Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients

Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals - not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance.A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important

Can Collegiate Hockey Players Accurately Predict Regional and Total Body Physiologic Changes throughout the Competitive Season?

A collegiate athlete’s body composition can fluctuate due to factors such as nutrition, sleep, and training load. As changes in body composition can affect an athlete’s level of performance, it may be beneficial if athlete’s can accurately predict these changes throughout a season. The purpose of this study was to determine how well a group of 23 male collegiate hockey players (age = 22.44 ± 1.16 years, height = 181.30 ± 6.99 cm, weight = 86.41 ± 8.32 kg) could predict their regional and total body lean and fat tissue mass throughout a hockey season (September to March). Total body, trunk, lower body, and upper body compositional changes were measured at the beginning and at the end of the competitive season using dual energy X-Ray absorptiometry (DXA). At the end of the season, a questionnaire was completed by each participant to explore how they perceived their body composition changes (losses or gains in lean tissue and fat mass) throughout the season. Overall, players had a difficult time identifying actual changes in lean tissue and fat mass throughout the season. Upper body fat and lean tissue changes were perceived most accurately, while perceptions of body fat were related to android adiposity but not visceral adiposity. These findings suggest that some regional areas of body composition changes may happen without being noticed. For strength and conditioning coaches, if athletes are made aware of these changes before they become exaggerated, proper dietary, and training adaptations can be made to enhance performance

Differences in Demographic, Behavioral, and Biological Variables Between Those With Valid and Invalid Accelerometry Data: Implications for Generalizability

Background: The exclusion of participants with invalid accelerometry data (IAD) may lead to biased results and/or lack of generalizability in large population studies. The purpose of this study was to investigate whether demographic, behavioral, and biological differences occur between those with IAD and valid accelerometry data (VAD) among adults using a representative sample of the civilian noninstitutionalized U.S. population. Methods: Ambulatory participants from NHANES (2003-2004) who were 20-85 years of age were included in the current study and wore an ActiGraph 7164 accelerometer for 7 days. A valid person was defined as those with 4 or more days of at least 10+ hrs of monitoring per day. Among adults (20-85 yrs), 3088 participants provided VAD and 987 provided IAD. Demographic, behavioral, and biological information were obtained from the household interview or from data obtained in a mobile examination center. Results: Differences were observed in age, BMI, ethnicity, education, smoking status, marital status, use of street drugs, current health status, HDL-cholesterol, C-reactive protein, self-reported vigorous physical activity, and plasma glucose levels between those with VAD and IAD. Conclusions: Investigators should take into consideration the potential cut-off bias in interpreting results based on data that excludes IAD participants

Investigating centrality in PTSD symptoms across diagnostic systems using network analysis*

Background: The posttraumatic stress disorder (PTSD) diagnosis has been widely debated since it was introduced into the diagnostic nomenclature four decades ago. Recently, the debate has focused on consequences of having two different descriptions of PTSD: 20 symptoms belonging to four symptom clusters in the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5), and three symptoms clusters in the 11th edition of the International Classification of Diseases (ICD-11) most often operationalized by six symptoms in the International Trauma Questionnaire (ITQ) (2017) and Hansen, Hyland, Armour, Shevlin, & Elklit (). Research has provided support for both models of PTSD, but at the same time indicates differences in estimated prevalence rates of PTSD (Hansen et al., , ). A growing body of research has modelled PTSD both theoretically and statistically as a network of interacting symptoms (Birkeland, Greene, & Spiller, ), yet it remains more unclear how the two diagnostic systems perform regarding which symptoms are more central/interconnected. Objectives and methods: We estimated two 23-item Gaussian Graphical Models to investigate whether ICD-11 or DSM-5 PTSD symptoms are more central in two trauma-exposed samples: a community sample (N = 2,367) and a military veteran sample (N = 657). PTSD DSM-5 was measured with the PTSD checklist-5 (PCL-5) and the PTSD ICD-11 was measure by the ITQ PTSD subscale. Results: Five of the six most central symptoms estimated via the expected influence centrality metric across the two samples were identical and represented symptoms from both diagnostic systems operationalized by the PCL-5 and the ITQ. Conclusions: The results of the present study underline that symptoms from both diagnostic systems hold central positions. The implications of the results are discussed from the perspectives of an indexical (i.e. the diagnostic systems reflect both shared and different aspects of PTSD) and a constitutive view (i.e., the diagnostic systems represent different disorders and the results cannot be reconciled per se) of mental health diagnoses (Kendler, )

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

Investigating the DSM-5 and the ICD-11 PTSD symptoms using network analysis across two distinct samples

Objective: Posttraumatic stress disorder (PTSD) has long been debated with a recent focus on the consequences of having two different diagnostic descriptions of PTSD (i.e., the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition [DSM-5] and the International Classification of Diseases-11th Edition [ICD-11]). Research has modeled PTSD as a network of interacting symptoms according to both diagnostic systems, but the relations between the two systems remain unclear regarding which symptoms are more central or interconnected. To answer this question, the present study is the first study to investigate the combined network structure of PTSD symptoms according to both systems using validated measurements (i.e., the International Trauma Questionnaire [ITQ] and the Posttraumatic Stress Disorder Checklist 5 [PCL-5] across two distinct trauma samples [a community sample, N = 2,367], and a military sample, N = 657). Method: We estimated two Gaussian Graphical Models of the combined ICD-11 and DSM-5 PTSD symptoms across the two samples. Results: Five of the six most central symptoms were the same across both samples. Conclusions: The results underline that a combination of five symptoms representing both diagnostic systems may hold central positions and potentially be important for treatment. However, the implications depend on if the different diagnostic descriptions can be reconciled in an indexical rather than constitutive perspective.Clinical Impact Statement Five identical posttraumatic stress disorder (PTSD) symptoms representing both diagnostic systems were identified across two distinct trauma exposed samples using network analysis. These symptoms may hold important positions compared with the remaining symptoms of the network and potentially be central for treatment. However, the implications depend on whether the results can be reconciled by viewing the two diagnostic descriptions of PTSD as indexical.Stress and Psychopatholog

Widespread sex differences in gene expression and splicing in the adult human brain

There is strong evidence to show that men and women differ in terms of neurodevelopment, neurochemistry and susceptibility to neurodegenerative and neuropsychiatric disease. The molecular basis of these differences remains unclear. Progress in this field has been hampered by the lack of genome-wide information on sex differences in gene expression and in particular splicing in the human brain. Here we address this issue by using post-mortem adult human brain and spinal cord samples originating from 137 neuropathologically confirmed control individuals to study whole-genome gene expression and splicing in 12 CNS regions. We show that sex differences in gene expression and splicing are widespread in adult human brain, being detectable in all major brain regions and involving 2.5% of all expressed genes. We give examples of genes where sex-biased expression is both disease-relevant and likely to have functional consequences, and provide evidence suggesting that sex biases in expression may reflect sex-biased gene regulatory structures

Hard Interactions of Quarks and Gluons: a Primer for LHC Physics

In this review article, we develop the perturbative framework for the calculation of hard scattering processes. We undertake to provide both a reasonably rigorous development of the formalism of hard scattering of quarks and gluons as well as an intuitive understanding of the physics behind the scattering. We emphasize the importance of logarithmic corrections as well as power counting of the strong coupling constant in order to understand the behavior of hard scattering processes. We include "rules of thumb" as well as "official recommendations", and where possible seek to dispel some myths. Experiences that have been gained at the Fermilab Tevatron are recounted and, where appropriate, extrapolated to the LHC.Comment: 118 pages, 107 figures; to be published in Reports on Progress in Physic