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Anorexia nervosa (AN) is a severe psychiatric disorder marked by extreme weight control behaviors and significant impacts on physical and psychosocial health. This study explores the relationship between vitamin D (Vit-D) levels and impulsivity in women with AN. Forty-six cisgender White women participants were assessed upon admission and before discharge from a specialized eating disorder treatment center, with an average duration of 2.5 ± 0.10 months. Methods included self-reported questionnaires and behavioral tasks to measure impulsivity, alongside serum Vit-D levels. Our results showed significant improvements in Vit-D levels and certain impulsivity measures, such as faster reaction times and fewer errors on the go/no-go task, correlating with higher Vit-D levels. However, no significant correlations were found between Vit-D levels and self-reported impulsivity. These findings suggest that adequate Vit-D levels may enhance cognitive functions related to impulse control in AN. Given this study's limitations, including its exclusive focus on women and small sample size, future research should involve larger, more diverse populations and randomized clinical trials to better understand the causal relationships and therapeutic potential of Vit-D in managing AN-related impulsivity.

Understanding the subprocesses of risky decision making is a prerequisite for understanding (dys-)functional decisions. For the present fMRI study, we designed a novel variant of the balloon-analog-risk task (BART) that measures three phases: decision making, reward anticipation, and feedback processing. Twenty-nine healthy young adults completed the BART. We analyzed neural activity and functional connectivity. Parametric modulation allowed assessing changes in brain functioning depending on the riskiness of the decision. Our results confirm involvement of nucleus accumbens, insula, anterior cingulate cortex, and dorsolateral prefrontal cortex in all subprocesses of risky decision-making. In addition, subprocesses were differentiated by the strength of activation in these regions, as well as by changes in activity and nucleus accumbens-connectivity by the riskiness of the decision. The presented fMRI-BART variant allows distinguishing activity and connectivity during the subprocesses of risky decision making and shows how activation and connectivity patterns relate to the riskiness of the decision. Hence, it is a useful tool for unraveling impairments in subprocesses of risky decision making in people with high risk behavior., (© 2024 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Previous research has revealed that daily variations in human neurobehavioral functions are driven in part by the endogenous circadian system. The objective of this study was to explore whether there exists a circadian influence on performance regarding a risky decision-making task and to determine whether the performance changes with sleep deprivation (SD). Thirteen participants underwent a 39 h constant routine (CR) protocol, during which they remained awake in constant conditions and performed the BART (balloon analogue risk task) every two hours. The mean pumps (gains) ( p < 0.001) and balloons popped (losses) ( p = 0.003) exhibited variation during the CR. The reaction time (RT) also showed significant variation across the CR ( p < 0.001), with slower mean RTs in the morning hours following SD. A greater risk propensity was observed around midday before SD and a lower risk propensity after 29.5 h of being awake. The sensitivity to punishment varied during the CR, but did not follow a predictable trend. Further research using real monetary incentives and neurophysiological measures is warranted to elucidate these findings.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER ., (© 2024. The Author(s).)

Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive ways. In this paper, we review existing variable selection approaches for the Bayesian additive regression trees (BART) model, a nonparametric regression model, which is flexible enough to capture the interactions between predictors and nonlinear relationships with the response. An emphasis of this review is on the ability to identify relevant predictors. We also propose two variable importance measures which can be used in a permutation-based variable selection approach, and a backward variable selection procedure for BART. We introduce these variations as a way of illustrating limitations and opportunities for improving current approaches and assess these via simulations.

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV., (© 2024. The Author(s).)

Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs)., Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers., Methods: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease-related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field., Results: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F 1 -score of 0.85 based on the RenD data set., Conclusions: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines., (©Hina Raja, Asim Munawar, Nikolaos Mylonas, Mohammad Delsoz, Yeganeh Madadi, Muhammad Elahi, Amr Hassan, Hashem Abu Serhan, Onur Inam, Luis Hernandez, Hao Chen, Sang Tran, Wuqaas Munir, Alaa Abd-Alrazaq, Siamak Yousefi. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.03.2024.)

In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set of elementary algebraic operators through compositions. Standard practice directly analyzes the high-dimensional candidate predictor space in a linear model; statistical analyses are then substantially hampered by the daunting challenge posed by the astronomically large number of correlated predictors with limited sample size. We formulate this problem as variable selection with operator-induced structure (OIS) and propose a new method to achieve unconventional dimension reduction by utilizing the geometry embedded in OIS. Although the model remains linear, we iterate nonparametric variable selection for effective dimension reduction. This enables variable selection based on ab initio primary features, leading to a method that is orders of magnitude faster than existing methods, with improved accuracy. To select the nonparametric module, we discuss a desired performance criterion that is uniquely induced by variable selection with OIS; in particular, we propose to employ a Bayesian Additive Regression Trees (BART)-based variable selection method. Numerical studies show superiority of the proposed method, which continues to exhibit robust performance when the input dimension is out of reach of existing methods. Our analysis of single-atom catalysis identifies physical descriptors that explain the binding energy of metal-support pairs with high explanatory power, leading to interpretable insights to guide the prevention of a notorious problem called sintering and aid catalysis design., Competing Interests: Disclosure Statement: The authors report there are no competing interests to declare.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time-to-event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate-dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user-friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood-borne cancer, where heteroskedasticity and nonproportional hazards are likely present., (© 2023 The International Biometric Society.)

Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high-dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects. We propose a Bayesian nonparametric approach that simultaneously (1) prioritizes inclusion of adjustment variables in accordance with existing principles of confounder selection; (2) estimates causal effects in a manner that permits complex relationships among confounders, exposures, and outcomes; and (3) provides causal estimates that account for uncertainty in the nature of confounding. The proposal relies on specification of multiple Bayesian additive regression trees models, linked together with a common prior distribution that accrues posterior selection probability to covariates on the basis of association with both the exposure and the outcome of interest. A set of extensive simulation studies demonstrates that the proposed method performs well relative to similarly-motivated methodologies in a variety of scenarios. We deploy the method to investigate the causal effect of emissions from coal-fired power plants on ambient air pollution concentrations, where the prospect of confounding due to local and regional meteorological factors introduces uncertainty around the confounding role of a high-dimensional set of measured variables. Ultimately, we show that the proposed method produces more efficient and more consistent results across adjacent years than alternative methods, lending strength to the evidence of the causal relationship between SO 2 emissions and ambient particulate pollution., (© 2023 The International Biometric Society.)

Background: Decision making under acute stress is frequent in daily life. While evidence suggests for a modulatory role of neuroticism on risky decision-making behaviors, the neural correlates underlying the association between neuroticism and risky decision-making under acute stress remain to be elucidated., Methods: Based on a modified Balloon Analogue Risk Task (BART) with concurrent functional magnetic resonance imaging, we evaluated the effect of acute stress on risk-taking behavior in 27 healthy male adults, and further assessed stress-induced changes in brain activation according to the individual differences in neuroticism., Results: Higher trait neuroticism levels positively correlated with increased stress-modulated activation of the right dorsal anterior cingulate cortex during risk-taking, and negatively correlated with decreased stress-modulated activation of the right dorsolateral prefrontal cortex during cash-outs., Limitations: Only male participants were recruited., Conclusions: We found a positive correlation between neuroticism and greater risk-taking behavior under acute stress. These results extend our understanding of the increased risk-taking propensity in high neurotic individuals under acute stress., Competing Interests: Declaration of competing interest There are no conflicts of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The application of Natural Language Processing (NLP) to medical data has revolutionized different aspects of health care. The benefits obtained from the implementation of this technique spill over into several areas, including in the implementation of chatbots, which can provide medical assistance remotely. Every possible application of NLP depends on one first main step: the pre-processing of the corpus retrieved. The raw data must be prepared with the aim to be used efficiently for further analysis. Considerable progress has been made in this direction for the English language but for other languages, such as Italian, the state of the art is not equivalently advanced, especially for texts containing technical medical terms. The aim of this work is to identify and develop a preprocessing pipeline suitable for medical data written in Italian. The pipeline has been developed in Python environment, employing Enchant, ntlk modules and Hugging Face's BERT and BART-based models. Then, it has been tested on real conversations typed between patients and physicians regarding medical questions. The algorithm has been developed within the MULTI-SITA project of the Italian Society of Anti-Infective Therapy (SITA), but shows a flexible structure that can adapt to a large variety of data.

The rational design of chemical entities with desired properties for a specific target is a long-standing challenge in drug design. Generative neural networks have emerged as a powerful approach to sample novel molecules with specific properties, termed as inverse drug design. However, generating molecules with biological activity against certain targets and predefined drug properties still remains challenging. Here, we propose a conditional molecular generation net (CMGN), the backbone of which is a bidirectional and autoregressive transformer. CMGN applies large-scale pretraining for molecular understanding and navigates the chemical space for specified targets by fine-tuning with corresponding datasets. Additionally, fragments and properties were trained to recover molecules to learn the structure-properties relationships. Our model crisscrosses the chemical space for specific targets and properties that control fragment-growth processes. Case studies demonstrated the advantages and utility of our model in fragment-to-lead processes and multi-objective lead optimization. The results presented in this paper illustrate that CMGN has the potential to accelerate the drug discovery process., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Humans globally are reaping the benefits of longer lives. Yet, longer life spans also require engaging with consequential but often uncertain decisions well into old age. Previous research has yielded mixed findings with regards to life span differences in how individuals make decisions under uncertainty. One factor contributing to the heterogeneity of findings is the diversity of paradigms that cover different aspects of uncertainty and tap into different cognitive and affective mechanisms. In this study, 175 participants (53.14% females, mean age = 44.9 years, SD = 19.0, age range = 16 to 81) completed functional neuroimaging versions of two prominent paradigms in this area, the Balloon Analogue Risk Task and the Delay Discounting Task. Guided by neurobiological accounts of age-related changes in decision-making under uncertainty, we examined age effects on neural activation differences in decision-relevant brain structures, and compared these across multiple contrasts for the two paradigms using specification curve analysis. In line with theoretical predictions, we find age differences in nucleus accumbens, anterior insula, and medial prefrontal cortex, but the results vary across paradigm and contrasts. Our results are in line with existing theories of age differences in decision making and their neural substrates, yet also suggest the need for a broader research agenda that considers how both individual and task characteristics determine the way humans deal with uncertainty., (© 2023. The Author(s).)

Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population., (© The Author 2021. Published by Oxford University Press.)

Pubertal development is a potential trigger for increases in risk-taking behaviours during adolescence. Here, we sought to investigate the relationship between puberty and neural activation during risky decision-making in males using functional magnetic resonance imaging (fMRI). Forty-seven males aged 12.5-14.5 years completed an fMRI risk-taking task (BART) and reported their tendencies for risky decision-making using a self-report questionnaire. Puberty was assessed through self-reported pubertal status and salivary testosterone levels. Testosterone concentration, but not physical pubertal status, was positively correlated with self-reported risk-taking behaviour, while neither was correlated with BART performance. Across the whole sample, participants had greater activation of the bilateral nucleus accumbens and right caudate on trials when they made a successful risky decision compared to trials when they made a safe choice or when their risky decision was unsuccessful. There was a negative correlation between pubertal stage and brain activation during unsuccessful risky decision-making trials compared within unsuccessful control trials. Males at a lower stage of pubertal development showed increased activation in the left insula, right cingulate cortex, dorsomedial prefrontal cortex (dmPFC), right putamen and right orbitofrontal cortex (OFC) relative to more pubertally mature males during trials when they chose to take a risk and the balloon popped compared to when they watched the computer make an unsuccessful risky decision. Less pubertally mature males also showed greater activation in brain regions including the dmPFC, right temporal and frontal cortices, right OFC, right hippocampus and occipital cortex in unsuccessful risky trials compared to successful risky trials. These results suggest a puberty-related shift in neural activation within key brain regions when processing outcomes of risky decisions, which may reduce their sensitivity to negative feedback, and in turn contribute to increases in adolescent risk-taking behaviours., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The framing effect refers to the phenomenon that different descriptions of the same option lead to a shift in the choice of the decision maker. Several studies have found that emotional contexts irrelevant to a decision in progress still influence the framing effect on decision making. However, little is known about the potential role of emotional contexts in the framing effect on outcome evaluation under uncertainty and the related neural mechanisms. The present study measured event-related potentials (ERPs) to capture the time series of brain activities during the processing of gain- and loss-framed choices and outcomes primed with neutral and negative emotional contexts. The results revealed that in the neutral emotional context, the P300 amplitudes following both positive and negative feedback were greater in the gain-framed condition than those in the loss-framed condition, demonstrating a framing effect, whereas in the negative emotional context, this effect was unstable and observed only following negative feedback. In contrast, regardless of whether the feedback was positive or negative, the framing effect on the feedback-related negativity (FRN) amplitudes was insensitive to neutral and negative emotional contexts. Furthermore, the time-frequency analysis showed that the framing effect on the theta power related to the FRN was also insensitive to neutral and negative emotional contexts. Our findings suggest that brain responses to framing effects on outcome evaluation in a later cognitive appraisal stage of decision making under uncertainty may depend on the emotional context, as the effects were observed only following negative feedback in the negative emotional context., (© 2022 Society for Psychophysiological Research.)

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and heterogeneous treatment effects can be hampered when data are structured within groups if we fail to correctly model the dependence between observations. Most machine learning methods do not readily accommodate such structure. This paper introduces a new algorithm, stan4bart, that combines the flexibility of Bayesian Additive Regression Trees (BART) for fitting nonlinear response surfaces with the computational and statistical efficiencies of using Stan for the parametric components of the model. We demonstrate how stan4bart can be used to estimate average, subgroup, and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data as well as multilevel approaches that have strict parametric forms.

Previous experience often guides people's decision-making. How the corresponding consecutive outcomes in the gain-loss frame modulate feedback evaluation in dynamic situations remains unclear. In this study, we used event-related potentials (ERPs) with a Balloon Analogue Risk Task (BART) to investigate the effect of the consistency with previous feedback on current choices under the gain-loss frame and the neural mechanism by coding ERP signals evoked by consecutive outcomes among adults (N = 42). Results indicated that the framing effect on feedback-related negativity (FRN) amplitudes following current feedback in decision-making was insensitive to prior feedback. In contrast, the P300 amplitudes following two inconsistent consecutive rounds of feedback were greater than that following two consistent consecutive rounds of feedback in the gain frame, while this effect was absent in the loss frame, revealing a more significant valance-framing effect. These findings demonstrate that two inconsistent rounds of feedback enhance the framing effect, and suggest that the framing effect is sensitive to prior feedback in a late cognitive appraisal stage in decision-making under uncertainty, rather than in an early feedback processing stage. The present study provides novel insights into how consecutive outcomes in the gain-loss frame modulate feedback evaluation in dynamic situations., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

The Balloon Analog Risk Task (BART) allows to experimentally assess individuals' risk-taking profiles in an ecologically sound setting. Many psychological and neuroscientific studies implemented the BART for its simplicity and intuitive nature. However, some issues in the design of the BART are systematically unconsidered in experimental paradigms, which may bias the estimation of individual risk-taking profiles. Since there are no methodological guidelines for implementing the BART, many variables (e.g., the maximum explosion probabilities, the rationale underlying stochastic events) vary inconstantly across experiments, possibly producing contrasting results. Moreover, the standard version of the BART is affected by the interaction of an individual-dependent, unavoidable source of stochasticity with a trial-dependent, more ambiguous source of stochasticity (i.e., the probability of the balloon to explode). This paper shows the most appropriate experimental choices for having the lowest error in the approximation of risk-taking profiles. Performance tests of a series of simulated data suggest that a more controlled, eventually non-stochastic version of the BART, better approximates original risk-taking profiles. Selecting optimal BART parameters is particularly important in neuroscience experiments to optimize the number of trials in a time window appropriate for acquiring neuroimaging data. We also provide helpful suggestions to researchers in many fields to allow the implementation of optimized risk-taking experiments using the BART., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Di Plinio, Pettorruso and Ebisch.)

In our previous work [8], we have shown that resting state (RS) functional connectivity metrics are significantly related with behavioural performance at Balloon Analogue Risk Task (BART). In the present study we investigated the hypothesis of an association between RS metrics and neural activity evoked by BART execution. A group-level whole-brain regression was run to assess whether RS metrics predict brain activation during the BART, in a sample of 35 young healthy adults (mean age 23 ± 2 years, 25 F). Results complete the previous findings showing that RS is also significantly associated with the neural activity during BART execution. Specifically, ALFF is positively associated with the activity of both the right inferior parietal lobule and the left caudate. These new results are coherent with previous evidence indicating RS abnormalities in clinical conditions characterised by symptoms of impulse control disorders, and further suggest that RS might be a stable predictor of both behavioural indices and neural correlates of impulsivity and of reward-guided decision-making., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The purpose of this ERP P3 study was to test a peer observation manipulation (being observed by a peer versus being alone) on neural markers of attention to reward (win-feedback) and punishment (loss-feedback) during the Balloon Analogue Risk Task. Participants (126 children, 53 % male, 8-10 years; 196 early adolescents, 50 % male, 11-13 years; and 121 mid-adolescents, 52 % male, 14-16 years) were assessed by age group and pubertal status. Individual differences in how participants felt about being observed by a peer, and self-report personality factors, also were examined. Findings indicated that early and mid-adolescents (and individuals in mid-puberty and late-puberty) were sensitive to peer observation as both groups showed larger neural responses to loss-feedback in the peer condition than in the alone condition. Conversely, children (and individuals in pre- and early-puberty) were unaffected by peer observation. In addition, there clearly were individual differences in how rewarding versus anxiety-provoking participants found the peer experience. Early adolescents and mid-adolescents (and individuals in mid- and late-puberty) who reported feeling more anxious about the peer observation elicited larger neural responses to loss-feedback, and individuals in mid- and late-puberty in particular reported higher worry and lower sensation-seeking scores than those who reported a positive experience., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Coupling loop-mediated isothermal amplification (LAMP) with a bioluminescent assay in real-time (LAMP-BART) is a strategy that can be readily leveraged to detect bacteria in particular samples of interest without the need for costly or complicated equipments. However, this approach exhibits poor sensitivity, and it additionally amplifies all target DNA including that derived from non-viable cells. Herein, we sought to overcome these traditional pyrophosphate bioluminescent assay limitations by utilizing 2-deoxyadenosine-5-(α-thio) -triphosphate (dATPαS) in place of dATP when conducting LAMP, thereby markedly reducing and stabilizing overall background signal levels, resulting in a detection limit of 3 CFU/μL. We were additionally able to ouple this LAMP-BART with propidium monoazide (PMAxx™) as a means of eliminating false-positive signals derived from nonviable cells. Herein, we detail the development of this PMAxx™-LAMP-BART assay and its use for the detection of live Lactobacillus salivarius. Our developed approach exhibited 100% specificity, with a 3 CFU/μL limit of detection (LOD) pure culture. In the application of feed, the LOD was 10 3  CFU per 10 g of spiked dry dog food and 10 2  CFU per 10 g of spiked chicken feed without enrichment. Traditional culture methods and a MALDI Biotyper were also used to confirm the accuracy of our novel assay system., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Epstein-Barr virus (EBV), which encodes >80 genes and nearly 50 non-coding RNAs, is a double-stranded DNA virus. EBV is associated with various types of lymphomas and lymphoproliferative disorders not only of B-cell but also T/NK-cell origin. However, the oncogenic mechanism remains poorly understood, including the EBV receptors expressed on T/NK cells, relationship of EBV with host genes, and epigenetic regulation of EBV and host genes. The roles of host and viral non-coding RNAs during tumorigenesis have been elucidated. EBV encodes at least 49 mature microRNAs (miRNAs), of which 44 are located in Bam HI-A rightward transcripts (BARTs) region, and the remaining five are located in Bam HI-H rightward fragment 1. BART miRNAs modulate cell differentiation, proliferation, apoptosis, and the cell cycle, and they are considered positive regulators of oncogenesis. We and others have recently reported that EBV-positive lymphomas frequently possess large deletions in BART miRNA clusters, suggesting that some viral miRNAs have suppressive effects on oncogenesis, and that deletion of these miRNAs may aid lymphoma formation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kimura, Okuno, Sato, Watanabe and Murata.)

This study examines the impact of peers' previous cautious versus risky choices on adolescents' risk-taking depending on the level of information about the risk. Adolescents completed an adaptation of the BART that manipulated social influence (cautious and risky) and risk information (i.e., informed, noninformed). Results showed that social influence impacts adolescents' decisions on the noninformed BART but not on the informed BART. In the noninformed BART, the peers' cautious choices strongly decreased risk-taking and led to greater performance. The peers' risky choices increase adolescents' risk-taking, but this effect is limited to situations involving minimal risk. Thus social experience may be a specific social context that represents a valuable source of information during adolescence, especially in situations with high uncertainty., (© 2021 Society for Research on Adolescence.)

Ultrasound Core Laboratories (UCL) are used in multicenter trials to assess imaging biomarkers to define robust phenotypes, to reduce imaging variability and to allow blinded independent review with the purpose of optimizing endpoint measurement precision. The Household Air Pollution Intervention Network, a multicountry randomized controlled trial (Guatemala, Peru, India and Rwanda), evaluates the effects of reducing household air pollution on health outcomes. Field studies using portable ultrasound evaluate fetal, lung and vascular imaging endpoints. The objective of this report is to describe administrative methods and training of a centralized clinical research UCL. A comprehensive administrative protocol and training curriculum included standard operating procedures, didactics, practical scanning and written/practical assessments of general ultrasound principles and specific imaging protocols. After initial online training, 18 sonographers (three or four per country and five from the UCL) participated in a 2 wk on-site training program. Written and practical testing evaluated ultrasound topic knowledge and scanning skills, and surveys evaluated the overall course. The UCL developed comprehensive standard operating procedures for image acquisition with a portable ultrasound system, digital image upload to cloud-based storage, off-line analysis and quality control. Pre- and post-training tests showed significant improvements (fetal ultrasound: 71% ± 13% vs. 93% ± 7%, p < 0.0001; vascular lung ultrasound: 60% ± 8% vs. 84% ± 10%, p < 0.0001). Qualitative and quantitative feedback showed high satisfaction with training (mean, 4.9 ± 0.1; scale: 1 = worst, 5 = best). The UCL oversees all stages: training, standardization, performance monitoring, image quality control and consistency of measurements. Sonographers who failed to meet minimum allowable performance were identified for retraining. In conclusion, a UCL was established to ensure accurate and reproducible ultrasound measurements in clinical research. Standardized operating procedures and training are aimed at reducing variability and enhancing measurement precision from study sites, representing a model for use of portable digital ultrasound for multicenter field studies., Competing Interests: Conflict of interest disclosure The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The parametric modeling approach typically used in practice relies on strong modeling assumptions for valid inference, and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health, and aging, and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.

Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity in parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope to leave the reader with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository., (© 2020 John Wiley & Sons Ltd.)

Background: Risky decision-making is an important facet of addiction. Individuals with alcohol dependence show abnormalities in gambling and other risk-taking tasks. In one such measure, the Balloon Analogue Risk Task (BART), participants sequentially choose to pump a virtual balloon to increase potential reward while the risk of explosion increases, or to cash-out and take earnings. In a prior study, alcohol-dependent participants differed from controls in brain activation during decision-making on the BART, but the relationship between risk/reward magnitude and brain activation was not studied, nor were participants compared to controls. Here we compared the degree to which risk and magnitude of reward influenced brain activation in alcohol-dependent participants vs. controls during decision-making on the BART., Methods: Thirty-two participants (16 alcohol-dependent, 16 control; 5 females/group) performed the BART during fMRI. A parametric analysis tested for a relationship between risk/reward magnitude and activation in rDLPFC and bilateral striatum regions of interest when participants chose to take risk or to cash out. An exploratory whole-brain voxel-wise analysis of mean activation during pumping, cash-out, and explosion events was also conducted., Results: Compared with controls, alcohol-dependent participants displayed less modulation of activation in the rDLPFC when taking risk. Exploratory analyses found that alcohol-dependent participants showed less activation than controls during explosions in a cluster including the insula. No differences were seen in striatal activation., Conclusions: Insensitivity of the rDLPFC to risk and of the insula to loss may contribute to decision-making deficits in alcohol dependence., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain-behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel-Berlin Risk Study ( N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention., (Copyright © 2020 Tisdall, Frey, Horn, Ostwald, Horvath, Pedroni, Rieskamp, Blankenburg, Hertwig and Mata.)

The underpinnings of poor decision-making in schizophrenia could reflect excessively risky or inhibited behaviors. This study employed the Balloon Analogue Risk Task (BART) to compare decision-making in schizophrenia cases to that of healthy controls. Individuals with schizophrenia performed significantly differently across three trials, failing to improve their performance as shown by the control group. In the control group, cognitive ability, measured with the Wechsler Adult Intelligence Scale (WAIS-III) showed that Perceptual Organization scores predicted Average Inflations per Trial, Total Balloon Pops, and Total Earnings. Although the schizophrenia cases failed to learn, group performance on the BART was not associated with cognitive ability, but regression analyses showed 41.4% of average inflations per trial were explained by Excitement, Delusions, Emotional Withdrawal, and Poor Rapport; total balloon pops were only explained by emotional withdrawal and Total Earnings were reduced by Delusions, Excitement and Poor Rapport. Only healthy participants demonstrated a relation between cognitive ability performance improvement across trials. Schizophrenia cases showed less risk-taking, and earned significantly less money overall. Identifying the determinants of poor decision-making could inform interventions and possible treatments to improve their function and perhaps be of relevance to public safety if decisions are overly risky., (Copyright © 2020. Published by Elsevier B.V.)

Adolescents demonstrate both heightened sensitivity to peer influence and increased risk-taking. The current study provides a novel test of how these two phenomena are related at behavioral and neural levels. Adolescent males (N = 83, 16-17 years) completed the Balloon Analogue Risk Task (BART) in an fMRI scanner. One week later, participants completed a driving task in which they drove alone and with a safety- or risk-promoting peer passenger. Results showed that neural responses during BART were associated with participants' behavioral conformity to safe vs. risky peer influence while later driving. First, the extent that neural activation in the anterior cingulate cortex (ACC) scaled with decision stakes in BART was associated with conformity to risky peer influence. Additionally, stake-modulated functional connectivity between ventral striatum (VS) and risk processing regions (including ACC and insula) was associated with safer driving under risky peer influence (i.e. resistance to risky peer influence), suggesting that connectivity between VS and ACC as well as insula may serve a protective role under risky peer influence. Together, these results suggest that adolescents' neural responses to risky decision making may modulate their behavioral conformity to different types of peer influence on risk taking., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Epstein-Barr virus (EBV) has been widely recognized to contribute to the development of nasopharyngeal carcinoma (NPC). The present study was to explore the association of plasma Epstein-Barr Virus LMP1 and EBER1 with circulating tumor cells (CTCs) and the metastasis of nasopharyngeal carcinoma. In the present study, we quantified the plasma levels of EBV DNA/RNAs, such as LMP1, LMP2, BART and EBER1 with real-time quantitative PCR, and CTCs with a CellSpotter Analyzer in NPC patients, with or without metastasis. Then the correlation of each biomarker with other biomarkers and tumor metastasis was analyzed. Our data indicated that the plasma levels of EBV LMP1, BART, EBER1, along with CTCs were significantly higher in metastatic NPC patients than in non-metastatic patients. Plasma LMP1 DNA and EBER1 discriminate metastatic NPC patients from non-metastatic patients, correlate with tumor stage and node stage for metastatic NPC patients. In summary, there were significantly higher plasma levels of Epstein-Barr Virus DNAs / RNAs in nasopharyngeal carcinoma patients. LMP1 DNA and EBER1 RNA correlated with the metastasis of nasopharyngeal carcinoma.

Neural correlates of decision making under risk are being increasingly utilized as biomarkers of risk for substance abuse and other psychiatric disorders, treatment outcomes, and brain development. This research relies on the basic assumption that fMRI measures of decision making represent stable, trait-like individual differences. However, reliability needs to be established for each individual construct. Here we assessed long-term test-retest reliability (TRR) of regional brain activations related to decision making under risk using the Balloon Analogue Risk Taking task (BART) and identified regions with good TRRs and familial influences, an important prerequisite for the use of fMRI measures in genetic studies. A secondary goal was to examine the factors potentially affecting fMRI TRRs in one particular risk task, including the magnitude of neural activation, data analytical approaches, different methods of defining boundaries of a region, and participant motion. For the average BOLD response, reliabilities ranged across brain regions from poor to good (ICCs of 0 to 0.8, with a mean ICC of 0.17) and highest reliabilities were observed for parietal, occipital, and temporal regions. Among the regions that were of a priori theoretical importance due to their reported associations with decision making, the activation of left anterior insula and right caudate during the decision period showed the highest reliabilities (ICCs of 0.54 and 0.63, respectively). Among the regions with highest reliabilities, the right fusiform, right rostral anterior cingulate and left superior parietal regions also showed high familiality as indicated by intrapair monozygotic twin correlations (ranging from 0.66 to 0.69). Overall, regions identified by modeling the average BOLD response to a specific event type (rather than its modulation by a parametric regressor), regions including significantly activated vertices (compared to a whole parcel), and regions with greater magnitude of task-related activations showed greater reliabilities. Participant motion had a moderate negative effect on TRR. Regions activated during decision period rather than outcome period of risky decisions showed the greatest TRR and familiality. Regions with reliable activations can be utilized as neural markers of individual differences or endophenotypes in future clinical neuroscience and genetic studies of risk-taking., Competing Interests: Declaration of competing interest All authors declare that they have no conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

An increased propensity for risk taking is a hallmark of adolescent behavior with significant health and social consequences. Here, we elucidated cortical and subcortical regions associated with risky and risk-averse decisions and outcome evaluation using the Balloon Analog Risk Task in a large sample of adolescents (n = 256, 56% female, age 14 ± 0.6), including the level of risk as a parametric modulator. We also identified sex differences in neural activity. Risky decisions engaged regions that are parts of the salience, dorsal attention, and frontoparietal networks, but only the insula was sensitive to increasing risks in parametric analyses. During risk-averse decisions, the same networks covaried with parametric levels of risk. The dorsal striatum was engaged by both risky and risk-averse decisions, but was not sensitive to escalating risk. Negative-outcome processing showed greater activations than positive-outcome processing. Insula, lateral orbitofrontal cortex, middle, rostral, and superior frontal areas, rostral and caudal anterior cingulate cortex were activated only by negative outcomes, with a subset of regions associated with negative outcomes showing greater activation in females. Taken together, these results suggest that safe decisions are predicted by more accurate neural representation of increasing risk levels, whereas reward-related processes play a relatively minor role., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

When facing difficult decisions, people typically believe that "two heads are better than one". However, findings from previous studies are inconsistent regarding the advantages of decision-making in groups as compared to individual decision-making. We hypothesize that personality traits may modulate risk-taking behavior and brain activity changes during group decision-making. In this study, we used event-related potentials (ERP) with a well-validated balloon analogue risk task (BART) paradigm to examine the relationships between personality traits, decision-making behavior, and brain activity patterns when a cohort of male participants make decisions and take risks both in groups and in isolation. We found significantly increased risk-taking behavior and reduced P300 component during group decision-making as compared to individual decision-making only for participants with high Agreeableness, but not for those with low Agreeableness. Moreover, Agreeableness scores correlated with risk-taking behavior and P300 amplitude changes in group decisions. These findings suggest that Agreeableness personality modulates risk-taking behavior and brain activity when people make decisions in groups, which have implications for future group decision research and practice., (Copyright © 2019. Published by Elsevier Inc.)

Epstein-Barr virus (EBV) establishes lifelong latent infection in humans and is associated with several lymphoid and epithelial cancers. In nasopharyngeal carcinoma (NPC), EBV expresses few viral proteins but elevated levels of Bam- HI A rightward transcripts (BARTs) RNA, which includes viral microRNAs and long non-coding RNAs (lncRNAs). BART lncRNAs localize within the nucleus of EBV-infected cells and knockdown of BART lncRNAs significantly affects the expression of genes associated with cell adhesion, oxidoreductase activity, inflammation, and immunity. Notably, downregulation of IKAROS family zinc finger 3 ( IKZF3/ Aiolos), which plays a role in lymphocyte development and cell attachment, occurred in NPC C666-1 cells following BART lncRNA-knockdown. Since Aiolos expression is normally restricted to lymphoid cells and rarely observed in epithelial cells, induction of Aiolos by BART lncRNA was confirmed by expressing the major BART lncRNA isoform, RPMS1 , in EBV-positive and -negative cells. BART lncRNA associated with the CBP/p300 complex and RNA polymerase II (Pol II) in the nucleus, suggesting that BART lncRNAs may mediate epigenetic regulation of gene expression through interaction with the chromatin remodeling machinery. This contention is further supported by evidence that BART lncRNA appears to stall Pol II at the promoter region and may regulate IFNB1 and CXCL8 expression by inhibiting transcription by Pol II in NPC. We hypothesize that EBV BART lncRNA expression modulates host gene expression and maintains EBV latency by interfering with histone methylation and acetylation processes. Aberrant expression of affected host genes mediated by BART lncRNA may lead to immune evasion, progression, and metastasis of NPC., (Copyright © 2019 Verhoeven, Tong, Mok, Liu, He, Zong, Chen, Tsao, Lung and Chen.)

The aim of the present study was to investigate the relationship between trait impulsivity, risk-taking, and decision-making performance. We recruited 20 healthy participants who performed the Iowa Gambling Task (IGT) and the Balloon Analog Risk Task (BART) to measure decision-making and risk-taking. The impulsivity was measured by the Barratt Impulsiveness Scale. Resting-state neural activity was recorded to explore whether brain oscillatory rhythms provide important information about the dispositional trait of impulsivity. We found a significant correlation between the ability to develop a successful strategy and the propensity to take more risks in the first trials of the BART. Risk-taking was negatively correlated with cognitive impulsivity in participants who were unable to develop a successful strategy. Neither risk-taking nor decision-making was correlated with cortical asymmetry. In a more exploratory approach, the group was sub-divided in function of participants' performances at the IGT. We found that the group who developed a successful strategy at the IGT was more prone to risk, whereas the group who failed showed a greater cognitive impulsivity. These results emphasize the need for individuals to explore their environment to develop a successful strategy in uncertain situations, which may not be possible without taking risks.

Impulsivity and risk-taking are known to have an important impact on problematic substance use and criminal behavior. This study examined the predictive value of baseline self-report and behavioral impulsivity and risk-taking measures [Delay Discounting Task (DDT), Balloon Analogue Risk Task (BART) and Behavioral Inhibition, Behavioral Activation Scale (BIS/BAS)] in 12-months follow-up substance use outcomes (e.g., use of alcohol, cannabis and other substances) and criminal recidivism (yes/no). Participants were 213 male offenders with a substance use disorder (SUD) under probation supervision. Bivariate regression analyses showed that BIS and BAS levels were associated (respectively) with the use of alcohol and cannabis. Multiple regression analysis showed that BIS was negatively associated with alcohol use at follow-up, whereas cannabis use at baseline and BAS predicted cannabis use at follow-up. At a trend level, interactions between delay discounting and risk-taking, and interactions between baseline cannabis use and BAS and BART predicted cannabis use at follow-up. Other substance use at follow-up was solely predicted by baseline other substance use. Overall, the findings provide marginal support for the predictive utility of impulsivity and risk-taking in accounting for variability in substance use among offenders with a SUD. This may be partly explained by the fact that only a limited number of psychological factors was assessed in this study. The studied population consists of a severe group, in which relapse into substance use or criminal behavior likely is related to complex, interacting biopsychosocial factors, of which impulsivity measures play a relatively small part., (Copyright © 2019 Rieser, Shaul, Blankers, Koeter, Schippers and Goudriaan.)

Epstein-Barr virus (EBV) genomes, particularly their latent genes, are heterogeneous among strains. The heterogeneity of EBV-encoded latent membrane protein 1 (LMP1) raises the question of whether there are functional differences between LMP1 expressed by cancer-associated EBV and that by non-cancerous strains. Here, we used bacterial artificial chromosome (BAC)-cloned EBV genomes retaining all virally encoded microRNA (miRNA) genes to investigate the functions of cancer-derived LMP1 in the context of the EBV genome. HEK293 cells were stably transfected with EBV-BAC clone DNAs encoding either nasopharyngeal carcinoma (NPC)-derived CAO-LMP1 (LMP1 CAO ) or LMP1 from a prototype B95-8 strain of EBV (LMP1 B95-8 ). When an EBV-BAC clone DNA encoding LMP1 CAO was stably transfected into HEK293 cells, it generated many more stable transformants than the control clone encoding LMP1 B95-8 . Furthermore, stably transfected HEK293 cells exhibited highly efficient production of progeny virus. Importantly, deletion of the clustered viral miRNA genes compromised the ability to produce progeny viruses. These results indicate that cancer-derived LMP1 and viral miRNAs together are necessary for efficient production of progeny virus, and that the resulting increase in efficiency contributes to EBV-mediated epithelial carcinogenesis.

[This corrects the article DOI: 10.3389/fnbeh.2015.00296.].

Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.

Many studies claim to measure decision-making under risk by employing the Domain-Specific Risk-Taking (DOSPERT) scale, a self-report measure, or the Balloon Analogue Risk Task (BART), a behavioural task. However, these tasks do not measure decision-making under risk but decision-making under uncertainty, a related but distinct concept. The present commentary discusses both the theoretical and empirical basis of the distinction between uncertainty and risk from the viewpoint of several scientific disciplines and reports how many studies wrongfully employ the DOSPERT scale and BART as risk-taking measures. Importantly, we call for proper distinguishing between (tasks measuring) decision-making under uncertainty and decision-making under risk in psychology, and related fields. We believe this is vital as research has shown that people's attitudes, behaviour, and brain activity differ between both concepts, indicating that confusing the concepts may lead researchers to erroneous conclusions.

Epstein-Barr virus (EBV) is a human γ-herpesvirus implicated in several human malignancies, including a wide range of lymphomas. Several molecules encoded by EBV in its latent state are believed to be related to EBV-induced lymphomagenesis, among which microRNAs-small RNAs with a posttranscriptional regulating role-are of great importance. The genome of EBV encodes 44 mature microRNAs belonging to two different classes, including BamHI-A rightward transcript ( BART ) and Bam HI fragment H rightward open reading frame 1 ( BHRF1 ), with different expression levels in different EBV latency types. These microRNAs might contribute to the pathogenetic effects exerted by EBV through targeting self mRNAs and host mRNAs and interfering with several important cellular mechanisms such as immunosurveillance, cell proliferation, and apoptosis. In addition, EBV microRNAs can regulate the surrounding microenvironment of the infected cells through exosomal transportation. Moreover, these small molecules could be potentially used as molecular markers. In this review, we try to present an updated and extensive view of the role of EBV-encoded miRNAs in human lymphomas., Competing Interests: The authors declare no conflict of interest.

Objective: Executive and motivational dysfunction have been associated with pediatric obesity. Poor sleep quality and psychopathology, often comorbid with obesity, are also associated with executive and motivational dysfunction. We examined the contribution of these comorbid factors to the association between obesity and executive function and reward-related decision-making., Methods: Seven- to 18-year-old children with and without obesity performed a working memory task with low and high loads, a response inhibition task, and a probabilistic reward-related decision-making task. Parents filled out standardized measures of executive function in everyday behavior, sleep health, and psychiatric symptoms. Analyses controlled for age, gender, IQ, and parental education., Results: Children with obesity showed worse working memory performance under higher load (p = 0.007), and worse parent-reported behavioral regulation (p = 0.05) and metacognition (p = 0.04) in everyday behavior and their reward-related decision-making was less consistent with learned probabilistic conditions (p = 0.04). Response inhibition did not differ between groups. Children with obesity had worse parent-reported sleep health (p < 0.01) and 4.27 greater odds of clinically relevant internalizing symptomology (p = 0.03), both of which mediated the effect of obesity on behavioral regulation (p's < 0.01) and metacognition (p's < 0.01). Performance-based assessments were not associated with sleep health or psychopathology., Conclusions: Sleep quality and internalizing psychopathology were worse in children with obesity and contributed to parent-reported executive dysfunction in their everyday behavior. Performance-based measures of working memory and decision-making were not associated with those comorbidities of obesity.

This study investigated changes in risk-taking propensity on a behavioral decision-making task as a function of varying social conditions with peers. In contrast to the effects of direct peer influence (pro-risk and anti-risk messages by peers), we included a socially ambiguous context (neutral messages by peers) and a no-peer control (participants alone) as comparison conditions. Using a counterbalanced mixed factorial design, college students (N = 187) completed the Balloon Analogue Risk Task-Youth (BART-Y) twice during two consecutive sessions, including once alone and once with a confederate; the control group completed two sessions of the task alone. The findings showed that, in general, direct pro-risk messages led to the most robust and consistent changes in risk-taking. The findings are discussed in terms of the multidimensional and multidirectional nature of peer influence during the college years.

Risky decision-making under a changing risk level is a complex process involving contextual information. The neural mechanism underlying how sleep deprivation (SD) influences risky decision-making behaviors with a changing risk level has yet to be elucidated. In this study, we used the Balloon Analogue Risk Task (BART) during functional magnetic resonance imaging to investigate the neural correlates of SD-induced changes on decision-making behaviors at different risk levels. Thirty-seven healthy male adults were recruited in this within-subjects, repeat-measure, counterbalanced study. These individuals were examined during a state of rested wakefulness state and after nearly 36 h of total SD. The results showed that SD increased the activation of risk modulation in the left inferior frontal gyrus and were positively correlated with risk-taking propensity after SD. Activation in the ventral striatum and thalamus during cash out was increased, and activation in the middle temporal gyrus after explosion (loss of money) was decreased in sleep-deprived subjects, providing additional evidence for greater risk-taking propensity after SD. These results extend our understanding of the neural mechanism underlying alteration of the risk-taking propensity in sleep-deprived individuals.

Background: Marijuana use has been associated with increased risk-taking and impulsive behavior. While pharmacologic effects of marijuana can lead to inhibitory impairment, expectancy of potential impairment may result in compensatory behavioral response by decreasing impulsive decisions and risky behaviors. With the increases in marijuana use and related problems, a better understanding of the individual characteristics associated with marijuana intoxication and risky behavior is needed. This study examined the role of impairment expectancies in marijuana's acute effects on behavioral measures of impulsivity and risk-taking., Methods: Participants (N=136) were regular marijuana users. A balanced placebo design (BPD) was used crossing marijuana administration (i.e., 0% Tetrahydrocannabinol (THC) vs. 2.8% THC) with stimulus expectancy (i.e., Told Placebo vs. Told THC). Marijuana outcome expectancies were measured by self-report and dependent measures included a number of behavioral impulsivity tasks and the balloon analogue risk task (BART)., Results: Among participants who received THC, higher expectancies for cognitive-behavioral impairment (CBI) were related to lower risk-taking on the BART. Among those who received placebo, there was no association between CBI expectancies and BART performance. CBI expectancies did not moderate the stimulus expectancy effect on the BART nor drug or stimulus expectancy effects on impulsivity measures., Conclusions: Results provide initial evidence that expectancies of greater impairment are associated with compensatory behavior on a risk-taking task under acute marijuana intoxication. Future studies should examine the role of impairment expectancies on risk behaviors of substantial public health concern, such as driving while under the influence of marijuana., (Published by Elsevier B.V.)

Background: A wide range of neuroimaging and neuromodulation studies have shown that the dorsolateral prefrontal cortex (DLPFC) plays a pivotal role in decision-making. Of particular interest is the question of its role in decision-making when conditions are uncertain and whether manipulating this neural substrate through neuromodulation changes subsequent risk-taking behaviour. Previous work using the Balloon Analogue Risk Task (BART) suggests that bilateral tDCS stimulation of the DLPFC reduces risk-taking behaviour but unilateral stimulation has no effect. However, participant numbers have been limited and may have biased the estimate of the size of the effect of the stimulation on task performance., Objectives/hypothesis: We aimed to test the robustness and generalizability of these previous findings by using a very similar methodology but with a much larger sample., Methods: During both 20- and 30-min tDCS stimulation at 2mA, we administered the BART to about 200 participants assigned to bilateral DLPFC stimulation of either right anodal/left cathodal, left anodal/right cathodal or sham (Study 1 and Study 2); and to unilateral stimulation conditions (Study 2): right anodal, left anodal or sham with the referent electrode over the contralateral supraorbital region., Results: In the first bilateral study, we found that risk-taking was greater for participants in the right anodal/left cathodal stimulation group compared to those who received left anodal/right cathodal stimulation, but not compared to sham. The results obtained in the bilateral and unilateral stimulation protocols implemented in Study 2 yielded no evidence of any effect of stimulation. Combining the data from both studies, we found no statistically significant differences between mean performances of the nine stimulation groups. Indeed, all 95% confidence intervals for the nine means overlapped, suggesting that these randomly vary around a common population mean., Conclusions: This study showed that there was no detectable effect of tDCS stimulation on risky decision-making under ambiguity, compared to sham stimulation. Hence, using a much larger sample, we did not replicate previous work reporting a reduction in risky decision-making by bilateral stimulation of the DLPFC compared to sham. When the results of our bilateral and unilateral stimulation studies were combined, it emerged that the most likely explanation for the apparent significant results in our bilateral stimulation study was random variation in performance. This outcome is a further reminder of the need for appropriately sized samples to potentially achieve reliable outcomes in brain modulation studies., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

This study analyzed the relationship between several Epstein-Barr virus (EBV) microRNA (miRNA) expression profiles and the clinicopathologic features of patients with EBV-associated gastric cancer. The miRNA expression was examined in 59 tumor and 39 paired normal mucosal tissues from available formalin-fixed paraffin embedded tissue samples. The expression levels of miR-BamHI fragment A rightward transcript (BART)1-5p, miR-BART4-5p, and miR-BART20-5p were determined using a quantitative real-time polymerase chain reaction. The expression of all three analyzed EBV microRNAs was significantly higher in the tumor tissue than in the paired normal tissue (P < 0.001 for each). When the median value of the EBV microRNA expression levels was used as the cutoff point, a high BART20-5p expression was associated with worse recurrence-free survival (P = 0.034) in a multivariate analysis including age and pathologic stage. In conclusion, the expression level of BART20-5p may predict recurrence-free survival for patients with EBV-associated gastric cancer. Further studies are warranted to clarify the roles of EBV BART microRNAs in the carcinogenesis, and their potential as a biomarker and therapeutic target for EBV-associated gastric cancer.