Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study

Background: Social anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. Objective: This study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. Methods: This study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naive Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. Results: The severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naive Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. Conclusions: This study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias

SH3 Domain-Peptide Binding Energy Calculations Based on Structural Ensemble and Multiple Peptide Templates

SH3 domains mediate signal transduction by recognizing short peptides. Understanding of the driving forces in peptide recognitions will help us to predict the binding specificity of the domain-peptide recognition and to understand the molecular interaction networks of cells. However, accurate calculation of the binding energy is a tough challenge. In this study, we propose three ideas for improving our ability to predict the binding energy between SH3 domains and peptides: (1) utilizing the structural ensembles sampled from a molecular dynamics simulation trajectory, (2) utilizing multiple peptide templates, and (3) optimizing the sequence-structure mapping. We tested these three ideas on ten previously studied SH3 domains for which SPOT analysis data were available. The results indicate that calculating binding energy using the structural ensemble was most effective, clearly increasing the prediction accuracy, while the second and third ideas tended to give better binding energy predictions. We applied our method to the five SH3 targets in DREAM4 Challenge and selected the best performing method

Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network.

RNA-binding proteins (RBPs) are important in gene expression regulations by post-transcriptional control of RNAs and immune system development and its function. Due to the help of sequencing technology, numerous RNA sequences are newly discovered without knowing their binding partner RBPs. Therefore, demands for accurate prediction method for RBP binding sites are increasing. There are many attempts for RBP binding site predictions using various machine-learning techniques combined with various RNA features. In this work, we present a new deep convolution neural network model trained on CLIP-seq datasets using multi-sized filters and multi-modal features to predict the binding property of RBPs. With this model, we integrated sequence and structure information to extract sequence motifs, structure motifs, and combined motifs at the same time. The RBP binding site prediction on RBP-24 dataset was compared with two multi-modal methods, GraphProt and Deepnet-rbp, using area under curve (AUC) of receiver-operating characteristics (ROC). Our method (average AUC = 0.920) outperformed 20 RBPs with GraphProt (average AUC = 0.888) and 15 RBP with Deepnet-rbp (average AUC = 0.902). The improvement was achieved by using multi-sized convolution filters, where average relative error reduction was 17%. By introducing new RNA structure representation, structure probability matrix, average relative error was reduced by 3% when compared to one-hot encoded secondary structure representation. Interestingly, structure probability matrix was more effective on ALKBH5, where relative error reduction was 30%. We developed new sequence motif enrichment method, which we stated as response enrichment method. We successfully enriched sequence motif for 12 RBPs, which had high resemblance with other literature evidences, RBPgroup and CISBP-RNA. Finally by analyzing these results altogether, we found intricate interplay between sequence motif and structure motif, which agreed with other researches

Reformulation-Linearization Technique Approach for Kidney Exchange Program IT Healthcare Platforms

Kidney exchange allows a potential living donor whose kidney is incompatible with his intended recipient to donate a kidney to another patient so that the donor’s intended recipient can receive a compatible kidney from another donor. These exchanges can include cycles of longer than two donor–patient pairs and chains produced by altruistic donors. Kidney exchange programs (KEPs) can be modeled as a maximum-weight cycle-packing problem in a directed graph. This paper develops a new integer programming model for KEPs by applying the reformulation-linearization technique (RLT) to enhance a lower bound obtained by its linear programming (LP) relaxation. Given the results obtained from the proposed model, the model is expected to be utilized in the integrated KEP IT (Information Technology) healthcare platform to obtain plans for optimized kidney exchanges

The Sharing of Benefits from a Logistics Alliance Based on a Hub-Spoke Network: A Cooperative Game Theoretic Approach

This study investigates a strategic alliance as a horizontal cooperation in the logistics and transportation industries by considering various sharing rules with a cooperative game approach. Through forging a strategic alliance, carriers gain extra benefits from resource sharing and high efficiency resource utilization. In particular, our research focuses on the cost savings from using larger vehicles utilizing collective market demand and regarding them as benefits of cooperation. The model conceptualizes the characteristic function of cost savings by coalitions that take into account the hub-spoke network which is common in transportation services. To share the improved profits fairly between members, we use different allocation schemes: the Shapley value, the core center, the τ -value, and the nucleolus. By analyzing those cooperative game theoretic solutions employing an alliance composed of three carriers, we investigate whether satisfaction in this specific coalition provides an incentive for carriers to join such a coalition. Our results from the analysis, with respect to fair allocation schemes, provide a practical and academic foundation for further research

Comparison to Other Binding Energy Calculation Methods.

<p>Area under ROC curves (AROC) are shown.</p><p>*Methods by Fernandez-Ballester <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012654#pone.0012654-FernandezBallester1" target="_blank">[19]</a> and Hou used different data sets<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012654#pone.0012654-Hou2" target="_blank">[11]</a>. Accordingly, our method was compared with the two methods separately.</p

Effect of structural ensemble sampled from MD simulation trajectory.

<p>The Pearson's correlation coefficients between the predicted binding energies and SPOT data are shown.</p><p>*Average correlation coefficient of 11 conformations.</p

Accounting for Fairness in a Two-Stage Stochastic Programming Model for Kidney Exchange Programs

Kidney exchange programs, which allow a potential living donor whose kidney is incompatible with his or her intended recipient to donate a kidney to another patient in return for a kidney that is compatible for their intended recipient, usually aims to maximize the number of possible kidney exchanges or the total utility of the program. However, the fairness of these exchanges is an issue that has often been ignored. In this paper, as a way to overcome the problems arising in previous studies, we take fairness to be the degree to which individual patient-donor pairs feel satisfied, rather than the extent to which the exchange increases social benefits. A kidney exchange has to occur on the basis of the value of the kidneys themselves because the process is similar to bartering. If the matched kidneys are not of the level expected by the patient-donor pairs involved, the match may break and the kidney exchange transplantation may fail. This study attempts to classify possible scenarios for such failures and incorporate these into a stochastic programming framework. We apply a two-stage stochastic programming method using total utility in the first stage and the sum of the penalties for failure in the second stage when an exceptional event occurs. Computational results are provided to demonstrate the improvement of the proposed model compared to that of previous deterministic models