VR/AR head-mounted display system based measurement and evaluation of dynamic visual acuity

This study evaluated the dynamic visual acuity of candidates by implementing a King–Devick (K-D) test chart in a virtual reality head-mounted display (VR HMD) and an augmented reality head-mounted display (AR HMD). Hard-copy KD (HCKD), VR HMD KD (VHKD), and AR HMD KD (AHKD) tests were conducted in 30 male and female candidates in the age of 10S and 20S and subjective symptom surveys were conducted. In the subjective symptom surveys, all except one of the VHKD questionnaire items showed subjective symptoms of less than 1 point. In the comparison between HCKD and VHKD, HCKD was measured more rapidly than VHKD in all tests. In the comparison between HCKD and AHKD, HCKD was measured more rapidly than AHKD in Tests 1, 2, and 3. In the comparison between VHKD and AHKD, AHKD was measured more rapidly than VHKD in Tests 1, 2, and 3. In the correlation analyses of test platforms, all platforms were correlated with each other, except for the correlation between HCKD and VHKD in Tests 1 and 2. There was no significant difference in the frequency of errors among Tests 1, 2, and 3 across test platforms. VHKD and AHKD, which require the body to be moved to read the chart, required longer measurement time than HCKD. In the measurements of each platform, AHKD was measured closer to HCKD than VHKD, which may be because the AHKD environment is closer to the actual environment than the VHKD environment. The effectiveness of VHKD and AHKD proposed in this research was evaluated experimentally. The results suggest that treatment and training could be performed concurrently through the use of clinical test and content development of VHKD and AHKD

CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images

Purpose: This study aimed to develop an open-source multimodal large language model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists Materials and Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLAVA network. Then, the model was fine-tuned, primarily using Dataset 2. The model's diagnostic performance for major pathological findings was evaluated, along with the acceptability of radiologic reports by human radiologists, to gauge its potential for autonomous reporting. Results: The model demonstrated impressive performance in test sets, achieving an average F1 score of 0.81 for six major pathological findings in the MIMIC internal test set and 0.62 for seven major pathological findings in the external test set. The model's F1 scores surpassed those of GPT-4-vision and Gemini-Pro-Vision in both test sets. In human radiologist evaluations of the external test set, the model achieved a 72.7% success rate in autonomous reporting, slightly below the 84.0% rate of ground truth reports. Conclusion: This study highlights the significant potential of multimodal LLMs for CXR interpretation, while also acknowledging the performance limitations. Despite these challenges, we believe that making our model open-source will catalyze further research, expanding its effectiveness and applicability in various clinical contexts. CXR-LLAVA is available at https://github.com/ECOFRI/CXR_LLAVA

Stability of a Jensen type equation in the space of generalized functions

AbstractWe reformulate and solve the stability problem of a Jensen type functional equation3f(x+y+z3)+f(x)+f(y)+f(z)−2f(x+y2)−2f(y+z2)−2f(z+x2)=0, in the spaces of some generalized functions such as tempered distributions and Fourier hyperfunctions

Self, Social, Team, and Situational Factors Influencing Televised Sports Viewership

This study examined personal, social, and team motives associated with the consumption of televised sports (CTS) while taking into consideration market constraints variables. Research participants (N = 304) were university students who responded to a questionnaire that consisted of four segments: (a) watching televised sports, (b) motives for watching televised sports, (c) situational constraints, and (d) demographics. Semi-structured interviews as an ad hoc study were conducted with additional 22 frequent viewers of televised sports to ensure inclusion of all relevant factors affecting CTS. Multiple regression analyses revealed that self, team, and social motives were significant factors (p \u3c .05) related to CTS. Two situational factors (weather and ticket availability) were found to have a significant (p \u3c .01) impact on the CTS. Findings from the interviews further revealed that four conceptual themes affected CTS: individual-related factors, team-related factors, event-related factors, and media features

Gene Expression Profiles are Altered in Human Papillomavirus-16 E6 D25E-Expressing Cell Lines

Previously, we have reported that the human papillomavirus (HPV) type 16 E6 D25E is the most prevalent variant in Korean women at high risk for cervical cancers. Several studies have identified an association between the increased frequency of this variant and the elevated risk of cervical intraepithelial neoplasia and invasive cervical carcinoma. To investigate whether the HPV-16 E6 D25E variant might influence cervical cancer progression, we used an oligonucleotide microarray approach to identify transcriptionally altered gene expression patterns in recombinant wild-type E6 or E6 D25E variant-expressing HPV-negative cancer cells. We found that 211 genes were significantly up- or down-regulated (at least 1.5-fold, p < 0.05). We identified 14 genes, nine down-regulated and five up-regulated upon E6 D25E expression, compared with wild-type E6 expression. These results further emphasize the unique biological activity of the HPV-16 E6 D25E variant

Primary Pulmonary Plasmacytoma Presenting as Multiple Lung Nodules

Extramedullary plasmacytoma is a plasma cell tumor arising outside the bone marrow and usually occurs as a solitary tumor in the upper respiratory tract, such as the pharynx, paranasal sinuses, nasal cavity, or oral cavity [1]. Other cases develop in the lymph nodes, skin, gastrointestinal tract, genitourinary tract, and other regions. Primary pulmonary plasmacytomas are very rare and usually present as solitary lung nodules or masses [2]. Unusual cases manifest as diffuse pulmonary infiltration [3,4]. We describe here a unique case of primary pulmonary plasmacytoma, which presented as multiple lung nodules during regular screening in a patient with systemic lupus erythematosus

One-hot Generalized Linear Model for Switching Brain State Discovery

Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits. Inferred neural interactions from neural signals primarily reflect functional interactions. In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states, and hence functional interactions can change over time. To model dynamically changing functional interactions, prior work employs state-switching generalized linear models with hidden Markov models (i.e., HMM-GLMs). However, we argue they lack biological plausibility, as functional interactions are shaped and confined by the underlying anatomical connectome. Here, we propose a novel prior-informed state-switching GLM. We introduce both a Gaussian prior and a one-hot prior over the GLM in each state. The priors are learnable. We will show that the learned prior should capture the state-constant interaction, shedding light on the underlying anatomical connectome and revealing more likely physical neuron interactions. The state-dependent interaction modeled by each GLM offers traceability to capture functional variations across multiple brain states. Our methods effectively recover true interaction structures in simulated data, achieve the highest predictive likelihood with real neural datasets, and render interaction structures and hidden states more interpretable when applied to real neural data

Trust transfer effects on values and attitudes toward China and the Shanghai Expo 2010

Despite the increasing popularity of events, the application of trust transfer concepts has yet to be investigated. Linking trust transfer, values, attitude, and behavioral intentions in a theoretical model, this study employs an on-site survey of visitors attending 2010 Shanghai World Expo to test the theory. The results suggest that positive emotional values attributed to the Expo were associated with positive emotions toward China. In addition, positive perceptions of functional values of the Expo carried over to improved perceived functional and emotional perceptions of China and positive attitudes towards the Expo