogeneous systems under influence of mechanisms of DPEI opens up new possibilities for their use as evidenced by this article. Under certain conditions it is possible to prepare a mushroom suspension with specified characteristics for drying and enhance medicinal properties of the obtained powder product while retaining all valuable components of feedstock.
 The article presents the results of research of DPEI-processing effect of the shiitake mushroom fruit body on the on physical and mechanical properties and structural characteristics of the mushroom suspension. The influence of hydro module, temperature of the suspension and the layout of the working bodies of the rotor-pulse apparatus (RPA) on its dynamic viscosity was studied and the possibility of reducing viscosity by 2-3 times is shown. An analysis of mushroom suspension microstructure has showed that with a certain layout of the working bodies of the RPA it is possible to control the degree of dispersion of particles and change the spatial structure of the aggregates in the volume of the dispersion medium. It is determined that self-organization of spatial aggregates from individual hyphae in such suspensions occurs over time. Moreover, the smaller the size of hyphae (≤ 25 microns after the RPA of the first version of the arrangement: rotor-stator-rotor) are, the larger the size of the spatial aggregates are formed. After the RPA with the second layout option, the fragments of hyphae had sizes ≥ 50 μm, but the dimensions of the spatial aggregates were three times smaller.
 It is found that after three times passing of the mushroom suspension through the RPA and its subsequent treatment in the cavitation device, the dynamic viscosity of the suspension is reduced by 20%.
 Confirmation of the the effectiveness of the DPEI-mechanisms in obtaining mushroom suspension is that due to the hydromechanical destruction of the polysaccharide structures of the chitin-glucan complex of the shiitake mushroom the content of the bioavailable complex of water-soluble oncostatic and immunoregulatory polysaccharides in the powder obtained by drying the mushroom suspension in an experimental spray dryer increased 6 times. References 13, figures 6.

Geography of Microcephaly in the Zika Era: A Study of Newborn Distribution and Socio-environmental Indicators in Recife, Brazil, 2015-2016.

We assessed sociodemographic and health care factors of mothers and newborns during a 2015-2016 outbreak of microcephaly in Recife, Brazil, and we analyzed the spatial distribution and incidence risk of newborns with microcephaly in relation to socio-environmental indicators. We collected data from August 2015 through May 2016 from Brazil's Live Birth Information System and Bulletin of Microcephaly Notification, and we geocoded the data by maternal residence. We constructed thematic maps of districts, according to socio-environmental and vector indicators. We identified spatial aggregates of newborns with microcephaly by using the Bernoulli model. We performed logistic regression analyses to compare the incidence risk of microcephaly within socio-environmental indicator groups. We geocoded 17 990 of 19 554 (92.0%) live births in Recife, of which 202 (1.1%) newborns were classified as having microcephaly, based on a head circumference of ≥2 standard deviations below the mean. Larger proportions of newborns with microcephaly (compared with newborns without microcephaly) were born to mothers who delivered in a public hospital, did not attend college, were aged ≤19, or were black or mixed race. A higher risk of microcephaly (incidence rate ratio [IRR] = 3.90; 95% confidence interval [CI], 1.88-8.06) occurred in districts with the lowest (vs highest) Municipal Human Development Index (ie, an index that assesses longevity, education, and income). The risk of microcephaly was significantly higher where rates of larvae density (IRR = 2.31; 95% CI, 1.19-4.50) and larvae detection (IRR = 2.04; 95% CI, 1.05-4.00) were higher and rates of sewage system (IRR = 2.20; 95% CI, 1.16-4.18) and garbage collection (IRR = 1.96; 95% CI, 0.99-3.88) were lower. Newborns with microcephaly lived predominantly in the poorest areas and in a high-risk cluster (relative risk = 1.89, P = .01) in the north. The disproportionate incidence of microcephaly in newborns in poor areas of Recife reinforces the need for government and public health authorities to formulate policies that promote social equity and support for families and their children with microcephaly.

Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland

There is a growing need for reliable medium to extended range hydrological forecasts in water and environmental management (e.g. hydro-power and agricultural production). The objective of this paper is a first assessment of the skill of hydrological forecasts based on Numerical Weather Predictions (NWPs) in comparison to the skill of forecasts based on climatology for monthly forecasts with daily resolutions and to identify possibilities of improvement by post-processing the hydrological forecasts. Various hydrological relevant model variables, such as the surface and subsurface runoff and the soil water content, will be analysed for entire Switzerland. The spatially aggregated predictions of these variables are compared to daily simulations and to long-term daily averages of simulations driven by meteorological observations (i.e. climatology). Besides this comparison of forecasts with simulations for model variables without direct measurements available, the skill of the monthly stream-flow forecasts is estimated at four catchments with discharge measurements. Additionally post-processing methods have been applied to remove bias and dispersion errors and to estimate the predictive uncertainty of the stream-flow. Some results of various verification measures like variants of the Geometric Mean for ratios of spatial aggregates and the Continuous Rank Probability Skill Score (CRPSS) will be shown. Apart from the indication of a strong diversity of upper limits of the forecast skill depending on catchment characteristics, the results of NWPs are generally superior to climatological predictions and could be applied gainfully for various kinds of long-term water management planning.