Hypotheses About the Relationship of Cognition With Psychopathology Should be Tested by Embedding Them Into Empirical Priors

Mechanistic hypotheses about psychiatric disorders are increasingly formalized as computational models of information-processing in the brain. Model parameters, characterizing for example decision-making biases, are hypothesized to correlate with clinical constructs. This is promising, but here we draw attention to some techniques used to minimize noise in parameter estimation which are in common use but may be unhelpful. Namely, the use of empirical priors that do not incorporate relationships between psychopathology and modelled processes will suppress the very relationships of interest. This is because the variability associated with psychopathology will be indistinguishable from that due to noise from the point of view of the hierarchical, or random-effects, fit that used the empirical priors in question. We advocate incorporating cross-domain, e.g. psychopathology-cognition relationships into the parameter inference itself

Error Rates, Decisive Outcomes and Publication Bias with Several Inferential Methods

Correction to this article published Hopkins, W.G. & Batterham, A.M. Sports Med (2016) 46: 923. doi:10.1007/s40279-016-0530-

Do eating behavior traits predict energy intake and body mass index? A systematic review and meta‐analysis

At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short-term energy intake (EI) and longer-term energy balance as estimated by body mass index (BMI). This systematic review examined the impact of EBT on BMI and laboratory-based measures of EI in adults (≥18 years) in any BMI category, excluding self-report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random-effects meta-analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m², mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694)

Flea Diversity as an Element for Persistence of Plague Bacteria in an East African Plague Focus

Plague is a flea-borne rodent-associated zoonotic disease that is caused by Yersinia pestis and characterized by long quiescent periods punctuated by rapidly spreading epidemics and epizootics. How plague bacteria persist during inter-epizootic periods is poorly understood, yet is important for predicting when and where epizootics are likely to occur and for designing interventions aimed at local elimination of the pathogen. Existing hypotheses of how Y. pestis is maintained within plague foci typically center on host abundance or diversity, but little attention has been paid to the importance of flea diversity in enzootic maintenance. Our study compares host and flea abundance and diversity along an elevation gradient that spans from low elevation sites outside of a plague focus in the West Nile region of Uganda (∼725–1160 m) to higher elevation sites within the focus (∼1380–1630 m). Based on a year of sampling, we showed that host abundance and diversity, as well as total flea abundance on hosts was similar between sites inside compared with outside the plague focus. By contrast, flea diversity was significantly higher inside the focus than outside. Our study highlights the importance of considering flea diversity in models of Y. pestis persistence

Maternal allergic contact dermatitis causes increased asthma risk in offspring

<p>Abstract</p> <p>Background</p> <p>Offspring of asthmatic mothers have increased risk of developing asthma, based on human epidemiologic data and experimental animal models. The objective of this study was to determine whether maternal allergy at non-pulmonary sites can increase asthma risk in offspring.</p> <p>Methods</p> <p>BALB/c female mice received 2 topical applications of vehicle, dinitrochlorobenzene, or toluene diisocyanate before mating with untreated males. Dinitrochlorobenzene is a skin-sensitizer only and known to induce a Th1 response, while toluene diisocyanate is both a skin and respiratory sensitizer that causes a Th2 response. Both cause allergic contact dermatitis. Offspring underwent an intentionally suboptimal protocol of allergen sensitization and aerosol challenge, followed by evaluation of airway hyperresponsiveness, allergic airway inflammation, and cytokine production. Mothers were tested for allergic airway disease, evidence of dermatitis, cellularity of the draining lymph nodes, and systemic cytokine levels. The role of interleukin-4 was also explored using interleukin-4 deficient mice.</p> <p>Results</p> <p>Offspring of toluene diisocyanate but not dinitrochlorobenzene-treated mothers developed an asthmatic phenotype following allergen sensitization and challenge, seen as increased Penh values, airway inflammation, bronchoalveolar lavage total cell counts and eosinophilia, and Th2 cytokine imbalance in the lung. Toluene diisocyanate treated interleukin-4 deficient mothers were able to transfer asthma risk to offspring. Mothers in both experimental groups developed allergic contact dermatitis, but not allergic airway disease.</p> <p>Conclusion</p> <p>Maternal non-respiratory allergy (Th2-skewed dermatitis caused by toluene diisocyanate) can result in the maternal transmission of asthma risk in mice.</p

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Background: Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective: This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods: Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results: The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions: Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms

Cortical Representation of Lateralized Grasping in Chimpanzees (Pan troglodytes): A Combined MRI and PET Study

Functional imaging studies in humans have localized the motor-hand region to a neuroanatomical landmark call the KNOB within the precentral gyrus. It has also been reported that the KNOB is larger in the hemisphere contralateral to an individual's preferred hand, and therefore may represent the neural substrate for handedness. The KNOB has also been neuronatomically described in chimpanzees and other great apes and is similarly associated with handedness. However, whether the chimpanzee KNOB represents the hand region is unclear from the extant literature. Here, we used PET to quantify neural metabolic activity in chimpanzees when engaged in unilateral reach-and-grasping responses and found significantly lateralized activation of the KNOB region in the hemisphere contralateral to the hand used by the chimpanzees. We subsequently constructed a probabilistic map of the KNOB region in chimpanzees in order to assess the overlap in consistency in the anatomical landmarks of the KNOB with the functional maps generated from the PET analysis. We found significant overlap in the anatomical and functional voxels comprising the KNOB region, suggesting that the KNOB does correspond to the hand region in chimpanzees. Lastly, from the probabilistic maps, we compared right- and left-handed chimpanzees on lateralization in grey and white matter within the KNOB region and found that asymmetries in white matter of the KNOB region were larger in the hemisphere contralateral to the preferred hand. These results suggest that neuroanatomical asymmetries in the KNOB likely reflect changes in connectivity in primary motor cortex that are experience dependent in chimpanzees and possibly humans

Galaxy And Mass Assembly (GAMA): the wavelength dependence of galaxy structure versus redshift and luminosity

We study how the sizes and radial profiles of galaxies vary with wavelength, by fitting Sersic functions simultaneously to imaging in nine optical and near-infrared bands. To quantify the wavelength dependence of effective radius we use the ratio, $\mathcal{R}$, of measurements in two restframe bands. The dependence of Sersic index on wavelength, $\mathcal{N}$, is computed correspondingly. Vulcani et al. (2014) have demonstrated that different galaxy populations present sharply contrasting behaviour in terms of $\mathcal{R}$ and $\mathcal{N}$. Here we study the luminosity dependence of this result. We find that at higher luminosities, early-type galaxies display a more substantial decrease in effective radius with wavelength, whereas late-types present a more pronounced increase in Sersic index. The structural contrast between types thus increases with luminosity. By considering samples at different redshifts, we demonstrate that lower data quality reduces the apparent difference between the main galaxy populations. However, our conclusions remain robust to this effect. We show that accounting for different redshift and luminosity selections partly reconciles the size variation measured by Vulcani et al. with the weaker trends found by other recent studies. Dividing galaxies by visual morphology confirms the behaviour inferred using morphological proxies, although the sample size is greatly reduced. Finally, we demonstrate that varying dust opacity and disc inclination can account for features of the joint distribution of $\mathcal{R}$ and $\mathcal{N}$ for late-type galaxies. However, dust does not appear to explain the highest values of $\mathcal{R}$ and $\mathcal{N}$. The bulge-disc nature of galaxies must also contribute to the wavelength-dependence of their structure

Reduction in Physical Match Performance at the Start of the Second Half in Elite Soccer

Purpose: Soccer referees' physical match performances at the start of the second half (46-60 min) were evaluated in relation to both the corresponding phase of the first half (0-15 min) and players' performances during the same match periods. Methods: Match analysis data were collected (Prozone, UK) from 12 soccer referees on 152 English Premier League matches during the 2008/09 soccer season. Physical match performance categories for referees and players were total distance, high-speed running distance (speed >5.5 m/s), and sprinting distance (>7.0 m/s). The referees' heart rate was recorded from the start of their warm-up to the end of the match. The referees' average distances (in meters) from the ball and fouls were also calculated. Results: No substantial differences were observed in duration (16:42 ± 2:35 vs 16:27 ± 1:00 min) or intensity (107 ± 11 vs 106 ± 14 beats/ min) of the referees' preparation periods immediately before each half. Physical match performance was reduced during the initial phase of the second half when compared with the first half in both referees (effect sizes-standardized mean differences-0.19 to 0.73) and players (effect sizes 0.20 to 1.01). The degree of the decreased performance was consistent between referees and players for total distance (4.7 m), high-speed running (1.5 m), and sprinting (1.1 m). The referees were closer to the ball (effect size 0.52) during the opening phase the second half. Conclusion: Given the similarity in the referees' preparation periods, it may be that the reduced physical match performances observed in soccer referees during the opening stages of the second half are a consequence of a slower tempo of play