Distinct subsets of hypothalamic genes are modulated by two different thermogenesis-inducing stimuli

Obesity results from an imbalance between food intake and energy expenditure, two vital functions that are tightly controlled by specialized neurons of the hypothalamus. The complex mechanisms that integrate these two functions are only beginning to be deciphered. The objective of this study was to determine the effect of two thermogenesis-inducing conditions, i.e., ingestion of a high-fat (HF) diet and exposure to cold environment, on the expression of 1,176 genes in the hypothalamus of Wistar rats. Hypothalamic gene expression was evaluated using a cDNA macroarray approach. mRNA and protein expressions were determined by reverse-transcription PCR (RT-PCR) and immunoblot. Cold exposure led to an increased expression of 43 genes and to a reduced expression of four genes. HF diet promoted an increased expression of 90 genes and a reduced expression of 78 genes. Only two genes (N-methyl-D-aspartate (NMDA) receptor 2B and guanosine triphosphate (GTP)-binding protein G-alpha-i1) were similarly affected by both thermogenesis-inducing conditions, undergoing an increment of expression. RT-PCR and immunoblot evaluations confirmed the modulation of NMDA receptor 2B and GTP-binding protein G-alpha-i1, only. This corresponds to 0.93% of all the responsive genes and 0.17% of the analyzed genes. These results indicate that distinct environmental thermogenic stimuli can modulate predominantly distinct profiles of genes reinforcing the complexity and multiplicity of the hypothalamic mechanisms that regulate energy conservation and expenditure.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Examples from trees, related to discrete subsets, pseudo-radiality and omega-boundedness

We construct some examples using trees. Some of them are consistent counterexamples for the discrete reflection of certain topological properties. All the properties dealt with here were already known to be non-discretely reflexive if we assume CH and we show that the same is true assuming the existence of a Suslin tree. In some cases we actually get some ZFC results. We construct also, using a Suslin tree, a compact space that is pseudo-radial but it is not discretely generated. With a similar construction, but using an Aronszajn tree, we present a ZFC space that is first countable, omega-bounded but is not strongly w-bounded, answering a question of Peter Nyikos. (C) 2008 Elsevier B.V. All rights reserved.