Some Aspects of Latent Structure Analysis

Latent structure models involve real, potentially observable variables and latent, unobservable variables. The framework includes various particular types of model, such as factor analysis, latent class analysis, latent trait analysis, latent profile models, mixtures of factor analysers, state-space models and others. The simplest scenario, of a single discrete latent variable, includes finite mixture models, hidden Markov chain models and hidden Markov random field models. The paper gives a brief tutorial of the application of maximum likelihood and Bayesian approaches to the estimation of parameters within these models, emphasising especially the fact that computational complexity varies greatly among the different scenarios. In the case of a single discrete latent variable, the issue of assessing its cardinality is discussed. Techniques such as the EM algorithm, Markov chain Monte Carlo methods and variational approximations are mentioned

Astronomical Distance Determination in the Space Age: Secondary Distance Indicators

The formal division of the distance indicators into primary and secondary leads to difficulties in description of methods which can actually be used in two ways: with, and without the support of the other methods for scaling. Thus instead of concentrating on the scaling requirement we concentrate on all methods of distance determination to extragalactic sources which are designated, at least formally, to use for individual sources. Among those, the Supernovae Ia is clearly the leader due to its enormous success in determination of the expansion rate of the Universe. However, new methods are rapidly developing, and there is also a progress in more traditional methods. We give a general overview of the methods but we mostly concentrate on the most recent developments in each field, and future expectations. © 2018, The Author(s)

Editorial Statement About JCCAP’s 2023 Special Issue on Informant Discrepancies in Youth Mental Health Assessments: Observations, Guidelines, and Future Directions Grounded in 60 Years of Research

Issue 1 of the 2011 Volume of the Journal of Clinical Child and Adolescent Psychology (JCCAP) included a Special Section about the use of multi-informant approaches to measure child and adolescent (i.e., hereafter referred to collectively as “youth”) mental health (De Los Reyes, 2011). Researchers collect reports from multiple informants or sources (e.g., parent and peer, youth and teacher) to estimate a given youth’s mental health. The 2011 JCCAP Special Section focused on the most common outcome of these approaches, namely the significant discrepancies that arise when comparing estimates from any two informant’s reports (i.e., informant discrepancies). These discrepancies appear in assessments conducted across the lifespan (Achenbach, 2020). That said, JCCAP dedicated space to understanding informant discrepancies, because they have been a focus of scholarship in youth mental health for over 60 years (e.g., Achenbach et al., 1987; De Los Reyes & Kazdin, 2005; Glennon & Weisz, 1978; Kazdin et al., 1983; Kraemer et al., 2003; Lapouse & Monk, 1958; Quay et al., 1966; Richters, 1992; Rutter et al., 1970; van der Ende et al., 2012). Thus, we have a thorough understanding of the areas of research for which they reliably appear when clinically assessing youth. For instance, intervention researchers observe informant discrepancies in estimates of intervention effects within randomized controlled trials (e.g., Casey & Berman, 1985; Weisz et al., 2017). Service providers observe informant discrepancies when working with individual clients, most notably when making decisions about treatment planning (e.g., Hawley & Weisz, 2003; Hoffman & Chu, 2015). Scholars in developmental psychopathology observe these discrepancies when seeking to understand risk and protective factors linked to youth mental health concerns (e.g., Hawker & Boulton, 2000; Hou et al., 2020; Ivanova et al., 2022). Thus, the 2011 JCCAP Special Section posed a question: Might these informant discrepancies contain data relevant to understanding youth mental health? Suppose none of the work in youth mental health is immune from these discrepancies. In that case, the answer to this question strikes at the core of what we produce―from the interventions we develop and implement, to the developmental psychopathology research that informs intervention development

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease