Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility

Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case scenarios such as predicting visit counts of individuals' for a given spatial area at a particular temporal resolution using raster/image format representation of the geographical region, since the movement patterns of an individual can be largely restricted and localized to a certain part of the raster. Additionally, current deep-learning approaches for solving such problem doesn't account for the geographical awareness of a region while modelling the spatio-temporal movement patterns of an individual. To address these limitations, there is a need to develop a novel strategy and modeling approach that can handle both sparse, irregular data while incorporating geo-awareness in the model. In this paper, we make use of quadtree as the data structure for representing the image and introduce a novel geo-aware enabled deep learning layer, GA-ConvLSTM that performs the convolution operation based on a novel geo-aware module based on quadtree data structure for incorporating spatial dependencies while maintaining the recurrent mechanism for accounting for temporal dependencies. We present this approach in the context of the problem of predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) through deep-learning based predictive model, GADST-Predict. Experimental results on two GPS based trace data shows that the proposed method is effective in handling frequency visits over different use-cases with considerable high accuracy

Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation

Aflatoxin is a carcinogenic toxin to humans and animals produced by mold fungi in staple crops. Surveys of Aflatoxin are expensive, and the results are usually not available for implementing within season mitigation strategies. Identification of high and low risk areas and years is essential to reduce the number of samples analyzed for Aflatoxin concentration. Previously a risk factors approach was developed to determine county level Aflatoxin contamination risk in southern Georgia, but Aflatoxin concentrations and risk factor data were not analyzed simultaneously and all risk factors had equal weight which is unrealistic. In the current paper we propose a regression approach to overcome these problems. Spatial Poisson profile regression identified clusters of counties which have similar Aflatoxin risk and risk factor profiles, whilst explicitly taking into account multicollinearity in the risk factor data and spatial autocorrelation in the Aflatoxin data. This approach allows examination of the utility of different highly correlated variables including remotely sensed data that could give information at the sub-county level. The results identify plausible clusters compared to previous work but also give the relative importance of the risk factors associated with those clusters. The approach also helps show that some factors like well-drained soil behave differently from expectations and irrigation data is not useful

How the COVID-19 Pandemic Has Changed Adolescent Health: Physical Activity, Sleep, Obesity, and Mental Health

The purpose of this study is to provide essential data for the establishment of education and policy for the formation of healthy lifestyles of adolescents in the future by analyzing the patterns of changes in society due to the prolonged COVID-19 in the physical activities, sleeping habits, obesity, and mental health of Korean adolescents. To this end, a total of 147,346 adolescents were selected and analyzed according to the purpose of the study in the 2018 (14th), 2019 (15th), and 2020 (16th) raw data of the “Youth Health Behavior Online Survey,” an annual national approval statistical survey conducted by a Korean government agency. The study examined changes in the physical activity, obesity, sleep, and mental health of Korean adolescents due to COVID-19. The physical activity rate of Korean adolescents in 2019 decreased by 5.3% from 2018. In addition, the physical activity rate in 2020 decreased by 2.1% compared to 2019. It was found that physical activity steadily decreased (p < 0.001). The obesity rate increased by 0.9% in 2019 compared to 2018 and by 1.8% in 2020 compared to 2019. Although the obesity rate steadily increased, it was found that it was accelerated due to COVID-19 (p < 0.001). Looking at the subjective sleep satisfaction rate of Korean adolescents, in 2019, it was 0.1% lower than in 2018, while in 2020, when COVID-19 began, it increased by 3.5% compared to 2019. It was found that satisfaction with sleep increased after COVID-19. Finally, the mental health characteristics of Korean adolescents by year were divided into stress and depression. Stress decreased by 1% compared to 2019 and 2018 and by 6.2% compared to 2020 and 2019. Depression increased by 1% in 2019 compared to 2018 and decreased by 3.4% in 2020 compared to 2019. In other words, stress and depression decreased after COVID-19. In 2020, when COVID-19 occurred, it was confirmed that there was a change in the health behavior of adolescents compared to 2018 and 2019. Therefore, active responses from schools, families, and communities are required to foster healthy lifestyle habits in social changes such as COVID-19

What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status

The purpose of the current study is to investigate the changes in physical activity (PA), sleep time (ST), and body weight (BW) Pre- and during COVID-19 in South Korea, and specifically, PA data were obtained during COVID-19 at three-time points based on the multilevel social distancing policies. All data were surveyed by questionnaires online and offline, and participants were required to fill in the monthly average of daily step counts were recorded an application on participants’ smartphone devices from Pre-COVID-19 (2019 year) and during COVID-19 (2020 year). Participants were 834 adults (males: 54.4%, female: 45.6%) and all statistical analyses were summarized by SPSS 25.0 program. The monthly average of daily step counts was 6747.09 during Pre-COVID-19, but the PA during COVID-19 was 5812.11 daily step counts per month. Also, there were significant pairwise differences between average PA Pre-COVID-19 and each level of social distancing (p < 0.001). After COVID-19, the participants who slept less than 7 h decreased by 3.6%, while those who slept more than 9 h increased by that much. As a result of BW, 269 participants responded their BW changed during COVID-19, and 199 of them reported they gained BW during COVID-19 (74.0%). Although self-reported questionnaires may have led to an under-or over-estimation of ST and BW, the present study found that the environment in which the COVID-19 is prevalent had adverse relationships on PA, ST, and BW. Therefore, it is important to identify strategies to motivate individuals for remaining physically active and getting adequate sleep while maintaining social distancing due to the presence of the COVID-19 global pandemic

Astrocytes Reduce Store-Operated Ca2+ Entry in Microglia under the Conditions of an Inflammatory Stimulus and Muscarinic Receptor Blockade

Inflammation and loss of cholinergic transmission are involved in neurodegenerative diseases, but possible interactions between them within neurons, astrocytes, and microglia have not yet been investigated. We aimed to compare store-operated Ca2+ entry (SOCE) in neurons, astrocytes, and microglia following cholinergic dysfunction in combination with (or without) an inflammatory stimulus and to investigate the effects of linalyl acetate (LA) on this process. We used the SH-SY5Y, U373, and BV2 cell lines related to neurons, astrocytes, and microglia, respectively. Scopolamine or lipopolysaccharide (LPS) was used to antagonize the muscarinic receptors or induce inflammatory responses, respectively. The concentration of intracellular Ca2+ was measured using Fura-2 AM. Treatment with scopolamine and LPS significantly increased SOCE in the neuron-like cells and microglia but not in the scopolamine-pretreated astrocytes. LA significantly reduced SOCE in the scopolamine-pretreated neuron-like cells and microglia exposed to LPS, which was partially inhibited by the Na+-K+ ATPase inhibitor ouabain and the Na+/Ca2+ exchanger (NCX) inhibitor Ni2+. Notably, SOCE was significantly reduced in the LPS plus scopolamine-pretreated cells mixed with astrocytes and microglia, with a two-fold increase in the applied number of astrocytes. LA may be useful in protecting neurons and microglia by reducing elevated SOCE that is induced by inflammatory responses and inhibiting the muscarinic receptors via Na+-K+ ATPase and the forward mode of NCX. Astrocytes may protect microglia by reducing increased SOCE under the conditions of inflammation and a muscarinic receptor blockade

Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital

Introduction: Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. Methods: This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. Results: Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. Conclusion: This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations

Real-time location system-based asset tracking in the healthcare field: lessons learned from a feasibility study

Abstract Background Numerous hospitals and organizations have recently endeavored to study the effects of real-time location systems. However, their experiences of system adoption or pilot testing via implementation were not shared with others or evaluated in a real environment. Therefore, we aimed to share our experiences and insight regarding a real-time location system, obtained via the implementation and operation of a real-time asset tracking system based on Bluetooth Low Energy/WiFi in a tertiary care hospital, which can be used to improve hospital efficiency and nursing workflow. Methods We developed tags that were attached to relevant assets paired with Bluetooth Low Energy sensor beacons, which served as the basis of the asset tracking system. Problems with the system were identified during implementation and operation, and the feasibility of introducing the system was evaluated via a satisfaction survey completed by end users after 3 months of use. Results The results showed that 117 nurses who had used the asset tracking system for 3 months were moderately satisfied (2.7 to 3.4 out of 5) with the system, rated it as helpful, and were willing to continue using it. In addition, we identified 4 factors (end users, target assets, tracking area, and type of sensor) that should be considered in the development of asset tracking systems, and 4 issues pertaining to usability (the active tag design, technical limitations, solution functions, and operational support). Conclusions The successful introduction of asset tracking systems based on real-time location in hospitals requires the selection of clear targets (e.g., users and assets) via analysis of the user environment and implementation of appropriate technical improvements in the system as required (e.g., miniaturization of the tag size and improvement of the sensing accuracy)