The Relationship Among Probable SARCopenia, Osteoporosis and SuprasPinatus Tendon Tears in Postmenopausal Women: The SARCOSP Study.

We aimed to investigate the relationship among probable sarcopenia, osteoporosis (OP) and supraspinatus tendon (SSP) tears in postmenopausal women. Postmenopausal women screened/followed for OP were recruited. Demographic data, comorbidities, exercise/smoking status, and handgrip strength values were recorded. Probable sarcopenia was diagnosed as handgrip strength values < 20 kg. Achilles and SSP thicknesses were measured using ultrasound. Among 1443 postmenopausal women, 268 (18.6%) subjects had SSP tears. Unilateral tears were on the dominant side in 146 (10.1%) and on the non-dominant side in 55 women (3.8%). In contrast to those without, women with SSP tears had older age, lower level of education, thinner SSP and lower grip strength (all p < 0.05). In addition, they had higher frequencies of hypertension, hyperlipidemia, DM, OP and probable sarcopenia, but lower exercise frequency (all p < 0.05). Binary logistic regression modeling revealed that age [odds ratio (OR): 1.046 (1.024-1.067 95% CI)], hypertension [OR: 1.560 (1.145-2.124 95% CI)], OP [OR: 1.371 (1.022-1.839 95% CI)] and probable sarcopenia [OR: 1.386 (1.031-1.861 95% CI)] were significant predictors for SSP tears (all p < 0.05). This study showed that age, presence of hypertension, probable sarcopenia and OP were related with SSP tears in postmenopausal women. To this end, although OP appeared to be related to SSP tears, SSP tear/thickness evaluation can be recommended for OP patients, especially those who have other risk factors such as older age, higher BMI, hypertension, and probable sarcopenia.

The relationship between sarcopenia-related measurements and osteoporosis: The SARCOP study.

As muscle and bone are closely-related, we have explored the association between sarcopenia-related measurements and bone mineral density (BMD) (and osteoporosis) in postmenopausal women. Grip strength, anterior thigh muscle thickness and chair stand test were found to be related with BMD. Additionally, grip strength < 22 kg increased the odds ratio of osteoporosis 1.6 times. INTRODUCTION: As muscle and bone are two closely related tissues, we aimed to investigate the association between sarcopenia-related measurements (i.e., sonographic anterior thigh muscle thickness, grip strength, chair stand test (CST), gait speed) and clinical factors, lumbar/femoral BMD, and the presence of osteoporosis (OP) in postmenopausal women. METHODS: Community dwelling postmenopausal women from two physical and rehabilitation medicine outpatient clinics were consecutively included in this cross-sectional study. Demographic data, age, weight, height, education/exercise status, smoking, and comorbidities were registered. BMD measurements were performed from lumbar vertebrae (L1-4) and femoral neck using dual energy X-ray absorptiometry (DXA). A T-score of ≤ -2.5 SD in the lumbar vertebrae (L1-L4) and/or femoral neck was accepted as OP. Anterior thigh muscle thickness (MT) at the midthigh level was measured sonographically using a linear probe. Grip strength was measured from the dominant side. Physical performance was assessed by CST and gait speed. RESULTS: Among 546 postmenopausal women, 222 (40.7%) had OP. Among sarcopenia-related parameters, grip strength and anterior thigh MT were positively associated with lumbar vertebral BMD. CST performance was positively associated with femoral neck BMD. After adjusting for confounding factors, low grip strength (< 22 kg) increased 1.6 times the risk of OP. CONCLUSION: Loss of muscle mass/function (i.e., sarcopenia) can coexist with loss of trabecular and cortical bone. To this end, grip strength and anterior thigh MT seem to be associated with the lumbar vertebral BMD, while CST is associated with the femoral neck BMD. Lastly, low grip strength might have an association with postmenopausal OP.

Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods.

OBJECTIVES: Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity. METHODS: In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey. RESULTS: Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria (DIC) scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error (MAE) as a comparison criteria. CONCLUSION: Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.

Data analysis and modeling pipelines for controlled networked social science experiments.

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

Protective effects of a Mediterranean-like dietary pattern on obesity, abdominal obesity and large neck circumference in a cohort of Turkish children aged 6-9 years.

BACKGROUND AND OBJECTIVES: This study evaluated the association of the Mediterranean Diet Quality Index (KIDMED index) with neck circumference, obesity, and abdominal obesity in a national sample of Turkish children aged 6-9 years. It was hypothesized that the Mediterranean diet has beneficial effects on BMI and waist and neck circumferences in children. METHODS AND STUDY DESIGN: The study sample consisted of 900 children aged 6-9 years recruited from seven primary schools in Ünye District in Ordu, Turkey. Anthropometric data were collected. The KIDMED score was used to assess the children's adherence to the Mediterranean diet. RESULTS: Compared with girls, a higher percentage of boys were obese (27.9% vs 22.2%), and the KIDMED score differed between boys (4.89±2.0) and girls (5.27±2.48; p<0.05). Only 18.7% of children had optimal KIDMED scores, and 35.7% had poor KIDMED scores. Compared with boys and girls with medium and optimal KIDMED scores, boys and girls with poor KIDMED scores had higher body weight, height, BMI, and waist and neck circumferences (p<0.05). KIDMED scores of overweight boys and girls were higher than those of obese boys and girls (p<0.05). An inverse correlation was found between KIDMED scores and body weight, BMI, and waist and neck circumferences. CONCLUSIONS: The Mediterranean dietary pattern is significantly associated with improved anthropometric measurements in children. These results demonstrate that providing a diet similar to the Mediterranean diet can prevent obesity and related disorders in children.

Opportunities to observe and measure intangible inputs to innovation: Definitions, operationalization, and examples.

Measuring the value of intangibles is not easy, because they are critical but usually invisible components of the innovation process. Today, access to nonsurvey data sources, such as administrative data and repositories captured on web pages, opens opportunities to create intangibles based on new sources of information and capture intangible innovations in new ways. Intangibles include ownership of innovative property and human resources that make a company unique but are currently unmeasured. For example, intangibles represent the value of a company's databases and software, the tacit knowledge of their workers, and the investments in research and development (R&D) and design. Through two case studies, the challenges and processes to both create and measure intangibles are presented using a data science framework that outlines processes to discover, acquire, profile, clean, link, explore the fitness-for-use, and statistically analyze the data. The first case study shows that creating organizational innovation is possible by linking administrative data across business processes in a Fortune 500 company. The motivation for this research is to develop company processes capable of synchronizing their supply chain end to end while capturing dynamics that can alter the inventory, profits, and service balance. The second example shows the feasibility of measurement of innovation related to the characteristics of open source software through data scraped from software repositories that provide this information. The ultimate goal is to develop accurate and repeatable measures to estimate the value of nonbusiness sector open source software to the economy. This early work shows the feasibility of these approaches.

Human Trophoblast Progenitor Cells Express and Release Angiogenic Factors.

Trophoblast stem cells develop from polar trophectoderm and give rise to the cells that generate the placenta. Trophoblast cells are responsible for the uterine invasion and vascular remodeling during the implantation of the embryo. However this knowledge is not yet to be confirmed for trophoblast progenitor cells (TPCs). In this study, we aimed to demonstrate that human TPCs (hTPCs) express and release angiogenic factors for the first time. TPCs were isolated from the term placenta. Then immunophenotyping was performed by FACS method by analyzing caudal type homeobox 2 (CDX2) and eomesodermin (EOMES). Immunofluorescence staining of CDX2 and EOMES was performed on these cells. Lastly, angiogenesis-related proteins were detected by western blot and ELISA assays. The isolated cells were positive for trophoblast stem cell markers CDX2 and EOMES in 92.5% and 92.7% of cells, respectively showing the characteristics of TPCs. The investigation of vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor 1 (VEGFR1), and vascular endothelial growth factor receptor 2 (VEGFR2) at protein and mRNA level in comparison with human umbilical vein endothelial cells (HUVECs), revealed that human TPCs (hTPCs) have higher levels of VEGF and VEGFR1 transcripts. Additionally soluble forms of VEGF and VEGFR1 were detected in supernatants of hTPCs. With this information, TPCs seem to be promising for regenerative cell therapies by increasing angiogenesis.

Multi-Source Models for Civil Unrest Forecasting.

Civil unrest events (protests, strikes, and "occupy" events) range from small, nonviolent protests that address specific issues to events that turn into large-scale riots. Detecting and forecasting these events is of key interest to social scientists and policy makers because they can lead to significant societal and cultural changes. We forecast civil unrest events in six countries in Latin America on a daily basis, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report (GSR), which is compiled by an independent group of social scientists and subject matter experts. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets. The experimental results, measured by F1-scores, are in the range 0.68 to 0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our method. The ablation study demonstrates the relative value of data sources for prediction. We find that social media and news are more informative than other data sources, including the political event databases, and enhance the prediction performance. However, social media increases the variation in the performance metrics.

Forecasting Social Unrest Using Activity Cascades.

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.