Emotion, Meaning, and Appraisal Theory

According to psychological emotion theories referred to as appraisal theory, emotions are caused by appraisals (evaluative judgments). Borrowing a term from Jan Smedslund, it is the contention of this article that psychological appraisal theory is “pseudoempirical” (i.e., misleadingly or incorrectly empirical). In the article I outline what makes some scientific psychology “pseudoempirical,” distinguish my view on this from Jan Smedslund’s, and then go on to show why paying heed to the ordinary meanings of emotion terms is relevant to psychology, and how appraisal theory is methodologically off the mark by employing experiments, questionnaires, and the like, to investigate what follows from the ordinary meanings of words. The overarching argument of the article is that the scientific research program of appraisal theory is fundamentally misguided and that a more philosophical approach is needed to address the kinds of questions it seeks to answer

Model-independent dark energy test with sigma_8 using results from the Wilkinson Microwave Anisotropy Probe

By combining the recent WMAP measurements of the cosmic microwave background anisotropies and the results of the recent luminosity distance measurements to type-Ia supernovae, we find that the normalization of the matter power spectrum on cluster scales, sigma_8, can be used to discriminate between dynamical models of dark energy (quintessence models) and a conventional cosmological constant model (LCDM).Comment: 5 pages, 6 figures. Additional discussion and reference, matches PRD accepted versio

The SPTPoL extended cluster survey

We describe the observations and resultant galaxy cluster catalog from the 2770 deg2 SPTpol Extended Cluster Survey (SPT-ECS). Clusters are identified via the Sunyaev-Zel'dovich (SZ) effect and confirmed with a combination of archival and targeted follow-up data, making particular use of data from the Dark Energy Survey (DES). With incomplete follow-up we have confirmed as clusters 244 of 266 candidates at a detection significance ξ ≥ 5 and an additional 204 systems at 4 4 threshold, and 10% of their measured SZ flux. We associate SZ-selected clusters, from both SPT-ECS and the SPT-SZ survey, with clusters from the DES redMaPPer sample, and we find an offset distribution between the SZ center and central galaxy in general agreement with previous work, though with a larger fraction of clusters with significant offsets. Adopting a fixed Planck-like cosmology, we measure the optical richness-SZ mass (l - M) relation and find it to be 28% shallower than that from a weak-lensing analysis of the DES data-a difference significant at the 4σ level-with the relations intersecting at λ = 60. The SPT-ECS cluster sample will be particularly useful for studying the evolution of massive clusters and, in combination with DES lensing observations and the SPT-SZ cluster sample, will be an important component of future cosmological analyses

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

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