Simulating Public Buses as a Mobile Platform for Deployment of Publicly Accessible Automated External Defibrillators.

Introduction: Public access defibrillation (PAD) programs seek to optimize locations of automated external defibrillators (AEDs) to minimize the time from out-of-hospital cardiac arrest (OHCA) recognition to defibrillation. Most PAD programs have focused on static AED (S-AED) locations in high traffic areas; pervasive electronic data infrastructure incorporating real-time geospatial data opens the possibility for AED deployment on mobile infrastructure for retrieval by nearby non-passengers. Performance characteristics of such systems are not known. Hypothesis: We hypothesized that publicly accessible AEDs located on buses would increase publicly accessible AED coverage and reduce AED retrieval time relative to statically located AEDs. Methods: S-AED sites in Pittsburgh, PA were identified and consolidated to 1 AED per building for analysis (n = 582). Public bus routes and schedules were obtained from the Port Authority of Allegheny County. OHCA locations and times were obtained from the Pittsburgh site of the Resuscitation Outcomes Consortium. Two simulations were conducted to assess the characteristics and impact of AEDs located on buses. In Simulation #1, geographic coverage area of AEDs located on buses (B-AEDs) was estimated using a 1/8th mile (201 m) retrieval radius during weekday, Saturday and Sunday periods. Cumulative geographic coverage across each period of the week was compared to S-AED coverage and the added coverage provided by B-AEDs was calculated. In Simulation #2, spatiotemporal event coverage was estimated for historical OHCA events, assuming constraints designed to reflect real world AED retrieval scenarios. Event coverage and AED retrieval time were compared between B-AEDs and S-AEDs across periods of the week and residential/nonresidential spatial areas. Results: Cumulative geographic coverage by S-AEDs was 23% across all periods, assuming uniform access hours. B-AEDs alone versus B-AEDs + S-AEDs covered 20% vs. 34% (weekday), 14% + 30% (Saturday), and 10% + 28% (Sunday). There was no statistically significant difference in 3-minute historical AED accessibility between only B-AEDs and only S-AEDs in standalone deployments (12% vs. 14%). However, when allowing for retrieval of either type of AED in the same scenario, event coverage was improved to 22% (p < 0.001). Conclusion: Deployment of B-AEDs may improve AED coverage but not as a standalone deployment strategy.

A Global Path Planner for Safe Navigation of Autonomous Vehicles in Uncertain Environments.

Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two components: sensor analytics and path finder. The sensor analytics component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level.

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.

Active and passive exploration for spatial knowledge acquisition: A meta-analysis.

Literature reported mixed evidence on whether active exploration benefits spatial knowledge acquisition over passive exploration. Active spatial learning typically involves at least physical control of one's movement or navigation decision-making, while passive participants merely observe during exploration. To quantify the effects of active exploration in learning large-scale, unfamiliar environments, we analysed previous findings with the multi-level meta-analytical model. Potential moderators were identified and examined for their contributions to the variability in effect sizes. Of the 128 effect sizes retrieved from 33 experiments, we observed a small to moderate advantage of active exploration over passive observation. Important moderators include gender composition, decision-making, types of spatial knowledge, and matched visual information. We discussed the implications of the results along with the limitations.

A straightforward cell culture insert model to incorporate biochemical and biophysical stromal properties into transplacental transport studies.

Introduction: The placental extracellular matrix (ECM) dynamically remodels over pregnancy and in disease. How these changes impact placental barrier function is poorly understood as there are limited in vitro models of the placenta with a modifiable stromal compartment to mechanistically investigate these extracellular factors. We developed a straightforward method to incorporate uniform hydrogels into standard cell culture inserts for transplacental transport studies. Methods: Uniform polyacrylamide (PAA) gels were polymerized within cell culture inserts by (re)using the insert packaging to create a closed, controllable environmental chamber. PAA pre-polymer solution was added dropwise via a syringe to the cell culture insert and the atmosphere was purged with an inert gas. Transport and cell culture studies were conducted to validate the model. Results: We successfully incorporated and ECM functionalized uniform PAA gels to cell culture inserts enable cell adhesion and monolayer formation. Imaging and analyte transport studies validated gel formation and expected mass transport results and successful cell studies confirmed cell viability, monolayer formation, and that the model could be used transplacental transport studies. Detailed methods and validation protocols are included. Discussion: It is well appreciated that ECM biophysical and biochemical properties impact cell phenotype and cell signaling in many tissues including the placenta. The incorporation of a PAA gel within a cell culture insert enables independent study of placental ECM biophysical and biochemical properties in the context of transplacental transport. These straightforward and low-cost methods to build three dimensional cellular models are readily adoptable by the wider scientific community.

Inflammation and immunological disarrays are associated with acute exercise in type 2 diabetes.

Objective: Type 2 diabetes (T2D) is the most common metabolic disorder that is associated with insulin resistance. The aim of the present study is to discover details of the molecular mechanism of exercise on control or progress of diabetic condition in patients via network analysis. Methods: Gene expression profiles of patients with T2D before and after doing exercise are retrieved from Gene Expression Omnibus (GEO) and are pre-evaluated by the GEO2R program. Data are studied based on expression values, regulatory relationships between the differentially expressed genes (DEGs), gene ontology analyses, and protein-protein interaction PPI network analysis. Results: A number of 118 significant DEGs were identified and classified based on fold change (FC) values as most dysregulated genes and dysregulated individuals. Action map analysis revealed that 18 DEGs appeared as the critical genes. Gene ontology analysis showed that 24 DEGs are connected to at least four pathways. JUN, IL6, IL1B, PTGS2, FOS, MYC, ATF3, CXCL8, EGR1, EGR2, NR4A1, PLK3, TTN, and UCP3 were identified as central DEGs. Conclusion: Finally; JUN, IL6, IL1B, PTGS2, FOS, ATF3, CXCL8, EGR1, and EGR2 were introduced as the critical targeted genes by exercise. Since the critical genes after exercise are upregulated and mostly are known as the risk factors of T2D, it can be concluded that unsuitable exercise can develop diabetic conditions in patients. Acute exercise-induced inflammation and immune disturbances seem to be associated with the development of T2D in patients.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Are accessible software accountable?: A commentary.

Accessible software is increasingly important as the incidence of disability continues to increase and the population ages globally, ensuring people are not left behind in the digital revolution. Likewise, there is increased interest in ensuring software is accountable such that it is clear about the information it uses and the actions that it takes. While there have been some agreed-upon definitions for accessible, interpretable, and transparent software, accountable software lacks a universal definition and methodology. We argue that for a software to be accountable, it must also be accessible, interpretable, and transparent, and provide a methodology for developing accountable software.

Regioselective sulfated chitosan produces a biocompatible and antibacterial wound dressing with low inflammatory response.

The aim of current study is to tailor chitosan derivate which is water-soluble while presents original biological features of chitosan. For this purpose, the 6-O chitosan sulfate (CS) with naked amine groups was synthesized via regioselective modification of chitosan (C) during which both crosslinking capacity and antibacterial properties of the C were remained intact. This was achieved by sulfation the C under controlled acidic conditions using chlorosulfonic acid/sulfuric acid mixture. Subsequently, a chemically crosslinked hydrogel of the CS was used as a wound dressing substrate. The modified sulfate groups retained the biocompatibility of C and showed antibacterial effects against gram-positive and gram-negative bacteria. In addition, the presence of sulfate groups in the CS chemical structure improved its anticoagulant activity compared to the unmodified C. Both in vitro and in vivo enzyme-linked immunosorbent assay (ELISA) measurements showed that CS had a higher potential to bind and scavenger anti-inflammatory cytokines, including IL-6 and transforming growth factor-ß (TGF-ß), both of which play critical roles in the early stage of the wound healing process. After treatment of full-thickness wounds with CS hydrogels, the macrophage cells (c.a. 6 × 104 cells) expressed significantly more M2 phenotype markers compared to the C group (4.2 × 104 cells). Furthermore, the CS hydrogel induced better re-epithelialization and vascularization of full-thickness wounds in mice compared to the C hydrogel during 30 days.

Immunoengineering Biomaterials in Cell-Based Therapy for Type 1 Diabetes.

Type 1 diabetes (T1D) is caused by low insulin production and chronic hyperglycemia due to destruction of pancreatic ß-cells. Cell transplantation is an attractive alternative approach compared to insulin injection. However, cell therapy has been limited by major challenges, including life-long requirement for immunosuppressive drugs to prevent host immune responses. Encapsulation of the transplanted cells can solve the problem of immune rejection, by providing a physical barrier between the transplanted cells and the recipient's immune cells. Despite current disputes in cell encapsulation approaches, thanks to recent advances in the fields of biomaterials and transplantation immunology, extensive effort has been dedicated to immunoengineering strategies, in combination with encapsulation technologies, to overcome the problem of host's immune responses. This review summarizes the most commonly used encapsulation and immunoengineering strategies combined with cell therapy, which have been applied as a novel approach to improve cell replacement therapies for management of T1D. Recent advances in the fields of biomaterial design, nanotechnology, as well as deeper knowledge about immune modulation had significantly improved cell encapsulation strategies. However, further progress requires combined application of novel immunoengineering approaches and islet/ß-cell transplantation. Impact statement Cell encapsulation shows promising potential in preventing host's immune responses and rejection of islets or ß-cells by providing a selectively permeable barrier between the host and the transplanted cells. Innovative materials, conformal nanocoatings, and immunomodulation have provided promising approaches in the field of encapsulation technology. Novel nanocarriers have been synthesized to release and deliver immunosuppressive agents to islets/ß-cells within the capsules in a controlled manner. The immunoengineering approach (immunosuppressive and immunomodulatory agents) could overcome the challenges of cell replacement therapy in type 1 diabetes.

Geographically-explicit Ecological Momentary Assessment (GEMA) Architecture and Components: Lessons Learned from PMOMS.

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively.

High-throughput modeling and analysis of protein structural dynamics.

Protein function is a dynamic property closely related to the conformational mechanisms of protein structure in its physiological environment. To understand and control the function of target proteins, it becomes increasingly important to develop methods and tools for predicting collective motions at the molecular level. In this article, we review computational methods for predicting conformational dynamics and discuss software tools for data analysis. In particular, we discuss a high-throughput, web-based system called iGNM for protein structural dynamics. iGNM contains a database of protein motions for more than 20 000 PDB structures and supports online calculations for newly deposited PDB structures or user-modified structures. iGNM allows dynamics analysis of protein structures ranging from enzymes to large complexes and assemblies, and enables the exploration of protein sequence-structure-dynamics-function relations.

Understanding Pregnancy and Postpartum Health Using Ecological Momentary Assessment and Mobile Technology: Protocol for the Postpartum Mothers Mobile Study.

BACKGROUND: There are significant racial disparities in pregnancy and postpartum health outcomes, including postpartum weight retention and cardiometabolic risk. These racial disparities are a result of a complex interplay between contextual, environmental, behavioral, and psychosocial factors. OBJECTIVE: This protocol provides a description of the development and infrastructure for the Postpartum Mothers Mobile Study (PMOMS), designed to better capture women's daily experiences and exposures from late pregnancy through 1 year postpartum. The primary aims of PMOMS are to understand the contextual, psychosocial, and behavioral factors contributing to racial disparities in postpartum weight and cardiometabolic health, with a focus on the daily experiences of stress and racism, as well as contextual forms of stress (eg, neighborhood stress and structural racism). METHODS: PMOMS is a longitudinal observation study that is ancillary to an existing randomized control trial, GDM2 (Comparison of Two Screening Strategies for Gestational Diabetes). PMOMS uses an efficient and cost-effective approach for recruitment by leveraging the infrastructure of GDM2, facilitating enrollment of participants while consolidating staff support from both studies. The primary data collection method is ecological momentary assessment (EMA) and through smart technology (ie, smartphones and scales). The development of the study includes: (1) the pilot phase and development of the smartphone app; (2) feedback and further development of the app including selection of key measures; and (3) implementation, recruitment, and retention. RESULTS: PMOMS aims to recruit 350 participants during pregnancy, to be followed through the first year after delivery. Recruitment and data collection started in December 2017 and are expected to continue through September 2020. Initial results are expected in December 2020. As of early May 2019, PMOMS recruited a total of 305 participants. Key strengths and features of PMOMS have included data collection via smartphone technology to reduce the burden of multiple on-site visits, low attrition rate because of participation in an ongoing trial in which women are already motivated and enrolled, high EMA survey completion and the use of EMA as a unique data collection method to understand daily experiences, and shorter than expected timeframe for enrollment because of the infrastructure of the GDM2 trial. CONCLUSIONS: This protocol outlines the development of the PMOMS, one of the first published studies to use an ongoing EMA and mobile technology protocol during pregnancy and throughout 1 year postpartum to understand the health of childbearing populations and enduring racial disparities in postpartum weight and cardiometabolic health. Our findings will contribute to the improvement of data collection methods, particularly the role of EMA in capturing multiple exposures and knowledge in real time. Furthermore, the results of the study will inform future studies investigating weight and cardiometabolic health during pregnancy and the postpartum period, including how social determinants produce population disparities in these outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13569.

TMpro web server and web service: transmembrane helix prediction through amino acid property analysis.

UNLABELLED: TMpro is a transmembrane (TM) helix prediction algorithm that uses language processing methodology for TM segment identification. It is primarily based on the analysis of statistical distributions of properties of amino acids in transmembrane segments. This article describes the availability of TMpro on the internet via a web interface. The key features of the interface are: (i) output is generated in multiple formats including a user-interactive graphical chart which allows comparison of TMpro predicted segment locations with other labeled segments input by the user, such as predictions from other methods. (ii) Up to 5000 sequences can be submitted at a time for prediction. (iii) TMpro is available as a web server and is published as a web service so that the method can be accessed by users as well as other services depending on the need for data integration. AVAILABILITY: http://linzer.blm.cs.cmu.edu/tmpro/ (web server and help), http://blm.sis.pitt.edu:8080/axis/services/TMProFetcherService (web service).

oGNM: online computation of structural dynamics using the Gaussian Network Model.

An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperative fluctuations accessible under native state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5-6 orders of magnitude faster than those using conventional normal mode analyses. Two cases studies illustrate the utility of oGNM. The first shows that the thermal fluctuations predicted for 1250 non-homologous proteins correlate well with X-ray crystallographic data over a broad range [7.3-15 A] of inter-residue interaction cutoff distances and the correlations improve with increasing observation temperatures. The second study, focused on 64 oligonucleotides and oligonucleotide-protein complexes, shows that good agreement with experiments is achieved by representing each nucleotide by three GNM nodes (as opposed to one-node-per-residue in proteins) along with uniform interaction ranges for all components of the complexes. These results open the way to a rapid assessment of the dynamics of DNA/RNA-containing complexes. The server can be accessed at http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm.

Using phone sensors and an artificial neural network to detect gait changes during drinking episodes in the natural environment.

BACKGROUND: Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC). METHODS: Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends. We collected 3-axis accelerometer, gyroscope, and magnetometer data from phone sensors, and computed 24 gait-related features using a sliding window technique. eBAC levels were calculated at each time point based on Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial neural network model to analyze associations between sensor features and eBACs in training (70% of the data) and validation and test (30% of the data) datasets. RESULTS: We analyzed 128 data points where both eBAC and gait-related sensor data were captured, either when not drinking (n=60), while eBAC was ascending (n=55) or eBAC was descending (n=13). 21 data points were captured at times when the eBAC was greater than the legal limit (0.08mg/dl). Using a Bayesian regularized neural network, gait-related phone sensor features showed a high correlation with eBAC (Pearson's r>0.9), and >95% of estimated eBAC would fall between -0.012 and +0.012 of actual eBAC. CONCLUSIONS: It is feasible to collect gait-related data from smartphone sensors during drinking occasions in the natural environment. Sensor-based features can be used to infer gait changes associated with elevated blood alcohol content.

Interactive, mobile, AGIle and novel education (IMAGINE): a conceptual framework to support students with mobility challenges in higher education.

Interactive, mobile, AGIle and novel education (IMAGINE) is a conceptual framework to help students with disabilities (SwD) participate more in the physical space and become more engaged in school. IMAGINE recommends and reminds students, and allows them to make requests of key learning resources (LRs). The goal of IMAGINE is to provide SwD with the location and time for attending a LR that is most optimal with respect to their learning style and preference, learning performance and other activities. IMAGINE will be a means through which SWD will be provided with tailored recommendations with respect to their daily activities to improve learning outcomes. A pilot was conducted with SwD who used IMAGINE's navigation and wayfinding functionality, and the subjects reported that it aligns well with their needs. Preliminary results suggest that after completing a training and using the tool, SwD reported that they are more likely to use the tool and their participation may increase as a result. In contrast to before the trial, the SwD were also able to better describe the tool's benefits and how to improve its functionality after using the tool for four weeks. Implications for Rehabilitation The IMAGINE tool may be a means through which SwD can be provided with tailored recommendations with respect to their daily activities to improve learning outcomes. PWD should be involved (as research study participants and research study team members) in the design and development of tools like IMAGINE to improve participation. IMAGINE and similar tools may not only encourage better learning outcomes, but also more physical participation in the community, and could be used across education and employment settings.

Adapting the Index of Relative Rurality (IRR) to Estimate Rurality at the ZIP Code Level: A Rural Classification System in Health Services Research.

BACKGROUND: Accurate analysis of health problems facing rural residents depends on how rurality is defined. Health services research relies frequently on the rural urban commuting area (RUCA) codes to estimate rurality at the small area level. We modified the county-level Index of Relative Rurality (IRR) to the ZIP code level (IRRZIP ) to create an alternative small-area-level rural classification system. We then compared how the 2 rural classification systems differ in how rural areas and populations are defined and in methodological analysis. METHODS: We linked data for veterans (n = 37,466) who attended the VA Pittsburgh Healthcare System to 2000 United States Census and the US Department of Agriculture's Economic Research Service data. RESULTS: The RUCA and the IRRZIP do not consistently classify the same ZIP code areas and populations as rural. Using the IRRZIP , each 10th increment in increased rurality was associated with a 2.6 increased odds of receiving primary care at a satellite clinic. CONCLUSIONS: The IRRZIP is a straightforward measure that is easy to use and interpret and may be a relevant alternative rural classification system that can be used in health services research.

Understanding route choices for wheelchair navigation.

PURPOSE: To validate a personalized routing technique with wheelchair users, understand their route choices and acquire their feedback on the necessity of wheelchair navigation and the importance of personalized routes. METHOD: A routing technique using a weighting method, called Absolute Restriction Method (ARM), was employed to compute personalized routes based on users' routing preferences. The evaluation involves five manual wheelchair users. The study protocol consists of three sessions: pre-activity, activity and post-activity sessions. The evaluation included a comparison between personalized routes and shortest feasible routes, in terms of route characteristics and users' ratings of important parameters. RESULTS: Subjects travelled a 14.64% longer distance along the personalized routes than the shortest feasible routes. However, all personalized routes had better path quality (slope and surface condition) than the shortest feasible routes. Four out of five subjects rated the parameters they deemed most important higher for the personalized route than for the shortest feasible route. CONCLUSIONS: The study confirmed that the shortest route criterion is not always suitable for individuals with mobility impairments. Personalized routes that take into account individual characteristics, route preferences and environmental characteristics are a promising solution to lessen the difficulties that manual wheelchair users face when navigating unfamiliar environments. IMPLICATIONS FOR REHABILITATION: Wheelchair users indicate the importance of personalized routes for individuals with mobility impairments. In regard to evaluation results, although subjects travelled 14.64% more distances in average along the personalized routes than the shortest feasible routes, they rated the personalized routes better path quality and less effort to travel.

Childhood abuse affects emotional closeness with family in mid- and later life.

OBJECTIVE: Knowledge about the effects of early life adversity on kin relationships in later years is sparse. The purpose of this study was to examine if childhood abuse and adversity negatively influences emotional closeness with family in mid- and later life. A second goal was to determine the role of psychosocial resources and personality traits in buffering the effects of early adversities. Gender and cohort differences were explored to see if men were differentially affected than women and whether middle-aged adults (35-49 years old) were differentially affected than older adults (50-74 years old) by the effects of childhood abuse and adversity. METHODS: Using retrospective accounts of early family abuse and adversities of 1,266 middle aged adults and 1,219 older adults from a large population-based survey, the National Survey of Midlife Development in United States (MIDUS), separate multiple regression analyses were conducted for the two cohorts to examine the effects of childhood emotional and physical abuse and family adversities on perceived emotional closeness with family. Interaction effects between childhood abuse and adversity (e.g., being expelled from school, death of sibling, parental divorce, losing a home to a natural disaster) with psychosocial resources (perceived control and self acceptance), personality characteristics (extraversion and neuroticism), and gender were examined. RESULTS: Results of OLS regressions suggest emotional and physical abuse predicted family closeness in middle-aged adults. Conversely, only emotional abuse predicted family closeness in older adults. Moderation models revealed that high levels of self acceptance were associated with better maintenance of emotional closeness among middle-aged adults who were emotionally and physically abused as children. Older adults with lower extraversion who experienced emotional abuse or reported greater number of adversities in childhood were found to be at higher risk for lower emotional closeness with family. Early life adversities were more detrimental for women. CONCLUSIONS: Findings suggest that the aftermath of childhood abuse does not dissipate with time, but continues to influence family relationships in mid- and later life. Identifying the links between childhood adversities and adult relationships can help identify strategic points for intervention to reduce the long-term effects of accumulated adverse experiences over the life course.