Learning Non-linear Structures with Gaussian Markov Random Fields

AbstractNowadays, one of the most changeling points in statistics is the analysis of high dimensional data. In such cases, it is commonly assumed that the dimensionality of the data is only artificially high: although each data point is described by thousands of features, it is assumed that it can be modeled as a function of only a few underlying parameters. Formally, it is assumed that the data points are samples from a low-dimensional manifold embedded in a high-dimensional space.In this paper, we discuss a recently proposed method, known as Maximum Entropy Unfolding (MEU), for learning non-linear structures that characterize high dimensional data.This method represents a new perspective on spectral dimensionality reduction and, joined with the theory of Gaussian Markov random fields, provides a unifying probabilistic approach to spectral dimensionality reduction techniques. Parameter estimation as well as approaches to learning the structure of the GMRF are discusse

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions

Exploratory factor analysis of large data matrices

Nowadays, the most interesting applications have data with many more variables than observations and require dimension reduction. With such data, standard exploratory factor analysis (EFA) cannot be applied. Recently, a generalized EFA (GEFA) model was proposed to deal with any type of data: both vertical data(fewer variables than observations) and horizontal data (more variables than observations). The associated algorithm, GEFALS, is very efficient, but still cannot handle data with thousands of variables. The present work modifies GEFALS and proposes a new very fast version, GEFAN. This is achieved by aligning the dimensions of the parameter matrices to their ranks, thus, avoiding redundant calculations. The GEFALS and GEFAN algorithms are compared numerically with well-known data

Sparse exploratory factor analysis

Sparse principal component analysis is a very active research area in the last decade. In the same time, there are very few works on sparse factor analysis. We propose a new contribution to the area by exploring a procedure for sparse factor analysis where the unknown parameters are found simultaneously

With or Without You: Altered Plant Response to Boron-Deficiency in Hydroponically Grown Grapevines Infected by Grapevine Pinot Gris Virus Suggests a Relation Between Grapevine Leaf Mottling and Deformation Symptom Occurrence and Boron Plant Availability

Despite the increasing spread of Grapevine Leaf Mottling and Deformation (GLMD) worldwide, little is known about its etiology. After identification of grapevine Pinot gris virus (GPGV) as the presumptive causal agent of the disease in 2015, various publications have evaluated GPGV involvement in GLMD. Nevertheless, there are only partial clues to explain the presence of GPGV in both symptomatic and asymptomatic grapevines and the mechanisms that trigger symptom development, and so a consideration of new factors is required. Given the similarities between GLMD and boron (B)-deficiency symptoms in grapevine plants, we posited that GPGV interferes in B homeostasis. By using a hydroponic system to control B availability, we investigated the effects of different B supplies on grapevine phenotype and those of GPGV infection on B acquisition and translocation machinery, by means of microscopy, ionomic and gene expression analyses in both roots and leaves. The transcription of the genes regulating B homeostasis was unaffected by the presence of GPGV alone, but was severely altered in plants exposed to both GPGV infection and B-deficiency, allowing us to speculate that the capricious and patchy occurrence of GLMD symptoms in the field may not be related solely to GPGV, but to GPGV interference in plant responses to different B availabilities. This hypothesis found preliminary positive confirmations in analyses on field-grown plants

Advancing Craniopharyngioma Management: A Systematic Review of Current Targeted Therapies and Future Perspectives

Craniopharyngiomas present unique challenges in surgical management due to their proximity to critical neurovascular structures. This systematic review investigates genetic and immunological markers as potential targets for therapy in craniopharyngiomas, assessing their involvement in tumorigenesis, and their influence on prognosis and treatment strategies. The systematic review adhered to PRISMA guidelines, with a thorough literature search conducted on PubMed, Ovid MED-LINE, and Ovid EMBASE. Employing MeSH terms and Boolean operators, the search focused on craniopharyngiomas, targeted or molecular therapy, and clinical outcomes or adverse events. Inclusion criteria encompassed English language studies, clinical trials (randomized or non-randomized), and investigations into adamantinomatous or papillary craniopharyngiomas. Targeted therapies, either standalone or combined with chemotherapy and/or radiotherapy, were examined if they included clinical outcomes or adverse event analysis. Primary outcomes assessed disease response through follow-up MRI scans, categorizing responses as follows: complete response (CR), near-complete response (NCR), partial response, and stable or progressive disease based on lesion regression percentages. Secondary outcomes included treatment type and duration, as well as adverse events. A total of 891 papers were initially identified, of which 26 studies spanning from 2000 to 2023 were finally included in the review. Two tables highlighted adamantinomatous and papillary craniopharyngiomas, encompassing 7 and 19 studies, respectively. For adamantinomatous craniopharyngiomas, Interferon-2α was the predominant targeted therapy (29%), whereas dabrafenib took precedence (70%) for papillary craniopharyngiomas. Treatment durations varied, ranging from 1.7 to 28 months. Positive responses, including CR or NCR, were observed in both types of craniopharyngiomas (29% CR for adamantinomatous; 32% CR for papillary). Adverse events, such as constitutional symptoms and skin changes, were reported, emphasizing the need for vigilant monitoring and personalized management to enhance treatment tolerability. Overall, the data highlighted a diverse landscape of targeted therapies with encouraging responses and manageable adverse events, underscoring the importance of ongoing research and individualized patient care in the exploration of treatment options for craniopharyngiomas. In the realm of targeted therapies for craniopharyngiomas, tocilizumab and dabrafenib emerged as prominent choices for adamantinomatous and papillary cases, respectively. While adverse events were common, their manageable nature underscored the importance of vigilant monitoring and personalized management. Acknowledging limitations, future research should prioritize larger, well-designed clinical trials and standardized treatment protocols to enhance our understanding of the impact of targeted therapies on craniopharyngioma patients

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods