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Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments allow us to estimate model parameters, other basic problems such as estimating the marginal likelihood, a fundamental tool in Bayesian model selection, remain challenging. This is an important scientific limitation because testing psychological hypotheses with hierarchical models has proven difficult with current model selection methods. We propose an efficient method for estimating the marginal likelihood for models where the likelihood is intractable, but can be estimated unbiasedly. It is based on first running a sampling method such as MCMC to obtain samples for the model parameters, and then using these samples to construct the proposal density in an importance sampling (IS) framework with an unbiased estimate of the likelihood. Our method has several attractive properties: it generates an unbiased estimate of the marginal likelihood, it is robust to the quality and target of the sampling method used to form the IS proposals, and it is computationally cheap to estimate the variance of the marginal likelihood estimator. We also obtain the convergence properties of the method and provide guidelines on maximizing computational efficiency. The method is illustrated in two challenging cases involving hierarchical models: identifying the form of individual differences in an applied choice scenario, and evaluating the best parameterization of a cognitive model in a speeded decision making context. Freely available code to implement the methods is provided. Extensions to posterior moment estimation and parallelization are also discussed., 38 pages, 4 tables, 5 appendices

With the advancement of technologies like in-car navigation and smartphones, concerns around how cognitive functioning is influenced by "workload" are increasingly prevalent. Research shows that spreading effort across multiple tasks can impair cognitive abilities through an overuse of resources, and that similar overload effects arise in difficult single-task paradigms. We developed a novel lab-based extension of the Detection Response Task, which measures workload, and paired it with a Multiple Object Tracking Task to manipulate cognitive load. Load was manipulated either by changing within-task difficulty or by the addition of an extra task. Using quantitative cognitive modelling we showed that these manipulations cause similar cognitive impairments through diminished processing rates, but that the introduction of a second task tends to invoke more cautious response strategies that do not occur when only difficulty changes. We conclude that more prudence should be exercised when directly comparing multi-tasking and difficulty-based workload impairments, particularly when relying on measures of central tendency.

Fusobacterium nucleatum is a ubiquitous opportunistic pathogen with an emerging role as an oncomicrobe in colorectal cancer and other cancer settings. F. nucleatum can adhere to and invade host cells in a manner that varies across F. nucleatum strains and host cell phenotypes. Here, we performed pairwise cocultures between three F. nucleatum strains and two immortalized primary host cell types (human colonic epithelial [HCE] cells and human carotid artery endothelial [HCAE] cells) followed by transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) to investigate transcriptional and epigenetic host cell responses. We observed that F. nucleatum-induced host cell transcriptional modulation involves strong upregulation of genes related to immune migration and inflammatory processes, such as TNF, CXCL8, CXCL1, and CCL20. Furthermore, we identified genes strongly upregulated in a cell line-specific manner. In HCE cells, overexpressed genes included UBD and DUOX2/DUOXA2, associated with p53 degradation-mediated proliferation and intestinal reactive oxygen species (ROS) production, respectively. In HCAE cells, overexpressed genes included EFNA1 and LIF, two genes commonly upregulated in colorectal cancer and associated with poor patient outcomes, and PTGS2 (COX2), a gene associated with the protective effect of aspirin in the colorectal cancer setting. Interestingly, we also observed downregulation of numerous histone modification genes upon F. nucleatum exposure. We used the ChIP-seq data to annotate chromatin states genome wide and found significant chromatin remodeling following F. nucleatum exposure in HCAE cells, with increased frequencies of active enhancer and low-signal/quiescent states. Thus, our results highlight increased inflammation and chemokine gene expression as conserved host cell responses to F. nucleatum exposure and extensive host cell epigenomic changes specific to host cell type. IMPORTANCE Fusobacterium nucleatum is a bacterium normally found in the healthy oral cavity but also has an emerging role in colorectal cancer and other cancer settings. The host-microbe interactions of F. nucleatum and its involvement in tumor initiation, progression, and treatment resistance are not fully understood. We explored host cell changes that occur in response to F. nucleatum. We identified key genes differentially expressed in response to various conditions of F. nucleatum exposure and determined that the conserved host cell response to F. nucleatum was dominated by increased inflammation and chemokine gene expression. Additionally, we found extensive host cell epigenomic changes as a novel aspect of host modulation associated with F. nucleatum exposure. These results extend our understanding of F. nucleatum as an emerging pathogen and highlight the importance of considering strain heterogeneity and host cell phenotypic variation when exploring pathogenic mechanisms of F. nucleatum.

Fusobacterium nucleatum (Fn) is a ubiquitous opportunistic pathogen with an emerging role as an oncomicrobe in colorectal and other cancer types. Fn can adhere to and invade host cells in a manner that varies across Fn strains and host cell phenotypes. Here we performed pairwise co-cultures between three Fn strains and two immortalized primary host cell types (colonic epithelial cells and vascular endothelial cells) followed by RNA-seq and ChIP-seq to investigate transcriptional and epigenetic host cell responses. We observed that Fn-induced host cell transcriptional modulation involves strong upregulation of genes related to immune migration and inflammatory processes, such as TNF, CXCL8, CXCL1, and CCL20. Further, we identified genes strongly upregulated specifically in conditions of host cell invasion, including overexpression of both EFNA1 and LIF, two genes commonly upregulated in colorectal cancer and associated with poor patient outcomes, and PTGS2 (COX2), a gene associated with the protective effect of aspirin in the colorectal cancer setting. Interestingly, we also observed downregulation of numerous histone modification genes upon Fn exposure. To further explore this relationship, we used the ChIP-seq data to annotate chromatin states genome-wide. We found significant chromatin remodeling following Fn exposure in conditions of host cell invasion, with substantial increases in the frequency of states corresponding to active enhancers as well as low signal or quiescent states. Thus, our results highlight increased inflammation and chemokine gene expression as conserved host cell responses to Fn exposure, and extensive host cell epigenomic changes associated with Fn host cell invasion. These results extend our understanding of Fn as an emerging pathogen and highlight the importance of considering strain heterogeneity and host cell phenotypic variation when exploring pathogenic mechanisms of Fn.

BackTrack is a multi-component, community-based intervention designed to build capacity amongst 14–17-year-old high risk young people. The aim of the current study seeks to explore community value and preferences for reducing youth crime and improving community safety using BackTrack in a rural setting in Armidale, New South Wales, Australia. The study design used discrete choice experiments (DCEs), designed in accordance with the 10-item checklist outlined by the International Society for Pharmacoeconomics and Outcomes Research. The DCE was pilot tested on 43 participants to test feasibility and comprehension. A revised version of the survey was subsequently completed by 282 people over a 12-day period between 30 May 2016 and 10 June 2016, representing a survey response rate of 35%. Ninety per cent of respondents were residents of Armidale, the local rural town where BackTrack was implemented. The DCE generated results that consistently demonstrated a preference for social programs to address youth crime and community safety in the Armidale area. Respondents chose BackTrack over Greater Police Presence 75% of the time with an annual benefit of Australian dollars (AUD) 150 per household, equivalent to a community benefit of AUD 2.04 million. This study estimates a strong community preference for BackTrack relative to more policing (a community willing to pay equivalent to AUD 2.04 million) highlighting the clear value of including community preferences when evaluating community-based programs for high-risk young people.

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalise beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to "borrow" strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an "assumption-light" tool for estimating the relatedness of cognitive processes across tasks, contexts, and time., Comment: 30 pages, 10 figures, 5 tables

Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers. Experimental Design: We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively. Results: We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression (P = 0.007), but immune-related CD8+ T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non–small cell lung cancer. Conclusions: Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type–specific testing.

To explore in a sample of medical oncology outpatients and their nominated support persons (SPs): (1) the relative influence of pain, consciousness and life extension on end-of-life choices using a discrete choice experiment (DCE); (2) the extent to which SPs can predict the choices of index patients and (3) whether having a previous end-of-life discussion was associated with dyad agreement. Adult medical oncology patients and their SPs were approached for consent to complete a survey containing a DCE. Participants chose between three unlabelled care scenarios characterised by three attributes: pain (mild, moderate or severe), consciousness (some, half or most of time) and extension of life (1, 2 or 3 weeks). Respondents selected (1) most-preferred and (2) least-preferred scenarios within each question. SPs answered the same questions but from patient’s perspective. A total of 110 patients and 64 SPs responded overall (42 matched patient-SP dyads). For patients, pain was the most influential predictor of most- and least-preferred scenarios (z = 12.5 and z = 12.9). For SPs, pain was the only significant predictor of most and least-preferred scenarios (z = 9.7 and z = 11.5). Dyad agreement was greater for choices about least- (69%) compared to most-preferred scenarios (55%). Agreement was slightly higher for dyads reporting a previous EOL discussion (68 versus 48%; p = 0.065). Patients and SPs place significant value on avoiding severe pain when making end-of-life choices, over and above level of consciousness or life extension. People’s views about end-of-life scenarios they most as well as least prefer should be sought.

Objective: Parenthood is central to the personal and social identity of many people. For individuals with psychotic disorders, parenthood is often associated with formidable challenges. We aimed to identify predictors of adequate parenting among parents with psychotic disorders. Methods: Data pertaining to 234 parents with psychotic disorders living with dependent children were extracted from a population-based prevalence study, the 2010 second Australian national survey of psychosis, and analysed using confirmatory factor analysis. Parenting outcome was defined as quality of care of children, based on participant report and interviewer enquiry/exploration, and included level of participation, interest and competence in childcare during the last 12 months. Results: Five hypothesis-driven latent variables were constructed and labelled psychosocial support, illness severity, substance abuse/dependence, adaptive functioning and parenting role. Importantly, 75% of participants were not identified to have any dysfunction in the quality of care provided to their child(ren). Severity of illness and adaptive functioning were reliably associated with quality of childcare. Psychosocial support, substance abuse/dependence and parenting role had an indirect relationship to the outcome variable via their association with either severity of illness and/or adaptive functioning. Conclusion: The majority of parents in the current sample provided adequate parenting. However, greater symptom severity and poorer adaptive functioning ultimately leave parents with significant difficulties and in need of assistance to manage their parenting obligations. As symptoms and functioning can change episodically for people with psychotic illness, provision of targeted and flexible support that can deliver temporary assistance during times of need is necessary. This would maximise the quality of care provided to vulnerable children, with potential long-term benefits.

Recently, Veksler, Myers, and Gluck (2015) proposed model flexibility analysis as a method that "aids model evaluation by providing a metric for gauging the persuasiveness of a given fit" (p. 755) Model flexibility analysis measures the complexity of a model in terms of the proportion of all possible data patterns it can predict. We show that this measure does not provide a reliable way to gauge complexity, which prevents model flexibility analysis from fulfilling either of the 2 aims outlined by Veksler et al. (2015): absolute and relative model evaluation. We also show that model flexibility analysis can even fail to correctly quantify complexity in the most clear cut case, with nested models. We advocate for the use of well-established techniques with these characteristics, such as Bayes factors, normalized maximum likelihood, or cross-validation, and against the use of model flexibility analysis. In the discussion, we explore 2 issues relevant to the area of model evaluation: the completeness of current model selection methods and the philosophical debate of absolute versus relative model evaluation. (PsycINFO Database Record

Listeners rely on multiple acoustic cues to recognize any phoneme. The relative contribution of these cues to listeners' perception is typically inferred from listeners' categorization of sounds in a two-alternative forced-choice task. Here we advocate the use of an evidence accumulation model to analyze categorization as well as response time data from such cue weighting paradigms in terms of the processes that underlie the listeners' categorization. We tested 30 Dutch listeners on their categorization of speech sounds that varied between typical /a/ and /a/ in vowel quality (F1 and F2) and duration. Using the linear ballistic accumulator model, we found that the changes in spectral quality and duration lead to changes in the speed of information processing, and the effects were larger for spectral quality. In addition, for stimuli with atypical spectral information, listeners accumulate evidence faster for /a/ compared to /ad. Finally, longer durations of sounds did not produce longer estimates of perceptual encoding time. Our results demonstrate the utility of evidence accumulation models for learning about the latent processes that underlie phoneme categorization. The implications for current theory in speech perception as well as future directions for evidence accumulation models are discussed. (C) 2016 Elsevier Ltd. All rights reserved.

Recent decades have witnessed amazing advances in both mathematical models of cognition and in the field of cognitive neuroscience. These developments were initially independent of one another, but recently the fields have started to become interested in joining forces. The resulting joint modeling of behavioral and neural data can be difficult, but has proved fruitful. We briefly review different approaches used in decision-making research for linking behavioral and neural data, and also provide an example. Our example provides a tight link between behavioral data and evoked scalp potentials measured during mental rotation. The example model illustrates a powerful hypothesis-driven way of linking such data sets. We demonstrate the use of such a model, provide a model comparison against interesting alternatives, and discuss the conclusions that follow from applying such a joint model.

Objective To explore the preferences of people with cancer regarding the timing and format of information provision about emotional concerns that may occur when undergoing medical procedures. Methods Eligible cancer survivors were mailed a survey containing discrete choice scenarios examining their timing and format preferences for information about potential emotional concerns associated with an upcoming hypothetical medical procedure. Results Of 356 eligible patients, 271 (76 %) completed the survey. Both face-to-face discussion and written materials were preferred as the mode of information delivery over access to a website. In order of descending preference, participants preferred to receive the information 1 week, 3 days and the day of the procedure. There were no differences in preferences for timing or format between subgroups based on age, gender, education and cancer type. Conclusion This study has demonstrated that cancer patients prefer receiving information about emotional concerns that might be experienced as part of a medical procedure in either written or via face-to-face format, and one week before the procedure. Practice Implications In order to provide patient-centred care, clinicians and the healthcare system more broadly should consider patient preferences for information delivery about upcoming medical procedures. Information preparation for medical procedures; discrete choice; oncology; patient preference; emotional response.

To explore in a sample of adult cancer patients: (1) the relative influence of initiation source, information format and consultation format on preferred approach to life expectancy disclosure using a discrete choice experiment (DCE); and (2) whether patient age, cancer type and perceived prognosis were associated with preferences within the three attributes. A DCE survey of adult solid tumour and haematological cancer patients. Participants chose between three hypothetical scenarios about life expectancy disclosure consisting of three attributes: initiation source (i.e. doctor versus patient-initiated discussion), information content (i.e. estimate presented as best-worst-typical length of life case scenario versus median survival time) and consultation format (i.e. two 20-min versus one 40-min consultation). Respondents selected their most preferred scenario within each question. Three hundred and two patients completed the DCE (78% consent rate). Initiation source was the most influential predictor of patient choice. More preferred a doctor deliver life expectancy information as soon as it is available rather than waiting for the patient to ask (59% vs 41% z = − 7.396, p

Neoantigens present unique and specific targets for personalized cancer immunotherapy strategies. Given the low mutational burden yet immunotherapy responsiveness of malignant mesothelioma (MM) when compared to other carcinogen-induced malignancies, identifying candidate neoantigens and T cells that recognize them has been a challenge. We used pleural effusions to gain access to MM tumor cells as well as immune cells in order to characterize the tumor-immune interface in MM. We characterized the landscape of potential neoantigens from SNVs identified in 27 MM patients and performed whole transcriptome sequencing of cell populations from 18 patient-matched pleural effusions. IFNγ ELISpot was performed to detect a CD8+ T cell responses to predicted neoantigens in one patient. We detected a median of 68 (range 7–258) predicted neoantigens across the samples. Wild-type non-binding to mutant binding predicted neoantigens increased risk of death in a model adjusting for age, sex, smoking status, histology and treatment (HR: 33.22, CI: 2.55–433.02, p = .007). Gene expression analysis indicated a dynamic immune environment within the pleural effusions. TCR clonotypes increased with predicted neoantigen burden. A strong activated CD8+ T-cell response was identified for a predicted neoantigen produced by a spontaneous mutation in the ROBO3 gene. Despite the challenges associated with the identification of bonafide neoantigens, there is growing evidence that these molecular changes can provide an actionable target for personalized therapeutics in difficult to treat cancers. Our findings support the existence of candidate neoantigens in MM despite the low mutation burden of the tumor, and may present improved treatment opportunities for patients.

Item does not contain fulltext Functional neuroimaging data indicate the dorsal striatum is engaged when people are required to vary the cautiousness of their decisions, by emphasizing the speed or accuracy of responding in laboratory-based decision tasks. However, the functional contribution of the striatum to decision making is unknown. In the current study we tested patients with focal ischemic lesions of the dorsal striatum and matched non-lesion control participants on a speed-accuracy tradeoff (SAT) task. Analysis using a computational model of response selection in a competitive and time-pressured context indicated that the decisions of patients with striatal lesions were less cautious than those of matched controls. This deficit was most prominent when the accuracy of decisions was emphasized. The results are consistent with the hypothesis that the striatum plays an important role in strategically setting response caution, an essential function for flexible behavior.

Organisms making repeated simple decisions are faced with a tradeoff between urgent and cautious strategies. While animals can adopt a statistically optimal policy for this tradeoff, findings about human decision-makers have been mixed. Some studies have shown that people can optimize this "speed-accuracy tradeoff", while others have identified a systematic bias towards excessive caution. These issues have driven theoretical development and spurred debate about the nature of human decision-making. We investigated a potential resolution to the debate, based on two factors that routinely differ between human and animal studies of decision-making: the effects of practice, and of longer-term feedback. Our study replicated the finding that most people, by default, are overly cautious. When given both practice and detailed feedback, people moved rapidly towards the optimal policy, with many participants reaching optimality with less than 1 h of practice. Our findings have theoretical implications for cognitive and neural models of simple decision-making, as well as methodological implications.

Strength imbalances between the hamstrings and quadriceps are an essential predictor for hamstring strain in soccer. The study aimed to investigate and compare the muscle strength imbalances of professional soccer players of different performance levels. One hundred and fifty nine senior male professional soccer players from first (n = 75) and second league (n = 84) Portuguese clubs participated in this study. Muscle strength was evaluated with a REV9000 isokinetic dynamometer. Maximal peak torque data were used to calculate quadriceps and hamstrings strength during concentric and eccentric actions, bilateral asymmetry, conventional strength ratios and dynamic control ratios. Second league athletes produced slightly lower conventional strength ratios in the right and left legs (ES = 0.22, p = 0.17 and ES = 0.36, p = 0.023, respectively) compared to the first league athletes. No significant differences were found in dynamic control ratios or in bilateral asymmetry among first and second league athletes. These findings do not show a clear link between the competitive level and injury risk in soccer players. However, some of the differences found, particularly in conventional strength ratios, highlight the importance of performing off-season and pre-season strength assessments to prescribe and adjust individual strength training programs among professional soccer players.

Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approaches have been employed in the study of perceptual decision-making, beginning with rudimentary linking of the data sets at a qualitative, structural level, culminating in sophisticated statistical approaches with quantitative links. We outline a new approach, in which a single model is developed that jointly addresses neural and behavioral data. This approach allows for specification and testing of quantitative links between neural and behavioral aspects of the model. Estimating the model in a Bayesian framework allows both data sets to equally inform the estimation of all model parameters. The use of a hierarchical model architecture allows for a model, which accounts for and measures the variability between neurons. We demonstrate the approach by re-analysis of a classic data set containing behavioral recordings of decision-making with accompanying single-cell neural recordings. The joint model is able to capture most aspects of both data sets, and also supports the analysis of interesting questions about prediction, including predicting the times at which responses are made, and the corresponding neural firing rates.

The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend upon the model selected and its estimated parameters. Modern approaches use hierarchical Bayesian models and Markov chain Monte-Carlo (MCMC) methods to estimate the posterior distribution of the parameters. Although there are several approaches available for model selection, they are all based on the posterior samples produced via MCMC, which means that the model selection inference inherits the properties of the MCMC sampler. To improve on current approaches to LBA inference we propose two methods that are based on recent advances in particle MCMC methodology; they are qualitatively different from existing approaches as well as from each other. The first approach is particle Metropolis-within-Gibbs; the second approach is density tempered sequential Monte Carlo. Both new approaches provide very efficient sampling and can be applied to estimate the marginal likelihood, which provides Bayes factors for model selection. The first approach is usually faster. The second approach provides a direct estimate of the marginal likelihood, uses the first approach in its Markov move step and is very efficient to parallelize on high performance computers. The new methods are illustrated by applying them to simulated and real data, and through pseudo code. The code implementing the methods is freely available., 35 pages, 6 figures, 7 tables

Only 2-3% of cancer patients enrol in a trial. We surveyed patients' willingness to change clinician or treating centre, or to travel, to participate in trials, to improve trial recruitment. Of 188 respondents, 79% were willing to participate in a trial in at least one scenario. Increasing travel time, change in oncologist, private health insurance and out of pocket expenses decreased likelihood of joining a trial. Rural and regional patients, and those from lower socio-economic areas, were more willing to travel. To optimise access to trials, clinicians should refer within and between institutions.

Objective Using a vignette-style DCE in a sample of oncology patients, this study explored: (1) the relative influence of the patient's level of concern about their depression on preferences for care, (2) the relative influence of depression severity according to a mental health checklist on preferred treatment-seeking options, and (3) whether patient age and gender were associated with depression care preference. Methods A discrete choice experiment (DCE) survey of cancer patients was conducted. Hypothetical vignettes to elicit care preferences were created using two attributes: the cancer patient's level of concern about depression (a little or a great deal) and results of a mental health checklist (not depressed or very depressed). Three response options for care preferences were presented, including a self-directed approach, shared care approach, and clinician-directed referral approach. Participants chose their most and least preferred options. Results A total of 281 cancer patients completed the survey. There was a significant association between level of concern and the most preferred option. Those with a great deal of concern about depression preferred to receive referral from their clinician more than those with a little concern about depression. Males were significantly more likely to select a self-directed approach as their most preferred option. Conclusions An oncology patient's level of concern about depression may influence the type of care they want to receive from their cancer doctor for depression. This finding has implications for depression screening in clinical practice.

The self-immunopeptidome is the repertoire of all self-peptides that can be presented by the combination of MHC variants carried by an individual, defined by their HLA genotype. Each MHC variant presents a distinct set of self-peptides, and the number of peptides in a set is variable. Subjects carrying MHC variants that present fewer self-peptides should also present fewer mutated peptides, resulting in decreased immune pressure on tumor cells. To explore this, we predicted peptide-MHC binding values using all unique 8-11mer human peptides in the human proteome and all available HLA class I allelic variants, for a total of 134 billion unique peptide--MHC binding predictions. From these predictions, we observe that most peptides are able to be presented by relatively few (< 250) MHC, while some can be presented by upwards of 1,500 different MHC. There is substantial overlap among the repertoires of peptides presented by different MHC and no relationship between the number of peptides presented and HLA population frequency. Nearly 30% of self-peptides are presentable by at least one MHC, leaving 70% of the human peptidome unsurveyed by T cells. We observed similar distributions of predicted self-immunopeptidome sizes in cancer subjects compared to controls, and within the pan-cancer population, predicted self-immunopeptidome size combined with mutational load to predict survival. Self-immunopeptidome analysis revealed evidence for tumor immunoediting and identified specific peptide positions that most influence immunogenicity. Because self-immunopeptidome size is defined by HLA genotypes and approximates neoantigen load, HLA genotyping could offer a rapid predictive biomarker for response to immunotherapy.

Neuroscientific theories of attention-deficit/hyperactivity disorder (ADHD) alternately posit that cognitive aberrations in the disorder are due to acute attentional lapses, slowed neural processing, or reduced signal-to-noise ratios. However, they make similar predictions about behavioral summary statistics (response times [RTs] and accuracy), hindering the field's ability to produce strong and specific tests of these theories. The current study uses the linear ballistic accumulator (LBA; BrownHeathcote, 2008), a mathematical model of choice RT tasks, to distinguish between competing theory predictions. Children with ADHD (n = 80) and age-matched controls (n = 32) completed a numerosity discrimination paradigm at 2 levels of difficulty, and RT data were fit to the LBA model to test theoretical predictions. Individuals with ADHD displayed slowed processing of evidence for correct responses (signal) relative to their peers but comparable processing of evidence for error responses (noise) and between-trial variability in processing (performance lapses). The findings are inconsistent with accounts that posit an increased incidence of attentional lapses in the disorder and provide partial support for those that posit slowed neural processing and lower signal-to-noise ratios. Results also highlight the utility of well-developed cognitive models for distinguishing between the predictions of etiological theories of psychopathology. (PsycINFO Database Record

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA's inferential properties, in simulation studies.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy., 0, info:eu-repo/semantics/published

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we asked people for prior and posterior inferences about the probability that 1 of 2 coins would generate certain outcomes. Most participants' inferences were inconsistent with Bayes' rule. Only in the simplest version of the task did the majority of participants adhere to Bayes' rule, but even in that case, there was a significant proportion that failed to do so. The current results highlight the importance of close quantitative comparisons between Bayesian inference and human data at the individual-subject level when evaluating models of cognition.

The dominant theoretical paradigm in explaining decision making throughout both neuroscience and cognitive science is known as “evidence accumulation”—the core idea being that decisions are reached by a gradual accumulation of noisy information. Although this notion has been supported by hundreds of experiments over decades of study, a recent theory proposes that the fundamental assumption of evidence accumulation requires revision. The “urgency gating” model assumes decisions are made without accumulating evidence, using only moment-by-moment information. Under this assumption, the successful history of evidence accumulation models is explained by asserting that the two models are mathematically identical in standard experimental procedures. We demonstrate that this proof of equivalence is incorrect, and that the models are not identical, even when both models are augmented with realistic extra assumptions. We also demonstrate that the two models can be perfectly distinguished in realistic simulated experimental designs, and in two real data sets; the evidence accumulation model provided the best account for one data set, and the urgency gating model for the other. A positive outcome is that the opposing modeling approaches can be fruitfully investigated without wholesale change to the standard experimental paradigms. We conclude that future research must establish whether the urgency gating model enjoys the same empirical support in the standard experimental paradigms that evidence accumulation models have gathered over decades of study.

We investigate a question relevant to the psychology and neuroscience of perceptual decision-making: whether decisions are based on steadily accumulating evidence, or only on the most recent evidence. We report an empirical comparison between two of the most prominent examples of these theoretical positions, the diffusion model and the urgency-gating model, via model-based qualitative and quantitative comparisons. Our findings support the predictions of the diffusion model over the urgency-gating model, and therefore, the notion that evidence accumulates without much decay. Gross qualitative patterns and fine structural details of the data are inconsistent with the notion that decisions are based only on the most recent evidence. More generally, we discuss some strengths and weaknesses of scientific methods that investigate quantitative models by distilling the formal models to qualitative predictions.

Constant decision-making underpins much of daily life, from simple perceptual decisions about navigation through to more complex decisions about important life events. At many scales, a fundamental task of the decision-maker is to balance competing needs for caution and urgency: fast decisions can be more efficient, but also more often wrong. We show how a single mathematical framework for decision-making explains the urgency/caution balance across decision-making at two very different scales. This explanation has been applied at the level of neuronal circuits (on a time scale of hundreds of milliseconds) through to the level of stable personality traits (time scale of years).

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.

We commonly determine the most preferred (best) and least preferred (worst) of a set of options, yet it is unclear whether the 2 choices are based on the same or different information. We examined best and worst choices using discrete choice tasks, in which participants selected either the best option from a set, the worst option, or selected both the best and the worst options. One experiment used perceptual judgments of area, and another used consumer preferences for various attributes of mobile phones. In both domains, we found that the task (best, worst, or best and worst) does not alter the preferences expressed for the best (respectively, the worst) option. We also observed that the choice probabilities were consistent with a single latent dimension—options that were frequently selected as best were infrequently selected as worst, and vice versa—both within and between respondents. A quantitative model of choice and response time provided convergent evidence on those relations, with model variants that assumed an inverse relationship between the estimated parameters for best and worst choices accounting well for the data.We conclude that the diverse types of best and worst choices that we studied can be conceived as opposing ends of a single continuous dimension rather than distinct latent entities. We discuss these results in the light of rather different results for accepting (e.g.,purchasing) and rejecting (e.g., not purchasing) options from a set Refereed/Peer-reviewed

Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice.

Summary No differences in blood transcriptome, virome, and B-cell/T-cell receptor patterns were found between chronic fatigue syndrome or alternatively diagnosed chronic Lyme syndrome patients and controls. Neither disease was found to be associated with transcriptionally mediated immune dysfunction or active viral infection., Background. Chronic fatigue syndrome (CFS) remains poorly understood. Although infections are speculated to trigger the syndrome, a specific infectious agent and underlying pathophysiological mechanism remain elusive. In a previous study, we described similar clinical phenotypes in CFS patients and alternatively diagnosed chronic Lyme syndrome (ADCLS) patients—individuals diagnosed with Lyme disease by testing from private Lyme specialty laboratories but who test negative by reference 2-tiered serologic analysis. Methods. Here, we performed blinded RNA-seq analysis of whole blood collected from 25 adults diagnosed with CFS and 13 ADCLS patients, comparing these cases to 25 matched controls and 11 patients with well-controlled systemic lupus erythematosus (SLE). Samples were collected at patient enrollment and not during acute symptom flares. RNA-seq data were used to study host gene expression, B-cell/T-cell receptor profiles (BCR/TCR), and potential viral infections. Results. No differentially expressed genes (DEGs) were found to be significant when CFS or ADCLS cases were compared to controls. Forty-two DEGs were found when SLE cases were compared to controls, consistent with activation of interferon signaling pathways associated with SLE disease. BCR/TCR repertoire analysis did not show significant differences between CFS and controls or ADCLS and controls. Finally, viral sequences corresponding to anelloviruses, human pegivirus 1, herpesviruses, and papillomaviruses were detected in RNA-seq data, but proportions were similar (P = .73) across all genus-level taxonomic categories. Conclusions. Our observations do not support a theory of transcriptionally mediated immune cell dysregulation in CFS and ADCLS, at least outside of periods of acute symptom flares.

In cued task switching, performance relies on proactive and reactive control processes. Proactive control is evident in the reduction in switch cost under conditions that promote advance preparation. However, the residual switch cost that remains under conditions of optimal proactive control indicates that, on switch trials, the target continues to elicit interference that is resolved using reactive control. We examined whether posttarget interference varies as a function of trial-by-trial variability in preparation. We investigated target congruence effects on behavior and target-locked ERPs extracted across the response time (RT) distribution, using orthogonal polynomial trend analysis (OPTA). Early N2, late N2, and P3b amplitudes were differentially modulated across the RT distribution. There was a large congruence effect on late N2 and P3b, which increased with RT for P3b amplitude, but did not vary with trial type. This suggests that target properties impact switch and repeat trials equally and do not contribute to residual switch cost. P3b amplitude was larger, and latency later, for switch than repeat trials, and this difference became larger with increasing RT, consistent with sustained carryover effects on highly prepared switch trials. These results suggest that slower, less prepared responses are associated with greater target-related interference during target identification and processing, as well as slower, more difficult decision processes. They also suggest that neither general nor switch-specific preparation can ameliorate the effects of target-driven interference. These findings highlight the theoretical advances achieved by integrating RT distribution analyses with ERP and OPTA to examine trial-by-trial variability in performance and brain function.

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology.

In most of the long history of the study of absolute identification--since Miller's (1956) seminal article--a severe limit on performance has been observed, and this limit has resisted improvement even by extensive practice. In a startling result, Rouder, Morey, Cowan, and Pfaltz (2004) found substantially improved performance with practice in the absolute identification of line lengths, albeit for only 3 participants and in a somewhat atypical paradigm. We investigated the limits of this effect and found that it also occurs in more typical paradigms, is not limited to a few virtuoso participants or due to relative judgment strategies, and generalizes to some (e.g., line inclination and tone frequency) but not other (e.g., tone loudness) dimensions. We also observed, apart from differences between dimensions, 2 unusual aspects of improvement with practice: (a) a positive correlation between initial performance and the effect of practice and (b) a large reduction in a characteristic trial-to-trial decision bias with practice.

Signal detection theory forms the core of many current models of cognition, including memory, choice, and categorization. However, the classic signal detection model presumes the a priori existence of fixed stimulus representations--usually Gaussian distributions--even when the observer has no experience with the task. Furthermore, the classic signal detection model requires the observer to place a response criterion along the axis of stimulus strength, and without theoretical elaboration, this criterion is fixed and independent of the observer's experience. We present a dynamic, adaptive model that addresses these 2 long-standing issues. Our model describes how the stimulus representation can develop from a rough subjective prior and thereby explains changes in signal detection performance over time. The model structure also provides a basis for the signal detection decision that does not require the placement of a criterion along the axis of stimulus strength. We present simulations of the model to examine its behavior and several experiments that provide data to test the model. We also fit the model to recognition memory data and discuss the role that feedback plays in establishing stimulus representations.

N. Stewart, G. D. A. Brown, and N. Chater's (2005) relative judgment model includes three core assumptions that enable it to predict accurately the vast majority of "classical" phenomena in absolute identification choices, but not the time taken to make them, including sequential effects, such as assimilation and contrast. These core assumptions, coupled with the parameter values used in the above-mentioned article, lead to the prediction that identification accuracy is low when a large stimulus on 1 trial is followed by a small stimulus on the next trial and vice versa. Data do not support this prediction. The authors identify a set of parameters that allow the model to better fit the data, but problems remain when the data are analyzed with a version of the discrimination measure (d') from signal detection theory. The fundamental problem is that the model fits data on average but at the expense of making incorrect predictions in detail.

Cognitive neuroscientists sometimes apply formal models to investigate how the brain implements cognitive processes. These models describe behavioral data in terms of underlying, latent variables linked to hypothesized cognitive processes. A goal of model-based cognitive neuroscience is to link these variables to brain measurements, which can advance progress in both cognitive and neuroscientific research. However, the details and the philosophical approach for this linking problem can vary greatly. We propose a continuum of approaches that differ in the degree of tight, quantitative, and explicit hypothesizing. We describe this continuum using four points along it, which we dub qualitative structural, qualitative predictive, quantitative predictive, and single model linking approaches. We further illustrate by providing examples from three research fields (decision making, reinforcement learning, and symbolic reasoning) for the different linking approaches.

Background Long non-coding RNAs (lncRNAs) are emerging as molecules that significantly impact many cellular processes and have been associated with almost every human cancer. Compared to protein-coding genes, lncRNA genes are often associated with transposable elements, particularly with endogenous retroviral elements (ERVs). ERVs can have potentially deleterious effects on genome structure and function, so these elements are typically silenced in normal somatic tissues, albeit with varying efficiency. The aberrant regulation of ERVs associated with lncRNAs (ERV-lncRNAs), coupled with the diverse range of lncRNA functions, creates significant potential for ERV-lncRNAs to impact cancer biology. Methods We used RNA-seq analysis to identify and profile the expression of a novel lncRNA in six large cohorts, including over 7,500 samples from The Cancer Genome Atlas (TCGA). Results We identified the tumor-specific expression of a novel lncRNA that we have named Endogenous retroViral-associated ADenocarcinoma RNA or ‘EVADR’, by analyzing RNA-seq data derived from colorectal tumors and matched normal control tissues. Subsequent analysis of TCGA RNA-seq data revealed the striking association of EVADR with adenocarcinomas, which are tumors of glandular origin. Moderate to high levels of EVADR were detected in 25 to 53% of colon, rectal, lung, pancreas and stomach adenocarcinomas (mean = 30 to 144 FPKM), and EVADR expression correlated with decreased patient survival (Cox regression; hazard ratio = 1.47, 95% confidence interval = 1.06 to 2.04, P = 0.02). In tumor sites of non-glandular origin, EVADR expression was detectable at only very low levels and in less than 10% of patients. For EVADR, a MER48 ERV element provides an active promoter to drive its transcription. Genome-wide, MER48 insertions are associated with nine lncRNAs, but none of the MER48-associated lncRNAs other than EVADR were consistently expressed in adenocarcinomas, demonstrating the specific activation of EVADR. The sequence and structure of the EVADR locus is highly conserved among Old World monkeys and apes but not New World monkeys or prosimians, where the MER48 insertion is absent. Conservation of the EVADR locus suggests a functional role for this novel lncRNA in humans and our closest primate relatives. Conclusions Our results describe the specific activation of a highly conserved ERV-lncRNA in numerous cancers of glandular origin, a finding with diagnostic, prognostic and therapeutic implications. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0142-6) contains supplementary material, which is available to authorized users.

The stop-signal paradigm is frequently used to study response inhibition. Inthis paradigm, participants perform a two-choice response time task wherethe primary task is occasionally interrupted by a stop-signal that promptsparticipants to withhold their response. The primary goal is to estimatethe latency of the unobservable stop response (stop signal reaction timeor SSRT). Recently, Matzke, Dolan, Logan, Brown, and Wagenmakers (inpress) have developed a Bayesian parametric approach that allows for theestimation of the entire distribution of SSRTs. The Bayesian parametricapproach assumes that SSRTs are ex-Gaussian distributed and uses Markovchain Monte Carlo sampling to estimate the parameters of the SSRT distri-bution. Here we present an efficient and user-friendly software implementa-tion of the Bayesian parametric approach —BEESTS— that can be appliedto individual as well as hierarchical stop-signal data. BEESTS comes withan easy-to-use graphical user interface and provides users with summarystatistics of the posterior distribution of the parameters as well various diag-nostic tools to assess the quality of the parameter estimates. The softwareis open source and runs on Windows and OS X operating systems. In sum,BEESTS allows experimental and clinical psychologists to estimate entiredistributions of SSRTs and hence facilitates the more rigorous analysis ofstop-signal data.

Prior information biases the decision process: actions consistent with prior information are executed swiftly, whereas actions inconsistent with prior information are executed slowly. How is this bias implemented in the brain? To address this question we conducted an experiment in which people had to decide quickly whether a cloud of dots moved coherently to the left or to the right. Cues provided probabilistic information about the upcoming stimulus. Behavioral data were analyzed with the linear ballistic accumulator (LBA) model, confirming that people used the cue to bias their decisions. The functional magnetic resonance imaging (fMRI) data showed that presentation of the cue differentially activated orbitofrontal cortex, hippocampus, and the putamen. Directional cues selectively activated the contralateral putamen. The fMRI analysis yielded results only when the LBA bias parameter was included as a covariate, highlighting the practical benefits of formal modeling. Our results suggest that the human brain uses prior information by increasing cortico-striatal activation to selectively disinhibit preferred responses.

Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The model's predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time.