Estimation and Q-matrix validation for diagnostic classification models

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects\u27 underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends on correct specification of a Q-matrix which is a binary matrix linking attribute patterns to items. Current literature on assessing the appropriateness of Q-matrix specifications has focused on validation methods for the deterministic-input, noisy-and-gate (DINA) model. The goal of the study is to develop general Q-matrix validation methods that can be applied to a wider class of DCMs. The study proposes a two-stage validation method which incorporates the idea of sequential searching based on the posterior distribution of attribute patterns and Bayesian model selection techniques. Simulation studies show that the proposed methods successfully detect and correct misspecifications in a Q-matrix for a complicated non-compensatory DCM, the reduced reparameterized unified model (RUM), and a compensatory DCM, the deterministic input, noisy-or-gate (DINO) model. Model estimation is the first step in validating a Q-matrix. The EM algorithm is shown to provide accurate estimates for the reduced RUM, with the advantage of significant computational time savings compared to estimation by Markov chain Monte Carlo (MCMC). In addition, factors affecting the performance of the validation methods are discussed. Suggestions on implementation of the methods under the case when items are from a combination of DCMs are given

How Low Nucleation Density of Graphene on CuNi Alloy is Achieved

CuNi alloy foils are demonstrated to be one of the best substrates for synthesizing large area single-crystalline graphene because a very fast growth rate and low nucleation density can be simultaneously achieved. The fast growth rate is understood to be due the abundance of carbon precursor supply, as a result of the high catalytic activity of Ni atoms. However, a theoretical understanding of the low nucleation density remains controversial because it is known that a high carbon precursor concentration on the surface normally leads to a high nucleation density. Here, the graphene nucleation on the CuNi alloy surfaces is systematically explored and it is revealed that: i) carbon atom dissolution into the CuNi alloy passivates the alloy surface, thereby drastically increasing the graphene nucleation barrier; ii) carbon atom diffusion on the CuNi alloy surface is greatly suppressed by the inhomogeneous atomic structure of the surface; and iii) a prominent increase in the rate of carbon diffusion into the bulk occurs when the Ni composition is higher than the percolation threshold. This study reveals the key mechanism for graphene nucleation on CuNi alloy surfaces and provides a guideline for the catalyst design for the synthesis of graphene and other 2D materials

BMP4 inhibits myogenic differentiation of bone marrow–derived mesenchymal stromal cells in mdx mice

AbstractBackground aimsBone marrow–derived mesenchymal stromal cells (BMSCs) are a promising therapeutic option for treating Duchenne muscular dystrophy (DMD). Myogenic differentiation occurs in the skeletal muscle of the mdx mouse (a mouse model of DMD) after BMSC transplantation. The transcription factor bone morphogenic protein 4 (BMP4) plays a crucial role in growth regulation, differentiation and survival of many cell types, including BMSCs. We treated BMSCs with BMP4 or the BMP antagonist noggin to examine the effects of BMP signaling on the myogenic potential of BMSCs in mdx mice.MethodsWe added BMP4 or noggin to cultured BMSCs under myogenic differentiation conditions. We then injected BMP4- or noggin-treated BMSCs into the muscles of mdx mice to determine their myogenic potential.ResultsWe found that the expression levels of desmin and myosin heavy chain decreased after treating BMSCs with BMP4, whereas the expression levels of phosphorylated Smad, a downstream target of BMP4, were higher in these BMSCs than in the controls. Mdx mouse muscles injected with BMSCs pretreated with BMP4 showed decreased dystrophin expression and increased phosphorylated Smad levels compared with muscles injected with non-treated BMSCs. The opposite effects were seen after pretreatment with noggin, as expected.ConclusionsOur results identified BMP/Smad signaling as an essential negative regulator of promyogenic BMSC activity; inhibition of this pathway improved the efficiency of BMSC myogenic differentiation, which suggests that this pathway might serve as a target to regulate BMSC function for better myogenic differentiation during treatment of DMD and degenerative skeletal muscle diseases