A neural network seismic detector

This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectro gram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.

Five gonadotrophin-releasing hormone receptors in a teleost fish: isolation, tissue distribution and phylogenetic relationships

Gonadotrophin-releasing hormone (GnRH) is the main neurohormone controlling gonadotrophin release in all vertebrates, and in teleost fish also of growth hormone and possibly of other adenohypophyseal hormones. Over 20 GnRHs have been identified in vertebrates and protochoordates and shown to bind cognate G-protein couple receptors (GnRHR). We have searched the puffer fish, Fugu rubripes, genome sequencing database, identified five GnRHR genes and proceeded to isolate the corresponding complementary DNAs in European sea bass, Dicentrachus labrax. Phylogenetic analysis clusters the European sea bass, puffer fish and all other vertebrate receptors into two main lineages corresponding to the mammalian type I and II receptors. The fish receptors could be subdivided in two GnRHR1 (A and B) and three GnRHR2 (A, B and C) subtypes. Amino acid sequence identity within receptor subtypes varies between 70 and 90% but only 50–55% among the two main lineages in fish. All European sea bass receptor mRNAs are expressed in the anterior and mid brain, and all but one are expressed in the pituitary gland. There is differential expression of the receptors in peripheral tissues related to reproduction (gonads), chemical senses (eye and olfactory epithelium) and osmoregulation (kidney and gill). This is the first report showing five GnRH receptors in a vertebrate species and the gene expression patterns support the concept that GnRH and GnRHRs play highly diverse functional roles in the regulation of cellular functions, besides the ‘‘classical’’ role of pituitary function regulation.

On-line operation of an intelligent seismic detector

This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA /LTA ratio-based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network's stations of the Institute of Meteorology of Portugal (IM). Records' spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. © 2013 Springer-Verlag Berlin Heidelberg.