Spatial inequalities in skilled birth attendance in India: a spatial-regional model approach

Background Despite a significant increase in the skilled birth assisted (SBA) deliveries in India, there are huge gaps in availing maternity care services across social gradients - particularly across states and regions. Therefore, this study applies the spatial-regression model to examine the spatial distribution of SBA across districts of India. Furthermore, the study tries to understand the spatially associated population characteristics that influence the low coverage of SBA across districts of India and its regions. Methods The study used national representative cross-sectional survey data obtained from the fourth round of National Family Health Survey, conducted in 2015-16. The effective sample size was 259,469 for the analysis. Moran’s I statistics and bivariate Local Indicator for Spatial Association maps were used to understand spatial dependence and clustering of deliveries conducted by SBA coverage in districts of India. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of deliveries conducted by SBA. Results Moran’s I value for SBA among women was 0.54, which represents a high spatial auto-correlation of deliveries conducted by SBA over 640 districts of India. There were 145 hotspots for deliveries conducted by SBA among women in India, which includes almost the entire southern part of India. The spatial error model revealed that with a 10% increase in exposure to mass media in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Interestingly, also with the 10% increase in the four or more antenatal care (ANC) in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Again, if there was a 10% increase of women with first birth order in a particular district, then the deliveries conducted by SBA significantly increased by 6.1%. If the district experienced an increase of 10% household as female-headed, then the deliveries conducted by SBA significantly increased by 1.4%. Conclusion The present study highlights the important role of ANC visits, mass media exposure, education, female household headship that augment the use of an SBA for delivery. Attention should be given in promoting regular ANC visits and strengthening women’s education.

Spatial and molecular profiling of the classic Hodgkin lymphoma microenvironment reveals an immunosuppressive mononuclear phagocyte network

Although a lymph node infiltrated by classic Hodgkin lymphoma is mostly composed of non-neoplastic immune cells, the malignant Hodgkin Reed-Sternberg cells (HRSC) successfully suppress an anti-tumor immune response, to create a cancer-permissive microenvironment. Accordingly, unleashing the dormant immune cells, for example by checkpoint inhibition, has been a central focus of recent therapeutic advances for this disease. Here, we profiled the global immune cell composition of normal and diseased lymph nodes by single-cell RNA sequencing, as a basis for interrogating the immediate vicinity of HRSC, first regionally and then at cellular resolution. Our analyses revealed specific immune cells and functional states associated with HRSC. Most prominently, we discovered a non-random spatial association of immunoregulatory mononuclear phagocytes positioned around HRSC, which express the immune checkpoints PD-L1, TIM-3, and the tryptophan-catabolizing protein IDO1. These findings provide a basis for rational targeting and activation of the anti-tumor immune response in classic Hodgkin lymphoma.

Integration of Information Theory, Fractal Theory and Statistical Analyses for Landslide Susceptibility Mapping at Regional Scales

A new model, integrating information theory, fractal theory and statistical model for accurate landslide susceptibility mapping (LSM) at regional scales, has been proposed. In this model, landslide conditional factors are firstly classified with an optimal number of classes, which is determined by maximizing their information coefficients estimated from Shannon’s entropy model. The spatial association between influencing factors and induced landslides has been measured by introducing the variable fractal dimension method (VFDM). The VFDM approach fully considers the characteristics of landslide fractal distribution. Then the fractal dimensions (\(D\)) are calculated to provide multiple factors with various numerical weights. The proposed model eventually combines the landslide frequency ratio (\(fr\)) of each factor with corresponding weight to achieve spatial prediction of landslides, illustrated by an example area in China. In the study area, 500 landslides have been identified by aerial photograph interpretation, extensive field investigations, historical and bibliographical landslide data. In the model, these landslides are randomly split into a training dataset (70 %)and a validating dataset (30 %) Seven factors are recognized and analyzed by frequency ratio (FR) method, including lithology, distance to fault, altitude, slope, aspect, distance to stream and distance to the road. The receiver operating characteristic curve (AUROC) has been adopted to compare and validate the model results. Results show that the proposed landslide model achieved a more accurate prediction with AUROC equal to 0.8467, over-performing than the conventional frequency ratio method (AUROC=0.8088). According to the final prognostic landslide susceptibility map, 16.37 % f the study area shows very high and high susceptibility, accounting for 63.55 % f the entire landslides. Evaluation of relative factor importance based on a one-by-one factor removal test indicates that the lithology factor contributes unique information for landslides. In conclusion, the example demonstrates that the proposed framework is promising for further improvement of LSM.

Socioeconomic conditions on poverty levels a case study: Central Java Province and Yogyakarta in 2016

This study aims to analyze how the influence of infrastructure availability, socioeconomic conditions, and the effect of location on poverty levels. The descriptive analysis is used to give a general description of poverty by using thematic charts and maps. The poverty map is analyzed by spatial autocorrelation of poverty levels by using a Moran Scatterplot and the Local Indicators of Spatial Association (LISA) Map. The results of the study indicate the existence of spatial linkages to poverty. The Increasing of other variables outside the model in neighboring regions will increase the level of poverty in a region. The infrastructures of road extension, clean water infrastructure, economic growth, quality of education, and health have a significant influence on the level of poverty, while the percentage of satisfactory sanitation did not demonstrate to affect the significant effect on poverty. The conclusion is that the level of poverty in the provinces of Central Java and Yogyakarta has an irregular distribution and a clustered spatial pattern.

Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for in-situ cell classification and neighborhood analysis

The human kidney is a complex organ with various cell types that are intricately organized to perform key physiological functions and maintain homeostasis. New imaging modalities such as mesoscale and highly multiplexed fluorescence microscopy are increasingly applied to human kidney tissue to create single cell resolution datasets that are both spatially large and multi-dimensional. These single cell resolution high-content imaging datasets have a great potential to uncover the complex spatial organization and cellular make-up of the human kidney. Tissue cytometry is a novel approach used for quantitative analysis of imaging data, but the scale and complexity of such datasets pose unique challenges for processing and analysis. We have developed the Volumetric Tissue Exploration and Analysis (VTEA) software, a unique tool that integrates image processing, segmentation and interactive cytometry analysis into a single framework on desktop computers. Supported by an extensible and open-source framework, VTEA's integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses for hyperdimensional large-scale imaging datasets. These novel capabilities enable the analysis of mesoscale two and three-dimensional multiplexed human kidney imaging datasets (such as CODEX and 3D confocal multiplexed fluorescence imaging). We demonstrate the utility of this approach in identifying cell subtypes in the kidney based on labels, spatial association and their microenvironment or neighborhood membership. VTEA provides integrated and intuitive approach to decipher the cellular and spatial complexity of the human kidney and complement other transcriptomics and epigenetic efforts to define the landscape of kidney cell types.

Robust Real-Time Traffic Surveillance with Deep Learning

Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for object detection and classification, especially in the last years, made this area even more appealing due to the effectiveness of these methods. This study presents TrafficSensor, a system that employs deep learning techniques for automatic vehicle tracking and classification on highways using a calibrated and fixed camera. A new traffic image dataset was created to train the models, which includes real traffic images in poor lightning or weather conditions and low-resolution images. The proposed system consists mainly of two modules, first one responsible of vehicle detection and classification and a second one for vehicle tracking. For the first module, several neural models were tested and objectively compared, and finally, the YOLOv3 and YOLOv4-based network trained on the new traffic dataset were selected. The second module combines a simple spatial association algorithm with a more sophisticated KLT (Kanade–Lucas–Tomasi) tracker to follow the vehicles on the road. Several experiments have been conducted on challenging traffic videos in order to validate the system with real data. Experimental results show that the proposed system is able to successfully detect, track, and classify vehicles traveling on a highway on real time.

Analysis of the Spatial Structure of Regional Enterprises and Its Impact on Regional Economic Development Based on the Data Envelopment Analysis Model (DEA)

This paper adopts the data envelopment analysis (DEA) model to conduct an in-depth study and analysis of the spatial structure of regional enterprises and their impact on regional economic development and to apply the analysis of the impact between the two. Firstly, the concepts of urban agglomeration, regional spatial pattern evolution, and coordinated regional development and their connotations are explained to define the research topic of this paper. Then, we systematically analyze the theory of regional economic growth, development stage theory, spatial polarization theory, and regional spatial association theory and review the existing studies on regional economic growth, regional development stage, regional spatial association, and pattern evolution, as well as the efficiency and quality of regional economic growth, which further clarify the research focus and research ideas of this paper. The time evolution, spatial divergence, and spatial association characteristics of the economic growth rate of the central urban agglomerations are measured, then the DEA model and entropy value method are used to calculate the economic growth efficiency and economic growth quality of the urban agglomerations, respectively, the time evolution characteristics and spatial pattern evolution characteristics of the economic growth efficiency and economic growth quality are analyzed, and finally, the coupled model of regional economic growth rate, efficiency, and quality is constructed. Finally, a coordination model of the coupling of regional economic growth rate, efficiency, and quality is constructed and the time-series evolution characteristics and spatial-temporal divergence characteristics of the coupling coordination degree of the three are comprehensively analyzed, and the spatial classification of the coupling coordination type of each county unit is made. The direction and path of regulation for the optimization of spatial structure and coordinated spatial development of urban agglomerations are proposed, and finally, the leading factors for the reorganization of the regional economic spatial structure are analyzed, and the regulatory countermeasures for the optimization of the economic spatial structure of urban agglomerations are proposed according to the spatial structure of urban agglomerations and their evolutionary characteristics.

Recognition of Maize Phenology in Sentinel Images with Machine Learning

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

The spatial association of accessory minerals with biotite in granitic rocks from the South Mountain Batholith, Nova Scotia, Canada

We use mineral liberation analysis (MLA) to quantify the spatial association of 15,118 grains of accessory apatite, monazite, xenotime, and zircon with essential biotite, and clustered with themselves, in a peraluminous biotite granodiorite from the South Mountain Batholith in Nova Scotia (Canada). A random distribution of accessory minerals demands that the proportion of accessory minerals in contact with biotite is identical to the proportion of biotite in the rock, and the binary touching factor (percentage of accessory mineral touching biotite divided by modal proportion of biotite) would be ~1.00. Instead, the mean binary touching factors for the four accessory minerals in relation to biotite are: apatite (5.06 for 11,168 grains), monazite (4.68 for 857 grains), xenotime (4.36 for 217 grains), and zircon (5.05 for 2876 grains). Shared perimeter factors give similar values. Accessory mineral grains that straddle biotite grain boundaries are larger than completely locked, or completely liberated, accessory grains. Only apatite-monazite clusters are significantly more abundant than expected for random distribution. The high, and statistically significant, binary touching factors and shared perimeter factors suggest a strong physical or chemical control on their spatial association. We evaluate random collisions in magma (synneusis), heterogeneous nucleation processes, induced nucleation in passively enriched boundary layers, and induced nucleation in actively enriched boundary layers to explain the significant touching factors. All processes operate during the crystallization history of the magma, but induced nucleation in passively and actively enriched boundary layers are most likely to explain the strong spatial association of phosphate accessories and zircon with biotite. In addition, at least some of the apatite and zircon may also enter the granitic magma as inclusions in grains of Ostwald-ripened xenocrystic biotite.