Cross-cultural validation of the educational needs assessment tool into Chinese for use in severe knee osteoarthritis

© 2018 Zhao et al. Background: Patient education is an integral part of the management of osteoarthritis. The educational needs assessment tool (ENAT) was developed in the UK to help direct needs-based patient education in rheumatic diseases. Aim: The aim of the study was to adapt and validate the ENAT into Chinese, for use in severe knee osteoarthritis (KOA). Methods: This cross-cultural validation study took two phases: 1) adaptation of the ENAT into Chinese (CENAT) and 2) validation of the CENAT. The Construct validity was determined using factor analysis and criterion-related validity by comparing data from CENAT with data from different self-efficacy scales: patient–physician interactions scale (PEPPI-10), self-efficacy for rehabilitation outcome scale (SER), and the self-efficacy for exercise scale (SEE). Results: The sample comprised 196 patients, with mean age 63.6±8.7 years, disease duration was 11.5 years, and 57.1% were female. The CENAT was found to have high internal consistency. The CENAT had weak correlations with the Chinese versions of PEPPI r=0.40, SER r=0.40, and SEE r=0.39. There were no correlations with age r=−0.03 or disease duration r=−0.11. Conclusion: The ENAT translated well into Chinese and has evidence of validity in KOA. Future studies will further inform its usefulness in clinics, community, and online settings

Target of Opportunity Observations Detectability of Kilonovae with WFST

Kilonovae are approximately thermal transients, produced by mergers of binary neutron stars (BNSs) and NS-black hole binaries. As the optical counterpart of the gravitational wave event GW170817, AT2017gfo is the first kilonova detected with smoking-gun evidence. Its observation offers vital information for constraining the Hubble constant, the source of cosmic $r$-process enrichment, and the equation of state of neutron stars. The 2.5-meter Wide-Field Survey Telescope (WFST) operates at six bands (u, g, r, i, z, w), spanning from 320 to 925 nm. It will be completed in the first half of 2023, and with a field-of-view diameter of 3 degrees, aims to detect kilonovae in the near future. In this article, considering the influence of the host galaxies and sky brightness, we generate simulated images to investigate WFST's ability to detect AT2017gfo-like kilonovae. Due to their spectra, host galaxies can significantly impact kilonova detection at a longer wavelength. When kilonovae are at peak luminosity, we find that WFST performs better in the g and r bands and can detect 90\% (50\%) kilonovae at a luminosity distance of 248 Mpc (338 Mpc) with 30 s exposures. Furthermore, to reflect actual efficiency under target-of-opportunity observations, we calculate the total time of follow-up under various localization areas and distances. We find that if the localization areas of most BNS events detected during the fourth observing (O4) run of LIGO and Virgo are hundreds of deg$^2$, WFST is expected to find $\sim$30\% kilonovae in the first two nights during O4 period.Comment: 18 pages, 11 figure

Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength

The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. With an analytical solution of the product of Gaussian distribution, we adopted variational inference to train the designed MVAE-PoE model to perform common latent feature extraction. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. Results: The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Compared to existing multi-view information fusion methods, the proposed MVAE-PoE achieved the best performance. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R^2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.Comment: 19 pages, 3 figure