Anorexia Nervosa and Bulimia Nervosa: The Patients\u27 Perspective

Eating-disorder clients show low motivation, poor follow-through, and inordinate premature dropout rates in treatment. To date, little research has been conducted that might provide clinicians with an understanding of the critical factors that may aid clients\u27 recovery. Such factors may be used by clinicians to better motivate clients to collaborate in treatment. The purpose of this study was to identify some of the critical factors that women with eating disorders believed were crucial in prompting or facilitating their recovery. Identification of these factors was accomplished through a systematic content analysis of semistructured interviews with recovered or recovering bulimics and anorexics. This study may contribute significantly to future research into the development of motivational supplements to eating disorder therapy (e.g., psychoeducational materials or therapy orientation programs). Of interest were what personal, interpersonal, or environmental factors anorexic and bulimic clients reported increased their motivation to recover, and prompted them to begin the recovery process, maintain recovery, and cope with the threat ofrelapse. Also, factors that subjects reported hindered their progress in recovery were examined. The anorexic and bulimic subjects reported social support as a critical factor across three stages of recovery, including beginning recovery, maintaining recovery, and coping with relapse. Being tired of the disorder and therapy were indicated to be relevant to beginning recovery. Improved self-esteem was deemed significant in helping subjects both maintain recovery and cope with the threat of relapse. Establishing healthy eating habits and attitudes was a necessary factor required to maintain recovery. Subjects shared that developing healthy ways to deal with emotions enabled them to deal successfully with the threat of relapse. Anorexic subjects reported that people and societal expectations, fear of becoming fat, incentive to numb emotions, and poor eating habits and attitudes impeded their recovery. Bulimic subjects indicated that people and societal expectations, incentive to numb emotions, lack of understanding, and poor eating habits and attitudes hindered their recovery

Feasibility of predicting performance degradation of airfoils in heavy rain

The heavy rain aerodynamic performance penalty program is detailed. This effort supported the design of a fullscale test program as well as examined the feasibility of estimating the degradation of performance of airfoils from first principles. The analytic efforts were supplemented by a droplet splashback test program in an attempt to observe the physics of impact and generation of ejecta. These tests demonstrated that the interaction of rain with an airfoil is a highly complex phenomenon and this interaction is not likely to be analyzed analytically with existing tools

Discovering hidden sectors with mono-photon Z' searches

In many theories of physics beyond the Standard Model, from extra dimensions to Hidden Valleys and models of dark matter, Z' bosons mediate between Standard Model particles and hidden sector states. We study the feasibility of observing such hidden states through an invisibly decaying Z' at the LHC. We focus on the process pp -> \gamma Z' -> \gamma X X*, where X is any neutral, (quasi-) stable particle, whether a Standard Model (SM) neutrino or a new state. This complements a previous study using pp -> Z Z' -> l+ l- X X*. Only the Z' mass and two effective charges are needed to describe this process. If the Z' decays invisibly only to Standard Model neutrinos, then these charges are predicted by observation of the Z' through the Drell-Yan process, allowing discrimination between Z' decays to SM neutrinos and invisible decays to new states. We carefully discuss all backgrounds and systematic errors that affect this search. We find that hidden sector decays of a 1 TeV Z' can be observed at 5 sigma significance with 50 fb^{-1} at the LHC. Observation of a 1.5 TeV state requires super-LHC statistics of 1 ab^{-1}. Control of the systematic errors, in particular the parton distribution function uncertainty of the dominant Z \gamma background, is crucial to maximize the LHC searchComment: 13 pages, 4 figure

Invisible Z′ at the CERN LHC

We study the feasibility of observing an invisibly decaying Z′ at the LHC through the process pp→ZZ′→ℓ^+ℓ^−XX^†, where X is any neutral, (quasi-) stable particle, whether a standard model neutrino or a new state. The measurement of the invisible width through this process facilitates both a model-independent measurement of Γ_(Z′→ṽν and potentially detection of light neutral hidden states. Such particles appear in many models, where the Z′ is a messenger to a hidden sector, and also if dark matter is charged under the U(1)′ of the Z′. We find that with as few as 30 fb^(−1) of data the invisibly decaying Z′ can be observed at 5σ over standard model background for a 1 TeV Z′ with reasonable couplings. If the Z′ does not couple to leptons and therefore cannot be observed in the Drell-Yan channel, this process becomes a discovery mode. For reasonable hidden sector couplings, masses up to 2 TeV can be probed at the LHC. If the Z′ does couple to leptons, then the rate for this invisible decay is predicted by on-peak data and the presence of additional hidden states can be searched for. With 100 fb^(−1) of data, the presence of excess decays to hidden states can be excluded at 95% C.L., if they comprise 20–30% of the total invisible cross section

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approachessupport vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R-2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha(-1). The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R-2 of 0.26 and RMSE of 70.1 Mg ha(-1). The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R-2 of 0.40 and an RMSE of 108.2 Mg ha(-1) compared to the full lidar coverage model with an R-2 of 0.58 and an RMSE of 96.0 Mg ha(-1). This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies

An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)

<p>Abstract</p> <p>Background</p> <p>Microarray gene expression time-course experiments provide the opportunity to observe the evolution of transcriptional programs that cells use to respond to internal and external stimuli. Most commonly used methods for identifying differentially expressed genes treat each time point as independent and ignore important correlations, including those within samples and between sampling times. Therefore they do not make full use of the information intrinsic to the data, leading to a loss of power.</p> <p>Results</p> <p>We present a flexible random-effects model that takes such correlations into account, improving our ability to detect genes that have sustained differential expression over more than one time point. By modeling the joint distribution of the samples that have been profiled across all time points, we gain sensitivity compared to a marginal analysis that examines each time point in isolation. We assign each gene a probability of differential expression using an empirical Bayes approach that reduces the effective number of parameters to be estimated.</p> <p>Conclusions</p> <p>Based on results from theory, simulated data, and application to the genomic data presented here, we show that BETR has increased power to detect subtle differential expression in time-series data. The open-source R package <it>betr </it>is available through Bioconductor. BETR has also been incorporated in the freely-available, open-source MeV software tool available from <url>http://www.tm4.org/mev.html</url>.</p