Serum inhibin A and angiogenic factor levels in pregnancies with previous preeclampsia and/or chronic hypertension: are they useful markers for prediction of subsequent preeclampsia?

OBJECTIVE: Our objective was to determine whether measurement of placenta growth factor (PLGF), inhibin A, or soluble fms-like tyrosine kinase-1 (sFlt-1) at 2 times during pregnancy would usefully predict subsequent preeclampsia ( PE) in women at high risk. STUDY DESIGN: We analyzed serum obtained at enrollment (12(0/7) to 19(6/7) weeks) and follow-up (24-28 weeks) from 704 patients with previous PE and/or chronic hypertension (CHTN) enrolled in a randomized trial for the prevention of PE. Logistic regression analysis assessed the association of log-transformed markers with subsequent PE; receiver operating characteristic analysis assessed predictive value. RESULTS: One hundred four developed preeclampsia: 27 at 37 weeks or longer and 77 at less than 37 weeks (9 at less than 27 weeks). None of the markers was associated with PE at 37 weeks or longer. Significant associations were observed between PE at less than 37 weeks and reduced PLGF levels at baseline (P =.022) and follow-up (P <.0001) and elevated inhibin A (P <.0001) and sFlt-1 (P =.0002) levels at follow-up; at 75% specificity, sensitivities ranged from 38% to 52%. Using changes in markers from baseline to follow-up, sensitivities were 52-55%. Associations were observed between baseline markers and PE less than 27 weeks (P <=.0004 for all); sensitivities were 67-89%, but positive predictive values (PPVs) were only 3.4-4.5%. CONCLUSION: Inhibin A and circulating angiogenic factors levels obtained at 12(0/7) to 19(6/7) weeks have significant associations with onset of PE at less than 27 weeks, as do levels obtained at 24-28 weeks with onset of PE at less than 37 weeks. However, because the corresponding sensitivities and/or PPVs were low, these markers might not be clinically useful to predict PE in women with previous PE and/or CHTN.

Low-level laser therapy (670 nm) on viability of random skin flap in rats

This study investigated the effects of 670 nm laser, at different fluences, on the viability of skin flap in rats. One hundred male animals were used. The animals were divided into control group; group treated with 3 J/cm(2); group treated with 6 J/cm(2); group treated with 12 J/cm(2) and group treated with 24 J/cm(2). The skin flap was made on the backs of all animals studied, with a plastic sheet interposed between the flap and the donor site. Laser irradiation was done immediately after the surgery and on days 1, 2, 3 and 4 after surgery. The percentage of necrosis of the flap was calculated at the 7th postoperative day. Additionally, a sample of each flap was collected to enable us to count the blood vessels. Treated animals showed a statistically significant smaller area of necrosis than did the control group. The necrosis in the treated groups was 41.82% (group 2), 36.51% (group 3), 29.45% (group 4) and 20.37% (group 5). We also demonstrated that laser irradiation at 670 nm, at all doses used, had a stimulatory effect on angiogenesis. Our study showed that the 670 nm laser was efficient to increase the viability of the skin flap, at all fluences used, with a tendency of reaching better results at higher doses.

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

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

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Prediction of R plus R-2 gravity for the deflection of a photon passing close to the Sun

The cross-section for the scattering of a photon by the Sun's gravitational field, treated as an external field, is computed in the framework of R + R-2 gravity. Using this result, we found that for a photon just grazing the Sun's surface the deflection is 1.75 which is exactly the same as that given by Einstein's theory. An explanation for this pseudo-paradox is provided.

Prediction of a weakly bound excited state in the He-4(2)-Li-7 molecule

A scale-independent approach, valid for weakly bound three-body systems, is used to analyze the existence of excited Thomas-Efimov states in molecular systems with three atoms: a helium dimer together with isotopes of lithium (Li-6 and Li-7) and sodium (Na-23). With the present study and the available data, we can clearly predict that the He-4(2)-Li-7 system supports an excited state with binding energy close to 2.31 mK. (C) 2000 American Institute of Physics. [S0021-9606(00)30442-1].

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate calibrations in gas chromatographic profiles for prediction of several physicochemical parameters of Brazilian commercial gasoline

Several Brazilian commercial gasoline physicochemical parameters, such as relative density, distillation curve (temperatures related to 10%, 50% and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and anti-knock index), hydrocarbon compositions (olefins, aromatics and saturates) and anhydrous ethanol and benzene content was predicted from chromatographic profiles obtained by flame ionization detection (GC-FID) and using partial least square regression (PLS). GC-FID is a technique intensively used for fuel quality control due to its convenience, speed, accuracy and simplicity and its profiles are much easier to interpret and understand than results produced by other techniques. Another advantage is that it permits association with multivariate methods of analysis, such as PLS. The chromatogram profiles were recorded and used to deploy PLS models for each property. The standard error of prediction (SEP) has been the main parameter considered to select the "best model". Most of GC-FID-PLS results, when compared to those obtained by the Brazilian Government Petroleum, Natural Gas and Biofuels Agency - ANP Regulation 309 specification methods, were very good. In general, all PLS models developed in these work provide unbiased predictions with lows standard error of prediction and percentage average relative error (below 11.5 and 5.0, respectively). (C) 2007 Elsevier B.V. All rights reserved.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

A emissão de CO2 do solo apresenta alta variabilidade espacial, devido à grande dependência espacial observada nas propriedades do solo que a influenciam. Neste estudo, objetivou-se: caracterizar e relacionar a variabilidade espacial da respiração do solo e propriedades relacionadas; avaliar a acurácia dos resultados fornecidos pelo método da krigagem ordinária e simulação sequencial gaussiana; e avaliar a incerteza na predição da variabilidade espacial da emissão de CO2 do solo e demais propriedades utilizando a simulação sequencial gaussiana. O estudo foi conduzido em uma malha amostral irregular com 141 pontos, instalada sobre a cultura de cana-de-açúcar. Nesses pontos foram avaliados a emissão de CO2 do solo, a temperatura do solo, a porosidade livre de água, o teor de matéria orgânica e a densidade do solo. Todas as variáveis apresentaram estrutura de dependência espacial. A emissão de CO2 do solo mostrou correlações positivas com a matéria orgânica (r = 0,25, p < 0,05) e a porosidade livre de água (r = 0,27, p <0,01) e negativa com a densidade do solo (r = -0,41, p < 0,01). No entanto, quando os valores estimados espacialmente (N=8833) são considerados, a porosidade livre de água passa a ser a principal variável responsável pelas características espaciais da respiração do solo, apresentando correlação de 0,26 (p < 0,01). As simulações individuais propiciaram, para todas as variáveis analisadas, melhor reprodução das funções de distribuição acumuladas e dos variogramas, em comparação à krigagem e estimativa E-type. As maiores incertezas na predição da emissão de CO2 estiveram associadas às regiões da área estudada com maiores valores observados e estimados, produzindo estimativas, ao longo do período estudado, de 0,18 a 1,85 t CO2 ha-1, dependendo dos diferentes cenários simulados. O conhecimento das incertezas gerado por meio dos diferentes cenários de estimativa pode ser incluído em inventários de gases do efeito estufa, resultando em estimativas mais conservadoras do potencial de emissão desses gases.

Prediction of dry matter intake based on ruminal degradation from milking cows grazing coast-cross grass

Dry matter intake (DMI) of coast-cross grazing by crossbred Holstein-Zebu and Zebu lactating cows was calculated using in vitro dry matter digestibility from extrusa (four esophageal fistulated cows) and fecal output estimate with mordent chromium. Pasture was rotationally grazed with three days grazing period and 27 days testing period, adopting a stocking rate of 1.6 and 3.2 cows/ha, during the dry and rainy season respectively. Voluntary DMI was estimated from degradation characteristics using different equations. Predicted coast-cross DMI varied with models. The prediction of tropical forages dry matter intake from equations based in ruminal degradation parameters needs farther investigation before being employed in practice.

USE OF PROGENY TESTING IN BEEF-CATTLE - PREDICTION OF GENETIC GAIN IN A NELORE CATTLE-BREEDING PROGRAM

Genetic gains predicted for selection, based on both individual performance and progeny testing, were compared to provide information to be used in implementation of progeny testing for a Nelore cattle breeding program. The prediction of genetic gain based on progeny testing was obtained from a formula, derived from methodology of Young and weller (J. Genetics 57: 329-338, 1960) for two-stage selection, which allows prediction of genetic gain per generation when the individuals under test have been pre-selected on the basis of their own performance. The application of this formula also allowed determination of the number of progeny per tested bull needed to maximize genetic gain, when the total number of tested progeny is limited.

Prediction of cassava starch edible film properties by chemometric analysis of infrared spectra

Cassava starch has been shown to make transparent and colorless flexible films without any previous chemical treatment. The functional properties of edible films are influenced by starch properties, including chain conformation, molecular bonding, crystallinity, and water content. Fourier-transform infrared (FTIR) spectroscopy in combination with attenuated total reflectance (ATR) has been applied for the elucidation of the structure and conformation of carbohydrates. This technique associated with chemometric data processing could indicate the relationship between the structural parameters and the functional properties of cassava starch-based edible films. Successful prediction of the functional properties values of the starch-based films was achieved by partial least squares regression data. The results showed that presence of the hydroxyl group on carbon 6 of the cyclic part of glucose is directly correlated with the functional properties of cassava starch films.