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Cybersickness poses a significant challenge to the widespread adoption of virtual reality (VR), as it impairs user experience and operational performance. This study proposes a physiological modeling approach to objectively assess cybersickness severity during VR experience. An interactive VR experiment was conducted, inducing varying levels of cybersickness through VR navigation tasks under different field-of-view and graphic quality settings. Physiological signals (i.e., electrodermal activity (EDA) and electrocardiogram (ECG)) were continuously recorded and extracted to build multiple machine learning regression models for cybersickness prediction. The results showed that EDA-based models consistently outperformed ECG-based models across all algorithms, with the Ensemble Learning model achieving the highest predictive accuracy (R2 = 0.98). In contrast, ECG-based models yielded limited predictive capability (R2 = 0.53). Combining ECG with EDA features showed little improvement in model accuracy, suggesting a limited complementary role of ECG features. SHAP-based feature importance analysis revealed that EDA features (e.g., mean, maximum, and variance of skin conductance) were the most effective features in cybersickness prediction, which captured both tonic arousal and phasic autonomic responses during the cybersickness process. ECG features such as SDNN and HRMAD contributed modestly, offering physiological interpretability despite being less effective in cybersickness prediction. The findings demonstrate the feasibility of using low-burden physiological signals for accurate and interpretable prediction of cybersickness severity. The proposed approach supports the development of lightweight, real-time monitoring systems for VR applications, offering practical advantages in terms of simplicity, adaptability, and deployment potential.

Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers’ mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers’ mental states.

Usability can influence patients' acceptance and adoption of a health information technology. However, little research has been conducted to study the usability of a self-management health care system, especially one geared toward elderly patients. This usability study evaluated a new computer-based self-management system interface for older adults with chronic diseases, using a paper prototype approach. Fifty older adults with different chronic diseases participated. Two usability evaluation methods were involved: (1) a heuristics evaluation and (2) end-user testing with a think-aloud testing method, audio recording, videotaping, and interviewing. A set of usability metrics was employed to determine the overall system usability, including task incompletion rate, task completion time, frequency of error, frequency of help, satisfaction, perceived usefulness, and perceived ease of use. Interviews were used to elicit participants' comments on the system design. The quantitative data were analyzed using descriptive statistics and the qualitative data were analyzed for content. The participants were able to perform the predesigned self-management tasks with the current system design and they expressed mostly positive responses about the perceived usability measures regarding the system interface. However, the heuristics evaluation, performance measures, and interviews revealed a number of usability problems related to system navigation, information search and interpretation, information presentation, and readability. Design recommendations for further system interface modifications were discussed. This study verified the usability of the self-management system developed for older adults with chronic diseases. Also, we demonstrated that our usability evaluation approach could be used to quickly and effectively identify usability problems in a health care information system at an early stage of the system development process using a paper prototype. Conducting a usability evaluation is an essential step in system development to ensure that the system features match the users' true needs, expectations, and characteristics, and also to minimize the likelihood of the users committing user errors and having difficulties using the system.

Background Mitochondrial dysfunction has been hypothesized to play a role in the etiology of congenital birth defects. However, evidence from observational studies is susceptible to bias and confounding. Mendelian randomization uses genetic variants as instrumental variables to investigate causal relationships. This study aimed to investigate the causal effect of mitochondrial proteins on risk of common congenital defects including orofacial clefts, congenital heart defects, external ear malformations, urinary system malformations, nervous system malformations, and limb malformations. Methods Summary statistics data on congenital birth defects were obtained from the FinnGen consortium. This included 1,994 cases of congenital heart malformations, 258 cases of nervous system malformations, 185 cases of ear malformations, 813 cases of urinary system malformations, 92 cases of limb malformations, and 181 cases of cleft lip and cleft palate, alongside 216,798 to 218,611 controls, depending on the defect type. Data on genetic variants associated with 66 mitochondrial proteins were extracted from the Human Plasma Proteome Atlas ( n  = 3,301 healthy individuals). The inverse-variance weighted method was applied as the primary analysis, with sensitivity analyses using MR-Egger regression, weighted median estimation, and MR-PRESSO to assess pleiotropy and outliers. Results Among the 66 mitochondrial protein traits examined, several displayed significant associations with congenital birth defects. Negative associations were found between pyruvate dehydrogenase kinase isozyme 1 and ATP synthase subunit beta mitochondrial levels and congenital heart malformation risk. GrpE protein homolog 1 mitochondrial was negatively associated with cleft lip/palate risk. 39S ribosomal protein L14 and GrpE protein homolog 1 mitochondrial showed positive and negative links with urinary malformations, respectively. Positive associations were noted between cytochrome c oxidase subunit 4 isoform 2, protein SCO1 homolog, and tRNA pseudouridine synthase A mitochondrial and nervous system malformations, while peptide chain release factor 1-like mitochondrial was negatively related. Cytochrome c oxidase subunit 7 A1 mitochondrial associated positively with ear malformations. Positive relationships were identified between cytochrome c oxidase subunit 7 A1, ADP-ribose pyrophosphatase, coiled-coil-helix-coiled-coil-helix domain-containing protein 10, NFU1 iron-sulfur cluster scaffold homolog mitochondrial, and limb malformation risk. Meanwhile, NADH dehydrogenase [ubiquinone] iron-sulfur protein 4 mitochondrial displayed a negative association. Conclusions This Mendelian randomization study provides evidence that mitochondrial protein levels may be causally implicated in congenital heart, urinary, nervous system, ear, and limb malformations. The findings highlight potential etiological roles for mitochondrial dysfunction in the pathogenesis of structural birth defects. Further large-scale and functional investigations are warranted to corroborate these genetic inference results and elucidate underlying mechanisms that may inform translational applications.

This study aims to delineate the burden of congenital birth defects (CBDs) in children under 14 years of age from 1990 to 2019, using an age-period-cohort framework to analyse data from the Global Burden of Disease Study (GBD). Data on prevalence cases, age-standardised prevalence rates (ASPRs), death cases, and age-standardised death rates (ASDRs) of congenital birth defects (CBDs) from 1990 to 2019 were obtained from GBD 2019. Using this data set, we conducted an age-period-cohort (APC) analysis to examine patterns and trends in mortality, prevalence, and disability-adjusted life years (DALYs) associated with CBDs, while exploring correlations with age, time periods, and generational birth cohorts. Furthermore, to quantify the temporal trends, we calculated the estimated annual percentage changes (EAPCs) for these parameters. The global prevalence of CBDs decreased from 1404.22 to 1301.66 per 100 000 with an EAPC of -0.18% from 1990 to 2019. CBD mortality decreased by 42.52% between 1990 and 2019, with the global age-standardised death rate declining from 49.72 to 25.58 per 100 000. The age-standardised DALY rate decreased from 4529.16 to 2393.61 per 100 000. Prevalence declined most notably among older children. The risk of CBDs reached its lowest during adolescence (10-14 years) across all regions. The most recent period (2015-2019) showed a reduced risk of prevalence compared to 2000-2004. Earlier birth cohorts displayed declining tendencies followed by slight increases in risk. This study demonstrates encouraging global reductions in the burden of CBDs among children over the past three decades. Prevalence, mortality, and DALYs attributable to CBDs have exhibited downward trajectories, although regional disparities remain. APC analysis provides valuable insights to inform prevention and management strategies for pediatric CBDs.

Comparative genomic analyses among closely related species can greatly enhance our understanding of plant gene and genome evolution. We report de novo-assembled AA-genome sequences for Oryza nivara , Oryza glaberrima , Oryza barthii , Oryza glumaepatula , and Oryza meridionalis . Our analyses reveal massive levels of genomic structural variation, including segmental duplication and rapid gene family turnover, with particularly high instability in defense-related genes. We show, on a genomic scale, how lineage-specific expansion or contraction of gene families has led to their morphological and reproductive diversification, thus enlightening the evolutionary process of speciation and adaptation. Despite strong purifying selective pressures on most Oryza genes, we documented a large number of positively selected genes, especially those genes involved in flower development, reproduction, and resistance-related processes. These diversifying genes are expected to have played key roles in adaptations to their ecological niches in Asia, South America, Africa and Australia. Extensive variation in noncoding RNA gene numbers, function enrichment, and rates of sequence divergence might also help account for the different genetic adaptations of these rice species. Collectively, these resources provide new opportunities for evolutionary genomics, numerous insights into recent speciation, a valuable database of functional variation for crop improvement, and tools for efficient conservation of wild rice germplasm. Significance Asian rice ( Oryza sativa ) is among the world’s most important crops. The genus Oryza has become a model for the study of plant genome structure, function, and evolution. We have undertaken de novo, full-genome sequence analysis of five diploid AA-genome species that are closely related to O. sativa . These species are native to quite different environments, representing four continents, thus exhibiting very different adaptations. Our studies identify specific genetic changes, in both gene copy number and the degree of diversifying natural selection, that indicate specific genes responsible for these adaptations, particularly in genes related to defense against pathogens and reproductive diversification. This genome discovery and comparative analysis provide a powerful tool for future Oryza study and rice improvement.

Circadian genes control most of the physiological functions in cancer cells, including cell proliferation, migration, and invasion. The CLOCK and BMAL1 complex plays a central role in circadian rhythms. Previous studies have shown that circadian genes may act as oncogenes or tumor-suppressor genes. In addition, F-actin, regulated by RHOA, has been shown to participate in tumor progression. However, the roles of the CLOCK and BMAL1 genes in the regulation of tumor progression via the RHOA-ROCK-CFL pathway remain largely unclear. Here we first indicate that the rearrangement of F-actin is regulated by CLOCK and BMAL1. We found that CLOCK and BMAL1 can upregulate RHOA expression by inhibiting CUL3-mediated ubiquitination and activate RHOA by reducing the interaction between RHOA and RhoGDI. Consequently, CLOCK and BMAL1 control the expression of the components of the RHOA-ROCK-CFL pathway, which alters the dynamics of F-actin/G-actin turnover and promotes cancer cell proliferation, migration, and invasion. In conclusion, our research proposes a novel insight into the role of CLOCK and BMAL1 in tumor cells. Cancer: Clocking the triggers for tumor progression Two proteins involved in regulating circadian rhythms may promote cancer tumor growth by changing the internal structures of cells. Circadian clock proteins play key roles in cell division, metabolism, and immune responses. Certain clock proteins are implicated in uncontrolled cell and tumor growth. Yong-xin Ma at Sichuan University in Chengdu, China and co-workers have shown that two clock proteins, CLOCK and BMAL1, regulate the arrangement of F-actin microfilaments in the cytoskeleton of cells. The researchers found that CLOCK and BMAL1 interact with an enzyme that promotes the formation of cellular fibers and regulates cell shape and motility. The overexpression of these clock proteins in cancers increases the levels of this enzyme, which changes F-actin structures inside cells. This promotes cancer cell proliferation, migration, and invasion.

Solar radiation forecasting is the basis of building a robust solar power system. Most ground-based forecasting methods are unable to consider the impact of cloud changes on future solar radiation. To alleviate this limitation, this study develops a hybrid network which relies on a convolutional neural network to extract cloud motion patterns from time series of satellite observations and a long short-term memory neural network to establish the relationship between future solar radiation and cloud information, as well as antecedent measurements. We carefully select the optimal scales to consider the spatial and temporal correlations of solar radiation and design test experiments at ten stations to check the model performance in various climate zones. The results demonstrate that the solar radiation forecasting accuracy is considerably improved, particularly in cloudy conditions, compared with purely ground-based models. The maximum magnitude of improvements reaches up to 50 W/m2 (15%) in terms of the (relative) root mean squared error (RMSE) for 1 h ahead forecasts. The network achieves superior forecasts with correlation coefficients varying from 0.96 at 1 h ahead to 0.85 at 6 h ahead. Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. This study ascertains that multi-source data fusion contributes to a better simulation of cloud impacts and a combination of different deep learning techniques enables more reliable forecasts of solar radiation. In addition, multi-step forecasts with a low latency make the advance planning and management of solar energy possible in practical applications.

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,⋯} ∈ “color” subspace yet cube ∈ “shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces (i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.