The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life.

Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns.

We demonstrate a time-lapse video approach that allows rapid examination of the spatio-temporal dynamics of Dictyostelium cell populations. Quantitative information was gathered by sampling life histories of more than 2,000 mutant clones from a large mutagenesis collection. Approximately 4% of the clonal lines showed a mutant phenotype at one stage. Many of these could be ordered by clustering into functional groups. The dataset allows one to search and retrieve movies on a gene-by-gene and phenotype-by-phenotype basis.

Dysregulated gene expression patterns have been reported in several mental disorders. Limited by the difficulty of obtaining samples, psychiatric molecular mechanism research still relies heavily on clues from genetics studies. By using reference data from brain expression studies, multiple types of comprehensive gene expression pattern analysis have been performed on psychiatric genetic results. These systems-level spatial-temporal expression pattern analyses provided evidence on specific brain regions, developmental stages and molecular pathways that are possibly involved in psychiatric pathophysiology. At present, there is no online tool for such systematic analysis, which hinders the applications of analysis by non-informatics researchers such as experimental biologists and clinical molecular biologists.

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran's Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran's Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

Pneumonia is one of the top 10 diseases by morbidity in Bhutan. This study aimed to investigate the spatial and temporal trends and risk factors of childhood pneumonia in Bhutan. A multivariable Zero-inflated Poisson regression model using a Bayesian Markov chain Monte Carlo simulation was undertaken to quantify associations of age, sex, altitude, rainfall, maximum temperature and relative humidity with monthly pneumonia incidence and to identify the underlying spatial structure of the data. Overall childhood pneumonia incidence was 143.57 and 10.01 per 1000 persons over 108 months of observation in children aged < 5 years and 5-14 years, respectively. Children < 5 years or male sex were more likely to develop pneumonia than those 5-14 years and females. Each 1 °C increase in maximum temperature was associated with a 1.3% (95% (credible interval [CrI] 1.27%, 1.4%) increase in pneumonia cases. Each 10% increase in relative humidity was associated with a 1.2% (95% CrI 1.1%, 1.4%) reduction in the incidence of pneumonia. Pneumonia decreased by 0.3% (CrI 0.26%, 0.34%) every month. There was no statistical spatial clustering after accounting for the covariates. Seasonality and spatial heterogeneity can partly be explained by the association of pneumonia risk to climatic factors including maximum temperature and relative humidity.

The temporal and spatial neural processing of faces has been investigated rigorously, but few studies have unified these dimensions to reveal the spatio-temporal dynamics postulated by the models of face processing. We used support vector machine decoding and representational similarity analysis to combine information from different locations (fMRI), time windows (EEG), and theoretical models. By correlating representational dissimilarity matrices (RDMs) derived from multiple pairwise classifications of neural responses to different facial expressions (neutral, happy, fearful, angry), we found early EEG time windows (starting around 130 ​ms) to match fMRI data from primary visual cortex (V1), and later time windows (starting around 190 ​ms) to match data from lateral occipital, fusiform face complex, and temporal-parietal-occipital junction (TPOJ). According to model comparisons, the EEG classification results were based more on low-level visual features than expression intensities or categories. In fMRI, the model comparisons revealed change along the processing hierarchy, from low-level visual feature coding in V1 to coding of intensity of expressions in the right TPOJ. The results highlight the importance of a multimodal approach for understanding the functional roles of different brain regions in face processing.

Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

In Tunisia, cases of zoonotic cutaneous leishmaniasis caused by Leishmania major are increasing and spreading from the south-west to new areas in the center. To improve the current knowledge on L. major evolution and population dynamics, we performed multi-locus microsatellite typing of human isolates from Tunisian governorates where the disease is endemic (Gafsa, Kairouan and Sidi Bouzid governorates) and collected during two periods: 1991-1992 and 2008-2012. Analysis (F-statistics and Bayesian model-based approach) of the genotyping results of isolates collected in Sidi Bouzid in 1991-1992 and 2008-2012 shows that, over two decades, in the same area, Leishmania parasites evolved by generating genetically differentiated populations. The genetic patterns of 2008-2012 isolates from the three governorates indicate that L. major populations did not spread gradually from the south to the center of Tunisia, according to a geographical gradient, suggesting that human activities might be the source of the disease expansion. The genotype analysis also suggests previous (Bayesian model-based approach) and current (F-statistics) flows of genotypes between governorates and districts. Human activities as well as reservoir dynamics and the effects of environmental changes could explain how the disease progresses. This study provides new insights into the evolution and spread of L. major in Tunisia that might improve our understanding of the parasite flow between geographically and temporally distinct populations.

Breast cancer is a leading tumor with a high mortality in women. This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012.

High-resolution Ca2+ imaging to study cellular Ca2+ behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization of Ca2+ signals as a function of time and space are often used. Methods of STMap analysis rely on a highly arduous process of user defined segmentation and event-based data retrieval. These methods are often time consuming, lack accuracy, and are extremely variable between users. We designed a novel automated machine-learning based plugin for the analysis of Ca2+ STMaps (STMapAuto). The plugin includes optimized tools for Ca2+ signal preprocessing, automated segmentation, and automated extraction of key Ca2+ event information such as duration, spatial spread, frequency, propagation angle, and intensity in a variety of cell types including the Interstitial cells of Cajal (ICC). The plugin is fully implemented in Fiji and able to accurately detect and expeditiously quantify Ca2+ transient parameters from ICC. The plugin's speed of analysis of large-datasets was 197-fold faster than the commonly used single pixel-line method of analysis. The automated machine-learning based plugin described dramatically reduces opportunities for user error and provides a consistent method to allow high-throughput analysis of STMap datasets.

This article explores, via three case studies, how spatio-temporal analysis can advance New Testament text interpretation. Acts 2, verse 9 to 11 is the text of study. Case study 1 applies network analysis to data representing the Roman road network constrained by parameters valid for ancient times. This analysis provided new information on the background of people attending a festival in Jerusalem. Case study 2 located geographical entities from the text in a cartographic visualization and provided supportive information to compare contemporary textual resources. For the disciplines of textual and conjectural criticism (case study 3), spatio-temporal analysis opens a new window to study what would be the most probable variant of the original text. The case study puts emendations that have been proposed over centuries in a 3D spatial context and provides in this way a sophisticated tool to relate different alternative variants of a specific text. From the case studies, it can be concluded that spatializing, visualizing, and spatially analyzing geographical concepts from the texts in Acts 2 contributes to the field of New Testament interpretation. Further work will elaborate on the findings.

Malaria incidence in Brazil reversed its decreasing trend when cases from recent years, as recent as 2015, exhibited an increase in the Brazilian Amazon basin, the area with the highest transmission of Plasmodium vivax and Plasmodium falciparum. In fact, an increase of more than 20% in the years 2016 and 2017 revealed possible vulnerabilities in the national malaria-control programme.

Wildfires in the Amazon significantly impact the forest structure and carbon cycle. Understanding the patterns of fire occurrence is crucial for effective management. A novel spatio-temporal point process framework was used to analyze changes in fire occurrence patterns in the Brazilian Amazon. A dynamic representation of a Log Gaussian Cox process was used to model the intensity function, which was decomposed into trend, seasonality, cycles, covariates, and spatial effects. The results show a marked decrease in long-term fire occurrence movements between the start of the sample and 2012, followed by an increase until the end of the sample, attributed to governance measures and market mechanisms. Spatial variability of fire occurrence rates in the Brazilian Amazon was successfully captured, with regions having more dry seasons experiencing higher fire occurrence rates. This analysis provides valuable insights into fire occurrence patterns in the Amazon region and the factors driving them.

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data.

Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.

Species that differ in their social system, and thus in traits such as group size and dispersal timing, may differ in their use of resources along spatial, temporal, or dietary dimensions. The role of sociality in creating differences in habitat use is best explored by studying closely related species or socially polymorphic species that differ in their social system, but share a common environment. Here we investigate whether five sympatric Anelosimus spider species that range from nearly solitary to highly social differ in their use of space and in their phenology as a function of their social system. By studying these species in Serra do Japi, Brazil, we find that the more social species, which form larger, longer-lived colonies, tend to live inside the forest, where sturdier, longer lasting vegetation is likely to offer better support for their nests. The less social species, which form single-family groups, in contrast, tend to occur on the forest edge where the vegetation is less robust. Within these two microhabitats, species with longer-lived colonies tend to occupy the potentially more stable positions closer to the core of the plants, while those with smaller and shorter-lived colonies build their nests towards the branch tips. The species further separate in their use of common habitat due to differences in the timing of their reproductive season. These patterns of habitat use suggest that the degree of sociality can enable otherwise similar species to differ from one another in ways that may facilitate their co-occurrence in a shared environment, a possibility that deserves further consideration.

To analyze the temporal trend and spatial patterns of schistosomiasis-related morbidity in Northeast Brazil, 2001-2017.

The maize endosperm consists of three major compartmentalized cell types: the starchy endosperm (SE), the basal endosperm transfer cell layer (BETL), and the aleurone cell layer (AL). Differential genetic programs are activated in each cell type to construct functionally and structurally distinct cells. To compare gene expression patterns involved in maize endosperm cell differentiation, we isolated transcripts from cryo-dissected endosperm specimens enriched with BETL, AL, or SE at 8, 12, and 16 days after pollination (DAP). We performed transcriptome profiling of coding and long noncoding transcripts in the three cell types during differentiation and identified clusters of the transcripts exhibiting spatio-temporal specificities. Our analysis uncovered that the BETL at 12 DAP undergoes the most dynamic transcriptional regulation for both coding and long noncoding transcripts. In addition, our transcriptome analysis revealed spatio-temporal regulatory networks of transcription factors, imprinted genes, and loci marked with histone H3 trimethylated at lysine 27. Our study suggests that various regulatory mechanisms contribute to the genetic networks specific to the functions and structures of the cell types of the endosperm.

The aim of this study was to assess the validity and test-retest reliability of an inertial measurement unit (IMU) system for gait analysis. Twenty-four healthy subjects conducted a 6-min walking test and were instrumented with seven IMUs and retroreflective markers. A kinematic approach was used to estimate the initial and terminal contact events in real-time. Based on these events twelve spatio-temporal parameters (STP) were calculated. A marker based optical motion capture (OMC) system provided the reference. Event-detection rate was about 99%. Detection offset was below 0.017 s. Relative root mean square error (RMSE) ranged from 0.90% to 4.40% for most parameters. However, the parameters that require spatial information of both feet showed higher errors. Step length showed a relative RMSE of 6.69%. Step width and swing width revealed the highest relative RMSE (34.34% and 35.20%). Test-retest results ranged from 0.67 to 0.92, except for the step width (0.25). Summarizing, it appears that the parameters describing the lateral distance between the feet need further improvement. However, the results of the validity and reliability of the IMU system encourage its validation in clinical settings as well as further research.