The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.

Introduction: Diabetes is progressive condition which requires various ways of treatment. Adequate therapy prescribed in the right time helps patient to postpone development of complications. Adherence to complicated therapy is challenge for both patients and HCPs and is subject of research in many disciplines. Improvement in communication between HCP and patients is very important in patient’s adherence to therapy. Aim: Aim of this research was to explore validity and reliability of modified SERVQUAL instrument in attempt to explore ways of motivating diabetic patient to accept prescribed insulin therapy. Material and Methods: We used modified SERVQUAL questionnaire as instrument in the research. It was necessary to check validity and reliability of the new modified instrument. Results: Results show that modified Servqual instrument has excellent reliability (α=0.908), so we could say that it measures precisely Expectations, Perceptions and Motivation at patients. Factor analysis (EFA method) with Varimax rotation extracted 4 factors which together explain 52.902% variance of the results on this subscale. Bifactorial solution could be seen on Scree-plot diagram (break at second factor). Conclusion: Results in this research show that modified Servqual instrument which is created in order to measure expectations and perceptions of the patients is valid and reliable. Reliability and validity are proven indeed in additional dimension which was created originally for this research - motivation to accept insulin therapy.

Reproducible experiments are a major requirement for transparent, comparable and verifiable results in the field of human-robot interaction (HRI). Furthermore, a version-controlled and well-structured "ready to deploy" system setup of soft- and hard-ware for an HRI experiment opens up a range of innovative possibilities for interdisciplinary efforts as well as simplified participation of collaborators in the research community. However, making experiments reproducible is not a trivial task. It stems from the lack of agreed upon methodologies, tools and the inherent technical complexity. In this work we present our latest efforts in the context of an international and interdisciplinary research project to enable robotics researchers, software engineers, and social scientists to work together to reproduce a behavioral HRI experiment. The successful reproduction demonstrates that our tool chain approach meets the proposed requirements of the reproducibility problem. To the best of our knowledge, this is the first time an integrated systemic approach allowed an identical instantiation of a complete HRI experiment at geographically distributed locations.

While researchers expect it will be technologically possible for robots to be widely available in society in the near future, the public shows negative attitudes toward robots that may impede their acceptance. Intergroup contact theory shows that positive contact with an outgroup reduces prejudice and increases positive emotions towards that outgroup. This was applied to an interaction between a participant and a humanoid robot to determine if those who interacted directly with, including touching, the robot would perceive all robots in a more positive manner and be more willing to interact with them. Results indicated that contact with the robot, compared with the Control condition, produced a marginally higher willingness to interact with robots.

Computer vision techniques that can anticipate people»s actions ahead of time could create more responsive and natural human-robot interaction systems. In this paper, we present a new human gesture forecasting framework for human-drone interaction. Our primary motivation is that despite growing interest in early recognition, little work has tried to understand how people experience these early recognition-based systems, and our human-drone forecasting framework will serve as a basis for conducting this human subjects research in future studies. We also introduce a new dataset with 22 videos of two human-drone interaction scenarios, and use it to test our gesture forecasting approach. Finally, we suggest follow-up procedures to investigate people»s experience in interacting with these early recognition-enabled systems.

As healthcare shifts towards a patient centered model, robotic technology can play an important role in monitoring, informing, supporting, and connecting independently living individuals with various physical and mental health conditions. As part of a study evaluating the use of the Socially Assistive Robot (SAR) Paro in the homes of older adults with depression, we performed two focus groups with clinicians to discuss how they might use sensor data collected by domestic SARs in clinical practice. In the first focus group, participants discussed potential uses of currently available SARs and sensors by them and their clients. The second focus group took place after sensor data from sensors onboard the Paro robot had been collected in older adults' homes. Clinicians considered the data and what information might be most useful for supporting clinical care. Data regarding monitoring client health, such as behavioral changes in sleep and daily activity levels, were of particular interest to clinicians. They also suggested using SARs to provide clients with information and interaction could help them develop coping skills and alleviate symptoms.