Evaluation of the Quantitative Myasthenia Gravis Score and Grip Strength in Chinese Patients With Myasthenia Gravis: An Observational Study

Introduction: The quantitative myasthenia gravis score is a commonly used scale for evaluating muscle weakness associated with myasthenia gravis (MG). It has been reported that some items used in the scale have low discriminative properties. However, there has been no research investigating the applicability of the quantitative MG score (QMGS) in Chinese patients with MG. In addition, the scoring method and ranges of grip strength items in QMGS need to be further evaluated.Methods: This study included 106 Chinese patients with MG, enrolled between September 2020 and February 2021, who were evaluated using the QMGS. Each item in the QMGS was analyzed for distribution. Three methods of evaluating grip strength, grip strength decrement, maximum grip strength, and relative grip strength, were compared. The correlation between the QMG total score minus grip strength score, and three evaluating methods, was analyzed.Results: The grip strength, swallowing, speech, diplopia, ptosis, and facial muscles items showed a clustered distribution. Most patients (94%) presented their maximum grip strength in the first four grip strength measurements. The QMG total score minus the grip strength score had a weak correlation with grip strength decrement (R grip r = 0.276; L grip r = 0.353, both p < 0.05) and moderate correlations with maximum grip strength (R grip r = −0.508; L grip r = −0.507; both p < 0.001) and relative grip strength (R grip r = −0.494; L grip r = −0.497, both p < 0.001).Conclusions: This study suggested that partial items in the QMGS have low discriminative properties for Chinese populations and the maximum grip strength value is the better method to evaluate grip strength compared to the other two scoring methods. Based on the quartiles of maximum grip strength, we propose new scoring ranges for the grip strength items.

Dynamics of Neuropsychological Symptoms during the Training of Executive Functions in Neurological Patients

Executive function disorder rehabilitation in neurological patients is associated with many difficulties. We investigated the effectiveness of group training, proposed by B. Wilson et al., which has the model of frontal lobes functioning by D. T. Stuss as the theoretical background. The study participants were 16 patients with executive function disorder caused by TBI, strokes, and infections. The training was shortened from 9 weeks to 3 and adopted to the conditions of the rehabilitation centre where the study was held. The evaluation of training effectiveness was carried out by the methods of neuropsychological diagnostics proposed by A. R. Luria as well as standardized quantitative tests (CWIT test, Raven test, FAB) and questionnaires (EBIQ) aimed at assessing the state of executive functions and general well-being. In result positive trends, but not reaching the level of significance, were revealed in the performance of all evaluating methods, with the exception of “arithmetic problems” and “inhibitory control” as part of the FAB test. Statistically significant result was obtained concerning such tests as “counting”, “analysis of story pictures”, and index of total uncorrected errors in the CWIT test. Thus, the results of eventual assessment showed positive dynamic of executive functions state.

The Piggy in the Middle

Researchers are becoming cognizant of the value of conducting predictive analysis using partial least squares structural equation modeling (PLS-SEM) for both the evaluation of overfit and to illustrate the practical value of models. Mediators are a popular mechanism for adding nuance and greater explanatory power to causal models. However, mediators pose a special challenge to generating predictions as they serve a dual role of antecedent and outcome. Solutions for generating predictions from mediated PLS-SEM models have not been suitably explored or documented, nor has there been exploration of whether the added model complexity of such mediators is justified in the light of predictive performance. We address that gap by evaluating methods for generating predictions from mediated models, and propose a simple metric that quantifies the predictive contribution of the mediator (PCM). We conduct Monte Carlo simulations and then apply the methods in an empirical demonstration. We find that there is no simple best solution, but that all three approaches have strengths and weaknesses. Further, the PCM metric performs well to quantify the predictive qualities of the mediator over-and-above the non-mediated alternative. We present guidelines on selecting the most appropriate method and applying PCM for additional evidence to support research conclusions.

Evaluating methods for reconstructing large gaps in historic snow depth time series

Historic measurements are often temporally incomplete and may contain longer periods of missing data, whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, for which even whole winters can be missing in a station record, and suitable methods have to be found to reconstruct the missing data. Daily in situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level are used to compare six different methods for reconstructing long gaps in manual HS time series by performing a “leave-one-winter-out” cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias-corrected data from the best-correlated neighboring station (BSC), inverse distance-weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, elastic net (ENET) regression, random forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. The median root mean square error (RMSE) in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be reproduced by ENET, RF, SM and WNR well, with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS>1 cm (dHS1) are clearly weaker and, except for BCS, positively biased with RMSE of 18–33 d. SM reveals the best performance with r2 of 0.93 and RMSE of 15 d for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

Robust Multimodal Emotion Recognition from Conversation with Transformer-Based Crossmodality the title Fusion

Decades of scientific research have been conducted on developing and evaluating methods for automated emotion recognition. With exponentially growing technology, there is a wide range of emerging applications that require emotional state recognition of the user. This paper investigates a robust approach for multimodal emotion recognition during a conversation. Three separate models for audio, video and text modalities are structured and fine-tuned on the MELD. In this paper, a transformer-based crossmodality fusion with the EmbraceNet architecture is employed to estimate the emotion. The proposed multimodal network architecture can achieve up to 65% accuracy, which significantly surpasses any of the unimodal models. We provide multiple evaluation techniques applied to our work to show that our model is robust and can even outperform the state-of-the-art models on the MELD.

Evaluating methods for reconstructing large gaps in historic snow depth time series

Historic measurements are often temporally incomplete and may contain longer periods of missing data whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, where even whole winters can be missing in a station record and suitable methods have to be found to reconstruct the missing data. Daily in-situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level is used to compare six different methods for reconstructing long gaps in manual HS time series by performing a "leave-one-winter-out" cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias corrected data from the best correlated neighboring station (BSC), inverse distance weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, Elastic Net (ENET) regression, Random Forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. Median RMSE in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be well reproduced by ENET, RF, SM and WNR with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS ≥ 1 cm (dHS1) are clearly weaker and except for BCS positively biased with RMSE of 18–33 days. SM reveals the best performance with r2 of 0.93 and RMSE of 15 days for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.