A series of unfortunate events: Do those who catastrophize learn more after negative outcomes?

Catastrophizing is a transdiagnostic construct that has been suggested to precipitate and maintain a multiplicity of psychiatric disorders, including anxiety, depression, post-traumatic stress disorder, and obsessive-compulsive disorder. However, the underlying cognitive mechanisms that result in catastrophizing are unknown. Relating reinforcement learning model parameters to catastrophizing may allow us to further understand the process of catastrophizing. Using a modified four-armed bandit task, we aimed to investigate the relationship between reinforcement learning parameters and self-report catastrophizing questionnaire scores to gain a mechanistic understanding of how catastrophizing may alter learning. We recruited 211 participants to complete a computerized four-armed bandit task and tested the fit of six reinforcement learning models on our data, including two novel models which both incorporated a scaling factor related to a history of negative outcomes variable. We investigated the relationship between self-report catastrophizing scores and free parameters from the overall best-fitting model, along with the best-fitting model to include history, using Pearson's correlations. Subsequently, we reassessed these relationships using multiple regression analyses to evaluate whether any observed relationships were altered when relevant IQ and mental health covariates were applied. Model-agnostic analyses indicated there were effects of outcome history on reaction time and accuracy, and that the effects on accuracy related to catastrophizing. The overall model of best fit was the Standard Rescorla–Wagner Model and the best-fitting model to include history was a model in which learning rate was scaled by history of negative outcome. We found no effect of catastrophizing on the scaling by history of negative outcome parameter (r = 0.003, p = 0.679), the learning rate parameter (r = 0.026, p = 0.703), or the inverse temperature parameter (r = 0.086, p = 0.220). We were unable to relate catastrophizing to any of the reinforcement learning parameters we investigated. This implies that catastrophizing is not straightforwardly linked to any changes to learning after a series of negative outcomes are received. Future research could incorporate further exploration of the space of models which include a history parameter

Catastrophizing and Risk-Taking

Background: Catastrophizing, when an individual overestimates the probability of a severe negative outcome, is related to various aspects of mental ill-health. Here, we further characterize catastrophizing by investigating the extent to which self-reported catastrophizing is associated with risk-taking, using an online behavioural task and computational modelling. Methods: We performed two online studies: a pilot study (n=69) and a main study (n=263). In the pilot study, participants performed the Balloon Analogue Risk Task (BART), alongside two other tasks (reported in the Supplement), and completed mental health questionnaires. Based on the findings from the pilot, we explored risk-taking in more detail in the main study using two versions of the Balloon Analogue Risk task (BART), with either a high or low cost for bursting the balloon. Results: In the main study, there was a significant negative relationship between self-report catastrophizing scores and risk-taking in the low (but not high) cost version of the BART. Computational modelling of the BART task revealed no relationship between any parameter and Catastrophizing scores in either version of the task. Conclusions: We show that increased self-reported catastrophizing may be associated with reduced behavioural measures of risk-taking, but were unable to identify a computational correlate of this effect

Dot1 binding induces chromatin rearrangements by histone methylation-dependent and -independent mechanisms

<p>Abstract</p> <p>Background</p> <p>Methylation of histone H3 lysine 79 (H3K79) by Dot1 is highly conserved among species and has been associated with both gene repression and activation. To eliminate indirect effects and examine the direct consequences of Dot1 binding and H3K79 methylation, we investigated the effects of targeting Dot1 to different positions in the yeast genome.</p> <p>Results</p> <p>Targeting Dot1 did not activate transcription at a euchromatic locus. However, chromatin-bound Dot1 derepressed heterochromatin-mediated gene silencing over a considerable distance. Unexpectedly, Dot1-mediated derepression was established by both a H3K79 methylation-dependent and a methylation-independent mechanism; the latter required the histone acetyltransferase Gcn5. By monitoring the localization of a fluorescently tagged telomere in living cells, we found that the targeting of Dot1, but not its methylation activity, led to the release of a telomere from the repressive environment at the nuclear periphery. This probably contributes to the activity-independent derepression effect of Dot1.</p> <p>Conclusions</p> <p>Targeting of Dot1 promoted gene expression by antagonizing gene repression through both histone methylation and chromatin relocalization. Our findings show that binding of Dot1 to chromatin can positively affect local gene expression by chromatin rearrangements over a considerable distance.</p

Adaptive learning from outcome contingencies in eating-disorder risk groups

Eating disorders are characterised by altered eating patterns alongside overvaluation of body weight or shape, and have relatively low rates of successful treatment and recovery. Notably, cognitive inflexibility has been implicated in both the development and maintenance of eating disorders, and understanding the reasons for this inflexibility might indicate avenues for treatment development. We therefore investigate one potential cause of this inflexibility: an inability to adjust learning when outcome contingencies change. We recruited (n = 82) three groups of participants: those who had recovered from anorexia nervosa (RA), those who had high levels of eating disorder symptoms but no formal diagnosis (EA), and control participants (HC). They performed a reinforcement learning task (alongside eye-tracking) in which the volatility of wins and losses was independently manipulated. We predicted that both the RA and EA groups would adjust their learning rates less than the control participants. Unexpectedly, the RA group showed elevated adjustment of learning rates for both win and loss outcomes compared to control participants. The RA group also showed increased pupil dilation to stable wins and reduced pupil dilation to stable losses. Their learning rate adjustment was associated with the difference between their pupil dilation to volatile vs. stable wins. In conclusion, we find evidence that learning rate adjustment is unexpectedly higher in those who have recovered from anorexia nervosa, indicating that the relationship between eating disorders and cognitive inflexibility may be complex. Given our findings, investigation of noradrenergic agents may be valuable in the field of eating disorders

A human gut bacterial genome and culture collection for improved metagenomic analyses

Understanding gut microbiome functions requires cultivated bacteria for experimental validation and reference bacterial genome sequences to interpret metagenome datasets and guide functional analyses. We present the Human Gastrointestinal Bacteria Culture Collection (HBC), a comprehensive set of 737 whole-genome-sequenced bacterial isolates, representing 273 species (105 novel species) from 31 families found in the human gastrointestinal microbiota. The HBC increases the number of bacterial genomes derived from human gastrointestinal microbiota by 37%. The resulting global Human Gastrointestinal Bacteria Genome Collection (HGG) classifies 83% of genera by abundance across 13,490 shotgun-sequenced metagenomic samples, improves taxonomic classification by 61% compared to the Human Microbiome Project (HMP) genome collection and achieves subspecies-level classification for almost 50% of sequences. The improved resource of gastrointestinal bacterial reference sequences circumvents dependence on de novo assembly of metagenomes and enables accurate and cost-effective shotgun metagenomic analyses of human gastrointestinal microbiota

Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns

Brass Happenings

Presenting a student recital for brass students in USU\u27s Music Department.https://digitalcommons.usu.edu/music_programs/1107/thumbnail.jp

Red-flag sepsis and SOFA identifies different patient population at risk of sepsis-related deaths on the general ward

Controversy exists regarding the best diagnostic and screening tool for sepsis outside the intensive care unit (ICU). Sequential organ failure assessment (SOFA) score has been shown to be superior to systemic inflammatory response syndrome (SIRS) criteria, however, the performance of “Red Flag sepsis criteria” has not been tested formally. The aim of the study was to investigate the ability of Red Flag sepsis criteria to identify the patients at high risk of sepsis-related death in comparison to SOFA based sepsis criteria. We also investigated the comparison of Red Flag sepsis to quick SOFA (qSOFA), SIRS, and national early warning score (NEWS) scores and factors influencing patient mortality. Patients were recruited into a 24-hour point-prevalence study on the general wards and emergency departments across all Welsh acute hospitals. Inclusion criteria were: clinical suspicion of infection and NEWS 3 or above in-line with established escalation criteria in Wales. Data on Red Flag sepsis and SOFA criteria was collected together with qSOFA and SIRS scores and 90-day mortality. 459 patients were recruited over a 24-hour period. 246 were positive for Red Flag sepsis, mortality 33.7% (83/246); 241 for SOFA based sepsis criteria, mortality 39.4% (95/241); 54 for qSOFA, mortality 57.4% (31/54), and 268 for SIRS, mortality 33.6% (90/268). 55 patients were not picked up by any criteria. We found that older age was associated with death with OR (95% CI) of 1.03 (1.02–1.04); higher frailty score 1.24 (1.11–1.40); DNA-CPR order 1.74 (1.14–2.65); ceiling of care 1.55 (1.02–2.33); and SOFA score of 2 and above 1.69 (1.16–2.47). The different clinical tools captured different subsets of the at-risk population, with similar sensitivity. SOFA score 2 or above was independently associated with increased risk of death at 90 days. The sequalae of infection-related organ dysfunction cannot be reliably captured based on routine clinical and physiological parameters alone