Morphology, geographical variation and the subspecies of marsh tit Poecile palustris in Britain and central Europe

Capsule: All British Marsh Tits belong to subspecies Poecile palustris dresseri, being smaller than nominate P. p. palustris of central Europe. Aims: Determining the subspecies of Marsh Tit in Britain to test whether ssp. P. p. palustris occurs in northern England and Scotland, by assessing regional variation in size compared with central European birds. Methods: 1147 wing length and 250 tail length measurements from 953 Marsh Tits were compared between eight British locations to test for regional variation. Biometrics were compared between birds from Britain and six locations within the continental European range of ssp. palustris. Results: There was no regional variation in wing or tail lengths among British Marsh Tits, indicating that all resident birds belong to ssp. dresseri. There was no evidence supporting the existence of ssp. palustris in northern England. British birds were significantly smaller than those from continental Europe, with proportionately shorter tails, consistent across all age and sex classes. Conclusion: All British Marsh Tits should be considered as ssp. dresseri, with ssp. palustris being limited to continental Europe. With no evidence of regional variation in size within Britain, reliable sexing methods based on biometrics could be applied in demographic studies throughout the country

Refined histopathological predictors of BRCA1 and BRCA2 mutation status: A large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling. Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Actual return reinforcement learning versus Temporal Differences: Some theoretical and experimental results

This paper argues that for many domains, we can expect credit-assignment methods that use actual returns to be more effective for reinforcement learning than the more commonly used temporal difference methods. We present analysis and empirical evidence from three sets of experiments in different domains to support this claim. A new algorithm we call C-Trace, a variant of the P-Trace RL algorithm is introduced, and some possible advantages of using algorithms of this type are discussed

Estimator Variance in Reinforcement Learning: Theoretical Problems and Practical Solutions

In reinforcement learning, as in many on-line search techniques, a large number of estimation parameters (e.g. Q-value estimates for 1-step Q-learning) are maintained and dynamically updated as information comes to hand during the learning process. Excessive variance of these estimators can be problematic, resulting in uneven or unstable learning, or even making effective learning impossible. Estimator variance is usually managed only indirectly, by selecting global learning algorithm parameters (e.g. for TD() based methods) that are a compromise between an acceptable level of estimator perturbation and other desirable system attributes, such as reduced estimator bias. In this paper, we argue that this approach may not always be adequate, particularly for noisy and non-Markovian domains, and present a direct approach to managing estimator variance, the new ccBeta algorithm. Empirical results in an autonomous robotics domain are also presented showing improved performance using the cc..