Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Shrinking gate fluorescence correlation spectroscopy yields equilibrium constants and separates photophysics from structural dynamics.

Fluorescence correlation spectroscopy is a versatile tool for studying fast conformational changes of biomolecules especially when combined with Förster resonance energy transfer (FRET). Despite the many methods available for identifying structural dynamics in FRET experiments, the determination of the forward and backward transition rate constants and thereby also the equilibrium constant is difficult when two intensity levels are involved. Here, we combine intensity correlation analysis with fluorescence lifetime information by including only a subset of photons in the autocorrelation analysis based on their arrival time with respect to the excitation pulse (microtime). By fitting the correlation amplitude as a function of microtime gate, the transition rate constants from two fluorescence-intensity level systems and the corresponding equilibrium constants are obtained. This shrinking-gate fluorescence correlation spectroscopy (sg-FCS) approach is demonstrated using simulations and with a DNA origami-based model system in experiments on immobilized and freely diffusing molecules. We further show that sg-FCS can distinguish photophysics from dynamic intensity changes even if a dark quencher, in this case graphene, is involved. Finally, we unravel the mechanism of a FRET-based membrane charge sensor indicating the broad potential of the method. With sg-FCS, we present an algorithm that does not require prior knowledge and is therefore easily implemented when an autocorrelation analysis is carried out on time-correlated single-photon data.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed).

BACKGROUND: Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. RESULTS: We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. CONCLUSIONS: Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Intrinsic neural timescales exhibit different lengths in distinct meditation techniques.

Meditation encompasses a range of practices employing diverse induction techniques, each characterized by a distinct attentional focus. In Mantra meditation, for instance, practitioners direct their attention narrowly to a given sentence that is recursively repeated, while other forms of meditation such as Shoonya meditation are induced by a wider attentional focus. Here we aimed to identify the neural underpinnings and correlates associated with this spectrum of distinct attentional foci. To accomplish this, we used EEG data to estimate the brain's intrinsic neural timescales (INTs), that is, its temporal windows of activity, by calculating the Autocorrelation Window (ACW) of the EEG signal. The autocorrelation function measures the similarity of a timeseries with a time-lagged version of itself by correlating the signal with itself on different time lags, consequently providing an estimation of INTs length. Therefore, through using the ACW metric, our objective was to explore whether there is a correspondence between the length of the brain's temporal windows of activity and the width of the attentional scope during various meditation techniques. This was performed on three groups of highly proficient practitioners belonging to different meditation traditions, as well as a meditation-naïve control group. Our results indicated that practices with a wider attentional focus, like Shoonya meditation, exhibit longer ACW durations compared to practices requiring a narrower attentional focus, such as Mantra meditation or body-scanning Vipassana meditation. Together, we demonstrated that distinct meditation techniques with varying widths of attentional foci exhibit unique durations in their brain's INTs. This may suggest that the width of the attentional scope during meditation relates and corresponds to the width of the brain's temporal windows in its neural activity. SIGNIFICANCE STATEMENT: Our research uncovered the neural mechanisms that underpin the attentional foci in various meditation techniques. We revealed that distinct meditation induction techniques, featured by their range of attentional widths, are characterized by varying lengths of intrinsic neural timescales (INTs) within the brain, as measured by the Autocorrelation Window function. This finding may bridge the gap between the width of attentional windows (subjective) and the width of the temporal windows in the brain's neural activity (objective) during different meditation techniques, offering a new understanding of how cognitive and neural processes are related to each other. This work holds significant implications, especially in the context of the increasing use of meditation in mental health and well-being interventions. By elucidating the distinct neural foundations of different meditation techniques, our research aims to pave the way for developing more tailored and effective meditation-based treatments.

iDHS-RGME: Identification of DNase I hypersensitive sites by integrating information on nucleotide composition and physicochemical properties.

As pivotal markers of chromatin accessibility, DNase I hypersensitive sites (DHSs) intimately link to fundamental biological processes encompassing gene expression regulation and disease pathogenesis. Developing efficient and precise algorithms for DHSs identification holds paramount importance for unraveling genome functionality and elucidating disease mechanisms. This study innovatively presents iDHS-RGME, an Extremely Randomized Trees (Extra-Trees)-based algorithm that integrates unique feature extraction techniques for enhanced DHSs prediction. Specifically, iDHS-RGME utilizes two feature extraction approaches: Reverse Complementary Kmer (RCKmer) and Geary Spatial Autocorrelation (GSA), which comprehensively capture sequence attributes from diverse angles, bolstering information richness and accuracy. To address data imbalance, Borderline-SMOTE is employed, followed by Maximum Information Coefficient (MIC) for meticulous feature selection. Comparative evaluations underscored the superiority of the Extra-Trees classifier, which was subsequently adopted for model prediction. Through rigorous five-fold cross-validation, iDHS-RGME achieved remarkable accuracies of 94.71 % and 95.07 % on two independent datasets, outperforming previous models in terms of both precision and effectiveness.

The effect of host admixture on wild house mouse gut microbiota is weak when accounting for spatial autocorrelation.

The question of how interactions between the gut microbiome and vertebrate hosts contribute to host adaptation and speciation is one of the major problems in current evolutionary research. Using bacteriome and mycobiome metabarcoding, we examined how these two components of the gut microbiota vary with the degree of host admixture in secondary contact between two house mouse subspecies (Mus musculus musculus and M. m. domesticus). We used a large data set collected at two replicates of the hybrid zone and model-based statistical analyses to ensure the robustness of our results. Assuming that the microbiota of wild hosts suffers from spatial autocorrelation, we directly compared the results of statistical models that were spatially naive with those that accounted for spatial autocorrelation. We showed that neglecting spatial autocorrelation can strongly affect the results and lead to misleading conclusions. The spatial analyses showed little difference between subspecies, both in microbiome composition and in individual bacterial lineages. Similarly, the degree of admixture had minimal effects on the gut bacteriome and mycobiome and was caused by changes in a few microbial lineages that correspond to the common symbionts of free-living house mice. In contrast to previous studies, these data do not support the hypothesis that the microbiota plays an important role in host reproductive isolation in this particular model system.

Declined terrestrial ecosystem resilience.

Terrestrial ecosystem resilience is crucial for maintaining the structural and functional stability of ecosystems following disturbances. However, changes in resilience over the past few decades and the risk of future resilience loss under ongoing climate change are unclear. Here, we identified resilience trends using two remotely sensed vegetation indices, analyzed the relative importance of potential driving factors to resilience changes, and finally assessed the risk of future resilience loss based on the output data of eight models from CMIP6. The results revealed that more than 60% of the ecosystems experienced a conversion from an increased trend to a declined trend in resilience. Attribution analysis showed that the most important driving factors of declined resilience varied regionally. The declined trends in resilience were associated with increased precipitation variability in the tropics, decreased vegetation cover in arid region, increased temperature variability in temperate regions, and increased average temperature in cold regions. CMIP6 reveals that terrestrial ecosystems under SPP585 are expected to experience more intense declines in resilience than those under SSP126 and SSP245, particularly in cold regions. These results highlight the risk of continued degradation of ecosystem resilience in the future and the urgency of climate mitigation actions.

Limited evidence of autocorrelation signaling upcoming affective episodes: a 12-month e-diary study in patients with bipolar disorder.

BACKGROUND: Increased autocorrelation (AR) of system-specific measures has been suggested as a predictor for critical transitions in complex systems. Increased AR of mood scores has been reported to anticipate depressive episodes in major depressive disorder, while other studies found AR increases to be associated with depressive episodes themselves. Data on AR in patients with bipolar disorders (BD) is limited and inconclusive. METHODS: Patients with BD reported their current mood via daily e-diaries for 12 months. Current affective status (euthymic, prodromal, depressed, (hypo)manic) was assessed in 26 bi-weekly expert interviews. Exploratory analyses tested whether self-reported current mood and AR of the same item could differentiate between prodromal phases or affective episodes and euthymia. RESULTS: A total of 29 depressive and 20 (hypo)manic episodes were observed in 29 participants with BD. Self-reported current mood was significantly decreased during the two weeks prior to a depressive episode (early prodromal, late prodromal), but not changed prior to manic episodes. The AR was neither a significant predictor for the early or late prodromal phase of depression nor for the early prodromal phase of (hypo)mania. Decreased AR was found in the late prodromal phase of (hypo)mania. Increased AR was mainly found during depressive episodes. CONCLUSIONS: AR changes might not be better at predicting depressive episodes than simple self-report measures on current mood in patients with BD. Increased AR was mostly found during depressive episodes. Potentially, changes in AR might anticipate (hypo)manic episodes.

Day-to-day affect fluctuations in adults with childhood trauma history: a two-week ecological momentary assessment study.

BACKGROUND: Childhood trauma (CT) may increase vulnerability to psychopathology through affective dysregulation (greater variability, autocorrelation, and instability of emotional symptoms). However, CT associations with dynamic affect fluctuations while considering differences in mean affect levels across CT status have been understudied. METHODS: 346 adults (age = 49.25 ± 12.55, 67.0% female) from the Netherlands Study of Depression and Anxiety participated in ecological momentary assessment. Positive and negative affect (PA, NA) were measured five times per day for two weeks by electronic diaries. Retrospectively-reported CT included emotional neglect and emotional/physical/sexual abuse. Linear regressions determined associations between CT and affect fluctuations, controlling for age, sex, education, and mean affect levels. RESULTS: Compared to those without CT, individuals with CT reported significantly lower mean PA levels (Cohen's d = -0.620) and higher mean NA levels (d = 0.556) throughout the two weeks. CT was linked to significantly greater PA variability (d = 0.336), NA variability (d = 0.353), and NA autocorrelation (d = 0.308), with strongest effects for individuals reporting higher CT scores. However, these effects were entirely explained by differences in mean affect levels between the CT groups. Findings suggested consistency of results in adults with and without lifetime depressive/anxiety disorders and across CT types, with sexual abuse showing the smallest effects. CONCLUSIONS: Individuals with CT show greater affective dysregulation during the two-week monitoring of emotional symptoms, likely due to their consistently lower PA and higher NA levels. It is essential to consider mean affect level when interpreting the impact of CT on affect dynamics.

The Influence of Fragmented Landscapes on Speciation.

In the face of unprecedented global transformations, unraveling the intricate mechanisms governing biodiversity patterns is imperative for predicting and interpreting species responses. An important element in this interplay is fragmentation and the spatial mosaic or arrangement of suitable sites within the landscape. Beyond its well-documented impact on biodiversity loss, fragmented landscapes also influence the origin of biodiversity, by influencing speciation dynamics. This research employs a model that integrates spatial configuration and dispersal abilities of individuals to investigate the impact of landscape configuration on species' evolutionary trajectories. Specifically, we propose a microevolutionary model where individuals are characterized by their dispersal ability and a genome, allowing population evolution and diversification. Space is explicitly characterized by suitable and unsuitable sites that define fragmented landscapes. Our model demonstrates how intermediate dispersal abilities enhance diversification. However, simulations of more fragmented landscapes result in a lower total number of individuals and a lower percentage of occupied sites by individuals, particularly when species have limited dispersal abilities. Furthermore, we have found that intermediate levels of fragmentation can stimulate greater species richness, while higher levels of speciation and extinction events tend to occur under higher fragmentations. Our results also show a non-monotonic dependence of richness on dispersal, supporting the intermediate dispersal hypothesis as promotor of diversification, demonstrating the synergistic effects of landscape configuration and species dispersal ability in the processes of speciation, extinction, and diversification. This impact of fragmentation poses a real challenge for biodiversity in the context of a dynamic world.

Mood regulation in euthymic patients with a history of antidepressant-induced mania.

INTRODUCTION: The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture of mood regulation in euthymic patients with and without a history of AIM. METHODS: Eighty-four euthymic participants were included. Participants rated their mood, anxiety and energy levels daily using an electronic (e-) visual analog scale, for a mean (SD) of 280.8(151.4) days. We analyzed their multivariate time series by computing each variable's auto-correlation, inter-variable cross-correlation, and composite multiscale entropy of mood, anxiety, and energy. Then, we compared the data features of participants with a history of AIM and those without AIM, using analysis of covariance, controlling for age, sex, and current treatment. RESULTS: Based on 18,103 daily observations, participants with AIM showed significantly stronger day-to-day auto-correlation and cross-correlation for mood, anxiety, and energy than those without AIM. The highest cross-correlation in participants with AIM was between mood and energy within the same day (median (IQR), 0.58 (0.27)). The strongest negative cross-correlation in participants with AIM was between mood and anxiety series within the same day (median (IQR), -0.52 (0.34)). CONCLUSION: Patients with a history of AIM have a different underlying mood architecture compared to those without AIM. Their mood, anxiety and energy stay the same from day-to-day; and their anxiety is negatively correlated with their mood.

Additive partitioning of multispecies distributional aggregation of local assemblages.

The distribution of species is not random in space. At the finest-resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors. In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling-derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect. Empirical studies from field amphibian surveys through line-transect sampling in southwestern China (Minya Konka) and central-southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m. In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research.

School-level intra-cluster correlation coefficients and autocorrelations for children's accelerometer-measured physical activity in England by age and gender.

BACKGROUND: Randomised, cluster-based study designs in schools are commonly used to evaluate children's physical activity interventions. Sample size estimation relies on accurate estimation of the intra-cluster correlation coefficient (ICC), but published estimates, especially using accelerometry-measured physical activity, are few and vary depending on physical activity outcome and participant age. Less commonly-used cluster-based designs, such as stepped wedge designs, also need to account for correlations over time, e.g. cluster autocorrelation (CAC) and individual autocorrelation (IAC), but no estimates are currently available. This paper estimates the school-level ICC, CAC and IAC for England children's accelerometer-measured physical activity outcomes by age group and gender, to inform the design of future school-based cluster trials. METHODS: Data were pooled from seven large English datasets of accelerometer-measured physical activity data between 2002-18 (> 13,500 pupils, 540 primary and secondary schools). Linear mixed effect models estimated ICCs for weekday and whole week for minutes spent in moderate-to-vigorous physical activity (MVPA) and being sedentary for different age groups, stratified by gender. The CAC (1,252 schools) and IAC (34,923 pupils) were estimated by length of follow-up from pooled longitudinal data. RESULTS: School-level ICCs for weekday MVPA were higher in primary schools (from 0.07 (95% CI: 0.05, 0.10) to 0.08 (95% CI: 0.06, 0.11)) compared to secondary (from 0.04 (95% CI: 0.03, 0.07) to (95% CI: 0.04, 0.10)). Girls' ICCs were similar for primary and secondary schools, but boys' were lower in secondary. For all ages, combined the CAC was 0.60 (95% CI: 0.44-0.72), and the IAC was 0.46 (95% CI: 0.42-0.49), irrespective of follow-up time. Estimates were higher for MVPA vs sedentary time, and for weekdays vs the whole week. CONCLUSIONS: Adequately powered studies are important to evidence effective physical activity strategies. Our estimates of the ICC, CAC and IAC may be used to plan future school-based physical activity evaluations and were fairly consistent across a range of ages and settings, suggesting that results may be applied to other high income countries with similar school physical activity provision. It is important to use estimates appropriate to the study design, and that match the intended study population as closely as possible.

Identification, spatial distribution, and associated factors of urban protected areas in China.

The increasing proximity between protected areas (PAs) and urban areas, which can lead to urban protected areas (UPAs), is now commonplace. Use of Euclidean distance to measure the distance between PAs and cities has not correctly portrayed the spatial relationship between PAs and cities. We devised an isochronous circle model to accurately measure the distance between 2706 national PAs in 5 categories and 2844 cities in China based on human accessibility to identify urban human activity-influenced protected areas (UHAIPAs) and to quantitatively analyze their distribution patterns and relationships with China's economy, population distribution patterns, and urban development indicators. Most of the PAs in China were established near cities. Of 2746 PAs in China, 18.35% (n = 504) became UPAs, and 58.27% (n = 1600), 16.72% (n = 459), and 3.31% (n = 91) of PAs were within 0-30, 30-60, and 60-90 min, respectively. Both UPAs and UHAIPAs in China in general exhibited obvious spatial aggregation characteristics (e.g., wetland parks and scenic areas), and there was a significant spatial dependence effect among characteristics. The degree of spatial distribution and aggregation of UPAs was correlated with 16 indicators across urban economic development, urban natural substrate, and urban policy support factors. Based on the results of our study, we call for various governments and scholars to focus on areas where wetland parks and PAs overlap with urban boundaries. It is important to emphasize the potential link between the development of agriculture, forestry, livestock and fisheries industries, and UPAs. Overall, we believe that examining the accessibility of PAs can more accurately measure the distance between PAs and cities, and more realistically reflect the possible impacts of urban human activities on PAs, which is helpful for strengthening the conservation and management of PAs.

Identificación, distribución espacial y factores asociados de las áreas urbanas protegidas en China Resumen La creciente proximidad entre las áreas protegidas (AP) y las áreas urbanas, que puede derivar en áreas urbanas protegidas (AUP), es muy común hoy en día. La distancia euclidiana no ha representado correctamente la distancia entre las AP y las ciudades. Por lo anterior, diseñamos un modelo de círculo isócrono para medir con certeza la distancia entre 2706 AP nacionales en cinco categorías y 2844 ciudades de China con base en la accesibilidad humana para identificar las áreas urbanas protegidas con influencia de actividad humana (AUPIAH). También lo usamos para analizar cuantitativamente los patrones de distribución y su relación con la economía china, los patrones de distribución poblacional y los indicadores de desarrollo urbano. La mayoría de las AP en China están establecidas cerca de las ciudades. De las 2746 AP, el 18.35% (n = 504) se convirtieron en AUP y el 58.27% (n = 1600), 16.72% (n = 459) y 3.31% (n = 91) se encontraban de 0­30 minutos, 30­60 y de 60­90 minutos de una ciudad, respectivamente. Tanto las AUP y las AUPIAH exhibieron características obvias de agregación espacial (p. ej.: parques de humedales y áreas escénicas) y hubo un efecto significativo de dependencia espacial entre dichas características. El nivel de distribución y agregación espacial de las AUP tuvo correlación con 16 indicadores de desarrollo económico urbano, sustrato natural urbano y factores de apoyo para las políticas urbanas. Con base en los resultados de nuestro estudio, hacemos un llamado para que diferentes gobiernos y académicos se enfoquen en las áreas en donde se traslapan las AP y los parques de humedales con los límites urbanos. Es importante resaltar la conexión potencial entre el desarrollo de la agricultura, silvicultura, ganadería y pesquería y las AUP. En general, consideramos que analizar la accesibilidad de las AP puede medir de forma más certera la distancia entre las AP y las ciudades, además de que refleja de forma más realista el posible impacto de las actividades humanas sobre las AP, lo cual es útil para fortalecer la conservación y gestión de las AP.

Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial-temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2 = 414.22, P < 0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran's I indices of the registered incidence of PTB were all >0 (P< 0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that 'high-high' clusters were mainly distributed in northern Jiangsu, and 'low-low' clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.

The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures.

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023.

BACKGROUND: Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district. METHODS: Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence. RESULTS: From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016-2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014-2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations. CONCLUSIONS: From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21.

BACKGROUND: Despite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India's population resides. METHODS: The study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran's I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population. RESULTS: In rural India, three out of every ten men were found to be anemic. The univariate Moran's I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent 'hotspots' of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49-54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption. CONCLUSIONS: Substantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.

Single voxel autocorrelation uncovers gradients of temporal dynamics in the hippocampus and entorhinal cortex during rest and navigation.

During navigation, information at multiple scales needs to be integrated. Single-unit recordings in rodents suggest that gradients of temporal dynamics in the hippocampus and entorhinal cortex support this integration. In humans, gradients of representation are observed, such that granularity of information represented increases along the long axis of the hippocampus. The neural underpinnings of this gradient in humans, however, are still unknown. Current research is limited by coarse fMRI analysis techniques that obscure the activity of individual voxels, preventing investigation of how moment-to-moment changes in brain signal are organized and how they are related to behavior. Here, we measured the signal stability of single voxels over time to uncover previously unappreciated gradients of temporal dynamics in the hippocampus and entorhinal cortex. Using our novel, single voxel autocorrelation technique, we show a medial-lateral hippocampal gradient, as well as a continuous autocorrelation gradient along the anterolateral-posteromedial entorhinal extent. Importantly, we show that autocorrelation in the anterior-medial hippocampus was modulated by navigational difficulty, providing the first evidence that changes in signal stability in single voxels are relevant for behavior. This work opens the door for future research on how temporal gradients within these structures support the integration of information for goal-directed behavior.