Dynamic Structural Equation Models with Missing Data: Data Requirements on <i>N</i> and <i>T</i>

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.</p

Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data: Implications for Longitudinal Study Designs

<p>Intraindividual variability can be measured by the intraindividual standard deviation (), intraindividual variance (), estimated <i>h</i>th-order autocorrelation coefficient (), and mean square successive difference (). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorrelations in the longitudinal responses; (2) the reliabilities of and have not been studied. The current study investigates reliabilities of , , , , and the intraindividual mean, with autocorrelated longitudinal data. Reliability estimates of the indicators were obtained through Monte Carlo simulations. The impact of influential factors on reliabilities of the intraindividual variability indicators is summarized, and the reliabilities are compared across the indicators. Generally, all the studied indicators of intraindividual variability were more reliable with a more reliable measurement scale and more assessments. The reliabilities of were generally lower than those of and , the reliabilities of were usually between those of and unless the scale reliability was large and/or the interindividual standard deviation in autocorrelation coefficients was large, and the reliabilities of the intraindividual mean were generally the highest. An R function is provided for planning longitudinal studies to ensure sufficient reliabilities of the intraindividual indicators are achieved.</p

A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis

<p>In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed.</p

Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning

Federated learning (FL) strives to enable collaborative training of deep models on the distributed clients of different data without centrally aggregating raw data and hence improving data privacy. Nevertheless, a central challenge in training classification models in the federated system is learning with non-IID data. Most of the existing work is dedicated to eliminating the heterogeneous influence of non-IID data in a federated system. However, in many real-world FL applications, the co-occurrence of data heterogeneity and long-tailed distribution is unavoidable. The universal class distribution is long-tailed, causing them to become easily biased towards head classes, which severely harms the global model performance. In this work, we also discovered an intriguing fact that the classifier logit vector (i.e., pre-softmax output) introduces a heterogeneity drift during the learning process of local training and global optimization, which harms the convergence as well as model performance. Therefore, motivated by the above finding, we propose a novel logit calibration FL method to solve the joint problem of non-IID and long-tailed data in federated learning, called Federated Learning with Logit Calibration (FedLC). First, we presented a method to mitigate the local update drift by calculating the Wasserstein distance among adjacent client logits and then aggregating similar clients to regulate local training. Second, based on the model ensemble, a new distillation method with logit calibration and class weighting was proposed by exploiting the diversity of local models trained on heterogeneous data, which effectively alleviates the global drift problem under long-tailed distribution. Finally, we evaluated FedLC using a highly non-IID and long-tailed experimental setting, comprehensive experiments on several benchmark datasets demonstrated that FedLC achieved superior performance compared with state-of-the-art FL methods, which fully illustrated the effectiveness of the logit calibration strategy

Distributional Knowledge Transfer for Heterogeneous Federated Learning

Federated learning (FL) produces an effective global model by aggregating multiple client weights trained on their private data. However, it is common that the data are not independently and identically distributed (non-IID) across different clients, which greatly degrades the performance of the global model. We observe that existing FL approaches mostly ignore the distribution information of client-side private data. Actually, the distribution information is a kind of structured knowledge about the data itself, and it also represents the mutual clustering relations of data examples. In this work, we propose a novel approach, namely Federated Distribution Knowledge Transfer (FedDKT), that alleviates heterogeneous FL by extracting and transferring the distribution knowledge from diverse data. Specifically, the server learns a lightweight generator to generate data and broadcasts it to the sampled clients, FedDKT decouples the feature representations of the generated data and transfers the distribution knowledge to assist model training. In other words, we exploit the similarity and shared parts of the generated data and local private data to improve the generalization ability of the FL global model and promote representation learning. Further, we also propose the similarity measure and attention measure strategies, which implement FedDKT by capturing the correlations and key dependencies among data examples, respectively. The comprehensive experiments demonstrate that FedDKT significantly improves the performance and convergence rate of the FL global model, especially when the data are extremely non-IID. In addition, FedDKT is also effective when the data are identically distributed, which fully illustrates the generalization and effectiveness of the distribution knowledge

DataSheet_1_Causal relationships between susceptibility and severity of COVID-19 and neuromyelitis optica spectrum disorder (NMOSD) in European population: a bidirectional Mendelian randomized study.docx

BackgroundNeurological disorders can be caused by viral infections. The association between viral infections and neuromyelitis optica spectrum disorder (NMOSD) has been well-documented for a long time, and this connection has recently come to attention with the occurrence of SARS-CoV-2 infection. However, the precise nature of the causal connection between NMOSD and COVID-19 infection remains uncertain.MethodsTo investigate the causal relationship between COVID-19 and NMOSD, we utilized a two-sample Mendelian randomization (MR) approach. This analysis was based on the most extensive and recent genome-wide association study (GWAS) that included SARS-CoV-2 infection data (122616 cases and 2475240 controls), hospitalized COVID-19 data (32519 cases and 2062805 controls), and data on severe respiratory confirmed COVID-19 cases (13769 cases and 1072442 controls). Additionally, we incorporated a GWAS meta-analysis comprising 132 cases of AQP4-IgG-seropositive NMOSD (NMO-IgG+), 83 cases of AQP4-IgG-seronegative NMOSD (NMO-IgG−), and 1244 controls.ResultsThe findings of our study indicate that the risk of developing NMO-IgG+ is elevated when there is a genetic predisposition to SARS-CoV-2 infection (OR = 5.512, 95% CI = 1.403-21.657, P = 0.014). Furthermore, patients with genetically predicted NMOSD did not exhibit any heightened susceptibility to SARS-CoV2 infection, COVID-19 hospitalization, or severity.Conclusionour study using Mendelian randomization (MR) revealed, for the first time, that the presence of genetically predicted SARS-CoV2 infection was identified as a contributing factor for NMO-IgG+ relapses.</p

Chemical Diversification Based on Substrate Promiscuity of a Standalone Adenylation Domain in a Reconstituted NRPS System

A nonribosomal peptide synthetase (NRPS) assembly line (sfa) in Streptomyces thioluteus that directs the formation of the diisonitrile chalkophore SF2768 (1) has been characterized by heterologous expression and directed gene knockouts. Herein, differential metabolic analysis of the heterologous expression strain and the original host led to the isolation of an SF2768 analogue (2, a byproduct of sfa) that possesses N-isovaleryl rather than 3-isocyanobutyryl side chains. The proposed biosynthetic logic of sfa and the structural difference between 1 and 2 suggested substrate promiscuity of the adenylate-forming enzyme SfaB. Further substrate scope investigation of SfaB and a successfully reconstituted NRPS system including a four-enzyme cascade enabled incorporation of diverse carboxylic acid building blocks into peptide scaffolds, and 30 unnatural products were thus generated. This structural diversification strategy based on substrate flexibility of the adenylation domain and in vitro reconstitution can be applied to other adenylation-priming pathways, thus providing a supplementary method for diversity-oriented total synthesis. Additionally, the biocatalytic process of the putative lysine δ-hydroxylase SfaE was validated through the derivatization of two key aldehyde intermediates (2a and 2b), thereby expanding the toolkit of enzymatic C–H bond activation

sj-sav-5-qjp-10.1177_17470218211047944 for Semantic feedback processing mechanism of the enactment effect: Evidence from event-related potentials

sj-sav-5-qjp-10.1177_17470218211047944 for Semantic feedback processing mechanism of the enactment effect: Evidence from event-related potentials by Lijuan Wang, Zhanyu Yu, Zhi Ren and Jialin Ma in Quarterly Journal of Experimental Psychology</p

sj-doc-6-qjp-10.1177_17470218211047944 – Supplemental material for Semantic feedback processing mechanism of the enactment effect: Evidence from event-related potentials

Supplemental material, sj-doc-6-qjp-10.1177_17470218211047944 for Semantic feedback processing mechanism of the enactment effect: Evidence from event-related potentials by Lijuan Wang, Zhanyu Yu, Zhi Ren and Jialin Ma in Quarterly Journal of Experimental Psychology</p

Additional file 2 of Hydrogen sulfide attenuates intracellular oxidative stress via repressing glycolate oxidase activities in Arabidopsis thaliana

Additional file 2: Table S1. List of primers and restriction enzymes used in this study