VGF changes during the estrous cycle: a novel endocrine role for TLQP peptides?

Although the VGF derived peptide TLQP-21 stimulates gonadotropin-releasing hormone (GnRH) and gonadotropin secretion, available data on VGF peptides and reproduction are limited. We used antibodies specific for the two ends of the VGF precursor, and for two VGF derived peptides namely TLQP and PGH, to be used in immunohistochemistry and enzyme-linked immunosorbent assay complemented with gel chromatography. In cycling female rats, VGF C-/N-terminus and PGH peptide antibodies selectively labelled neurones containing either GnRH, or kisspeptin (VGF N-terminus only), pituitary gonadotrophs and lactotrophs, or oocytes (PGH peptides only). Conversely, TLQP peptides were restricted to somatostatin neurones, gonadotrophs, and ovarian granulosa, interstitial and theca cells. TLQP levels were highest, especially in plasma and ovary, with several molecular forms shown in chromatography including one compatible with TLQP-21. Among the cycle phases, TLQP levels were higher during metestrus-diestrus in median eminence and pituitary, while increased in the ovary and decreased in plasma during proestrus. VGF N- and C-terminus peptides also showed modulations over the estrous cycle, in median eminence, pituitary and plasma, while PGH peptides did not. In ovariectomised rats, plasmatic TLQP peptide levels showed distinct reduction suggestive of a major origin from the ovary, while the estrogen-progesterone treatment modulated VGF C-terminus and TLQP peptides in the hypothalamus-pituitary complex. In in vitro hypothalamus, TLQP-21 stimulated release of growth hormone releasing hormone but not of somatostatin. In conclusion, various VGF peptides may regulate the hypothalamus-pituitary complex via specific neuroendocrine mechanisms while TLQP peptides may act at further, multiple levels via endocrine mechanisms involving the ovary

Message from the ICSC 2012 workshop co-chairs

Welcome to the proceedings containing the papers from two workshops selected for presentation at the Sixth IEEE International Conference on Semantic Computing (ICSC 2012) in Palermo, Italy, September 19–21, 2012

Learning terminological Naïve Bayesian classifiers under different assumptions on missing knowledge

Knowledge available through Semantic Web standards can easily be missing, generally because of the adoption of the Open World Assumption (i.e. the truth value of an assertion is not necessarily known). However, the rich relational structure that characterizes ontologies can be exploited for handling such missing knowledge in an explicit way. We present a Statistical Relational Learning system designed for learning terminological naïve Bayesian classifiers, which estimate the probability that a generic individual belongs to the target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself

A graph regularization based approach to transductive class-membership prediction

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of propagating class-membership information among similar individuals; it is non-parametric in nature and characterised by interesting complexity properties, making it a potential candidate for large-scale transductive inference. We also evaluate its effectiveness with respect to other approaches based on inductive inference in SW literature

Artificial Intelligence Algorithms in Precision Medicine: A New Approach in Clinical Decision-Making

US National Institutes of Health described the precision medicine as ‘an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.’ In other words, on the basis of the definition, the precision medicine allows to treat patients based on their genetic, lifestyle, and environmental data. Nevertheless, the complexity and rise of data in healthcare arising from cheap genome sequencing, advanced biotechnology, health sensors patients use at home, and the collection of information about patients’ journey in healthcare with hand-held devices unquestionably require a suitable toolkit and advanced analytics for processing the huge information. The artificial intelligence algorithms (AI) can remarkably improve the ability to use big data to make predictions by reducing the cost of making predictions. The advantages of artificial intelligence algorithms have been extensively discussed in the medical literature. In this paper based on the collection of the data relevant for the health of a given individual and the inference obtained by AI, we provide a simulation environment for understanding and suggesting the best actions that need to be performed to improve the individual’s health. Such simulation modelling can help improve clinical decision-maing and the fundamental understanding of the healthcare system and clinical process

Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones

On the dust and gas content of high-redshift galaxies hosting obscured AGN in the CDF–S

Submillimeter Galaxies (SMGs) at high redshift are among the best targets to investigate the early evolutionary phases in the lifetime of massive systems, during which large gas reservoirs sustain vigorous star formation and efficiently feed the central, buried Super Massive Black Hole (SMBH), until it enters into luminous Quasar (QSO) phase, quenching the star formation. I present the analysis of new ALMA band 4 (1.8-2.4 mm) data of six obscured QSOs (log NH > 23) hosted by SMGs at redshift > 2.5 in the 7 Ms Chandra Deep Field South (CDF-S), showing their properties in terms of continuum dust emission and high-J CO transitions. Sizes and masses of the galaxies are measured to estimate whether and to which extent the host ISM may contribute to the nuclear absorption, assuming different geometries. The derived column densities suggest that the galaxy ISM can substantially contribute to the AGN obscuration. I also discuss the kinematics and morphology in some of these object, finding that two of the sources present unambiguous features of a rotating system, while a third source is possibly undergoing a merger

Diagnostics of the tropical tropopause layer from in-situ observations and CCM data

A suite of diagnostics is applied to in-situ aircraft measurements and one Chemistry-Climate Model (CCM) data to characterize the vertical structure of the Tropical Tropopause Layer (TTL). The diagnostics are based on vertical tracer profiles and relative vertical tracer gradients, using tropopause-referenced coordinates, and tracer-tracer relationships in the tropical Upper Troposphere/Lower Stratosphere (UT/LS). Observations were obtained during four tropical campaigns performed from 1999 to 2006 with the research aircraft Geophysica and have been compared to the output of the ECHAM5/MESSy CCM. The model vertical resolution in the TTL (~500 m) allows for appropriate comparison with high-resolution aircraft observations and the diagnostics used highlight common TTL features between the model and the observational data. The analysis of the vertical profiles of water vapour, ozone, and nitrous oxide, in both the observations and the model, shows that concentration mixing ratios exhibit a strong gradient change across the tropical tropopause, due to the role of this latter as a transport barrier and that transition between the tropospheric and stratospheric regimes occurs within a finite layer. The use of relative vertical ozone and carbon monoxide gradients, in addition to the vertical profiles, helps to highlight the region where this transition occurs and allows to give an estimate of its thickness. The analysis of the CO-O3 and H2O-O3 scatter plots and of the Probability Distribution Function (PDF) of the H2O-O3 pair completes this picture as it allows to better distinguish tropospheric and stratospheric regimes that can be identified by their different chemical composition. The joint analysis and comparison of observed and modelled data allows to state that the model can represent the background TTL structure and its seasonal variability rather accurately. The model estimate of the thickness of the interface region between tropospheric and stratospheric regimes agrees well with average values inferred from observations. On the other hand, the measurements can be influenced by regional scale variability, local transport processes as well as deep convection, that can not be captured by the model