BACKGROUND: The mortality of type 2 diabetes mellitus (T2DM) can be affected by environmental factors. However, few studies have explored the effects of environmental factors across diverse regions over time. Given the vulnerability observed in the elderly group in previous research, this research applied Bayesian spatiotemporal models to assess the associations in the elderly group.
METHODS: Data on T2DM death in the elderly group (aged over 60 years old) at the county level were collected from the National Death Surveillance System between 1st January 2013 and 31st December 2019 in Shandong Province, China. A Bayesian spatiotemporal model was employed with the integrated Nested Laplace Approach to explore the associations between socio-environmental factors (i.e., temperatures, relative humidity, the Normalized Difference Vegetation Index (NDVI), particulate matter with a diameter of 2.5 μm or less (PM2.5) and gross domestic product (GDP)) and T2DM mortality.
RESULTS: T2DM mortality in the elderly group was found to be associated with temperature and relative humidity (i.e., temperature: Relative Risk (RR) = 1.41, 95% Credible Interval (CI): 1.27-1.56; relative humidity: RR = 1.05, 95% CI:1.03-1.06), while no significant associations were found with NDVI, PM2.5 and GDP. In winter, significant impacts from temperature (RR = 1.18, 95% CI: 1.06-1.32) and relative humidity (RR = 0.94, 95% CI: 0.89-0.99) were found. Structured and unstructured spatial effects, temporal trends and space-time interactions were considered in the model.
CONCLUSIONS: Higher mean temperatures and relative humidities increased the risk of elderly T2DM mortality in Shandong Province. However, a higher humidity level decreased the T2DM mortality risk in winter in Shandong Province. This research indicated that the spatiotemporal method could be a useful tool to assess the impact of socio-environmental factors on health by combining the spatial and temporal effects.

In cellular contexts, the oscillation of calcium ions (Ca2+) is intricately linked to various physiological processes, such as cell proliferation, metabolism, and survival. Stromal interaction molecule 1 (STIM1) proteins form a crucial regulatory component in the store-operated calcium entry (SOCE) process. The structural attributes of STIM1 are vital for its functionality, encompassing distinct domains situated in the endoplasmic reticulum (ER) lumen and the cytoplasm. The intraluminal domain enables the timely detection of diminishing Ca2+ concentrations, prompting structural modifications that activate the cytoplasmic domain. This activated cytoplasmic domain undergoes conformational alterations and engages with membrane components, opening a channel that facilitates the influx of Ca2+ from the extracellular environment. Given its multiple domains and interaction mechanisms, STIM1 plays a foundational role in cellular biology. This review focuses on the design of optogenetic tools inspired by the structure and function of STIM1. These tools offer a groundbreaking approach for studying and manipulating intracellular Ca2+ signaling with precisely spatiotemporal control. We further explore the practical applications of these tools, spanning fundamental scientific research, clinical studies, and their potential for translational research.

BACKGROUND: Sanqi, the root of Panax notoginseng, has long been recognized for its therapeutic effects on cardiovascular diseases. Saponins, including ginsenosides and notoginsenosides, are the main bioactive components of P. notoginseng. The biosynthesis of saponins is closely related to the defense responses orchestrated by endogenous hormones.
RESULTS: To provide new insights into the underlying role of phytohormone jasmonic acid (JA) in the synthesis and regulation of saponins, we performed an ultra-performance liquid chromatography analysis of different tissues of P. notoginseng aged 2-4 years. Moreover, by combined evaluation of saponin content and transcriptome profiling of each tissue, the spatial and temporal distribution of saponins was analyzed. N notoginsenoside R1, ginsenoside Rb1 and ginsenoside Rd accumulated in the underground tissues, including the root, tuqi, fibril and rhizome. In agreement with this data, the corresponding genes of the endogenous hormone JAs, especially coronatine insensitive 1 (COI1) and myelocytomatosis proteins 2 (MYC2), were predominantly expressed in the underground tissues. The tissue- and age-specific distribution of saponins was consistent with the expression of genes involved in JA biosynthetic, metabolic and signaling pathways.
CONCLUSIONS: The present study has revealed the temporal and spatial effects of endogenous phtohormones in the synthesis and regulation of notoginsenosides, which will provide a significant impact on improving the ecological planting technology, cultivating new high-quality varieties and protecting the rare resources of medicinal P. notoginseng. © 2024 Society of Chemical Industry.

BACKGROUND: The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.
METHODS: Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.
RESULTS: Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.
CONCLUSIONS: Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD-GAT-BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

UNASSIGNED: In recent years, the incidence of measles in China has consistently remained below 1 per 100,000 population, yet the disease has not been eliminated. This study aims to comprehensively analyze the epidemiological characteristics of measles from 2005 to 2022, identify high-risk populations and areas, and propose targeted interventions.
UNASSIGNED: We utilized data from the China Disease Prevention and Control Information System for our comprehensive analysis. Spatial autocorrelation was employed to examine the spatial clustering of measles, while spatiotemporal scanning analysis was used to detect spatiotemporal clustering to describe measles epidemiology during the study period.
UNASSIGNED: Between 2005 and 2022, 732,218 measles cases were reported in China. Overall, the incidence of measles exhibited a downward trend, particularly during the periods of 2008-2011 and 2015-2022. In 2022, the incidence rate reached its historical low at 0.039 per 100,000 population. Measles predominantly affects young children. Since 2017, global spatial clustering has diminished, although hotspot areas persist in the western provinces. Spatial-temporal scanning identified a high-incidence cluster from 2005 to 2008, comprising 15 provinces in the western, central, and northern regions of China. Conversely, from 2016 to 2022, a low-incidence cluster was detected in the southern and central provinces.
UNASSIGNED: China has made significant progress in measles prevention and control. The recent low incidence and absence of substantial spatiotemporal clustering indicate that China is nearing measles elimination. However, there is a continuing need to enhance prevention and control efforts among very young children and in historic incidence hotspots in western provinces. Additionally, improving the diagnosis of vaccine-associated rash illnesses is essential.

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform-1D, 2D, or 3D-would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.

Understanding patterns of species diversity is crucial for ecological research and conservation, and this understanding may be improved by studying patterns in the two components of species diversity, species richness and evenness of abundance of species. Variation in species richness and evenness has previously been linked to variation in total abundance of communities as well as productivity gradients. Exploring both components of species diversity is essential because these components could be unrelated or driven by different mechanisms. The aim of this study was to investigate the relationship between species richness and evenness in European bird communities along an extensive latitudinal gradient. We examined their relationships with latitude and Net Primary Productivity, which determines energy and matter availability for heterotrophs, as well as their responses to territory densities (i.e. the number of territories per area) and community biomass (i.e. the bird biomass per area). We applied a multivariate Poisson log-normal distribution to unique long-term, high-quality time-series data, allowing us to estimate species richness of the community as well as the variance of this distribution, which acts as an inverse measure of evenness. Evenness in the distribution of abundance of species in the community was independent of species richness. Species richness increased with increasing community biomass, as well as with increasing density. Since both measures of abundance were explained by NPP, species richness was partially explained by energy-diversity theory (i.e. the more energy, the more species sustained by the ecosystem). However, species richness did not increase linearly with NPP but rather showed a unimodal relationship. Evenness was not explained either by productivity nor by any of the aspects of community abundance. This study highlights the importance of considering both richness and evenness to gain a better understanding of variation in species diversity. We encourage the study of both components of species diversity in future studies, as well as use of simulation studies to verify observed patterns between richness and evenness.

Under-five children\'s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children\'s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children\'s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

OBJECTIVE: In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space-time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster.
METHODS: Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent\'s postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space-time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster.
RESULTS: Eight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters.
CONCLUSIONS: Our findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.