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Purpose: Immersive virtual reality (IVR) has been frequently proposed as a promising tool for learning. However, researchers have commonly implemented a plethora of design elements in these IVR systems, which makes the specific aspects of the system that are necessary to achieve beneficial outcomes unclear. Against this background, this study aims to combine the literature on presence with learning theories to propose that the ability of IVR to present 3D objects to users improves the presence of these objects in the virtual environment compared with 2D objects, leading to increased learning performance. Design/methodology/approach: To test this study's hypotheses, the authors conducted a 2 (training condition: approach vs avoid) x 2 (object presence: high vs low) between-subjects laboratory experiment that used IVR with 83 female participants. Findings: The results support this study's hypotheses and show that training with high object presence leads to greater reactions to cues (chocolate cravings) and improved health behaviour (chocolate consumption). Originality/value: This study shows that increased object presence leads to unique experiences for users, which help reinforce training effects. Moreover, this work sheds further light on how immersive computer technologies can affect user attitudes and behaviour. Specifically, this work contributes to IVR research by showing that learning effects can be enhanced through an increased degree of object presence. [ABSTRACT FROM AUTHOR]

Background: Mutation trees are rooted trees in which nodes are of arbitrary degree and labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular Robinson–Foulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels. Results: We generalize the Robinson–Foulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. We show the basic version of the Bourque distance for mutation trees can be computed in linear time. We also make a connection between the Robinson–Foulds distance and the nearest neighbor interchange distance. [ABSTRACT FROM AUTHOR]

Purpose: Toxic behavior in multiplayer video games diminishes the potential revenue of gaming companies by spreading a bad mood, negatively affecting game play, and subsequently leading to the churn of players. However, research investigating why toxic behavior occurs is still scarce. To address this issue, this study disjunctively tests three different theoretical approaches (social cognitive theory, theory of planned behavior, and online disinhibition effect) to explain toxic behavior and propose a unified theory of toxic behavior. Design/methodology/approach: In total, 320 respondents participated in a questionnaire study. This study analyzes the data with covariance-based statistics (i.e. regression analysis and structural equation modelling), and the approach is twofold. First, the hypotheses of three theories are disjunctively tested. Second, a unified theory of toxic behavior is proposed. Findings: The results of this study indicate that online disinhibition best explains toxic behavior, whereby toxic behavior victimization, attitude, and behavioral control also play an important role. Research limitations/implications: The findings of this study offer an opportunity to better understand a contemporary and especially meaningful form of negative behavior online. Practical implications: To maintain revenue and popularity, the computer game industry can use the findings of this study to prevent and better address toxic behavior and its negative consequences. Originality/value: Toxic behavior among video game players is a relatively new and unexplored phenomenon; therefore, this study makes a valuable contribution to the research field by testing the explanatory power of three theoretical approaches and proposing a unified theory of toxic behavior. [ABSTRACT FROM AUTHOR]

Purpose: Toxic behavior in multiplayer video games diminishes the potential revenue of gaming companies by spreading a bad mood, negatively affecting game play, and subsequently leading to the churn of players. However, research investigating why toxic behavior occurs is still scarce. To address this issue, this study disjunctively tests three different theoretical approaches (social cognitive theory, theory of planned behavior, and online disinhibition effect) to explain toxic behavior and propose a unified theory of toxic behavior. Design/methodology/approach: In total, 320 respondents participated in a questionnaire study. This study analyzes the data with covariance-based statistics (i.e. regression analysis and structural equation modelling), and the approach is twofold. First, the hypotheses of three theories are disjunctively tested. Second, a unified theory of toxic behavior is proposed. Findings: The results of this study indicate that online disinhibition best explains toxic behavior, whereby toxic behavior victimization, attitude, and behavioral control also play an important role. Research limitations/implications: The findings of this study offer an opportunity to better understand a contemporary and especially meaningful form of negative behavior online. Practical implications: To maintain revenue and popularity, the computer game industry can use the findings of this study to prevent and better address toxic behavior and its negative consequences. Originality/value: Toxic behavior among video game players is a relatively new and unexplored phenomenon; therefore, this study makes a valuable contribution to the research field by testing the explanatory power of three theoretical approaches and proposing a unified theory of toxic behavior. [ABSTRACT FROM AUTHOR]

The question of the optimal level of diversification in marketing research has a long history and shows an inconsistent empirical picture. Based on assumptions from the media management field, consumer engagement literature and social identity approach, this paper is aimed at obtaining a better understanding of diversification as a competitive advantage for organizations in eSports. Accordingly, we collected a sample of 216 League of Legends consumers, via the crowdsourcing marketplace Mechanical Turk- to explain the phenomenon of customer loyalty' for different eSports franchises. Using a quasi-experimental approach and structural equation modeling, our results show support for an inverted u-shaped curve of diversification. Medium diversified franchises seem to have the most beneficial positions in the eSports market. Furthermore, our holistic model illustrates that franchise identification has the potential to explain media engagement. Additionally, the two dimensions of media engagement – consumption and contribution – affect franchise loyalty in a positive manner, contrary to the third-dimension creation. We further discuss the contributions of our analyses and identify potential paths for future research in this paper. [ABSTRACT FROM AUTHOR]

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby. [ABSTRACT FROM AUTHOR]

Background: Genes occurring co-localized in multiple genomes can be strong indicators for either functional constraints on the genome organization or remnant ancestral gene order. The computational detection of these patterns, which are usually referred to as gene clusters, has become increasingly sensitive over the past decade. The most powerful approaches allow for various types of imperfect cluster conservation: Cluster locations may be internally rearranged. The individual cluster locations may contain only a subset of the cluster genes and may be disrupted by uninvolved genes. Moreover cluster locations may not at all occur in some or even most of the studied genomes. The detection of such low quality clusters increases the risk of mistaking faint patterns that occur merely by chance for genuine findings. Therefore, it is crucial to estimate the significance of computational gene cluster predictions and discriminate between true conservation and coincidental clustering. Results: In this paper, we present an efficient and accurate approach to estimate the significance of gene cluster predictions under the approximate common intervals model. Given a single gene cluster prediction, we calculate the probability to observe it with the same or a higher degree of conservation under the null hypothesis of random gene order, and add a correction factor to account for multiple testing. Our approach considers all parameters that define the quality of gene cluster conservation: the number of genomes in which the cluster occurs, the number of involved genes, the degree of conservation in the different genomes, as well as the frequency of the clustered genes within each genome. We apply our approach to evaluate gene cluster predictions in a large set of well annotated genomes. [ABSTRACT FROM AUTHOR]

Background: It has recently been shown that fractionation, the random loss of excess gene copies after a whole genome duplication event, is a major cause of gene order disruption. When estimating evolutionary distances between genomes based on chromosomal rearrangement, fractionation inevitably leads to significant overestimation of classic rearrangement distances. This bias can be largely avoided when genomes are preprocessed by "consolidation", a procedure that identifies and accounts for regions of fractionation. Results: In this paper, we present a new consolidation algorithm that extends and improves previous work in several directions. We extend the notion of the fractionation region to use information provided by regions where this process is still ongoing. The new algorithm can optionally work with this new definition of fractionation region and is able to process not only tetraploids but also genomes that have undergone hexaploidization and polyploidization events of higher order. Finally, this algorithm reduces the asymptotic time complexity of consolidation from quadratic to linear dependence on the genome size. The new algorithm is applied both to plant genomes and to simulated data to study the effect of fractionation in ancient hexaploids. [ABSTRACT FROM AUTHOR]

Background: Mancheron, Uricaru and Rivals (Nucleic Acids Res. 39:e101, 2011) recently introduced a new approach in the context of multiple genome comparison that allows to detect regions of strong overlaps in a set of pairwise local alignments between several reference genomes and one target genome. Such overlap regions are an important source of information in genome annotation. Results: In this paper we introduce a series of algorithms that improve over the approach of Mancheron et al., both in terms of computational complexity and in practical runtime. We also extend the problem definition such that overlaps to different reference genomes can be rated differently and regions overlapping only a subset of the reference genomes are detected. [ABSTRACT FROM AUTHOR]

Whole genome comparison based on the analysis of gene cluster conservation has become a popular approach in comparative genomics. While gene order and gene content as a whole randomize over time, it is observed that certain groups of genes which are often functionally related remain co-located across species. However, the conservation is usually not perfect which turns the identification of these structures, often referred to as approximate gene clusters, into a challenging task. In this article, we present an efficient set distance based approach that computes approximate gene clusters by means of reference occurrences. We show that it yields highly comparable results to the corresponding non-reference based approach, while its polynomial runtime allows for approximate gene cluster detection in parameter ranges that used to be feasible only with simpler, e.g., max-gap based, gene cluster models. To illustrate further the performance and predictive power of our algorithm, we compare it to a state-of-the art approach for max-gap gene cluster computation. [ABSTRACT FROM AUTHOR]

Gamification is often equipped with the promise to increase motivation and performance. However, research explaining which gamification design elements are effective and the mechanisms through which these effects can be explained is still at an early stage. By drawing on the three psychological needs – competence, autonomy, and social relatedness – proposed by self-determination theory, we develop a model to explain the effects of feedback and avatar design on reuse. We test these effects with a 2 (avatar similarity: low vs. high) × 2 (embodied feedback: no feedback vs. embodied feedback) × 2 (status feedback: no feedback vs. score and leaderboard) + 1 (control group) experiment. Additionally, we use structural equation modeling to test the derived model. Our results support evidence that different forms of feedback and avatar design influence reuse by satisfying the three psychological needs. Furthermore, our findings also reveal that autonomy for decision freedom is negatively related to reuse intention, which disagrees with existing research and may provide insights into why results on gamification elements are inconsistent. • Reuse intention was analyzed in an experimental approach using a gamified version of the manikin task. • Gamification elements satisfy reuse intention and the psychological needs through different working mechanisms. • The three psychological needs show unique relationships towards reuse intention. • Differentiating between autonomy of decision freedom and task meaningfulness reveals converse correlational patterns. [ABSTRACT FROM AUTHOR]

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Author summary: Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways. [ABSTRACT FROM AUTHOR]

Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. Results: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is effecient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. Conclusions: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably. [ABSTRACT FROM AUTHOR]

Germinal centers (GCs) are specialized compartments within the secondary lymphoid organs where B cells proliferate, differentiate, and mutate their antibody genes in response to the presence of foreign antigens. Through the GC lifespan, interclonal competition between B cells leads to increased affinity of the B cell receptors for antigens accompanied by a loss of clonal diversity, although the mechanisms underlying clonal dynamics are not completely understood. We present here a multi-scale quantitative model of the GC reaction that integrates an intracellular component, accounting for the genetic events that shape B cell differentiation, and an extracellular stochastic component, which accounts for the random cellular interactions within the GC. In addition, B cell receptors are represented as sequences of nucleotides that mature and diversify through somatic hypermutations. We exploit extensive experimental characterizations of the GC dynamics to parameterize our model, and visualize affinity maturation by means of evolutionary phylogenetic trees. Our explicit modeling of B cell maturation enables us to characterise the evolutionary processes and competition at the heart of the GC dynamics, and explains the emergence of clonal dominance as a result of initially small stochastic advantages in the affinity to antigen. Interestingly, a subset of the GC undergoes massive expansion of higher-affinity B cell variants (clonal bursts), leading to a loss of clonal diversity at a significantly faster rate than in GCs that do not exhibit clonal dominance. Our work contributes towards an in silico vaccine design, and has implications for the better understanding of the mechanisms underlying autoimmune disease and GC-derived lymphomas. [ABSTRACT FROM AUTHOR]

Background: Automatic processes to approach smoking-related cues have been repeatedly linked to smoking status, intensity of smoking, and cigarette craving. Moreover, recent findings suggest that targeting those tendencies directly by means of approach bias modification (ABM) has merit in changing maladaptive approach tendencies for drug cues and reducing drug consumption. However, training effects tend to be small. Embedding the training into virtual reality (VR) technology could be a promising way to improve training efficacy. The present protocol describes a randomized controlled trial that aims to assess the efficacy of a newly developed VR-ABM as a means of reducing smoking-related approach biases or nicotine consumption in smokers seeking abstinence.Methods: One hundred daily smokers who are motivated to quit smoking will be recruited into the randomized controlled trial. All participants will attend a brief smoking cessation intervention (TAU) and will be randomly assigned either to the experimental (VR-avoidance training) or the placebo-control group (VR-placebo training). During the VR-avoidance training, participants are implicitly instructed to make an avoidance movement in response to smoking-related objects (e.g., cigarettes) and an approach movement in response to alternative objects (e.g., healthy food). During the VR-placebo training, no such contingency between arm movement and item content exists. Trainings are administered in six sessions within two weeks. Training effects on automatic approach tendencies and smoking behavior are measured immediately after training and at a 7-week follow-up.Discussion: Embedding the training into virtual reality (VR) technology could be a promising new way to improve ecological validity, realism, and immersion and thereby increase ABM training effects. The results of this study can inform future research in the optimization and advancement of treatment for addiction.Trial Registration: Registered with Current Controlled Trials: study ID ISRCTN16006023. Registered on 28 March 2019. [ABSTRACT FROM AUTHOR]

Background: Automatic tendencies to approach drug-related cues have been linked to the development and maintainance of harmful drug-taking behavior. Recent studies have demonstrated that these automatic approach tendencies can be targeted directly by means of cognitive bias modification (CBM). Moreover, changing those approach tendencies may enhance treatment outcomes. However, training and therapy effects tend to be rather small and adherence to the training might be impaired by time-consuming multiple laboratory training sessions. Here, we present a protocol for a randomized controlled design to improve CBM training efficiency and facilitate access to the training by providing mobile-phone-based training sessions at home to current smokers motivated to quit smoking.Methods: Participants (n = 100) are current smokers who smoke at least six cigarettes per day for at least 6 months and are willing to quit smoking. All participants attend a brief behavioral smoking cessation intervention (TAU) and are randomly assigned either to an experimental (TAU + training) or a control group. Participants in the experimental condition are given access to a training application (app) aimed at retraining automatic approach biases for smoking cues. Participants are instructed to perform the app training outside the laboratory context on a daily basis for 14 consecutive days. Participants in the control group do not receive the training. Primary outcome measures are changes in smoking-related approach biases and reductions in daily nicotine consumption as assessed at baseline, post-training and at 6-week follow up. Secondary outcome measures include approach biases for alternative stimuli or smoking stimuli to which participants were not exposed during training, attentional and association biases, biochemical outcomes, and self-reported smoking behavior, also measured at three different time points (baseline, post-training, and follow up). After completion of the study, smokers in the control condition will receive access to the training app.Discussion: This randomized controlled trial is the first to test the effectiveness of an app-based CBM intervention as an adjunct to a brief smoking cessation intervention in smokers motivated to quit smoking. The results of this study can inform future research in the optimization and advancement of CBM treatment for addiction.Trial Registration: Current Controlled Trials, ISRCTN15690771. Registered on 20 November 2018. [ABSTRACT FROM AUTHOR]

Approach bias modification (ApBM) has shown promise in addiction treatment, but effects are small and ecological validity suffers from completing trainings in the laboratory. Providing app-based trainings via mobile phones could increase training efficacy. One-hundred-and-thirty-one smokers seeking abstinence attended a smoking cessation intervention and were thereafter randomized to one of three conditions: (a) app-based ApBM-training; (b) app-based sham training; (c) no training. App trainings were performed on 14 consecutive days at home. Behavioral and self-report data was assessed at pretest, posttest, and a 6-week follow-up. App-based ApBM led to stronger reductions in cigarette smoking as compared to no training. However, both training variants led to stronger declines in nicotine dependence and the sham training was superior in reducing alcohol consumption. Although approach biases for positive cues increased following ApBM training, this effect did not mediate treatment outcome. Other smoking-related cognitive biases did not change after training. Our results can inform future research in the optimization and advancement of ApBM treatment for addiction.Registered with Current Controlled Trials: study ID ISRCTN15690771. Registered on 20 November 2018; http://www.isrctn.com/ISRCTN15690771.Trial registration: Approach bias modification (ApBM) has shown promise in addiction treatment, but effects are small and ecological validity suffers from completing trainings in the laboratory. Providing app-based trainings via mobile phones could increase training efficacy. One-hundred-and-thirty-one smokers seeking abstinence attended a smoking cessation intervention and were thereafter randomized to one of three conditions: (a) app-based ApBM-training; (b) app-based sham training; (c) no training. App trainings were performed on 14 consecutive days at home. Behavioral and self-report data was assessed at pretest, posttest, and a 6-week follow-up. App-based ApBM led to stronger reductions in cigarette smoking as compared to no training. However, both training variants led to stronger declines in nicotine dependence and the sham training was superior in reducing alcohol consumption. Although approach biases for positive cues increased following ApBM training, this effect did not mediate treatment outcome. Other smoking-related cognitive biases did not change after training. Our results can inform future research in the optimization and advancement of ApBM treatment for addiction.Registered with Current Controlled Trials: study ID ISRCTN15690771. Registered on 20 November 2018; http://www.isrctn.com/ISRCTN15690771. [ABSTRACT FROM AUTHOR]

Background: Comparative analyses of chromosomal gene orders are successfully used to predict gene clusters in bacterial and fungal genomes. Present models for detecting sets of co-localized genes in chromosomal sequences require prior knowledge of gene family assignments of genes in the dataset of interest. These families are often computationally predicted on the basis of sequence similarity or higher order features of gene products. Errors introduced in this process amplify in subsequent gene order analyses and thus may deteriorate gene cluster prediction. Results: In this work, we present a new dynamic model and efficient computational approaches for gene cluster prediction suitable in scenarios ranging from traditional gene family-based gene cluster prediction, via multiple conflicting gene family annotations, to gene family-free analysis, in which gene clusters are predicted solely on the basis of a pairwise similarity measure of the genes of different genomes. We evaluate our gene family-free model against a gene family-based model on a dataset of 93 bacterial genomes. Conclusions: Our model is able to detect gene clusters that would be also detected with well-established gene family-based approaches. Moreover, we show that it is able to detect conserved regions which are missed by gene family-based methods due to wrong or deficient gene family assignments. [ABSTRACT FROM AUTHOR]

Automatic approach biases toward smoking-related cues have been implicated in the development and maintenance of addictive behaviors. Studies aiming at modifying such biases have shown promise in changing maladaptive approach tendencies for smoking cues and reducing smoking behavior. However, training effects tend to be small and partly inconsistent. The present randomized-controlled trial incorporated virtual reality (VR) technology into Approach Bias Modification (ABM) to improve efficacy. One-hundred-eight smokers attended behavioral counseling for smoking cessation and were thereafter randomized to receive VR-ABM or VR-control training. During VR-ABM, participants trained to implicitly avoid smoking-related objects and to approach alternative objects, while no such contingency existed in the VR-control condition. Trainings were administered in six sessions within a two-week period. Assessments were conducted at baseline, post-intervention (three weeks after baseline), and at follow-up (seven weeks after baseline). VR-ABM did not change approach biases, nor other cognitive biases, but it was superior in reducing daily smoking. However, this effect was limited to the two-week training period. Both groups improved in other smoking- and health-related variables across time. Future work should continue to investigate working mechanisms of ABM, in particular crucial training ingredients. VR could prove valuable for public health as the potential of VR-based treatments is large and not fully explored. • Approach Bias Retraining has shown promise in treating substance-use disorders • A novel VR-based Approach Bias Retraining for smoking did not change approach biases • As long as the training was administered, it was superior in reducing cigarette smoking compared to sham-training • No other training-specific effects were found • Other smoking-related and clinical outcome variables improved across time [ABSTRACT FROM AUTHOR]