Rebuilding Shattered Worlds

Rebuilding Shattered Worlds explores the ways a demolished neighborhood in Easton, Pennsylvania, still resonates in the imaginations of displaced residents. Drawing on six years of ethnographic research, the authors highlight the intersecting languages of blight, race, and place as elderly interlocutors attempt to make sense of the world they lost when urban renewal initiatives razed “Syrian Town”—a densely packed neighborhood of Lebanese American, Italian American, and African American residents.This ethnography of remembering shows how former residents engage collective memory-making through their shared place, language, and class position within the larger cityscape. Demonstrating the creative power of linguistic resources, material traces, and absent spaces, Rebuilding Shattered Worlds brings together insights from linguistic anthropology and material studies, foregrounding the role language plays in signaling “pastness.

Scoring Model Predictions using Cross-Validation

We formalize a framework for quantitatively assessing agreement between two datasets that are assumed to come from two distinct data generating mechanisms. We propose a methodology for prediction scoring which provides a measure of the distance between two unobserved data generating mechanisms (DGMs), along the dimension of a particular model. The cross-validated scores can be used to evaluate preregistered hypotheses and to perform model validation in the face of complex statistical models. Using human behavior data from the Next Generation Social Science (NGS2) program, we demonstrate that prediction scores can be used as model assessment tools and that they can reveal insights based on data collected from different populations and across different settings. Our proposed cross-validated prediction scores are capable of quantifying true differences between data generating mechanisms, allow for the validation and assessment of complex models, and serve as valuable tools for reproducible research

Sustainability of Free/Libre Open Source Projects: A Longitudinal Study

This paper examines the factors that influence the long-term sustainability of FLOSS projects. A model of project sustainability based on organizational ecology is developed and tested empirically. Data about activity and contribution patterns over the course of five years for 2,772 projects registered with SourceForge is analyzed. Our results suggest that the size of the project’s development base, project age and the size of niche occupied by the project are positively related to the project’s ability to attract user and/or developer resources. The ability to attract resources is an indicator of the perceived project legitimacy, which in turn is a strong predictor of the project’s future sustainability. Thus a project’s ability to attract developer and user resources is shown to play a mediating role between the demographic (size and age) and ecological (niche) characteristics of the project and its future sustainability. Our results support the applicability of tenets of organizational ecology related to the liability of smallness, the liability of newness, and population characteristics (niche size) to the FLOSS development environment. The implications of the results for future research and practice are discussed

Differential gene expression in multiple neurological, inflammatory and connective tissue pathways in a spontaneous model of human small vessel stroke

Aims: Cerebral small vessel disease (SVD) causes a fifth of all strokes plus diffuse brain damage leading to cognitive decline, physical disabilities and dementia. The aetiology and pathogenesis of SVD are unknown, but largely attributed to hypertension or microatheroma. Methods: We used the spontaneously hypertensive stroke-prone rat (SHRSP), the closest spontaneous experimental model of human SVD, and age-matched control rats kept under identical, non-salt-loaded conditions, to perform a blinded analysis of mRNA microarray, qRT-PCRand pathway analysis in two brain regions (frontal and midcoronal) commonly affected by SVD in the SHRSP at age five, 16 and 21 weeks. Results: We found gene expression abnormalities, with fold changes ranging from 2.5 to 59 for the 10 most differentially expressed genes, related to endothelial tight junctions (reduced), nitric oxide bioavailability (reduced), myelination (impaired), glial and microglial activity (increased), matrix proteins (impaired), vascular reactivity (impaired) and albumin (reduced), consistent with protein expression defects in the same rats. All were present at age 5 weeks thus pre-dating blood pressure elevation. ‘Neurological’ and ‘inflammatory’ pathways were more affected than ‘vascular’ functional pathways. Conclusions: This set of defects, although individually modest, when acting in combination could explain the SHRSP's susceptibility to microvascular and brain injury, compared with control rats. Similar combined, individually modest, but multiple neurovascular unit defects, could explain susceptibility to spontaneous human SVD

Assessment of Cancer Survivors’ Experiences of Using a Publicly Available Physical Activity Mobile Application

Background: Regular participation in physical activity (PA) is associated with improved physical and psychosocial outcomes in cancer survivors. However, PA levels are low during and after cancer treatment. Interventions to promote PA in this population are needed. PA mobile apps are popular and have potential to increase PA participation, but little is known about how appropriate or relevant they are for cancer survivors. Objective: This study aims to (1) assess recruitment, study uptake, and engagement for a publicly available PA mobile app (GAINFitness) intervention in cancer survivors; (2) assess cancer survivors’ attitudes towards the app; (3) understand how the app could be adapted to better meet the needs of cancer survivors; and (4) to determine the potential for change in PA participation and psychosocial outcomes over a 6-week period of using the app. Methods: The present study was a one-arm, pre-post design. Cancer survivors (N=11) aged 33 to 62 years with a mean (SD) age of 45 (9.4), and 82% (9/11) female, were recruited (via community/online convenience sampling to use the app for 6 weeks). Engagement with the app was measured using self-reported frequency and duration of usage. Qualitative semi-structured telephone interviews were conducted after the 6-week study period and were analyzed using thematic analysis. PA, well-being, fatigue, quality of life (QOL), sleep quality, and anxiety and depression were self-reported at baseline and at a 6-week follow-up using the Godin Leisure Time Exercise Questionnaire (GLTEQ), the Functional Assessment of Cancer Therapy-General (FACT-G), the Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue Scale Questionnaire, the Health and Quality of Life Outcomes (EQ5D) Questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Hospital Anxiety and Depression Scale (HADS), respectively. Results: Of the people who responded to the study advertisement, 73% (16/22) agreed to participate and 100% (11/11) of the participants who started the study completed all baseline and follow-up outcome measures and the telephone interview. On average, participants used the app twice a week for 25 minutes per session. Four themes were identified from the qualitative interviews surrounding the suitability of the app for cancer survivors and how it could be adapted: (1) barriers to PA, (2) receiving advice about PA from reliable sources, (3) tailoring the application to one’s lifestyle, and (4) receiving social support from others. Pre-post comparison showed significant increases in strenuous PA, improvements in sleep quality, and reductions in mild PA. There were no significant changes in moderate PA or other psychosocial outcomes. Conclusions: All participants engaged with the app and qualitative interviews highlighted that the app was well-received. A generic PA mobile app could bring about positive improvements in PA participation and psychosocial outcomes among cancer survivors. However, a targeted PA app aimed specifically towards cancer survivors may increase the relevance and suitability of the app for this population

School-Based Nutrition and Physical Activity Program for Rural Elementary School Students: A Pilot Study

Children who eat unhealthy diets and engage in limited physical activity are susceptible to adverse health effects, such as obesity. This pilot intervention study examined the immediate impact of a health education program, Get Charmed, which used a short-run incentive program as a strategy for motivating rural elementary school students to learn about physical activity and healthy eating behaviors. We assessed kindergarten through fifth grade students’ knowledge of physical activity, healthy eating, and water consumption, at baseline and immediately following the intervention. Get Charmed is a six-week program geared toward elementary-aged children, with aims to increase participants’ knowledge and awareness around healthier lifestyle behaviors. A pre-post evaluation assessed knowledge about healthy eating, physical activity, and hydration among elementary school-aged children (n = 22) enrolled in grades k-5. Frequencies were calculated for the number of correct responses for each item. A series of Wilcoxon signed rank tests were performed to assess changes in knowledge from baseline to post-test. Average knowledge scores for the 3rd-5th grade students increased from 15.56 (± 1.88) to 16.78 (± 1.20), which was statistically significant (z = -2.41, p = 0.016). Average baseline knowledge for the kindergarten to 2nd grade students increased from 9.54 (± 1.66) to 10.46 (± 0.66). For the kindergarten to 2nd grade students, a statistically significant proportion of participants (six out of 13 participants) increased knowledge (z = -1.98, p = 0.048). Implementing Get Charmed with short-run incentives in rural school-based settings is a practical and economical approach to introducing new foods while increasing rural elementary students’ knowledge in the areas of physical activity, nutrition, and hydration

Toward a taxonomy of trust for probabilistic machine learning

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (i) in the translation of real-world goals to goals on a particular set of training data, (ii) in the translation of abstract goals on the training data to a concrete mathematical problem, (iii) in the use of an algorithm to solve the stated mathematical problem, and (iv) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights not only steps where existing research work on trust tends to concentrate and but also steps where building trust is particularly challenging