Age-dependent variation in the terminal investment threshold in male crickets

This is the author accepted manuscript, made available online by the publisher 1st February 2018. Final version to be available from the publisher via the DOI in this record.The terminal investment hypothesis proposes that decreased expectation of future reproduction (e.g., arising from a threat to survival) should precipitate increased investment in current reproduction. The level at which a cue of decreased survival is sufficient to trigger terminal investment (i.e., the terminal investment threshold) may vary according to other factors that influence expectation for future reproduction. We test whether the terminal investment threshold varies with age in male crickets, using heat-killed bacteria to simulate an immune-inducing infection. We measured calling effort (a behavior essential for mating) and hemolymph antimicrobial activity in young and old males across a gradient of increasing infection cue intensity. There was a significant interaction between the infection cue and age in their effect on calling effort, confirming the existence of a dynamic terminal investment threshold: young males reduced effort at all infection levels, whereas old males increased effort at the highest levels relative to naïve individuals. A lack of a corresponding decrease in antibacterial activity suggests that altered reproductive effort is not traded against investment in this component of immunity. Collectively, these results support the existence of a dynamic terminal investment threshold, perhaps accounting for some of the conflicting evidence in support of terminal investment. This article is protected by copyright. All rights reserved.This research was funded, in part, by a grant from the National Science Foundation IOS 16-54028 (SKS, BMS, and JH), grants from the Beta Lambda Chapter of the Phi Sigma Biological Honor Society, Graduate Student Association of Illinois State University, Animal Behavior Society, and Orthopterists‘ Society to KRD, and an Illinois State University Summer Faculty Fellowship and Faculty Research Award to SKS

Drug survival of adalimumab, ustekinumab and secukinumab in patients with psoriasis: a prospective cohort study from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR).

BACKGROUND: Real-world biologic drug survival is an important proxy measure for effectiveness. Predictors of drug survival may help patients with psoriasis choose between biologic therapies. OBJECTIVES: (i) To assess the relative drug survival of adalimumab, ustekinumab and secukinumab in patients with psoriasis. (ii) To investigate predictors of biologic drug survival. METHODS: A prospective cohort study was performed in the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) between November 2007 and August 2019. We performed survival analysis and fitted a flexible parametric survival model for biologic discontinuation due to ineffectiveness. RESULTS: In total 9652 patients were included: 5543 starting on adalimumab (57·4%), 991 on secukinumab (10·3%) and 3118 on ustekinumab (32·3%). The overall drug survivals of adalimumab, secukinumab and ustekinumab in year 1 were 0·78 [95% confidence interval (CI) 0·77-0·79], 0·88 (95% CI 0·86-0·91) and 0·88 (95% CI 0·87-0·89), respectively. The adjusted hazard ratios (adjHRs) for discontinuation of adalimumab and secukinumab compared with ustekinumab were 2·11 (95% CI 1·76-2·54) and 0·67 (95% CI 0·40-1·11), respectively. The presence of psoriatic arthritis predicted for survival in the adalimumab and secukinumab cohorts (adjHR 0·67, 95% CI 0·51-0·88 and 0·70, 95% CI 0·40-1·24, respectively), but for discontinuation in the ustekinumab cohort (adjHR 1·42, 95% CI 1·12-1·81). Previous exposure to biologic therapies predicted for discontinuation in the ustekinumab and secukinumab cohorts (adjHR 1·54, 95% CI 1·26-1·89 and 1·49, 95% CI 0·91-2·45, respectively) and for survival in the adalimumab cohort (adjHR 0·71, 95% CI 0·55-0·92). CONCLUSIONS: Secukinumab and ustekinumab have similar sustained drug survival, while adalimumab has a lower drug survival in patients with psoriasis. Psoriatic arthritis and previous biologic experience were predictors with differential effects between the biologic therapies. What is already known about this topic? There is conflicting evidence over the real-world drug survival of secukinumab in patients with psoriasis. Data from registries to date suggest that secukinumab has a lower drug survival than that reported from clinical trials. What does this study add? This study found that secukinumab and ustekinumab had similar sustained drug survival in the real world, while the drug survival of adalimumab was lower, suggesting that the real-world drug survival of secukinumab is higher than previously reported. We found that psoriatic arthritis and previous biologic experience had differential effects on drug discontinuation in the three biologic cohorts. These predictors may help patients and clinicians choose the most appropriate biologic therapy

A standardization approach to compare treatment safety and effectiveness outcomes between clinical trials and real‐world populations in psoriasis

Background: Patients recruited in randomized controlled trials (RCTs) for biologic therapies in psoriasis are not fully representative of the real‐world psoriasis population. Objectives: Firstly, to investigate whether patient characteristics are associated with being included in a psoriasis RCT. Secondly, to estimate the differences in the incidence of severe adverse events (SAEs) and the response rate between RCT and real‐world populations of patients on biologic therapies for psoriasis using a standardization method. Methods: Data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) were appended to individual participant‐level data from two RCTs assessing ustekinumab in patients with psoriasis. Baseline variables were assessed for association of being in an RCT using a multivariable logistic regression model. Propensity score weights were derived to reweigh the registry population so that variables had the distribution of the trial population. We measured the C‐statistic of the model with trial status as the dependent variable, and the risk differences in the incidence rate of SAEs in the first year and Psoriasis Area and Severity Index (PASI) after 6 months in the BADBIR cohort before and after weighting. Results: In total 6790 registry and 2021 RCT participants were included. The multivariable logistic regression model had a C‐statistic of 0.82 [95% confidence interval (CI) 0.81–0.83]. The risk differences for the incidence rate of SAEs and the proportion of patients with PASI < 1.5 were 9.27 (95% CI −3.91–22.5) per 1000 person‐years and 0.95 (95% CI −1.98–4.15), respectively. Conclusions: Our results suggest that RCTs of biologic therapies in patients with psoriasis are not fully representative of the real‐world population, but this lack of external validity does not account for the efficacy–effectiveness gap

Game theory of mind

This paper introduces a model of ‘theory of mind’, namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a ‘game theory of mind’. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a ‘stag-hunt’. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution

Becoming original: effects of strategy instruction

Visual arts education focuses on creating original visual art products. A means to improve originality is enhancement of divergent thinking, indicated by fluency, flexibility and originality of ideas. In regular arts lessons, divergent thinking is mostly promoted through brainstorming. In a previous study, we found positive effects of an explicit instruction of metacognition on fluency and flexibility in terms of the generation of ideas, but not on the originality of ideas. Therefore, we redesigned the instruction with a focus on building up knowledge about creative generation strategies by adding more complex types of association, and adding generation through combination and abstraction. In the present study, we examined the effects of this intervention by comparing it with regular brainstorming instruction. In a pretest-posttest control group design, secondary school students in the comparison condition received the brainstorm lesson and students in the experimental condition received the newly developed instruction lesson. To validate the effects, we replicated this study with a second cohort. The results showed that in both cohorts the strategy instruction of 50 min had positive effects on students' fluency, flexibility and originality. This study implies that instructional support in building up knowledge about creative generation strategies may improve students' creative processes in visual arts education

Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain

Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain updates reward prediction through stimulus–reward experiences. It remains unknown, however, how sensory processing of reward-predicting stimuli contributes to the computation of reward prediction error. To elucidate this issue, we examined the relation between stimulus discriminability of the reward-predicting stimuli and the reward prediction error signal in the brain using functional magnetic resonance imaging (fMRI). Before main experiments, subjects learned an association between the orientation of a perceptually salient (high-contrast) Gabor patch and a juice reward. The subjects were then presented with lower-contrast Gabor patch stimuli to predict a reward. We calculated the correlation between fMRI signals and reward prediction error in two reinforcement learning models: a model including the modulation of reward prediction by stimulus discriminability and a model excluding this modulation. Results showed that fMRI signals in the midbrain are more highly correlated with reward prediction error in the model that includes stimulus discriminability than in the model that excludes stimulus discriminability. No regions showed higher correlation with the model that excludes stimulus discriminability. Moreover, results show that the difference in correlation between the two models was significant from the first session of the experiment, suggesting that the reward computation in the midbrain was modulated based on stimulus discriminability before learning a new contingency between perceptually ambiguous stimuli and a reward. These results suggest that the human reward system can incorporate the level of the stimulus discriminability flexibly into reward computations by modulating previously acquired reward values for a typical stimulus

Evidence for a heritable predisposition to Chronic Fatigue Syndrome

<p>Abstract</p> <p>Background</p> <p>Chronic Fatigue Syndrome (CFS) came to attention in the 1980s, but initial investigations did not find organic causes. Now decades later, the etiology of CFS has yet to be understood, and the role of genetic predisposition in CFS remains controversial. Recent reports of CFS association with the retrovirus xenotropic murine leukemic virus-related virus (XMRV) or other murine leukemia related retroviruses (MLV) might also suggest underlying genetic implications within the host immune system.</p> <p>Methods</p> <p>We present analyses of familial clustering of CFS in a computerized genealogical resource linking multiple generations of genealogy data with medical diagnosis data of a large Utah health care system. We compare pair-wise relatedness among cases to expected relatedness in the Utah population, and we estimate risk for CFS for first, second, and third degree relatives of CFS cases.</p> <p>Results</p> <p>We observed significant excess relatedness of CFS cases compared to that expected in this population. Significant excess relatedness was observed for both close (p <0.001) and distant relationships (p = 0.010). We also observed significant excess CFS relative risk among first (2.70, 95% CI: 1.56-4.66), second (2.34, 95% CI: 1.31-4.19), and third degree relatives (1.93, 95% CI: 1.21-3.07).</p> <p>Conclusions</p> <p>These analyses provide strong support for a heritable contribution to predisposition to Chronic Fatigue Syndrome. A population of high-risk CFS pedigrees has been identified, the study of which may provide additional understanding.</p

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups