PACAP in developing sensory and peripheral organs of the zebrafish, Danio rerio

The anatomical distribution of PACAP-like immunoreactivity was investigated in sensory and peripheral organs of the zebrafish, Danio rerio, during the pharyngula, hatching and larval periods, by using indirect immunofluorescence methods. First PACAP-like immunoreactive (ir) elements appeared during the pharyngula period, at 24 hours post fertilization (hpf), within the most superficial layer of the retina and the dorsal aorta. At 48 hpf, additional ir cells were found in the olfactory placode and esophagus. At 72 hpf (hatching period), PACAP-like immunoreactivity was first detected in the ganglion cell layer of the retina, the otic sensory epithelium, pharyngeal arches, swim bladder and pancreatic progenitor cells. During day 5 of larval development, new groups of ir cells appeared in the liver, whereas no ir elements were observed in the olfactory placode. Subsequently, at day 13 of larval development, additional ir elements were found for the first time in some gut epithelial cells while those previously observed in the retina and otic sensory epithelium were absent. The transient expression of PACAP-like ir material in sensory organs suggests that the peptide could be implicated in neurotrophic activities and neurosensorial connections in the migration and/or differentiation processes. The appearance of PACAP-like ir elements in peripheral organs at different developmental stages, indicates that this peptide could be involved in the control of more specific functions as soon as these peripheral structures begin to operate

Apoptosis, cell proliferation and serotonin immunoreactivity in gut of Liza aurata from natural heavy metal polluted environments: preliminary observations.

In the present paper, the effect of natural environment nonlethal heavy metal concentration on cell renewal of Liza aurata intestinal epithelium, was studied by the TUNEL (terminal deoxynucleotidyltransferase-mediated dUTP nick end labelling) method and anti-PCNA (proliferating cell nuclear antigen) immunohistochemistry, in order to detect, respectively, apoptosis and cell proliferation. In addition, the presence and distribution of the cell renewal regulator, serotonin, was immunohistochemically investigated. In order to reduce variability, only immature specimens were considered. The results indicated that in the control specimens from non-polluted areas, the PCNA immunoreactive nuclei of the proximal intestinal epithelium were only located at the bottom of the intestinal folds, together with a few TUNEL-positive nuclei, and goblet mucous differentiated cells. In the specimens from polluted areas, the number of PCNA immunoreactive cells was greatly enhanced, and they extended along the mid portion of the intestinal folds; the number of TUNEL-positive nuclei was enhanced as well, but they were almost exclusively detected in the third apical portion of the intestinal folds. Serotonin immunoreactive nerve elements were more frequently detected in the intestinal wall of L. aurata specimens from polluted areas, and besides that, some serotonin immunoreactive endocrine cells were also present. Variations in distribution and frequency of TUNEL-positive nuclei, PCNA immunoreactive nuclei, and serotonin immunoreactivity put in evidence an alteration of cell renewal with an enhancement of cell proliferation, probably leading to morphological intestinal fold changes

Structured Sparsity: Discrete and Convex approaches

Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance interpretability in machine learning and statistics applications: While the ambient dimension is vast in modern data analysis problems, the relevant information therein typically resides in a much lower dimensional space. However, many solutions proposed nowadays do not leverage the true underlying structure. Recent results in CS extend the simple sparsity idea to more sophisticated {\em structured} sparsity models, which describe the interdependency between the nonzero components of a signal, allowing to increase the interpretability of the results and lead to better recovery performance. In order to better understand the impact of structured sparsity, in this chapter we analyze the connections between the discrete models and their convex relaxations, highlighting their relative advantages. We start with the general group sparse model and then elaborate on two important special cases: the dispersive and the hierarchical models. For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations. We also consider more general structures as defined by set functions and present their convex proxies. Further, we discuss efficient optimization solutions for structured sparsity problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure

Systems Biology Approach Predicts Antibody Signature Associated with Brucella melitensis Infection in Humans

A complete understanding of the factors that determine selection of antigens recognized by the humoral immune response following infectious agent challenge is lacking. Here we illustrate a systems biology approach to identify the antibody signature associated with Brucella melitensis (Bm) infection in humans and predict proteomic features of serodiagnostic antigens. By taking advantage of a full proteome microarray expressing previously cloned 1406 and newly cloned 1640 Bm genes, we were able to identify 122 immunodominant antigens and 33 serodiagnostic antigens. The reactive antigens were then classified according to annotated functional features (COGs), computationally predicted features (e.g., subcellular localization, physical properties), and protein expression estimated by mass spectrometry (MS). Enrichment analyses indicated that membrane association and secretion were significant enriching features of the reactive antigens, as were proteins predicted to have a signal peptide, a single transmembrane domain, and outer membrane or periplasmic location. These features accounted for 67% of the serodiagnostic antigens. An overlay of the seroreactive antigen set with proteomic data sets generated by MS identified an additional 24%, suggesting that protein expression in bacteria is an additional determinant in the induction of Brucella-specific antibodies. This analysis indicates that one-third of the proteome contains enriching features that account for 91% of the antigens recognized, and after B. melitensis infection the immune system develops significant antibody titers against 10% of the proteins with these enriching features. This systems biology approach provides an empirical basis for understanding the breadth and specificity of the immune response to B. melitensis and a new framework for comparing the humoral responses against other microorganisms

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

Geodesic Gaussian kernels for value function approximation

The least-squares policy iteration approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular and useful choice as a basis function. However, it does not allow for discontinuity which typically arises in real-world reinforcement learning tasks. In this paper, we propose a new basis function based on geodesic Gaussian kernels, which exploits the non-linear manifold structure induced by the Markov decision processes. The usefulness of the proposed method is successfully demonstrated in simulated robot arm control and Khepera robot navigation