Use of self-organizing maps and molecular descriptors to predict the cytotoxic activity of sesquiterpene lactones

Some sesquiterpene lactones (SLs) are the active compounds of a great number of traditionally medicinal plants from the Asteraceae family and possess considerable cytotoxic activity. Several studies in vitro have shown the inhibitory activity against cells derived from human carcinoma of the nasopharynx (KB). Chemical studies showed that the cytotoxic activity is due to the reaction of alpha,beta-unsaturated carbonyl structures of the SLs with thiols, such as cysteine. These studies support the view that SLs inhibit tumour growth by selective alkylation of growth-regulatory biological macromolecules, such as key enzymes, which control cell division, thereby inhibiting a variety of cellular functions, which directs the cells into apoptosis. In this study we investigated a set of 55 different sesquiterpene lactones, represented by 5 skeletons (22 germacranolides, 6 elemanolides, 2 eudesmanolides, 16 guaianolides and nor-derivatives and 9 pseudoguaianolides), in respect to their cytotoxic properties. The experimental results and 3D molecular descriptors were submitted to Kohonen self-organizing map (SOM) to classify (training set) and predict (test set) the cytotoxic activity. From the obtained results, it was concluded that only the geometrical descriptors showed satisfactory values. The Kohonen map obtained after training set using 25 geometrical descriptors shows a very significant match, mainly among the inactive compounds (similar to 84%). Analyzing both groups, the percentage seen is high (83%). The test set shows the highest match, where 89% of the substances had their cytotoxic activity correctly predicted. From these results, important properties for the inhibition potency are discussed for the whole dataset and for subsets of the different structural skeletons. (C) 2008 Elsevier Masson SAS. All rights reserved.

A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Genome-wide association mapping in a wild avian population identifies a link between genetic and phenotypic variation in a life-history trait

Understanding the genetic basis of traits involved in adaptation is a major challenge in evolutionary biology but remains poorly understood. Here, we use genome-wide association mapping using a custom 50 k single nucleotide polymorphism (SNP) array in a natural population of collared flycatchers to examine the genetic basis of clutch size, an important life-history trait in many animal species. We found evidence for an association on chromosome 18 where one SNP significant at the genome-wide level explained 3.9% of the phenotypic variance. We also detected two suggestive quantitative trait loci (QTLs) on chromosomes 9 and 26. Fitness differences among genotypes were generally weak and not significant, although there was some indication of a sex-by-genotype interaction for lifetime reproductive success at the suggestive QTL on chromosome 26. This implies that sexual antagonism may play a role in maintaining genetic variation at this QTL. Our findings provide candidate regions for a classic avian life-history trait that will be useful for future studies examining the molecular and cellular function of, as well as evolutionary mechanisms operating at, these loci.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Genetic parameters for body weight, carcass chemical composition and yield in a broiler-layer cross developed for QTL mapping

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transcriptomics and systems biology analysis in identification of specific pathways involved in cacao resistance and susceptibility to witches' broom disease

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Caracterização da resistência do algodoeiro a Ramularia areola e variabilidade molecular do patógeno

This research was conducted with the aim to study the genetic and pathogenic structure of Ramularia areola isolates collected in Brazil and to characterize the resistance response in cotton plants to ramularia spot. The genetic variability of 28 isolates of R. areola was studied using RAPD markers. The pathogenicity evaluation was realized by the inoculation of 6 isolates on cotton varieties Guazuncho-2 (Gossypium hirsutum) and VH8-4602 (Gossypium barbadense). The inheritance of disease resistance was studied using an artificially inoculated population of F2 individuals derived from the intercross of Guazuncho-2 (susceptible variety) end VH8-4602 (resistant variety), and also the parents and F1 individuals. Molecular polymorphism between the G. hisutum varieties DeltaOpal (suscetible) and CNPA CO-11612 (resistant) was estimated by 118 SSR and 24 AFLP markers. The parental genotypes Guazuncho-2 and VH8-4602 were selected for mapping, and then Recombinant Inbred Lines (RIL´s) derived from this crossing were evaluated with SSR 12 markers. The analysis of population structure of R. areola revealed that the three subpopulations were genetically simillar (Gst=0.18), and the isolates from Goiás and Minas Gerais were more similar to each other (0,92). This probability can be related to the relatively high gene flow among the three subpopulations (Nm=2.20). The isolates R. areola 9.1, from Minas Gerais State and 8.1 and 8.3 from Goiás State were the most aggressive ones to the susceptible variety Guazuncho-2. The variety VH8-4602 presented high level of resistance to ramularia spot. No differential interaction was observed between the pathogens and the analyzed varieties, and the resistance was classified as horizontal. The quantification of disease by number of necrotic lesions and number of spores in individual plants of F1 and F2 generations from the crossing between the varieties Guazuncho-2 and VH8-4602 presented continuous distribution, suggesting polygenic resistance. The resistance is probabilly recessive, since necrotic lesions and sporulation were observed on F1 plants. The molecular polymorphism between DeltaOpal e CNPA CO-11612 lineages was low (6%), then would be difficult to accomplish molecular mapping of disease resistance using this intercross. With the genotyping of the RIL s it was verified that 25% of the markers segregated in the proportions proposed by Mendel s Law and 75% of the studied markers presented segregation distortion in favor to the parental G. hirsutum. Both the low genetic variability of the pathogen and the number of resistance genes suggest that durable genetic resitance may be achieved

Application of neural networks to identify features of dynamical grounding systems

The accurate identification of features of dynamical grounding systems are extremely important to define the operational safety and proper functioning of electric power systems. Several experimental tests and theoretical investigations have been carried out to obtain characteristics and parameters associated with the technique of grounding. The grounding system involves a lot of non-linear parameters. This paper describes a novel approach for mapping characteristics of dynamical grounding systems using artificial neural networks. The network acts as identifier of structural features of the grounding processes. So that output parameters can be estimated and generalized from an input parameter set. The results obtained by the network are compared with other approaches also used to model grounding systems.

Analysis of plant LTR-retrotransposons at the fine-scale family level reveals individual molecular patterns

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular detection of Paracoccidioides brasiliensis in road-killed wild animals

Paracoccidioides brasiliensis infections have been little studied in wild and/or domestic animals, which may represent an important indicator of the presence of the pathogen in nature. Road-killed wild animals have been used for surveillance of vectors of zoonotic pathogens and may offer new opportunities for eco-epidemiological studies of paracoccidiodomycosis (PCM). The presence of P. brasiliensis infection was evaluated by Nested-PCR in tissue samples collected from 19 road-killed animals; 3 Cavia aperea (guinea pig), 5 Cerdocyon thous (crab-eating-fox), 1 Dasypus novemcinctus (nine-banded armadillo), 1 Dasypus septemcinctus (seven-banded armadillo), 2 Didelphis albiventris (white-eared opossum), 1 Eira barbara (tayra), 2 Gallictis vittata (grison), 2 Procyon cancrivorus (raccoon) and 2 Sphiggurus spinosus (porcupine). Specific P. brasiliensis amplicons were detected in (a) several organs of the two armadillos and one guinea pig, (b) the lung and liver of the porcupine, and (c) the lungs of raccoons and grisons. P. brasiliensis infection in wild animals from endemic areas might be more common than initially postulated. Molecular techniques can be used for detecting new hosts and mapping 'hot spot' areas of PCM.

Physical mapping of the Nile tilapia (Oreochromis niloticus) genome by fluorescent in situ hybridization of repetitive DNAs to metaphase chromosomes - a review

The Nile tilapia (Oreochromis niloticus) has received increasing scientific interest over the past few decades for two reasons: first, tilapia is an enormously important species in aquaculture worldwide, especially in regions where there is a chronic shortage of animal protein; and second, this teleost fish belongs to the fascinating group of cichlid fishes that have undergone a rapid and extensive radiation of much interest to evolutionary biologists. Currently, studies based on physical and genetic mapping of the Nile tilapia genome offer the best opportunities for applying genomics to such diverse questions and issues as phylogeography, isolation of quantitative trait loci involved in behaviour, morphology, and disease, and overall improvement of aquacultural stocks. In this review, we have integrated molecular cytogenetic data for the Nile tilapia describing the chromosomal location of the repetitive DNA sequences, satellite DNAs, telomeres, 45S and 5S rDNAs, and the short and long interspersed nucleotide elements [short interspersed nuclear elements (SINEs) and long interspersed nuclear elements (LINEs)], and provide the beginnings of a physical genome map for this important teleost fish. (C) 2004 Elsevier B.V. All rights reserved.

Karyotypic conservatism in samples of Characidium cf. zebra (Teleostei, Characiformes, Crenuchidae): physical mapping of ribosomal genes and natural triploidy

Basic and molecular cytogenetic analyses were performed in specimens of Characidium cf. zebra from five collection sites located throughout the Tietê, Paranapanema and Paraguay river basins. The diploid number in specimens from all samples was 2n = 50 with a karyotype composed of 32 metacentric and 18 submetacentric chromosomes in both males and females. Constitutive heterochromatin was present at the centromeric regions of all chromosomes and pair 23, had additional interstitial heterochromatic blocks on its long arms. The nucleolar organizer regions (NORs) were located on the long arms of pair 23, while the 5S rDNA sites were detected in different chromosomes among the studied samples. One specimen from the Alambari river was a natural triploid and had two extra chromosomes, resulting in 2n = 77. The remarkable karyotypic similarity among the specimens of C. cf. zebra suggests a close evolutionary relationship. on the other hand, the distinct patterns of 5S rDNA distribution may be the result of gene flow constraints during their evolutionary history.