5 research outputs found
The reinforcement paradox: monetary incentives and Bayesian updating
We report the results of two pre-registered experiments designed to study the reinforcement paradox: increased incentives often fail to increase and sometimes even decrease performance in Bayesian updating tasks. We argue that, in the presence of win/loss cues, higher incentives have two countervailing effects: increased error rates for reinforcement behavior (win-stay, lose-shift) and increased performance for decisions resulting from Bayesian updating. We find some evidence that incentives increase performance when the win/loss cue is removed whereas when reinforcement is active the effects of incentives are mixed
GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
G-protein coupled receptors (GPCRs) are key players in signal transduction and
therefore a large proportion of pharmaceutical drugs target these receptors.
Structural data of GPCRs are sparse yet important for elucidating the
molecular basis of GPCR-related diseases and for performing structure-based
drug design. To ameliorate this problem, GPCR-SSFE 2.0 (http://www.ssfa-
7tmr.de/ssfe2/), an intuitive web server dedicated to providing three-
dimensional Class A GPCR homology models has been developed. The updated web
server includes 27 inactive template structures and incorporates various new
functionalities. Uniquely, it uses a fingerprint correlation scoring strategy
for identifying the optimal templates, which we demonstrate captures
structural features that sequence similarity alone is unable to do. Template
selection is carried out separately for each helix, allowing both single-
template models and fragment-based models to be built. Additionally, GPCR-SSFE
2.0 stores a comprehensive set of pre-calculated and downloadable homology
models and also incorporates interactive loop modeling using the tool SL2,
allowing knowledge-based input by the user to guide the selection process. For
visual analysis, the NGL viewer is embedded into the result pages. Finally,
blind-testing using two recently published structures shows that GPCR-SSFE 2.0
performs comparably or better than other state-of-the art GPCR modeling web
servers
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe