A comparison of methods for centering covariates in cross-classified random effects models

The cross-classified random-effects model (CCREM) is used to handle cross-classified data in which units are nested within multiple higher-level dimensions that are not clustered within each other. The focus of interest in this study is the exogeneity assumption in CCREM, which refers to the assumed independence between covariates and random effects at level-2. If the exogeneity assumption is violated, it affects the robustness of the statistical inferences made when estimating the CCREM. Certain methods for centering a covariate can reduce the impact of violating exogeneity. For unbalanced cross-classified data, Raudenbush (2009) proposed the general model of the adaptive centering approach using cluster-mean centering. However, there are several alternatives in addition to this model, including the correlated random effects (RE) model, cell-mean centering, fixed effects (FE) using cluster robust variance estimation (CRVE), and the FE-RE hybrid model. The correlated RE model explicitly models between-cluster variability of the level-1 covariate as level-2 predictors, simultaneously estimating both within- and between-cluster effects. Another approach called cell-mean centering centers covariates around the cell mean instead of the cluster mean and considers the interaction between the two dimensions of the data. If a researcher is interested primarily in level-1 covariates, the FE approach has often been used for handling violations of exogeneity (Wooldridge, 2010). The FE model can be used along with two-way CRVE, an extension of one-way CRVE that accounts for the dependence of errors within clusters (Cameron et al., 2011). The final alternative is an FE-RE hybrid model, which incorporates the FE and RE approaches by modeling one dimension as fixed effects and the other dimension as random effects. This approach requires fewer assumptions while benefiting from the use of the RE model for the selected dimension. However, covariate-centering strategies have only been examined for the hierarchical linear model, not for the CCREM. Thus, extended research on CCREM is needed to demonstrate and evaluate the impact of centering options on the model’s performance and statistical inferences. In this dissertation, I first reviewed the current practice of centering with the CCREM and described the benefits and limitations of covariate centering methods with the CCREM. Next, I presented the results of two empirical applications comparing the use of different centering alternatives. Then, I conducted a systematic review examining how assumptions were tested and how centering was used when estimating the CCREM in applied education and social science research. Finally, I performed a simulation study to compare the performance of alternative centering approaches in scenarios in which the exogeneity assumption is violated.Educational Psycholog

The Effects of Heat and Massage Application on Autonomic Nervous System

∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2011 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens

Simple coordination complex-derived three-dimensional mesoporous graphene as an efficient bifunctional oxygen electrocatalyst

3D mesoporous graphene (mesoG) was synthesized from [Ni<inf>2</inf>(EDTA)] (EDTA = ethylenediaminetetraacetate). The material is comprised of interconnected 4 nm-sized hollow carbon shells composed of 3-4 layers of graphene and exhibits high bifunctional electrocatalytic activity as well as high durability for use in oxygen evolution and reduction reactions. This journal is ??? 2015 The Royal Society of Chemistryopen11

Drosophila CrebB is a Substrate of the Nonsense-Mediated mRNA Decay Pathway that Sustains Circadian Behaviors

Post-transcriptional regulation underlies the circadian control of gene expression and animal behaviors. However, the role of mRNA surveillance via the nonsense-mediated mRNA decay (NMD) pathway in circadian rhythms remains elusive. Here, we report that Drosophila NMD pathway acts in a subset of circadian pacemaker neurons to maintain robust 24 h rhythms of free-running locomotor activity. RNA interference-mediated depletion of key NMD factors in timeless-expressing clock cells decreased the amplitude of circadian locomotor behaviors. Transgenic manipulation of the NMD pathway in clock neurons expressing a neuropeptide PIGMENT-DISPERSING FACTOR (PDF) was sufficient to dampen or lengthen free-running locomotor rhythms. Confocal imaging of a transgenic NMD reporter revealed that arrhythmic Clock mutants exhibited stronger NMD activity in PDF-expressing neurons than wild-type. We further found that hypomorphic mutations in Suppressor with morphogenetic effect on geni-talia 5 (Smg5) or Smg6 impaired circadian behaviors. These NMD mutants normally developed PDF-expressing clock neurons and displayed daily oscillations in the transcript levels of core clock genes. By contrast, the loss of Smg5 or Smg6 function affected the relative transcript levels of cAMP response element-binding protein B (CrebB) in an isoform-specific manner. Moreover, the overexpression of a transcriptional repressor form of CrebB rescued free-running locomotor rhythms in Smg5-depleted flies. These data demonstrate that CrebB is a rate-limiting substrate of the genetic NMD pathway important for the behavioral output of circadian clocks in Drosophila

Collagen microarchitecture mechanically controls myofibroblast differentiation.

Altered microarchitecture of collagen type I is a hallmark of wound healing and cancer that is commonly attributed to myofibroblasts. However, it remains unknown which effect collagen microarchitecture has on myofibroblast differentiation. Here, we combined experimental and computational approaches to investigate the hypothesis that the microarchitecture of fibrillar collagen networks mechanically regulates myofibroblast differentiation of adipose stromal cells (ASCs) independent of bulk stiffness. Collagen gels with controlled fiber thickness and pore size were microfabricated by adjusting the gelation temperature while keeping their concentration constant. Rheological characterization and simulation data indicated that networks with thicker fibers and larger pores exhibited increased strain-stiffening relative to networks with thinner fibers and smaller pores. Accordingly, ASCs cultured in scaffolds with thicker fibers were more contractile, expressed myofibroblast markers, and deposited more extended fibronectin fibers. Consistent with elevated myofibroblast differentiation, ASCs in scaffolds with thicker fibers exhibited a more proangiogenic phenotype that promoted endothelial sprouting in a contractility-dependent manner. Our findings suggest that changes of collagen microarchitecture regulate myofibroblast differentiation and fibrosis independent of collagen quantity and bulk stiffness by locally modulating cellular mechanosignaling. These findings have implications for regenerative medicine and anticancer treatments

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials.Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:1805.0390

Achieving Fairness-aware Two-level Scheduling for Heterogeneous Distributed Systems

In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness