Influences of Ketogenic Diet on Body Fat Percentage, Respiratory Exchange Rate, and Total Cholesterol in Athletes: A Systematic Review and Meta-Analysis

(1) Background: The purpose of the current meta-analysis was to investigate any positive or negative effects of ketogenic diets in athletes and provide an assessment of the size of these effects. (2) Methods: Databases were used to select relevant studies up to January 2021 regarding the effects of ketogenic diets in athletes. Inclusion criteria were as follows: data before and after ketogenic diet use, being randomized controlled trials and presenting ketogenic diets and assessments of ketone status. Study subjects were required to be professional athletes. Review studies, pilot studies, and studies in which non-athletes were included were excluded from this meta-analysis. The outcome effect sizes in these selected studies were calculated by using the standardized mean difference statistic. (3) Results: Eight studies were selected for this meta-analysis. Athletes who consumed the ketogenic diet had reduced body fat percentages, respiratory exchange rates, and increased total cholesterol compared to athletes who did not consume this diet. However, body mass index, cardiorespiratory fitness, heart rate, HDL cholesterol, glucose level, and insulin level were unaffected by the diet. (4) Conclusions: Ketogenic diets had a beneficial effect by decreasing body fat percentage, but athletes with high total cholesterol level need to be monitored when instituting a ketogenic diet. Our study sample size was limited; therefore, additional studies may be needed to confirm the current findings. Further studies need to be conducted on changes in LDL cholesterol, HDL cholesterol, total cholesterol and ratio of LDL to HDL cholesterol.

A Blind Spot in the Use of the Weibull Function for Modeling Diameter Distributions

Diameter distribution models based on probability density functions are integral to many forest growth and yield systems, where they are used to estimate product volumes within diameter classes. The three-parameter Weibull function with a constrained nonnegative lower bound is commonly used because of its flexibility and ease of fitting. This study compared Weibull and reverse Weibull functions with and without a lower bound constraint and left-hand truncation, across three large unthinned plantation cohorts in which 81% of plots had negatively skewed diameter distributions. Near-optimal lower bounds for the unconstrained Weibull function were negative for negatively skewed data, and the left-truncated Weibull using these bounds was 14.2% more accurate than the constrained Weibull, based on the Kolmogorov-Smirnov statistic. The truncated reverse Weibull fit dominant tree distributions 23.7% more accurately than the constrained Weibull, based on a mean absolute difference statistic. This work indicates that a blind spot may have developed in plantation growth modeling systems deploying constrained Weibull functions, and that left-truncation of unconstrained functions could substantially improve model accuracy for negatively skewed distributions.

Deletion statistic accuracy in confirmatory factor models

Social science researchers now routinely use confirmatory factor models in scale development and validation studies. Methodologists have known for some time that the results of fitting a confirmatory factor model can be unduly influenced by one or a few cases in the data. However, there has been little development and use of case diagnostics for identifying influential cases with confirmatory factor models. A few case deletion statistics have been proposed to identify influential cases in confirmatory factor models. However, these statistics have not been systematically evaluated or compared for their accuracy. This study evaluated the accuracy of three case deletion statistics found in the R package influence.SEM. The accuracy of the case deletion statistics was also compared to Mahalanobis distance, which is commonly used to screen for unusual cases in multivariate applications. A statistical simulation was used to compare the accuracy of the statistics in identifying target cases generated from a model in which variables were uncorrelated. The results showed that Likelihood distance and generalized Cook’s distance detected the target cases more effectively than the Chi-square difference statistic. The accuracy of the Likelihood distance and generalized Cook’s distance statistics was unaffected by model misspecification. The results of this study suggest that Likelihood distance and generalized Cook’s distance are more accurate under more varied conditions in identifying target cases in confirmatory factor models.

Self-Compassion: Evaluation of a Psychoeducational Website

Background: Low levels of self-compassion are associated with a number of mental health problems. Recent findings suggest that self-compassion can be significantly increased through traditional therapist led interventions such as Mindful Self-Compassion (MSC). Aims: The goal of this study was to assess the impact of a psychoeducational website modeled on MSC on measured levels of self-compassion and two related constructs: shame and rumination. Method: Participants (N = 9) were recruited from a pool of university students and employees. Single case AB protocols were used. Results: Analyses using the Critical Difference statistic revealed significant improvement in self-compassion and shame for the majority of participants, but limited changes in rumination. Conclusions: These preliminary findings suggest that self-compassion may be enhanced through the use of a psychoeducational website. It may be worthwhile to develop a structured internet-based treatment course for individuals who would not otherwise access traditional psychological services.

Image Retrieval Based on Color-Statistic Feature

Color is the most widely used visual feature in content based image retrieval. The visual coherence color space, HSV, is adopted to represent image. Hue component is used to denote color. Hue difference statistic is proposed to extract color change information as supplement to color feature. The image is divided into sub images equally. Color and change information is extracted in each region. After feature vector clustering and coding, image content can be expressed as vector codes. The text based analysis technology is used for image retrieval. Experiments show that the proposed method can realize efficient retrieval for unconstrained scene images.

Change in mood instability (MI) with time and treatment

IntroductionThe literature indicates that most patients with Major Depression mention sudden short “mood swings” (MI) when asked. MI is known to be distressing but little is known about the treatment.ObjectivesTo determine whether MI changes with community treatment of depression.AimTo study changes in MI with 3-6 months of treatment for depression in patients with Major Depression and complaints of MI.Methods34 patients with Major Depression and complaints of “mood swings” were recruited from 4 psychiatric practices. They were interviewed with the MINI diagnostic interview and the Mood Disorders Questionnaire. They completed the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory Trait Form (STAI-T), and Visual Analogue Scales for Depressed Mood (VAS) and Anxious Mood twice a day for a week. The Mean Square Successive Difference Statistic (MSSD) was calculated from the VAS readings. The BDI, STAI-T, and VAS were repeated after 6 months of treatment.Results25/34 patients reported past hypomania. Most patients were treated with a combination of antidepressants and mood stabilizers. The BDI and STAI-T scores improved with treatment. There was no overall change in depressed and anxious MI. Change in Depressed MI and Anxiety MI correlates with change in BDI from T1 to T2ConclusionsAnxiety and depression improved with treatment as expected. Change in MI is inconsistent. Research into medications and psychosocial treatments that improve MI is needed and this will probably improve depression treatment outcome.

Gene Expression Profiling Reveals Reproducible Human Lung Adenocarcinoma Subtypes in Multiple Independent Patient Cohorts

Purpose Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes. Methods Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes distinguishing subtypes were identified using the weighted difference statistic. Gene lists were compared across cohorts using centroids and gene set enrichment analysis. Results Cohorts of 31, 72, and 128 adenocarcinomas were generated for a total of 231 microarrays, each with 2,553 reliable genes. Three adenocarcinoma subtypes were identified in each cohort. These were named bronchioid, squamoid, and magnoid according to their respective correlations with gene expression patterns from histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma. Tumor subtypes were distinguishable by many hundreds of genes, and lists generated in one cohort were predictive of tumor subtypes in the two other cohorts. Tumor subtypes correlated with clinically relevant covariates, including stage-specific survival and metastatic pattern. Most notably, bronchioid tumors were correlated with improved survival in early-stage disease, whereas squamoid tumors were associated with better survival in advanced disease. Conclusion DNA microarray analysis of lung adenocarcinomas identified reproducible tumor subtypes which differ significantly in clinically important behaviors such as stage-specific survival.