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In contrast to unidimensional item response models that postulate a single underlying proficiency, cognitive diagnosis models (CDMs) posit multiple, discrete skills or attributes, thus allowing CDMs to provide a finer-grained assessment of examinees’ test performance. A common component of CDMs for specifying the attributes required for each item is the Q-matrix. Although construction of Q-matrix is typically performed by domain experts, it nonetheless, to a large extent, remains a subjective process, and misspecifications in the Q-matrix, if left unchecked, can have important practical implications. To address this concern, this paper proposes a discrimination index that can be used with a wide class of CDM subsumed by the generalized deterministic input, noisy “and” gate model to empirically validate the Q-matrix specifications by identifying and replacing misspecified entries in the Q-matrix. The rationale for using the index as the basis for a proposed validation method is provided in the form of mathematical proofs to several relevant lemmas and a theorem. The feasibility of the proposed method was examined using simulated data generated under various conditions. The proposed method is illustrated using fraction subtraction data.

Background The plasticity along the epithelial-mesenchymal transition (EMT) spectrum has been shown to be regulated by various epigenetic repertoires. Emerging evidence of local chromatin conformation changes suggests that regulation of EMT may occur at a higher order of three-dimensional genome level. Results We perform Hi-C analysis and combine ChIP-seq data across cancer cell lines representing different EMT states. We demonstrate that the epithelial and mesenchymal genes are regulated distinctively. We find that EMT genes are regulated within their topologically associated domains (TADs), with only a subset of mesenchymal genes being influenced by A/B compartment switches, indicating topological remodeling is required in the transcriptional regulation of these genes. At the TAD level, epithelial and mesenchymal genes are associated with different regulatory trajectories. The epithelial gene-residing TADs are enriched with H3K27me3 marks in the mesenchymal-like states. The mesenchymal gene-residing TADs, which do not show enrichment of H3K27me3 in epithelial-like states, exhibit increased interaction frequencies with regulatory elements in the mesenchymal-like states. Conclusions We propose a novel workflow coupling immunofluorescence and dielectrophoresis to unravel EMT heterogeneity at single-cell resolution. The predicted three-dimensional structures of chromosome 10, harboring Vimentin, identify cell clusters of different states. Our results pioneer a novel avenue to decipher the complexities underlying the regulation of EMT and may infer the barriers of plasticity in the 3D genome context.

The focus of cognitive diagnosis (CD) is on evaluating an examinee’s strengths and weaknesses in terms of cognitive skills learned and skills that need study. Current methods for fitting CD models (CDMs) work well for large-scale assessments, where the data of hundreds or thousands of examinees are available. However, the development of CD-based assessment tools that can be used in small-scale test settings, say, for monitoring the instruction and learning process at the classroom level has not kept up with the rapid pace at which research and development proceeded for large-scale assessments. The main reason is that the sample sizes of the small-scale test settings are simply too small to guarantee the reliable estimation of item parameters and examinees’ proficiency class membership. In this article, a general nonparametric classification (GNPC) method that allows for assigning examinees to the correct proficiency classes with a high rate when sample sizes are at the classroom level is proposed as an extension of the nonparametric classification (NPC) method (Chiu and Douglas in J Classif 30:225–250, 2013 ). The proposed method remedies the shortcomings of the NPC method and can accommodate any CDM. The theoretical justification and the empirical studies are presented based on the saturated general CDMs, supporting the legitimacy of using the GNPC method with any CDM. The results from the simulation studies and real data analysis show that the GNPC method outperforms the general CDMs when samples are small.

Computerized adaptive testing (CAT) is characterized by its high estimation efficiency and accuracy, in contrast to the traditional paper-and-pencil format. CAT specifically for cognitive diagnosis (CD-CAT) carries the same advantages and has been seen as a tool for advancing the use of cognitive diagnosis (CD) assessment for educational practice. A powerful item selection method is the key to the success of a CD-CAT program, and to date, various parametric item selection methods have been proposed and well-researched. However, these parametric methods all require large samples, to secure high-precision calibration of the items in the item bank. Thus, at present, implementation of parametric methods in small-scale educational settings, such as classroom, remains challenging. In response to this issue, Chang, Chiu, and Tsai (Appl Psychol Meas 43:543–561, 2019) proposed the nonparametric item selection (NPS) method that does not require parameter calibration and outperforms the parametric methods for settings with only small or no calibration samples. Nevertheless, the NPS method is not without limitations; extra assumptions are required to guarantee a consistent estimator of the attribute profiles when data conform to complex models. To remedy this shortcoming, the general nonparametric item selection (GNPS) method that incorporates the newly developed general NPC (GNPC) method (Chiu et al. in Psychometrika 83:355–375, 2018) as the classification vehicle is proposed in this study. The inclusion of the GNPC method in the GNPS method relaxes the assumptions imposed on the NPS method. As a result, the GNPS method can be used with any model or multiple models without abandoning the advantage of being a small-sample technique. The legitimacy of using the GNPS method in the CD-CAT system is supported by Theorem 1 proposed in the study. The efficiency and effectiveness of the GNPS method are confirmed by the simulation study that shows the outperformance of the GNPS method over the compared parametric methods when the calibration samples are small.

Diagnostic classification models in educational measurement describe ability in a knowledge domain as a composite of specific binary skills called “cognitive attributes,” each of which an examinee may or may not have mastered. Attribute Hierarchy Models (AHMs) account for the possibility that attributes are dependent by imposing a hierarchical structure such that mastery of one or more attributes is a prerequisite of mastering one or more other attributes. Thus, the number of meaningfully defined attribute combinations is reduced, so that constructing a complete Q-matrix may be challenging. (The Q-matrix of a cognitively diagnostic test documents which attributes are required for solving which item; the Q-matrix is said to be complete if it guarantees the identifiability of all realizable proficiency classes among examinees.) For structured Q-matrices (i.e., the item attribute profiles are restricted to reflect the hierarchy postulated to underlie the attributes), the conditions of completeness have been established. However, sometimes, a structured Q-matrix cannot be assembled because the items of the test in question have attribute profiles that do not conform to the prerequisite structure imposed by the postulated attribute hierarchy. A Q-matrix composed of such items is called “unstructured.” In this article, the completeness conditions of unstructured Q-matrices for the DINA model are presented. Specifically, there exists an entire range of Q-matrices that are all complete for DINA-AHMs. Thus, the theoretical results presented here can be combined with extant insights about Q-completeness for models without attribute hierarchies into a unified framework on the completeness of Q-matrices for the DINA model.

A trend in educational testing is to go beyond unidimensional scoring and provide a more complete profile of skills that have been mastered and those that have not. To achieve this, cognitive diagnosis models have been developed that can be viewed as restricted latent class models. Diagnosis of class membership is the statistical objective of these models. As an alternative to latent class modeling, a nonparametric procedure is introduced that only requires specification of an item-by-attribute association matrix, and classifies according to minimizing a distance measure between observed responses, and the ideal response for a given attribute profile that would be implied by the item-by-attribute association matrix. This procedure requires no statistical parameter estimation, and can be used on a sample size as small as 1. Heuristic arguments are given for why the nonparametric procedure should be effective under various possible cognitive diagnosis models for data generation. Simulation studies compare classification rates with parametric models, and consider a variety of distance measures, data generation models, and the effects of model misspecification. A real data example is provided with an analysis of agreement between the nonparametric method and parametric approaches.

Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, K -means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice.

The Q-matrix of a cognitively diagnostic test is said to be complete if it allows for the identification of all possible proficiency classes among examinees. Completeness of the Q-matrix is therefore a key requirement for any cognitively diagnostic test. However, completeness of the Q-matrix is often difficult to establish, especially, for tests with a large number of items involving multiple attributes. As an additional complication, completeness is not an intrinsic property of the Q-matrix, but can only be assessed in reference to a specific cognitive diagnosis model (CDM) supposed to underly the data—that is, the Q-matrix of a given test can be complete for one model but incomplete for another. In this article, a method is presented for assessing whether a given Q-matrix is complete for a given CDM. The proposed procedure relies on the theoretical framework of general CDMs and is therefore legitimate for CDMs that can be reparameterized as a general CDM.

Epithelial-mesenchymal transition (EMT) is a dynamic process enabling polarized epithelial cells to acquire mesenchymal features implicated in development and carcinoma progression. As our understanding evolves, it is clear the reversible execution of EMT arises from complex epigenomic regulation involving histone modifications and 3-dimensional (3D) genome structural changes, leading to a cascade of transcriptional events. This review summarizes current knowledge on chromatin organization in EMT, with a focus on hierarchical structures of the 3D genome and chromatin accessibility changes. A Mini Review summarizing the current understanding of epithelial-mesenchymal transition (EMT) and 3D genome organization, focusing on the role of chromatin conformation and accessibility changes during the transitional phases of EMT.

Parametric likelihood estimation is the prevailing method for fitting cognitive diagnosis models—also called diagnostic classification models (DCMs). Nonparametric concepts and methods that do not rely on a parametric statistical model have been proposed for cognitive diagnosis. These methods are particularly useful when sample sizes are small. The general nonparametric classification (GNPC) method for assigning examinees to proficiency classes can accommodate assessment data conforming to any diagnostic classification model that describes the probability of a correct item response as an increasing function of the number of required attributes mastered by an examinee (known as the “monotonicity assumption”). Hence, the GNPC method can be used with any model that can be represented as a general DCM. However, the statistical properties of the estimator of examinees’ proficiency class are currently unknown. In this article, the consistency theory of the GNPC proficiency-class estimator is developed and its statistical consistency is proven.