Feature screening with latent responses

A novel feature screening method is proposed to examine the correlation between latent responses and potential predictors in ultrahigh-dimensional data analysis. First, a confirmatory factor analysis (CFA) model is used to characterize latent responses through multiple observed variables. The expectation-maximization algorithm is employed to estimate the parameters in the CFA model. Second, R-Vector (RV) correlation is used to measure the dependence between the multivariate latent responses and covariates of interest. Third, a feature screening procedure is proposed on the basis of an unbiased estimator of the RV coefficient. The sure screening property of the proposed screening procedure is established under certain mild conditions. Monte Carlo simulations are conducted to assess the finite-sample performance of the feature screening procedure. The proposed method is applied to an investigation of the relationship between psychological well-being and the human genome.     

Cryptides: latent peptides everywhere

Proteomic surveys with top-down platforms are today revealing thousands of naturally occurring fragments of bigger proteins. Some of them have not functional meaning because they derive from pathways responsible for protein degradation, but many have specific functions, often completely different from that one of the parent proteins. These peptides encrypted in the protein sequence are nowadays called cryptides. They are frequent in the animal and plant kingdoms and represent a new interesting –omic field of investigation. To point out how much widespread is their presence, we describe here the most studied cryptides from very common sources such as serum albumin, immunoglobulins, hemoglobin, and from saliva and milk proteins. Given its vastness, it is unfeasible to cover the topic exhaustively, therefore only several selected examples of cryptides from other sources are thereafter reported. Demanding is the development of new –omic platforms for the functional screening of new cryptides, which could provide suggestion for peptides and peptido-mimetics with variegate fields of application.     

Integrin alphaVbeta6-mediated activation of latent TGF-beta requires the latent TGF-beta binding protein-1

Transforming growth factor-betas (TGF-beta) are secreted as inactive complexes containing the TGF-beta, the TGF-beta propeptide, also called the latency-associated protein (LAP), and the latent TGF-beta binding protein (LTBP). Extracellular activation of this complex is a critical but incompletely understood step in TGF-beta regulation. We have investigated the role of LTBP in modulating TGF-beta generation by the integrin alphaVbeta6. We show that even though alphavbeta6 recognizes an RGD on LAP, LTBP-1 is required for alphaVbeta6-mediated latent TGF-beta activation. The domains of LTBP-1 necessary for activation include the TGF-beta propeptide-binding domain and a basic amino acid sequence (hinge domain) with ECM targeting properties. Our results demonstrate an LTBP-1 isoform-specific function in alphaVbeta6-mediated latent TGF-beta activation; LTBP-3 is unable to substitute for LTBP-1 in this assay. The results reveal a functional role for LTBP-1 in latent TGF-beta activation and suggest that activation of specific latent complexes is regulated by distinct mechanisms that may be determined by the LTBP isoform and its potential interaction with the matrix.     

Detection of latent sequence periodicities.

A method is proposed for the automatic detection of serial periodicities in a linear sequence. Its application to DNA subtelomeric sequences from two lower eukaryotes, P.falciparum and S.cerevisiae, reveals ordered patterns organised in hierarchical periodicities, not easily recognizable by other methods. The possible implications concerning the evolution of tandemly repetitive arrays are discussed in light of a model which involves, as successive steps, random repeat modification, the fusion of differently modified repeat versions into longer units, and the amplification of (and/or homogenization to) the more recent repeat units.     

Reconnaissance and latent learning in ants

We show that ants can reconnoitre their surroundings and in effect plan for the future. Temnothorax albipennis colonies use a sophisticated strategy to select a new nest when the need arises. Initially, we presented colonies with a new nest of lower quality than their current one that they could explore for one week without a need to emigrate. We then introduced a second identical low quality new nest and destroyed their old nest so that they had to emigrate. Colonies showed a highly significant preference for the (low quality) novel new nest over the identical but familiar one. In otherwise identical experiments, colonies showed no such discrimination when the choice was between a familiar and an unfamiliar high-quality nest. When, however, either all possible pheromone marks were removed, or landmarks were re-orientated, just before the emigration, the ants chose between identical low-quality new nests at random. These results demonstrate for the first time that ants are capable of assessing and retaining information about the quality of potential new nest sites, probably by using both pheromones and landmark cues, even though this information may only be of strategic value to the colony in the future. They seem capable, therefore, of latent learning and, more explicitly, learning what not to do.     

Latent class regression on latent factors

In the research of public health, psychology, and social sciences, many research questions investigate the relationship between a categorical outcome variable and continuous predictor variables. The focus of this paper is to develop a model to build this relationship when both the categorical outcome and the predictor variables are latent (i.e. not observable directly). This model extends the latent class regression model so that it can include regression on latent predictors. Maximum likelihood estimation is used and two numerical methods for performing it are described: the Monte Carlo expectation and maximization algorithm and Gaussian quadrature followed by quasi-Newton algorithm. A simulation study is carried out to examine the behavior of the model under different scenarios. A data example involving adolescent health is used for demonstration where the latent classes of eating disorders risk are predicted by the latent factor body satisfaction.