Virtual learning

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin...

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV...

Holographic displays can generate light fields by dynamically modulating the wavefront of a coherent beam of light using a spatial light modulator, promising rich virtual and augmented reality applications. However, the limited spatial resolution of existing dynamic spatial light modulators imposes a tight bound on the diffraction angle. As a result, modern holographic displays possess low étendue, which is the product of the display area and the maximum solid angle of diffracted light. The low étendue forces a sacrifice of either the field-of-view (FOV) or the display size...

BACKGROUND: Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy...

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait...

Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' stain unstained tissue images indistinguishable from chemically stained tissue. Frameworks such as deep convolutional GANs (DCGAN) and conditional GANs (CGANs) have successfully generated highly reproducible 'stained' images. However, their utility may be limited by requiring registered, paired images which can be difficult to obtain...

Strengthening online learning outcomes requires the establishment of strong student-teacher relationships to engage students actively in learning activities. Teacher-student relationships are also pivotal factors for enhancing academic motivation for online learning engagement. Generally, however, research on online teaching remains underdeveloped. We aimed, in this study, to investigate the complex interplay in higher education in Pakistan between teacher-student relationships, academic motivation, and online learning engagement...

PURPOSE: To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method. THEORY AND METHODS: Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model...

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape...

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties...

Learning new motor skills is often challenged by sensory mismatches. For reliable sensory information, people have actively employed sensory intervention methods. Visual assistance is the most popular method to provide sensory information, which is equivalent to the knowledge of performance (KP) in motor tasks. However, its efficacy is questionable because of visual-proprioceptive mismatch as well as heavy intrinsic visual and cognitive engagement in motor tasks. Electrotactile intervention is a promising technique to address the current limitations, as it provides KP using tactile feedback that has a close neurophysiological association with proprioception...

Using spatial mapping processes to learn about threat and safety in an environment is crucial for survival. Research using conditioning paradigms has explored the effects of state (transient arousal) and trait anxiety (anxiety as an aspect of personality) on threat learning and acquisition. However, results are mixed, and little is known regarding why some individuals do not learn to discriminate between threat and safety during contextual conditioning. We used a virtual reality (VR) contextual threat conditioning paradigm to elucidate the effects of state and trait anxiety on contextual threat learning...

This qualitative study was conducted with the aim of investigating the experience of mothers in Tehran in the field of virtual education during the COVID-19 pandemic. The participants in this study were 17 mothers of school children who experienced virtual education in Tehran during the COVID-19 pandemic. Data collection was done through semi-structured interviews, over the phone, and lasted for approximately 30-45 min. The interviews were audio recorded with the permission of the participants, transcribed verbatim, and analyzed using the method of conventional content analysis...

BACKGROUND: Virtual mindfulness may be helpful for individuals with intellectual disabilities in the context of COVID-related disruptions of in-person programming, such as Special Olympics (SO). This study examined the feasibility of a virtual mindfulness intervention for SO athletes and their caregivers. METHOD: SO athletes (n = 44) and their caregivers (n = 29) participated in a 6-week adapted virtual mindfulness intervention. Athletes completed mindfulness and well-being questionnaires prior to, immediately following, and 3-months post-intervention...

Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction...

Virtual fencing technology provides an opportunity to rethink the management of intensive grazing systems in general, yet most studies have used products developed and applied to more extensive livestock systems. This research aimed to assess the application of a virtual fencing technology developed for the intensive pastoral dairy industry. The Halter system uses 2 primary cues, sound and vibration, and one aversive secondary cue, a low energy electrical pulse, to confine cows to a pasture allocation and remotely herd cows...

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels...

Virtual reality (VR) has increasingly been used in several disciplines, including nursing, a profession in high demand that is now facing a shortage. This study investigated the effectiveness and efficacy of VR hybrid training over traditional training (TR) methods. Sixteen college students were recruited and randomly assigned to get 100% TR or 50% TR and 50% VR (VR50). Participants attended a three-day program with a registered nurse, consisting of lectures and practical lab sessions. Participants' performance, training time, cognitive development, physical development, mental workload, user experience, Students' Satisfaction and Self-Confidence, and team learning were evaluated...

BACKGROUND: The use of virtual reality (VR) in healthcare education is on the increase. In disaster medicine, it could be a solution to the cost and logistic constraints for a "full-scale" scenarios. However, VR is mainly designed for single players, which is not appropriate for the objectives pursued in disaster medicine. We decided to evaluate the educational value of using individual VR simulation in disaster medicine on a group of learners. METHODS: The VR scenario used was a reproduction of a major train crash, with 21 victims and whose objectives were START triage and first aid techniques...

BACKGROUND: Regarding competency of nursing students in cardiopulmonary resuscitation (CPR), nursing students frequently exhibit inadequate performance and low satisfaction levels regarding CPR training methods. The problem-based learning (PBL) method, characterized by a constructivist approach, has been underutilized for CPR training, particularly in a virtual format. Hence, this study aims to assess the influence of virtual problem-based learning in cardiopulmonary resuscitation on the satisfaction and performance of fourth-year nursing students...