The Sleep Benefit in Episodic Memory: An Integrative Review and a Meta-Analysis

People recall more information after sleep than after an equally long period of wakefulness. This sleep benefit in episodic memory has been documented in almost a century of research. However, an integrative review of hypothesized underlying processes, a comprehensive quantification of the benefit, and a systematic investigation of potential moderators has been missing so far. Here, we address these issues by analyzing 823 effect sizes from 271 independent samples that were reported in 177 articles published between 1967 and 2019. Using multilevel meta-regressions with robust variance estimates, we found a moderate overall sleep benefit in episodic memory (g = 0.44). Moderator analyses revealed four important findings: First, the sleep benefit is larger when stimuli are studied multiple times instead of just once. Second, for word materials, the effect size depends on the retrieval procedure: It is largest in free recall, followed by cued recall and recognition tasks. Third, the sleep benefit is stronger in pre-post difference measures of retention than in delayed memory tests. Fourth, sleep benefits are larger for natural sleep and nighttime naps than foralternative sleep-study designs (e.g., SWS-deprived sleep, daytime naps). Although there was no obvious evidence for selective reporting, it is a potential threat to the validity of the results. When accounting for selective reporting bias, the overall effect of sleep on episodic memory is reduced but still significant (g = 0.28). We argue that our results support an integrative, multi-causal theoretical account of sleep-induced episodic memory benefits and provide guidance to increase their replicability.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models which attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlledly on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This work elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.

Symptoms COVID 19 Positive Vapers Compared to COVID 19 Positive Non-vapers

Objectives: The purpose of the present study was to assess and describe the severity of symptoms reported by Covid-19 positive patients who vaped (smoked e-cigarettes) when compared to those who did not vape or smoke at the time of the diagnosis of Covid-19. Methods: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between March 1, 2020 and February 28, 2021; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Among the 1734 eligible patients, 289 patients reported current vaping. The cohort of vapers (N = 289) was age and gender matched to 1445 covid-19 positive patients who did not vape. The data analyzed included: date of birth, gender, ethnicity, race, marital status, as well as lifestyle history such as vaping and smoking and reported covid-19 symptoms experienced. Results: A logistic regression analysis was performed separately for each symptom using generalized estimating equations (GEE) with robust variance estimates in order to account for the 1:5 age, sex, and race matched set study design. Patients who vaped and developed Covid-19 infection were more likely to have chest pain or tightness (16% vs 10%, vapers vs non vapers, P = .005), chills (25% vs 19%, vapers vs non vapers, P = .0016), myalgia (39% vs 32%, vapers vs non vapers, P = .004), headaches (49% vs 41% vapers vs non vapers, P = .026), anosmia/dysgeusia (37% vs 30%, vapers vs non vapers, P = .009), nausea/vomiting/abdominal pain (16% vs 10%, vapers vs non vapers, P = .003), diarrhea (16% vs 10%, vapers vs non vapers, P = .004), and non-severe light-headedness (16% vs 9%, vapers vs non vapers, P < .001). Conclusion: Vapers experience higher frequency of covid-19 related symptoms when compared with age and gender matched non-vapers. Further work should examine the impact vaping has on post-covid symptom experience.

Combining Cluster Sampling and Link-Tracing Sampling to Estimate Totals and Means of Hidden Populations in Presence of Heterogeneous Probabilities of Links

We propose Horvitz-Thompson-like and Hájek-like estimators of the total and mean of a response variable associated with the elements of a hard-to-reach population, such as drug users and sex workers. A portion of the population is assumed to be covered by a frame of venues where the members of the population tend to gather. An initial cluster sample of elements is selected from the frame, where the clusters are the venues, and the elements in the sample are asked to name their contacts who belong to the population. The sample size is increased by including in the sample the named elements who are not in the initial sample. The proposed estimators do not use design-based inclusion probabilities, but model-based inclusion probabilities which are derived from a Rasch model and are estimated by maximum likelihood estimators. The inclusion probabilities are assumed to be heterogeneous, that is, they depend on the sampled people. Variance estimates are obtained by bootstrap and are used to construct confidence intervals. The performance of the proposed estimators and confidence intervals is evaluated by two numerical studies, one of them based on real data, and the results show that their performance is acceptable.

Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

The burgeoning advances in high-throughput technologies have posed a great challenge to the identification of novel biomarkers for diagnosing, by contemporary models and methods, through bioinformatics-driven analysis. Diagnostic performance metrics such as the partial area under the ROC (pAUC) indexes exhibit limitations to analysing genomic data. Among other issues, the inability to differentiate between biomarkers whose ROC curves cross each other with the same pAUC value, the inappropriate expression of non-concave ROC curves, and the lack of a convenient interpretation, restrict their use in practice. Here, we have proposed the fitted partial area index (FpAUC), which is computable through an algorithm valid for any ROC curve shape, as an alternative performance summary for the evaluation of highly sensitive biomarkers. The proposed approach is based on fitter upper and lower bounds of the pAUC in a high-sensitivity region. Through variance estimates, simulations, and case studies for diagnosing leukaemia, and ovarian and colon cancers, we have proven the usefulness of the proposed metric in terms of restoring the interpretation and improving diagnostic accuracy. It is robust and feasible even when the ROC curve shows hooks, and solves performance ties between competitive biomarkers.

Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

Selection indices for fruit yield improvement in elite cucumber (Cucumis sativus L.) cultivars

Cucumber (Cucumis sativus L.) is an important vegetable crop, rich in vitamins and minerals and eaten fresh as a dessert. Its fruit yield is relatively low, though could be improved through knowledge of character association with it and selection of desirable materials for improvement programmes. Fifteen cultivars of Cucumber were evaluated at two locations (Abeokuta and Ibadan), South West, Nigeria in a randomized complete block design with three replicates in order to determine heritability, correlation, direct and indirect effects of characters on fruit yield. Data collected on agro-morphological characters were subjected to analysis of variance, estimates of heritability, correlation, and path analysis. Significant variations (p <0.05) were observed in the cultivars. High heritability estimates (>90%) was observed for fruit length at both locations. A significant phenotypic and genotypic correlation was observed between fruit yield and fruit weight. Number of days to 50% flowering and fruit width could also be selected directly for improvement of fruit yield in cucumber.

PSXIV-19 Heritability for growth and resistance to gastrointestinal parasitism in meat goats and hair sheep

Genetic selection for resistance to internal parasitism is of great research interest. Heritabilities were determined for average daily gain (ADG), logarithmic transformed fecal egg count (FEC), packed cell volume (PCV), and serum immunoglobin (Ig) levels of growing male meat goats and hair sheep from different farms in the southcentral USA during three consecutive central performance tests (CPT). Tests entailed 7–10 wk of data collection after artificial infection with Haemonchus contortus. In year 1, animals evaluated were selected randomly and in years 2 and 3 progeny of CPT sires classified as highly or moderately resistant, which included 46, 50, and 51 Boer, Kiko, and Spanish and 59, 61, 34, and 46 Dorper, Katahdin-farm A, Katahdin-farm B, and St. Croix, respectively. Females were classified accordingly on-farm based on FEC and FAMACHA. Pedigree records consisted of 32 and 57 known sires, 95 and 152 known dams including 4 and 10 full-sibs and 97 and 149 half-sibs for goats and sheep, respectively. Variance components and heritabilities were estimated by AIREML using WOMBAT with a multivariate animal model. Heritability estimates were 0.48 ± 0.214 and 0.85 ± 0.157 of ADG, 0.31 ± 0.237 and 0.20 ± 0.172 of FEC, 0.60 ± 0.206 and 0.24 ± 0.185 of PCV, 0.26 ± 0.172 and 0.51 ± 0.167 of IgA, 0.335 and 0.543 of IgM, and 0.14 ± 0.192 and 0.31 ± 0.190 of IgG for goats and sheep, respectively. Reasons for relatively high heritabilities for all traits include the low residual variance estimates due primarily to a standardized environment in the performance test. In conclusion, moderate to high heritabilities were found for growth performance and response to parasite infection for growing meat goat and hair sheep males under a standardized environment that suggests considerable for genetic improvement through selection.

A flexible point and variance estimator to assess bird/bat fatality from carcass searches

Estimation of bird and bat fatalities due to collision with anthropogenic structures (such as power lines or wind turbines) is an important ecological issue. However, searching for collision victims usually only detects a proportion of the true number of collided individuals. Various mortality estimators have previously been proposed to correct for this incomplete detection, based on regular carcass searches and additional field experiments. However, each estimator implies specific assumptions/restrictions, which may easily be violated in practice. In this study, we extended previous approaches and developed a versatile algorithm to compute point and variance estimates for true carcass numbers. The presented method allows for maximal flexibility in the data structure. Using simulated data, we showed that our point and variance estimators ensured unbiased estimates under various challenging data conditions. The presented method may improve the estimation of true collision numbers, as an important pre-condition for calculating collision rates and evaluating measures to reduce collision risks, and may thus provide a basis for management decisions and/or compensation actions with regard to planned or existing wind turbines and power lines.