Developing a Novel Measure of Body Satisfaction Using Virtual Reality

<div><p>Body image disturbance (BID), considered a key feature in eating disorders, is a pervasive issue among young women. Accurate assessment of BID is critical, but the field is currently limited to self-report assessment methods. In the present study, we build upon existing research, and explore the utility of virtual reality (VR) to elicit and detect changes in BID across various immersive virtual environments. College-aged women with elevated weight and shape concerns (<i>n</i> = 38) and a non-weight and shape concerned control group (<i>n</i> = 40) were randomly exposed to four distinct virtual environments with high or low levels of body salience and social presence (i.e., presence of virtual others). Participants interacted with avatars of thin, normal weight, and overweight body size (BMI of approximately 18, 22, and 27 respectively) in virtual social settings (i.e., beach, party). We measured state-level body satisfaction (state BD) immediately after exposure to each environment. In addition, we measured participants’ minimum interpersonal distance, visual attention, and approach preference toward avatars of each size. Women with higher baseline BID reported significantly higher state BD in all settings compared to controls. Both groups reported significantly higher state BD in a beach with avatars as compared to other environments. In addition, women with elevated BID approached closer to normal weight avatars and looked longer at thin avatars compared to women in the control group. Our findings indicate that VR may serve as a novel tool for measuring state-level BID, with applications for measuring treatment outcomes. Implications for future research and clinical interventions are discussed.</p></div

Antarctic Seasonal Pressure Reconstructions 1905-2013

<div><h3>Overview:</h3><p>This project created seasonal reconstructions for many of the long-term Antarctic station records, in order to understand better the relative roles of natural variability and change during the 20th Century. Using midlatitude pressure records that were significantly correlated to the individual station being reconstructed, a principal component regression reconstruction technique was employed. The records were extended back to 1905 for all locations, and several different approaches were attempted:</p><li>Reconstructions based on groups of midlatitude predictor stations that were correlated at <i>p</i><0.05 and <i>p</i><0.10, termed the 5% and 10% networks, respectively;</li><li>Reconstructions based on detrended and original predictor and predictand seasonal pressure data;</li><li>Reconstructions with predictor and predictand data ending in 2011 vs. 2013;</li><li>Reconstructions calibrated over 1957-2011 (or 2013, whichever the ending year is), and validated using a leave-one-out cross validation procedure, termed the 'full period' reconstructions;</li><li>Reconstructions calibrated during the first 30 years (1957-1986) and validated over the last 25-27 years (1987-2011 or 1987-2013), termed the 'early' reconstructions;</li><li>Reconstructions calibrated during last 30-32 years (1982-2011 or 1982-2013) and validated over the first 25 years (1957-1981), termed the 'late' period reconstructions;</li><li>Reconstructions using all of the above mentioned methods with now incorporating in reanalysis data from HadSLP2 and NOAA 20CR, termed the 'pseudo' reconstructions.</li><br><b>NOTE:</b> Any reconstructions termed 'original' reconstructions are any reconstructions not using 'pseudo' data. Reconstructions using 'pseudo' data from reanalysis products are termed 'pseudo' reconstructions. <br><br>We provide here all the reconstruction data for each station (which can be accessed by downloading the data attached), including the best overall reconstructions for all stations.<p></p><b></b><p><b>Acknowledgments:</b> <br>This work is supported by funding from the National Science Foundation, through the <a href="http://www.nsf.gov/awardsearch/showAward?AWD_ID=1341621&HistoricalAwards=false" target="_blank">Antarctic Oceanic and Atmospheric Sciences award PLR-1341621</a></p><b><p>Relevant Publications:</p></b><p>For further information on the <b><u>reconstruction methodology</u></b>, please see the <a href="http://polarmet.osu.edu/ACD/sam/sam_recon.html" target="_blank">seasonal SAM index reconstructions</a>, or the following publications:</p><li>Jones, J. M., R. L. Fogt, M. Widmann, G. J. Marshall, P. D. Jones, and M. Visbeck, 2009: Historical SAM Variability. Part I: Century length seasonal reconstructions. <i>J. Climate</i>, <b>22</b>, 5319-5345, doi: 10.1175/2009JCLI2785.1</li><li>Fogt, R. L., J. Perlwitz, A. J. Monaghan, D. H. Bromwich, J. M. Jones, and G. J. Marshall, 2009: Historical SAM Variability. Part II: 20th century variability and trends from reconstructions, observations, and the IPCC AR4 Models. <i>J. Climate</i>, <b>22</b>, 5346-5365, doi: 10.1175/2009JCLI2786.1<br><br></li>For details on the <b><u>Antarctic station-based pressure reconstructions</u></b>, please see the following publications:<li>Fogt, R. L., C. A. Goergens, M. E. Jones, G. A. Witte, M. Y. Lee, and J. M. Jones, 2016: Antarctic station-based pressure reconstructions since 1905: 1. Reconstruction evaluation. <i>J. Geophysical Res.-Atmospheres</i>, <b>21</b>, 2814-2835, doi:10.1002/2015JD024564.  <a href="http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015JD024564/full" target="_blank">Access here from Wiley online library</a></li><li>Fogt, R. L., J. M. Jones, C. A. Goergens, M. E. Jones, G. A. Witte, and M. Y. Lee, 2016: Antarctic station-based pressure reconstructions since 1905: 2. Variability and trends during the twentieth century. <i>J. Geophysical Res.-Atmospheres</i>, <b>21</b>, 2836-2856, doi:10.1002/2015JD024565.  <a href="http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015JD024565/full" target="_blank">Access here from Wiley online library</a><p></p><b></b><p><b>Contacts:</b> <br>For additional information, please feel free to email <i>Dr. Ryan L. Fogt</i> (<a href="mailto:[email protected]">[email protected]</a>)</p><hr><p><b>RECONSTRUCTION PERFORMANCE</b><br>The evaluation statistics for the best performing original reconstructions for all the 'full period' reconstructions are summarized in the tables below. Full details on the length of the records (both for midlatitude and Antarctic stations reconstructed) and other skill measures can be found in <a href="http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015JD024564/full" target="_blank">Fogt et al. 2016</a>.</p><p><b></b></p><p><b>December-January-February (DJF)</b></p><p></p><table><tbody><tr><th>Stations</th><th>Calibration Correlation</th><th>Validation Correlation</th><th>Reduction of Error</th><th>Coefficient of Efficiency</th></tr><tr><td>Amundsen-Scott<br>Bellingshausen<br>Byrd<br>Casey<br>Davis<br>Dumont<br>Esperanza<br>Faraday<br>Halley<br>Marambio<br>Marsh / O'Higgins<br>Mawson<br>McMurdo / Scott Base<br>Mirny<br>Novolazarevskaya<br>Rothera<br>Syowa<br>Vostok<br></td><td>0.859<br>0.830<br>0.826<br>0.794<br>0.754<br>0.816<br>0.909<br>0.899<br>0.923<br>0.760<br>0.819<br>0.885<br>0.872<br>0.842<br>0.873<br>0.886<br>0.773<br>0.832<br></td><td>0.790<br>0.733<br>0.732<br>0.746<br>0.660<br>0.779<br>0.813<br>0.820<br>0.890<br>0.637<br>0.725<br>0.813<br>0.824<br>0.737<br>0.843<br>0.805<br>0.710<br>0.774<br></td><td>0.737<br>0.761<br>0.745<br>0.749<br>0.765<br>0.750<br>0.826<br>0.808<br>0.852<br>0.742<br>0.743<br>0.783<br>0.760<br>0.709<br>0.780<br>0.798<br>0.671<br>0.792<br></td><td>0.615<br>0.652<br>0.617<br>0.675<br>0.647<br>0.685<br>0.652<br>0.665<br>0.789<br>0.659<br>0.635<br>0.655<br>0.674<br>0.528<br>0.729<br>0.652<br>0.598<br>0.702<br></td></tr></tbody></table><b><p>March-April-May (MAM)</p></b><p></p><table><tbody><tr><th>Stations</th><th>Calibration Correlation</th><th>Validation Correlation</th><th>Reduction of Error</th><th>Coefficient of Efficiency</th></tr><tr><td>Amundsen-Scott<br>Bellingshausen<br>Byrd<br>Casey<br>Davis<br>Dumont<br>Esperanza<br>Faraday<br>Halley<br>Marambio<br>Marsh / O'Higgins<br>Mawson<br>McMurdo / Scott Base<br>Mirny<br>Novolazarevskaya<br>Rothera<br>Syowa<br>Vostok<br></td><td>0.721<br>0.853<br>0.668<br>0.559<br>0.738<br>0.660<br>0.785<br>0.819<br>0.608<br>0.725<br>0.719<br>0.742<br>0.678<br>0.717<br>0.779<br>0.699<br>0.719<br>0.660<br></td><td>0.678<br>0.818<br>0.603<br>0.486<br>0.660<br>0.606<br>0.748<br>0.778<br>0.529<br>0.670<br>0.770<br>0.671<br>0.635<br>0.677<br>0.732<br>0.635<br>0.638<br>0.609<br></td><td>0.520<br>0.739<br>0.473<br>0.313<br>0.554<br>0.441<br>0.615<br>0.672<br>0.369<br>0.637<br>0.565<br>0.551<br>0.459<br>0.514<br>0.627<br>0.503<br>0.545<br>0.464<br></td><td>0.456<br>0.682<br>0.385<br>0.222<br>0.438<br>0.353<br>0.557<br>0.601<br>0.269<br>0.586<br>0.559<br>0.438<br>0.401<br>0.456<br>0.570<br>0.411<br>0.430<br>0.409<br></td></tr></tbody></table><b><p>June-July-August (JJA)</p></b><p></p><table><tbody><tr><th>Stations</th><th>Calibration Correlation</th><th>Validation Correlation</th><th>Reduction of Error</th><th>Coefficient of Efficiency</th></tr><tr><td>Amundsen-Scott<br>Bellingshausen<br>Byrd<br>Casey<br>Davis<br>Dumont<br>Esperanza<br>Faraday<br>Halley<br>Marambio<br>Marsh / O'Higgins<br>Mawson<br>McMurdo / Scott Base<br>Mirny<br>Novolazarevskaya<br>Rothera<br>Syowa<br>Vostok<br></td><td>0.685<br>0.914<br>0.563<br>0.765<br>0.683<br>0.731<br>0.853<br>0.871<br>0.721<br>0.814<br>0.884<br>0.667<br>0.793<br>0.787<br>0.818<br>0.810<br>0.574<br>0.723<br></td><td>0.578<br>0.884<br>0.391<br>0.712<br>0.595<br>0.650<br>0.823<br>0.841<br>0.612<br>0.760<br>0.838<br>0.555<br>0.632<br>0.648<br>0.689<br>0.765<br>0.423<br>0.659<br></td><td>0.469<br>0.836<br>0.376<br>0.586<br>0.492<br>0.534<br>0.733<br>0.758<br>0.519<br>0.776<br>0.809<br>0.444<br>0.630<br>0.619<br>0.675<br>0.644<br>0.376<br>0.535<br></td><td>0.316<br>0.779<br>0.213<br>0.503<br>0.372<br>0.412<br>0.680<br>0.706<br>0.365<br>0.737<br>0.746<br>0.290<br>0.375<br>0.398<br>0.472<br>0.571<br>0.220<br>0.446<br></td></tr></tbody></table><b><p>September-October-November (SON)</p></b><p></p><table><tbody><tr><th>Stations</th><th>Calibration Correlation</th><th>Validation Correlation</th><th>Reduction of Error</th><th>Coefficient of Efficiency</th></tr><tr><td>Amundsen-Scott<br>Bellingshausen<br>Byrd<br>Casey<br>Davis<br>Dumont<br>Esperanza<br>Faraday<br>Halley<br>Marambio<br>Marsh / O'Higgins<br>Mawson<br>McMurdo / Scott Base<br>Mirny<br>Novolazarevskaya<br>Rothera<br>Syowa<br>Vostok<br></td><td>0.619<br>0.853<br>0.765<br>0.698<br>0.623<br>0.641<br>0.762<br>0.769<br>0.676<br>0.697<br>0.711<br>0.616<br>0.731<br>0.635<br>0.581<br>0.623<br>0.594<br>0.615<br></td><td>0.395<br>0.819<br>0.621<br>0.529<br>0.545<br>0.540<br>0.712<br>0.747<br>0.536<br>0.633<br>0.647<br>0.557<br>0.612<br>0.534<br>0.505<br>0.522<br>0.546<br>0.514<br></td><td>0.383<br>0.745<br>0.637<br>0.461<br>0.405<br>0.411<br>0.581<br>0.591<br>0.457<br>0.579<br>0.601<br>0.370<br>0.534<br>0.445<br>0.332<br>0.434<br>0.363<br>0.385<br></td><td>0.085<br>0.689<br>0.448<br>0.224<br>0.295<br>0.277<br>0.502<br>0.557<br>0.262<br>0.514<br>0.530<br>0.291<br>0.357<br>0.285<br>0.250<br>0.362<br>0.304<br>0.259<br></td></tr></tbody></table></li></div><div><p><b>DATA</b></p><p>Please <a href="http://www.scalialab.com/best_recons_all.xlsx"><b>click here</b></a> for access to all of the best performing reconstructions in an MS Excel spreadsheet. </p><p><br>To access more data pertaining to each station individually, please download individual station data provided above on this page. The attached .txt files for each individual station provide the overall best reconstructions by season. The .xlsx files provide all reconstructions for each station and method used.</p><p></p><hr><p>Last Revised: May 2016</p></div

Means and 95% confidence intervals for the total percent of time within populated environments participants spent looking at avatar dyads of each body size.

<p>Means and 95% confidence intervals for the total percent of time within populated environments participants spent looking at avatar dyads of each body size.</p

Mean BPSS-R Body Subscale scores for women with and without elevated weight-and-shape concerns.

<p>Error bars represent 95% confidence intervals.</p

Aerial depiction of the method for calculating visual gaze and interpersonal distance.

<p>The participant’s movement from her starting point at T₁ to her idling position at T₂ is recorded by the optical tracking system in the laboratory room. At T₂, line C indicates the participant’s entire horizontal field of view of 102⁰ within the HMD. Avatar A is within the participant’s range of view, whereas Avatar B is not in view. A vector (line A) was extended from the center of the participant's head along the z-axis (i.e., extending out from the nose), and another vector (line D) was drawn between the participant and Avatar A, providing a measurement for angle E. The participant is considered to be looking at a given avatar if angle E is less than half of her entire field of view, or less than 51⁰. Line B demonstrates the distance between the participant at T₂ and Avatar B, measured as the distance from the participant to the outside edge of a cylinder with a radius equal to the width of Avatar B, as measured in Inspector.</p

Female avatars from beach and party environments representing thin (BMI = 18), average (BMI = 23) and overweight (BMI = 28) from left to right.

<p>Female avatars from beach and party environments representing thin (BMI = 18), average (BMI = 23) and overweight (BMI = 28) from left to right.</p

Proportion of avatars approached first by body size and populated environment.

<p>Proportion of avatars approached first by body size and populated environment.</p

Boletín de Segovia: Número 129 - 1841 octubre 28

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

Identifying potential high-risk zones for land-derived plastic litter to marine megafauna and key habitats within the North Atlantic

The pervasive use of plastic in modern society has led to plastic litter becoming ubiquitous within the ocean. Land-based sources of plastic litter are thought to account for the majority of plastic pollution in the marine environment, with plastic bags, bottles, wrappers, food containers and cutlery among the most common items found. In the marine environment, plastic is a transboundary pollutant, with the potential to cause damage far beyond the political borders from where it originated, making the management of this global pollutant particularly complex. In this study, the risks of land-derived plastic litter (LDPL) to major groups of marine megafauna – seabirds, cetaceans, pinnipeds, elasmobranchs, turtles, sirenians, tuna and billfish – and a selection of productive and biodiverse biogenic habitats – coral reefs, mangroves, seagrass, saltmarsh and kelp beds – were analysed using a Spatial Risk Assessment approach. The approach combines metrics for vulnerability (mechanism of harm for megafauna group or habitat), hazard (plastic abundance) and exposure (distribution of group or habitat). Several potential high-risk zones (HRZs) across the North Atlantic were highlighted, including the Azores, the UK, the French and US Atlantic coasts, and the US Gulf of Mexico. Whilst much of the modelled LDPL driving risk in the UK originated from domestic sources, in other HRZs, such as the Azores archipelago and the US Gulf of Mexico, plastic originated almost exclusively from external (non-domestic) sources. LDPL from Caribbean islands - some of the largest generators of marine plastic pollution in the dataset of river plastic emissions used in the study - was noted as a significant input to HRZs across both sides of the Atlantic. These findings highlight the potential of Spatial Risk Assessment analyses to determine the location of HRZs and understand where plastic debris monitoring and management should be prioritised, enabling more efficient deployment of interventions and mitigation measures.</p

Performance statistics of three top models of pathogen and toxicant exposure factors relating to ultimate cause of mortality for 67 fishers (<i>Pekania pennanti</i>) within the two isolated populations, northern California and southern Sierra Nevada.

<p>The one factor in the final model was ARNUM (number of different types of anticoagulant rodenticides to which the fisher was exposed). TOXO_high refers to exposure to <i>T</i>. <i>gondii</i> using the isotype IgM.</p