Virtual reality (VR) is a valuable tool for the assessment of human perception and behavior in a risk-free environment. Investigators should, however, ensure that the used virtual environment is validated in accordance with the experiment's intended research question since behavior in virtual environments has been shown to differ to behavior in real environments. This article presents the street crossing decisions of 30 participants who were facing an approaching vehicle and had to decide at what moment it was no longer safe to cross, applying the step-back method. The participants executed the task in a real environment and also within a highly immersive VR setup involving a head-mounted display (HMD). The results indicate significant differences between the two settings regarding the participants' behaviors. The time-to-contact of approaching vehicles was significantly lower for crossing decisions in the virtual environment than for crossing decisions in the real one. Additionally, it was demonstrated that participants based their crossing decisions in the real environment on the temporal distance of the approaching vehicle (i.e., time-to-contact), whereas the crossing decisions in the virtual environment seemed to depend on the vehicle's spatial distance, neglecting the vehicle's velocity. Furthermore, a deeper analysis suggests that crossing decisions were not affected by factors such as the participant's gender or the order in which they faced the real and the virtual environment.
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http://dx.doi.org/10.1016/j.aap.2019.105356 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFJ Sex Med
January 2025
Department of Surgery, Section of Urology, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, United States.
Background: Understanding patient goals for metoidioplasty and phalloplasty gender-affirming surgery (MaPGAS) is paramount to achieving satisfactory, preference-sensitive outcomes, yet there is a lack of understanding of MaPGAS priorities and how these may vary between transgender men and non-binary individuals assigned female at birth (AFAB).
Aim: To understand the surgical goals of transgender men and non-binary individuals AFAB considering MaPGAS.
Methods: An online survey was created following literature review and qualitative interviews and distributed via social media and a community health center to participants AFAB aged ≥18 years who had considered but not yet undergone MaPGAS.
JMIR Res Protoc
January 2025
South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.
View Article and Find Full Text PDFContraception
January 2025
Department of Obstetrics and Gynecology, Sidney Kimmel Medical College at Thomas Jefferson University, 833 Chestnut Street, Philadelphia PA 19107.
Objective: To assess the perceived impact of state and institutional policies on managing pregnancies of unknown location (PUL) at U.S. Ryan residency programs.
View Article and Find Full Text PDFPhys Eng Sci Med
January 2025
Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, India.
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification.
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