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This article develops and assesses the concept of triple neighborhood disadvantage. We argue that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods its residents visit and are visited by, connections that form through networks of everyday urban mobility. We construct measures of mobilitybased disadvantage using geocoded patterns of movement estimated from hundreds of millions of tweets sent by nearly 400,000 Twitter users over 18 months. Analyzing nearly 32,000 neighborhoods and 9,700 homicides in 37 of the largest U. S. cities, we show that neighborhood triple disadvantage independently predicts homicides, adjusting for traditional neighborhood correlates of violence, spatial proximity to disadvantage, prior homicides, and city fixed effects. Not only is triple disadvantage a stronger predictor than traditional measures, it accounts for a sizable portion of the association between residential neighborhood disadvantage and homicides. In turn, potential mechanisms such as neighborhood drug activity, interpersonal friction, and gun crime prevalence account for much of the association between triple disadvantage and homicides. These findings implicate structural mobility patterns as an important source of triple (dis) advantage for neighborhoods and have implications for a broad range of phenomena beyond crime, including community capacity, gentrification, transmission in a pandemic, and racial inequality.

Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviours in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighbourhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighbourhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision-makers, and disaster managers alike.

Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with non-trivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One simple question of this type is that of the conditions needed for social forces to stabilize a particular structure. We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change-scores of the sufficient statistics vis-a-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the ``stable cone'' within the parameter space as a starting point. In addition, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one. We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing over time.

Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviors in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighborhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighborhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision makers, and disaster managers alike.

Traumatic brain injury (TBI) is a leading cause of disability. Sequelae can include functional impairments and psychiatric syndromes such as post-traumatic stress disorder (PTSD), depression and anxiety. Special Operations Forces (SOF) veterans (SOVs) may be at an elevated risk for these complications, leading some to seek underexplored treatment alternatives such as the oneirogen ibogaine, a plant-derived compound known to interact with multiple neurotransmitter systems that has been studied primarily as a treatment for substance use disorders. Ibogaine has been associated with instances of fatal cardiac arrhythmia, but coadministration of magnesium may mitigate this concern. In the present study, we report a prospective observational study of the Magnesium–Ibogaine: the Stanford Traumatic Injury to the CNS protocol (MISTIC), provided together with complementary treatment modalities, in 30 male SOVs with predominantly mild TBI. We assessed changes in the World Health Organization Disability Assessment Schedule from baseline to immediately (primary outcome) and 1 month (secondary outcome) after treatment. Additional secondary outcomes included changes in PTSD (Clinician-Administered PTSD Scale for DSM-5), depression (Montgomery–Åsberg Depression Rating Scale) and anxiety (Hamilton Anxiety Rating Scale). MISTIC resulted in significant improvements in functioning both immediately ( P corrected  < 0.001, Cohen’s d  = 0.74) and 1 month ( P corrected  < 0.001, d  = 2.20) after treatment and in PTSD ( P corrected  < 0.001, d  = 2.54), depression ( P corrected  < 0.001, d  = 2.80) and anxiety ( P corrected  < 0.001, d  = 2.13) at 1 month after treatment. There were no unexpected or serious adverse events. Controlled clinical trials to assess safety and efficacy are needed to validate these initial open-label findings. ClinicalTrials.gov registration: NCT04313712 . Military veterans with traumatic brain injuries who received treatment with the psychoactive compound ibogaine and magnesium experienced improvements in disability, psychiatric symptoms and cognition.

Susceptibility to a clinically significant drug hypersensitivity syndrome associated with abacavir use seems to have a strong genetic component. We have previously shown that the presence of HLA-B*5701 strongly predicts abacavir hypersensitivity and have identified a potential susceptibility locus within a 300-kb region between the MEGT1 and C4A6 loci in the central MHC. We now report the results of fine recombinant genetic mapping in an expanded patient population of 248 consecutive, fully ascertained, abacavir-exposed individuals in the Western Australian HIV Cohort Study, in which 18 cases of definite abacavir hypersensitivity (7.3%) and 230 tolerant controls were identified. Haplotype mapping within patients with allelic markers of the 57.1 ancestral haplotype suggests a susceptibility locus within the 14-kb Hsp70 gene cluster. HLA-B*5701 was present in 94.4% of hypersensitive cases compared with 1.7% of controls (odds ratio, 960; P < 0.00001). A haplotypic nonsynonymous polymorphism of Hsp70-Hom (HspA1L, resulting from the substitution of residue M493T in the peptide-binding subunit) was found in combination with HLA-B*5701 in 94.4% of hypersensitive cases and 0.4% of controls (odds ratio, 3,893; P < 0.00001). Individuals with abacavir hypersensitivity demonstrated increased monocyte tumor necrosis factor expression in response to ex vivo abacavir stimulation, which was abrogated with CD8+T cell depletion. These data indicate that the concurrence of HLA-B*5701 and Hsp70-Hom M493T alleles is necessary for the development of abacavir hypersensitivity, which is likely to be mediated by an HLA-B*5701-restricted immune response to abacavir.

Background Previous research suggests that perceived discrimination is associated with cognitive function impairment, and such association is mediated by depression. With minority populations continuously growing, it is crucial to investigate such relationships in diverse populations. This study aims to examine and compare the above relationships among non‐Hispanic white (NHW), Mexican American (MA), and African American (AA) participants. Method A sample size of 1,129 participants (640 AAs, 248 NHWs, 241 MAs) aged 50+ came from the Health and Aging Brain Study – Health Disparities (HABS‐HD). Multiple linear regression was conducted to explore the direct association between perceived discrimination, measured by the Everyday Discrimination Scale mean score, and cognitive function, measured by the Mini Mental State Examination (MMSE) Score. The mediation effect of depression, measured by the Geriatric Depression Scale total score, was evaluated by the indirect effect estimate and confidence interval through Monte Carlo simulations. Result Everyday perceived discrimination negatively influenced cognitive function, and the effect was fully mediated by depression across the three populations (estimate for indirect effect = ‐0.16, 95% CI = [‐0.24, ‐0.09]). When stratified, the mediation effect of depression on the association between discrimination and cognitive function remained significant for NHW (estimate for indirect effect = ‐0.37, 95% CI = [‐0.63, ‐0.16]) and MA (estimate for indirect effect = ‐0.42, 95% CI = [‐0.74, ‐0.16]). However, such mediation effect was not observed for the AA population. Conclusion Depression mediates the link between everyday discrimination and cognitive decline, but differences between racial/ethnic groups underscore the need for further research into underlying mechanisms among minority groups, including Mexican American and African American populations. Depression interventions may mitigate negative cognitive effects from discrimination. Tailoring such interventions by race/ethnicity and targeting at‐risk groups could optimally promote cognitive health.

Background: The compounding effects of climate change catastrophes such as bushfires and pandemics impose significant burden on individuals, societies, and their economies. The enduring effects of such syndemics on mental health remain poorly understood, particularly for at-risk populations (e.g., pregnant women and newborns). The aim of this study was to investigate the impact of direct and indirect exposure to the 2019/20 Australian Capital Territory and South-Eastern New South Wales bushfires followed by COVID-19 on the mental health and wellbeing of pregnant women and mothers with newborn babies. Methods: All women who were pregnant, had given birth, or were within three months of conceiving during the 2019/2020 bushfires, lived within the catchment area, and provided consent were invited to participate. Those who consented were asked to complete three online surveys. Mental health was assessed with the DASS-21 and the WHO-5. Bushfire, smoke, and COVID-19 exposures were assessed by self-report. Cross-sectional associations between exposures and mental health measures were tested with hierarchical regression models. Results: Of the women who participated, and had minimum data (n = 919), most (>75%) reported at least one acute bushfire exposure and 63% reported severe smoke exposure. Compared to Australian norms, participants had higher depression (+12%), anxiety (+35%), and stress (+43%) scores. Women with greater exposure to bushfires/smoke but not COVID-19 had poorer scores on all mental health measures. Conclusions: These findings provide novel evidence that the mental health of pregnant women and mothers of newborn babies is vulnerable to major climate catastrophes such as bushfires.

Non-human primate (NHP) animal models are an integral part of the drug research and development process. For some biothreat pathogens, animal model challenge studies may offer the only possibility to evaluate medical countermeasure efficacy. A thorough understanding of host immune responses in such NHP models is therefore vital. However, applying antibody-based immune characterization techniques to NHP models requires extensive reagent development for species compatibility. In the case of studies involving high consequence pathogens, further optimization for use of inactivated samples may be required. Here, we describe the first optimized CO-Detection by indEXing (CODEX) multiplexed tissue imaging antibody panel for deep profiling of spatially resolved single-cell immune responses in rhesus macaques. This 21-marker panel is composed of a set of 18 antibodies that stratify major immune cell types along with a set three Ebola virus (EBOV)-specific antibodies. We validated these two sets of markers using immunohistochemistry and CODEX in fully inactivated Formalin-Fixed Paraffin-Embedded (FFPE) tissues from mock and EBOV challenged macaques respectively and provide an efficient framework for orthogonal validation of multiple antibody clones using CODEX multiplexed tissue imaging. We also provide the antibody clones and oligonucleotide tag sequences as a valuable resource for other researchers to recreate this reagent set for future studies of tissue immune responses to EBOV infection and other diseases.