Using Immersive Virtual Reality Distraction to Reduce Fear and Anxiety before Surgery.

Presurgical anxiety is very common and is often treated with sedatives. Minimizing or avoiding sedation reduces the risk of sedation-related adverse events. Reducing sedation can increase early cognitive recovery and reduce time to discharge after surgery. The current case study is the first to explore the use of interactive eye-tracked VR as a nonpharmacologic anxiolytic customized for physically immobilized presurgery patients. Method: A 44-year-old female patient presenting for gallbladder surgery participated. Using a within-subject repeated measures design (treatment order randomized), the participant received no VR during one portion of her preoperative wait and interactive eye-tracked virtual reality during an equivalent portion of time in the presurgery room. After each condition (no VR vs. VR), the participant provided subjective 0-10 ratings and state-trait short form Y anxiety measures of the amount of anxiety and fear she experienced during that condition. Results: As predicted, compared to treatment as usual (no VR), the patient reported having 67% lower presurgical anxiety during VR. She also experienced "strong fear" (8 out of 10) during no VR vs. "no fear" (0 out of 10) during VR. She reported a strong sense of presence during VR and zero nausea. She liked VR, she had fun during VR, and she recommended VR to future patients during pre-op. Interactive VR distraction with eye tracking was an effective nonpharmacologic technique for reducing anticipatory fear and anxiety prior to surgery. The results add to existing evidence that supports the use of VR in perioperative settings. VR technology has recently become affordable and more user friendly, increasing the potential for widespread dissemination into medical practice. Although case studies are scientifically inconclusive by nature, they help identify new directions for future larger, carefully controlled studies. VR sedation is a promising non-drug fear and anxiety management technique meriting further investigation.

Desktop Virtual Reality Offers a Novel Approach to Minimize Pain and Anxiety during Burn Wound Cleaning/Debridement in Infants and Young Children: A Randomized Crossover Pilot Study.

Although most scald burn injuries involve children under six, because of the challenges of using head mounted displays with young children there is very little research exploring the use of VR in children under six. The current clinical pilot study measured the analgesic effectiveness of our new desktop VR system (with no VR helmet) in children under six during burn wound care (a within-subjects design with randomized treatment order). Between December 2021-April 2022, nine children with burn injuries (10 months to 5 years age, mean = 18 months) participated. The mean burn size was 10% Total Body Surface Area, range 2-22%. Using nurse's ratings, VR significantly reduced children's pain during burn wound care by 40% on the observational Faces, Legs, Activity, Crying, and Consolability (FLACC) pain scale. Specifically, non-parametric within-subject sign tests compared nurse's ratings of the young patients' pain during burn wound care using usual pain medications with no VR = 6.67, (SD = 2.45) vs. adjunctive Animal Rescue World VR (VR = 4.00, SD = 2.24, p < 0.01). The observational Procedure-Behavior Checklist (PBCL) nurse's scale measured a 34% reduction in anxiety with VR as compared to pharmacologic treatment alone (p < 0.005). Similarly, when using single graphic rating scales the patients' parents reported a significant 36% decrease in their child's pain during VR (p < 0.05), a 38% (p < 0.005) decrease in their child's anxiety during VR, and a significant increase in patients' joy during VR. It can be concluded that during burn wound care with no distraction (traditional pain medications), children under 6 years old experienced severe pain during a 10 min burn wound cleaning session. During burn wound care combining desktop virtual reality and traditional pain medications, the same pediatric patients experienced only mild pain during burn wound cleaning/debridement. VR significantly reduced the children's pain and anxiety during burn wound care.

Adding tactile feedback increases avatar ownership and makes virtual reality more effective at reducing pain in a randomized crossover study.

Severe pain is a widespread health problem in need of novel treatment approaches. In the current study we used real water to give virtual objects (i.e., animated virtual water) more realistic physical properties (wet liquid qualities). Healthy volunteers aged 18-34 participated in a within-subject randomized study comparing participants' worst pain during brief thermal stimuli with (1) No Immersive Virtual Reality (VR), versus (2) during VR + no tactile feedback versus (3) VR + real water (with tactile feedback from co-located real objects). Tactile feedback significantly decreased pain intensity (VR analgesia, p < 0.01), compared to VR with no tactile feedback, and compared to No VR (baseline). Tactile feedback made the virtual water feel significantly more real, increased participant's sense of presence, and both VR conditions were distracting (significantly reduced accuracy on an attention demanding task). As a non-pharmacologic analgesic, mixed reality reduced pain by 35% in the current study, comparable to the analgesia from a moderate dose of hydromorphone in previous published experimental studies. Tactile feedback also significantly increased avatar embodiment, the participants illusion of ownership of the virtual hands, which has potential to improve the effectiveness of avatar therapy for chronic pain in future studies. Mixed reality should be tested as treatment in pain patients.

The Emerging Role of Virtual Reality as an Adjunct to Procedural Sedation and Anesthesia: A Narrative Review.

Over the past 20 years, there has been a significant reduction in the incidence of adverse events associated with sedation outside of the operating room. Non-pharmacologic techniques are increasingly being used as peri-operative adjuncts to facilitate and promote anxiolysis, analgesia and sedation, and to reduce adverse events. This narrative review will briefly explore the emerging role of immersive reality in the peri-procedural care of surgical patients. Immersive virtual reality (VR) is intended to distract patients with the illusion of "being present" inside the computer-generated world, drawing attention away from their anxiety, pain, and discomfort. VR has been described for a variety of procedures that include colonoscopies, venipuncture, dental procedures, and burn wound care. As VR technology develops and the production costs decrease, the role and application of VR in clinical practice will expand. It is important for medical professionals to understand that VR is now available for prime-time use and to be aware of the growing body in the literature that supports VR.

Classification of clinically actionable genetic mutations in cancer patients.

Personalized medicine in cancer treatment aims to treat each individual's cancer tumor uniquely based on the genetic sequence of the cancer patient and is a much more effective approach compared to traditional methods which involve treating each type of cancer in the same, generic manner. However, personalized treatment requires the classification of cancer-related genes once profiled, which is a highly labor-intensive and time-consuming task for pathologists making the adoption of personalized medicine a slow progress worldwide. In this paper, we propose an intelligent multi-class classifier system that uses a combination of Natural Language Processing (NLP) techniques and Machine Learning algorithms to automatically classify clinically actionable genetic mutations using evidence from text-based medical literature. The training data set for the classifier was obtained from the Memorial Sloan Kettering Cancer Center and the Random Forest algorithm was applied with TF-IDF for feature extraction and truncated SVD for dimensionality reduction. The results show that the proposed model outperforms the previous research in terms of accuracy and precision scores, giving an accuracy score of approximately 82%. The system has the potential to revolutionize cancer treatment and lead to significant improvements in cancer therapy.

Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning.

Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303-5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA.

Virtual Reality Animal Rescue World: Pediatric virtual reality analgesia during just noticeable pressure pain in children aged 2-10 years old (crossover design).

Background and aims: Excessive pain during medical procedures is a worldwide medical problem. Most scald burns occur in children under 6, who are often undermedicated. Adjunctive Virtual Reality (VR) distraction has been shown to reduce pain in children aged 6-17, but little is known about VR analgesia in young children. This study tests whether desktop VR (VR Animal Rescue World) can reduce the just noticeable pressure pain of children aged 2-10. Methods: A within-subject repeated measures design was used. With treatment order randomized, each healthy volunteer pediatric participant underwent brief cutaneous pressure stimuli under three conditions: (1) no distraction, (2) a verbal color naming task (no VR), and (3) a large TV-based desktop VR distraction. A hand-held Wagner pressure pain stimulation device was used to generate just noticeable pain sensations. Participants indicated when a steadily increasing non-painful pressure stimulus first turned into a painful pressure sensation (just noticeable pain). Results: A total of 40 healthy children participated (43% aged 2-5 years; and 57% aged 6-10 years). Compared to the no distraction condition, the 40 children showed significant VR analgesia (i.e., a significant reduction in pain sensitivity during the VR Animal Rescue World condition), t(39) = 9.83, p < 0.001, SD = 6.24. VR was also significantly more effective at reducing pain sensitivity vs. an auditory color naming task, t(39) = 5.42, p < 0.001, SD = 5.94. The subset of children aged 2-5 showed significant reductions in pain during VR. Children under 6 showed greater sensitivity to pain during no distraction than children aged 6-10. Conclusion: During no distraction, children under 6 years old were significantly more sensitive to pain than children aged 6-10. Virtual reality (VR) significantly reduced the "just noticeable" pressure pain sensitivity of children in both age groups.

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection.

BACKGROUND: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. METHODS: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. RESULTS: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. CONCLUSIONS: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.

An MRI Scans-Based Alzheimer's Disease Detection via Convolutional Neural Network and Transfer Learning.

Alzheimer's disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.

Robustness modularity in complex networks.

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

An Exploratory Study on the Effectiveness of Virtual Reality Analgesia for Children and Adolescents with Kidney Diseases Undergoing Venipuncture.

The current study evaluated the effectiveness of VR analgesia among pediatric and adolescent patients with kidney disease undergoing venipuncture. Patients at an Italian Children's hospital (N = 82, age range 7-17 years) undergoing venipuncture were randomly assigned to a No VR group (non-medical conversation) vs. a Yes VR group (VR analgesia). After the procedure, patients gave 0-10 Verbal Numeric Pain Scale ratings. Compared with patients in the No VR Group, patients in the Yes VR group reported significantly lower "Pain intensity"(No VR mean = 2.74, SD = 2.76 vs. Yes VR mean = 1.56, SD = 1.83) and the VR group also rated "Pain unpleasantness" significantly lower than the No VR group (No VR mean = 2.41, SD = 0.94 vs. Yes VR mean = 1.17, SD = 1.80). Patients distracted with VR also reported having significantly more fun during the venipuncture procedure. No side effects emerged. In addition to reducing pain intensity, VR has the potential to make venipuncture a more fun and less unpleasant experience for children with CKD, as measured in the present study for the first time. Finally, in exploratory analyses, children aged 7-11 in the VR group reported 55% lower worst pain than control subjects in the same age range, whereas children aged 12 to 17 in the VR group only reported 35% lower worst pain than control subjects. Additional research and development using more immersive VR is recommended.

Smart Health System to Detect Dementia Disorders Using Virtual Reality.

Smart health technology includes physical sensors, intelligent sensors, and output advice to help monitor patients' health and adjust their behavior. Virtual reality (VR) plays an increasingly larger role to improve health outcomes, being used in a variety of medical specialties including robotic surgery, diagnosis of some difficult diseases, and virtual reality pain distraction for severe burn patients. Smart VR health technology acts as a decision support system in the diseases diagnostic test of patients as they perform real world tasks in virtual reality (e.g., navigation). In this study, a non-invasive, cognitive computerized test based on 3D virtual environments for detecting the main symptoms of dementia (memory loss, visuospatial defects, and spatial navigation) is proposed. In a recent study, the system was tested on 115 real patients of which thirty had a dementia, sixty-five were cognitively healthy, and twenty had a mild cognitive impairment (MCI). The performance of the VR system was compared with Mini-Cog test, where the latter is used to measure cognitive impaired patients in the traditional diagnosis system at the clinic. It was observed that visuospatial and memory recall scores in both clinical diagnosis and VR system of dementia patients were less than those of MCI patients, and the scores of MCI patients were less than those of the control group. Furthermore, there is a perfect agreement between the standard methods in functional evaluation and navigational ability in our system where P-value in weighted Kappa statistic= 100% and between Mini-Cog-clinical diagnosis vs. VR scores where P-value in weighted Kappa statistic= 93%.

Community detection in networks using graph embeddings.

Graph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So, it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.

Fast consensus clustering in complex networks.

Algorithms for community detection are usually stochastic, leading to different partitions for different choices of random seeds. Consensus clustering has proven to be an effective technique to derive more stable and accurate partitions than the ones obtained by the direct application of the algorithm. However, the procedure requires the calculation of the consensus matrix, which can be quite dense if (some of) the clusters of the input partitions are large. Consequently, the complexity can get dangerously close to quadratic, which makes the technique inapplicable on large graphs. Here, we present a fast variant of consensus clustering, which calculates the consensus matrix only on the links of the original graph and on a comparable number of additional node pairs, suitably chosen. This brings the complexity down to linear, while the performance remains comparable as the full technique. Therefore, our fast consensus clustering procedure can be applied on networks with millions of nodes and links.

Virtual Reality Analgesia With Interactive Eye Tracking During Brief Thermal Pain Stimuli: A Randomized Controlled Trial (Crossover Design).

In light of growing concerns about opioid analgesics, developing new non-pharmacologic pain control techniques has become a high priority. Adjunctive virtual reality can help reduce acute pain during painful medical procedures. However, for some especially painful medical procedures such as burn wound cleaning, clinical researchers recommend that more distracting versions of virtual reality are needed, to further amplify the potency of virtual reality analgesia. The current study with healthy volunteers explores for the first time whether interacting with virtual objects in Virtual Reality (VR) via "hands free" eye-tracking technology integrated into the VR helmet makes VR more effective/powerful than non-interactive/passive VR (no eye-tracking) for reducing pain during brief thermal pain stimuli. METHOD: Forty eight healthy volunteers participated in the main study. Using a within-subject design, each participant received one brief thermal pain stimulus during interactive eye tracked virtual reality, and each participant received another thermal pain stimulus during non-interactive VR (treatment order randomized). After each pain stimulus, participants provided subjective 0-10 ratings of cognitive, sensory and affective components of pain, and rated the amount of fun they had during the pain stimulus. RESULTS: As predicted, interactive eye tracking increased the analgesic effectiveness of immersive virtual reality. Compared to the passive non-interactive VR condition, during the interactive eye tracked VR condition, participants reported significant reductions in worst pain (p < 0.001) and pain unpleasantness (p < 0.001). Participants reported a significantly stronger illusion of presence (p < 0.001), and significantly more fun in VR (p < 0.001) during the interactive condition compared to during passive VR. In summary, as predicted by our primary hypothesis, in the current laboratory acute pain analog study with healthy volunteers, increasing the immersiveness of the VR system via interactive eye tracking significantly increased how effectively VR reduced worst pain during a brief thermal pain stimulus. Although attention was not directly measured, the pattern of pain ratings, presence ratings, and fun ratings are consistent with an attentional mechanism for how VR reduces pain. Whether the current results generalize to clinical patient populations is another important topic for future research. Additional research and development is recommended.

Virtual Reality Analgesia for Pediatric Dental Patients.

Background: Dental procedures often elicit pain and fear in pediatric dental patients. Aim: To evaluate the feasibility and effectiveness of immersive virtual reality as an attention distraction analgesia technique for pain management in children and adolescents undergoing painful dental procedures. Design: Using a within-subjects design, five patients (mean age 13.20 years old, SD 2.39) participated. Patients received tethered immersive interactive virtual reality distraction in an Oculus Rift VR helmet (experimental condition) during one dental procedure (a single dental filling or tooth extraction). On a different visit to the same dentist (e.g., 1 week later), each patient also received a comparable dental procedure during the control condition "treatment as usual" (treatment order randomized). After each procedure, children self-rated their "worst pain," "pain unpleasantness," "time spent thinking about pain," "presence in VR," "fun," and "nausea" levels during the dental procedures, using graphic rating scales. Results: Patients reported significantly lower "worst pain" and "pain unpleasantness," and had significantly more fun during VR, compared to a comparable dental procedure with No VR. Using Oculus Rift VR goggles, patients reported a "strong sense of going inside the computer-generated world," without side effects. The dentist preferred having the patients in VR. Conclusion: Results of this pilot study provide preliminary evidence of the feasibility of using immersive, interactive VR to distract pediatric dental patients and increase fun of children during dental procedures.

Virtual Reality Analgesia During Venipuncture in Pediatric Patients With Onco-Hematological Diseases.

Background: Venipuncture is described by children as one of the most painful and frightening medical procedures. Objective: To evaluate the effectiveness of Virtual Reality (VR) as a distraction technique to help control pain in children and adolescents undergoing venipuncture. Methods: Using a within-subjects design, fifteen patients (mean age 10.92, SD = 2.64) suffering from oncological or hematological diseases received one venipuncture with "No VR" and one venipuncture with "Yes VR" on two separate days (treatment order randomized). "Time spent thinking about pain", "Pain Unpleasantness", "Worst pain" the quality of VR experience, fun during the venipuncture and nausea were measured. Results: During VR, patients reported significant reductions in "Time spent thinking about pain," "Pain unpleasantness," and "Worst pain". Patients also reported significantly more fun during VR, and reported a "Strong sense of going inside the computer-generated world" during VR. No side effects were reported. Conclusion: VR can be considered an effective distraction technique for children and adolescents' pain management during venipuncture. Moreover, VR may elicit positive emotions, more than traditional distraction techniques. This could help patients cope with venipuncture in a non-stressful manner. Additional research and development is needed.

The Use of Virtual Reality Facilitates Dialectical Behavior Therapy® "Observing Sounds and Visuals" Mindfulness Skills Training Exercises for a Latino Patient with Severe Burns: A Case Study.

Sustaining a burn injury increases an individual's risk of developing psychological problems such as generalized anxiety, negative emotions, depression, acute stress disorder, or post-traumatic stress disorder. Despite the growing use of Dialectical Behavioral Therapy® (DBT®) by clinical psychologists, to date, there are no published studies using standard DBT® or DBT® skills learning for severe burn patients. The current study explored the feasibility and clinical potential of using Immersive Virtual Reality (VR) enhanced DBT® mindfulness skills training to reduce negative emotions and increase positive emotions of a patient with severe burn injuries. The participant was a hospitalized (in house) 21-year-old Spanish speaking Latino male patient being treated for a large (>35% TBSA) severe flame burn injury. Methods: The patient looked into a pair of Oculus Rift DK2 virtual reality goggles to perceive the computer-generated virtual reality illusion of floating down a river, with rocks, boulders, trees, mountains, and clouds, while listening to DBT® mindfulness training audios during 4 VR sessions over a 1 month period. Study measures were administered before and after each VR session. Results: As predicted, the patient reported increased positive emotions and decreased negative emotions. The patient also accepted the VR mindfulness treatment technique. He reported the sessions helped him become more comfortable with his emotions and he wanted to keep using mindfulness after returning home. Conclusions: Dialectical Behavioral Therapy is an empirically validated treatment approach that has proved effective with non-burn patient populations for treating many of the psychological problems experienced by severe burn patients. The current case study explored for the first time, the use of immersive virtual reality enhanced DBT® mindfulness skills training with a burn patient. The patient reported reductions in negative emotions and increases in positive emotions, after VR DBT® mindfulness skills training. Immersive Virtual Reality is becoming widely available to mainstream consumers, and thus has the potential to make this treatment available to a much wider number of patient populations, including severe burn patients. Additional development, and controlled studies are needed.

The Use of Virtual Reality to Facilitate Mindfulness Skills Training in Dialectical Behavioral Therapy for Borderline Personality Disorder: A Case Study.

Borderline personality disorder (BPD) is a severe mental disorder characterized by a dysfunctional pattern of affective instability, impulsivity, and disturbed interpersonal relationships. Dialectical Behavior Therapy (DBT®) is the most effective treatment for Borderline Personality Disorder, but demand for DBT® far exceeds existing clinical resources. Most patients with BPD never receive DBT®. Incorporating computer technology into the DBT® could help increase dissemination. Immersive Virtual Reality technology (VR) is becoming widely available to mainstream consumers. This case study explored the feasibility/clinical potential of using immersive virtual reality technology to enhance DBT® mindfulness skills training of a 32 year old female diagnosed with BPD. Prior to using VR, the patient experienced difficulty practicing DBT® mindfulness due to her emotional reactivity, and difficulty concentrating. To help the patient focus her attention, and to facilitate DBT® mindfulness skills learning, the patient looked into virtual reality goggles, and had the illusion of slowly "floating down" a 3D computer-generated river while listening to DBT® mindfulness training audios. Urges to commit suicide, urges to self harm, urges to quit therapy, urges to use substances, and negative emotions were all reduced after each VR mindfulness session and VR mindfulness was well accepted/liked by the patient. Although case studies are scientifically inconclusive by nature, results from this feasibility study were encouraging. Future controlled studies are needed to quantify whether VR-enhanced mindfulness training has long term benefits e.g., increasing patient acceptance and/or improving therapeutic outcome. Computerizing some of the DBT® skills treatment modules would reduce cost and increase dissemination.

Efficient solution for finding Hamilton cycles in undirected graphs.

The Hamilton cycle problem is closely related to a series of famous problems and puzzles (traveling salesman problem, Icosian game) and, due to the fact that it is NP-complete, it was extensively studied with different algorithms to solve it. The most efficient algorithm is not known. In this paper, a necessary condition for an arbitrary un-directed graph to have Hamilton cycle is proposed. Based on this condition, a mathematical solution for this problem is developed and several proofs and an algorithmic approach are introduced. The algorithm is successfully implemented on many Hamiltonian and non-Hamiltonian graphs. This provides a new effective approach to solve a problem that is fundamental in graph theory and can influence the manner in which the existing applications are used and improved.