Characterizing equitable access to grocery stores during disasters using location-based data.

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents' ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities.

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Impact of online Mindfulness-Based Cognitive Therapy on suicidal ideation: A secondary analysis of a randomized trial of Mindful Mood Balance.

BACKGROUND: To address the elevated prevalence of depression, suicide, and suicidal ideation, patients require increased access to effective interventions. Mindfulness-Based Cognitive Therapy has a strong evidence base in relapse prophylaxis and can be delivered digitally through Mindful Mood Balance (MMB). METHODS: This study was a secondary analysis of the impact of MMB paired with usual depression care (UDC) compared to UDC alone on patients in a randomized clinical trial for residual depression (Segal et al., 2020) who had a history of attempted suicide or reported current suicidal ideation (N = 109). RESULTS: MMB relative to UDC was associated with a greater rate of reduction in suicidal ideation (SI; t(103) = 2.50, p = 0.014, d = 0.49, 95% CI [0.09-0.88]) and a greater likelihood of being in a lower severity category of SI (t(103) = 2.02, p = 0.046, odds ratio = 3.43, 95% CI [1.02-11.53]). There was also evidence that MMB reduces depression severity outcomes among this at risk group (t(105) = 2.38, p < 0.02, d = 0.46, 95% CI [0.07-0.85]). LIMITATIONS: Reported findings are based on a subgroup of patients in a clinical trial originally designed to treat residual depressive symptoms. CONCLUSIONS: Online interventions, such as MMB, may offer one solution to the challenge of expanding the reach of services for patients with residual depression who are at risk of suicidal ideation and behavior.

The Delayed Neuropathological Consequences of Traumatic Brain Injury in a Community-Based Sample.

The late neuropathological effects of traumatic brain injury have yet to be fully elucidated, particularly with respect to community-based cohorts. To contribute to this critical gap in knowledge, we designed a multimodal neuropathological study, integrating traditional and quantitative approaches to detect pathologic changes in 532 consecutive brain autopsies from participants in the Adult Changes in Thought (ACT) study. Diagnostic evaluation including assessment for chronic traumatic encephalopathy (CTE) and quantitative immunoassay-based methods were deployed to examine levels of pathological (hyperphosphorylated) tau (pTau) and amyloid (A) ß in brains from ACT participants with (n = 107) and without (n = 425) history of remote TBI with loss of consciousness (w/LOC). Further neuropathological assessments included immunohistochemistry for α-synuclein and phospho-TDP-43 pathology and astro- (GFAP) and micro- (Iba1) gliosis, mass spectrometry analysis of free radical injury, and gene expression evaluation (RNA sequencing) in a smaller sub-cohort of matched samples (49 cases with TBI and 49 non-exposed matched controls). Out of 532 cases, only 3 (0.6%-none with TBI w/LOC history) showed evidence of the neuropathologic signature of chronic traumatic encephalopathy (CTE). Across the entire cohort, the levels of pTau and Aß showed expected differences for brain region (higher levels in temporal cortex), neuropathological diagnosis (higher in participants with Alzheimer's disease), and APOE genotype (higher in participants with one or more APOE ε4 allele). However, no differences in PHF-tau or Aß1-42 were identified by Histelide with respect to the history of TBI w/LOC. In a subset of TBI cases with more carefully matched control samples and more extensive analysis, those with TBI w/LOC history had higher levels of hippocampal pTau but no significant differences in Aß, α-synuclein, pTDP-43, GFAP, Iba1, or free radical injury. RNA-sequencing also did not reveal significant gene expression associated with any measure of TBI exposure. Combined, these findings suggest long term neuropathological changes associated with TBI w/LOC may be subtle, involve non-traditional pathways of neurotoxicity and neurodegeneration, and/or differ from those in autopsy cohorts specifically selected for neurotrauma exposure.

Determinants of Risk Disparity Due to Infrastructure Service Losses in Disasters: A Household Service Gap Model.

The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at the household level by examining service disruptions as threats, level of tolerance of households to disruptions as susceptibility, and experienced hardship as an indicator for the realized impacts of risk. The concept of "zone of tolerance" for the service disruptions was encapsulated to account for different capabilities of the households to endure the adverse impacts. The model was tested and validated in the context of power outages through survey data from the residents of Harris County in the aftermath of Hurricane Harvey in 2017. The results show that households' need for utility service, preparedness level, the existence of substitutes, possession of social capital, previous experience with disasters, and risk communication affect the zone of tolerance within which households cope with service outages. In addition, sociodemographic characteristics, such as race and residence type, are shown to influence the zone of tolerance, and hence the level of hardship experienced by the affected households. The results reveal that population subgroups show variations in the tolerance level of service disruptions. The findings highlight the importance of integrating social dimensions into the resilience planning of infrastructure systems. The proposed model and results enable human-centric hazards mitigation and resilience planning to effectively reduce the risk disparity of vulnerable populations to service disruptions in disasters.

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data.

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics-low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics-high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.

Detecting Early-Warning Signals in Time Series of Visits to Points of Interest to Examine Population Response to COVID-19 Pandemic.

The objective of this paper is to examine population response to COVID-19 and associated policy interventions through detecting early-warning signals in time series of visits to points of interest (POIs). Complex systems, such as cities, would demonstrate early-warning signals (e.g., increased autocorrelation and standard deviation) when they approach phase transitions responding to external perturbation, such as crises, policy changes, and human behavior changes. In urban systems, population visits to POIs, such as restaurants, museums, and hospitals, represent a state of cities as complex systems. These states may undergo phase transitions due to population response to pandemic risks and intervention policies (e.g., social distancing and shelter-in-place orders). In this study, we conducted early-warning signal detection on population visits to POIs to examine population response to pandemic risks, and we evaluated time lags between detected early-warning dates and dates of first cases and policy interventions. We examined two early-warning signals, the increase of autocorrelation at-lag-1 and standard deviation, in time series of population visits to POIs in 17 metropolitan cities in the United States of America. We examined visits to grouped POIs according to two categories of essential services and non-essential services. The results show that: (1) early-warning signals for population response to COVID-19 were detected between February 14 and March 11, 2020 in 17 cities; (2) detected population response had started prior to shelter-in-place orders in 17 cities; (3) early-warning signals detected from the essential POIs visits appeared earlier than those from non-essential POIs; and 4) longer time lags between detected population response and shelter-in-place orders led to a less decrease in POI visits. The results show the importance of detecting early-warning signals during crises in cities as complex systems. Early-warning signals could provide important insights regarding the timing and extent of population response to crises to inform policymakers.

Comparison of regional flortaucipir PET with quantitative tau immunohistochemistry in three subjects with Alzheimer's disease pathology: a clinicopathological study.

BACKGROUND: The objective of this study was to make a quantitative comparison of flortaucipir PET retention with pathological tau and ß-amyloid across a range of brain regions at autopsy. METHODS: Patients with dementia (two with clinical diagnosis of AD, one undetermined), nearing the end of life, underwent 20-min PET, beginning 80 min after an injection of ~370 mBq flortaucipir [18F]. Neocortical, basal ganglia, and limbic tissue samples were obtained bilaterally from 19 regions at autopsy and subject-specific PET regions of interest corresponding to the 19 sampled target tissue regions in each hemisphere were hand drawn on the PET images. SUVr values were calculated for each region using a cerebellar reference region. Abnormally phosphorylated tau (Ptau) and amyloid-ß (Aß) tissue concentrations were measured for each tissue region with an antibody capture assay (Histelide) using AT8 and H31L21 antibodies respectively. RESULTS: The imaging-to-autopsy interval ranged from 4-29 days. All three subjects had intermediate to high levels of AD neuropathologic change at autopsy. Mean cortical SUVr averaged across all three subjects correlated significantly with the Ptau immunoassay (Pearson r = 0.81; p < 0.0001). When Ptau and Aß1-42 were both included in the model, the Ptau correlation with flortaucipir SUVr was preserved but there was no correlation of Aß1-42 with flortaucipir. There was also a modest correlation between limbic (hippocampal/entorhinal and amygdala) flortaucipir SUVr and Ptau (Pearson r = 0.52; p < 0.080). There was no significant correlation between SUVr and Ptau in basal ganglia. CONCLUSIONS: The results of this pilot study support a quantitative relationship between cortical flortaucipir SUVr values and quantitative measures of Ptau at autopsy. Additional research including more cases is needed to confirm the generalizability of these results. Trial registration, NIH Clinicaltrials.gov NCT # 02516046. Registered August 27, 2015. https://clinicaltrials.gov/ct2/show/NCT02516046?term=02516046&draw=2&rank=1.

Neuropathological and transcriptomic characteristics of the aged brain.

As more people live longer, age-related neurodegenerative diseases are an increasingly important societal health issue. Treatments targeting specific pathologies such as amyloid beta in Alzheimer's disease (AD) have not led to effective treatments, and there is increasing evidence of a disconnect between traditional pathology and cognitive abilities with advancing age, indicative of individual variation in resilience to pathology. Here, we generated a comprehensive neuropathological, molecular, and transcriptomic characterization of hippocampus and two regions cortex in 107 aged donors (median = 90) from the Adult Changes in Thought (ACT) study as a freely-available resource (http://aging.brain-map.org/). We confirm established associations between AD pathology and dementia, albeit with increased, presumably aging-related variability, and identify sets of co-expressed genes correlated with pathological tau and inflammation markers. Finally, we demonstrate a relationship between dementia and RNA quality, and find common gene signatures, highlighting the importance of properly controlling for RNA quality when studying dementia.

Biochemical and kinetic characterization of radical S-adenosyl-L-methionine enzyme HydG.

The radical S-adenosyl-L-methionine (AdoMet) enzyme HydG is one of three maturase enzymes involved in [FeFe]-hydrogenase H-cluster assembly. It catalyzes L-tyrosine cleavage to yield the H-cluster cyanide and carbon monoxide ligands as well as p-cresol. Clostridium acetobutylicum HydG contains the conserved CX3CX2C motif coordinating the AdoMet binding [4Fe-4S] cluster and a C-terminal CX2CX22C motif proposed to coordinate a second [4Fe-4S] cluster. To improve our understanding of the roles of each of these iron-sulfur clusters in catalysis, we have generated HydG variants lacking either the N- or C-terminal cluster and examined these using spectroscopic and kinetic methods. We have used iron analyses, UV-visible spectroscopy, and electron paramagnetic resonance (EPR) spectroscopy of an N-terminal C96/100/103A triple HydG mutant that cannot coordinate the radical AdoMet cluster to unambiguously show that the C-terminal cysteine motif coordinates an auxiliary [4Fe-4S] cluster. Spectroscopic comparison with a C-terminally truncated HydG (ΔCTD) harboring only the N-terminal cluster demonstrates that both clusters have similar UV-visible and EPR spectral properties, but that AdoMet binding and cleavage occur only at the N-terminal radical AdoMet cluster. To elucidate which steps in the catalytic cycle of HydG require the auxiliary [4Fe-4S] cluster, we compared the Michaelis-Menten constants for AdoMet and L-tyrosine for reconstituted wild-type, C386S, and ΔCTD HydG and demonstrate that these C-terminal modifications do not affect the affinity for AdoMet but that the affinity for L-tyrosine is drastically reduced compared to that of wild-type HydG. Further detailed kinetic characterization of these HydG mutants demonstrates that the C-terminal cluster and residues are not essential for L-tyrosine cleavage to p-cresol but are necessary for conversion of a tyrosine-derived intermediate to cyanide and CO.

Glucose-induced replicative senescence in mesenchymal stem cells.

Mesenchymal stem cells (MSCs) show great promise for use in a variety of cell-based therapies. Because isolated primary mesenchymal stem cells are low in numbers, in vitro expansion is necessary. However, the expansion potential is limited and in vitro aging leads to loss of multipotency and replicative senescence. Stress induced by culture conditions is likely to be a major cause of replicative senescence and reduced multipotency of MSC and optimization of culture conditions might be able to reduce this. Caloric restriction (CR) is the only established method to delay aging and extend lifespan. In vitro caloric restriction experiments are rare, but have demonstrated beneficial effects. Therefore, we investigated the effect of culture medium glucose concentration on the proliferative and differentiation potential of mesenchymal stem cells. Reduction in glucose concentrations led to decreased apoptosis and an increased rate of MSC proliferation and increased the number and size of fibroblastic colonies in the colony-forming unit assay.