Is there any influence of breastfeeding on the cerebral blood flow? A review of 256 healthy newborns

OBJECTIVE: To investigate whether breastfeeding influence the cerebral blood-flow velocity. MATERIALS AND METHODS: The present study included 256 healthy term neonates, all of them with appropriate weight for gestational age, 50.8% being female. Pulsatility index, resistance index and mean velocity were measured during breastfeeding or resting in the anterior cerebral artery, in the left middle cerebral artery, and in the right middle cerebral artery of the neonates between their first 10 and 48 hours of life. The data were analyzed by means of a paired t-test, Brieger's f-test for analysis of variance and linear regression, with p < 0.01 being accepted as statistically significant. RESULTS: Mean resistance index decreased as the mean velocity increased significantly during breastfeeding. Pulsatility index values decreased as much as the resistance index, but in the right middle cerebral artery it was not statistically significant. CONCLUSION: Breastfeeding influences the cerebral blood flow velocities.

O modelo AM1 na previsão de frequências vibracionais

We analyse vibrational frequencies of 168 compounds with the AM1 model concerning its experimentally observed gaseous frequencies. Stretching of CH, NH, OH and CO bonds, its related bending frequencies, and the CC frame movements are the studied vibrations. The results show problems with the AM1 vibrational splittings. Often symmetric stretching frequencies, like in CH3, CH2 and NH3, appear switched with the corresponding antisymmetrical ones. Among the studied vibrations many stretchings are overestimated, while bendings oscillate around experimental values. Fluorine stretchings, NN, OO, CH, double and triples CC bonds and cyclic hydrocarbon breathing modes are always overestimated while torsions, umbrella modes and OH/SH stretching are, in average, underestimated. Graphical analysis show that compounds with the lowest molecular masses are the ones with the largest difference to the experimental values. From our results it is not possible to fit confortably the calculated frequencies by a simple linear relationship of the type, n(obs)=a*n(AM1). Better aggreement is obtained when different curves are adjusted for the stretching and bending modes, and when a complete linear function is used. Among our studies the best obtained statistical results are for CH, NH and OH. The conclusions obtained in this work will improve the AM1 calculated frequencies leading to accurate results for these properties.

Proposição, validação e análise dos modelos que correlacionam estrutura química e atividade biológica

The present paper aims to bring under discussion some theoretical and practical aspects about the proposition, validation and analysis of QSAR models based on multiple linear regression. A comprehensive approach for the derivation of extrathermodynamic equations is reviewed. Some examples of QSAR models published in the literature are analyzed and criticized.

Aplicação de algoritmos genéticos na seleção de variáveis em espectroscopia no infravermelho médio: determinação simultânea de glicose, maltose e frutose

Genetic algorithm was used for variable selection in simultaneous determination of mixtures of glucose, maltose and fructose by mid infrared spectroscopy. Different models, using partial least squares (PLS) and multiple linear regression (MLR) with and without data pre-processing, were used. Based on the results obtained, it was verified that a simpler model (multiple linear regression with variable selection by genetic algorithm) produces results comparable to more complex methods (partial least squares). The relative errors obtained for the best model was around 3% for the sugar determination, which is acceptable for this kind of determination.

Aplicação de Zr/Ti-PILC no processo de adsorção de Cu(II), Co(II) e Ni(II) utilizando modelos físico-químicos de adsorção e termodinâmica do processo

The aim of this investigation is to study how Zr/Ti-PILC adsorbs metals. The physico-chemical proprieties of Zr/Ti-PILC have been optimized with pillarization processes and Cu(II), Ni(II) and Co(II) adsorption from aqueous solution has been carried out, with maximum adsorption values of 8.85, 8.30 and 7.78 x10-1 mmol g-1, respectively. The Langmuir, Freundlich and Temkin adsorption isotherm models have been applied to fit the experimental data with a linear regression process. The energetic effect caused by metal interaction was determined through calorimetric titration at the solid-liquid interface and gave a net thermal effect that enabled the calculation of the exothermic values and the equilibrium constant.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

Estatística aplicada à química: dez dúvidas comuns

Ten common doubts of chemistry students and professionals about their statistical applications are discussed. The use of the N-1 denominator instead of N is described for the standard deviation. The statistical meaning of the denominators of the root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV) are given for researchers using multivariate calibration methods. The reason why scientists and engineers use the average instead of the median is explained. Several problematic aspects about regression and correlation are treated. The popular use of triplicate experiments in teaching and research laboratories is seen to have its origin in statistical confidence intervals. Nonparametric statistics and bootstrapping methods round out the discussion.

Cinética e caracterização de ramnolipídeos produzidos por Pseudomonas aeruginosa MSIC02 utilizando glicerol como fonte de carbono

Glycerol, a co-product of biodiesel production, was used as a carbon source for the kinetics studies and production of biosurfactants by P. aeruginosa MSIC02. The highest fermentative parameters (Y PX = 3.04 g g-1; Y PS = 0.189 g g-1, P B = 31.94 mg L-1 h-1 and P X = 10.5 mg L-1 h-1) were obtained at concentrations of 0.4% (w/v) NaNO3 and 2% (w/v) glycerol. The rhamnolipid exhibited 80% of emulsification on kerosene, surface tension of 32.5 mN m-1, CMC = 28.2 mg L-1, C20 (concentration of surfactant in the bulk phase that produces a reduction of 20 dyn/cm in the surface tension of the solvent) = 0.99 mg L-1, Γm (surface concentration excess) = 2.4 x 10-26 mol Å-2 and S (surface area) = 70.4 Ų molecule-1 with solutions containing 10% NaCl. A mathematical model based on logistic equation was considered to representing the process. Model parameters were estimated by non-linear regression method. This approach was able to give a good description of the process.

Automatic evaluation and data generation for analytical chemistry instrumental analysis exercises

In general, laboratory activities are costly in terms of time, space, and money. As such, the ability to provide realistically simulated laboratory data that enables students to practice data analysis techniques as a complementary activity would be expected to reduce these costs while opening up very interesting possibilities. In the present work, a novel methodology is presented for design of analytical chemistry instrumental analysis exercises that can be automatically personalized for each student and the results evaluated immediately. The proposed system provides each student with a different set of experimental data generated randomly while satisfying a set of constraints, rather than using data obtained from actual laboratory work. This allows the instructor to provide students with a set of practical problems to complement their regular laboratory work along with the corresponding feedback provided by the system's automatic evaluation process. To this end, the Goodle Grading Management System (GMS), an innovative web-based educational tool for automating the collection and assessment of practical exercises for engineering and scientific courses, was developed. The proposed methodology takes full advantage of the Goodle GMS fusion code architecture. The design of a particular exercise is provided ad hoc by the instructor and requires basic Matlab knowledge. The system has been employed with satisfactory results in several university courses. To demonstrate the automatic evaluation process, three exercises are presented in detail. The first exercise involves a linear regression analysis of data and the calculation of the quality parameters of an instrumental analysis method. The second and third exercises address two different comparison tests, a comparison test of the mean and a t-paired test.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Evaluation of phenotypic stability of resistance to Phytophthora spp. in cacao clones

Resistance of fourteen Theobroma cacao clones to Phytophthora spp. was evaluated using stem inoculations on grafted seedlings. Concepts of phenotypic stability were used to interpret the results and to express horizontality of the resistance. The linear regression coefficient 'b', the determination coefficient (R²) and average lesion size were used to determine the level of horizontal resistance, the phenotypic stability and the predictability of all clones. The results indicated that clones P 7 and MA 15 present highest levels of horizontal resistance and stability, but with moderate predictability. Clones CAS 1 and CEPEC 13 were classified as those with high horizontal resistance, stability and predictability, while clones PA 30, UF 650 and SIAL 88 and EET 59 showed intermediate resistance and stability and high predictability. Clones SPA 17, OC 61, PA 150, SIAL 505, ICS 1 and R 41 presented high susceptibility and intermediate or low stability and moderate or high predictability.

Determination of Alkalinity and dissociation constants of high salinity waters: use of F5BC titration function

Measurements of parameters expressed in terms of carbonic species such as Alkalinity and Acidity of saline waters do not analyze the influence of external parameters to the titration such as Total free and associated Carbonic Species Concentration, activity coefficient, ion pairing formation and Residual Liquid Junction Potential in pH measurements. This paper shows the development of F5BC titration function based on the titrations developed by Gran (1952) for the carbonate system of natural waters. For practical use, samples of saline waters from Pocinhos reservoir in Paraiba were submitted to titration and linear regression analysis. Results showed that F5BC involves F1x and F2x Gran functions determination, respectively, for Alkalinity and Acidity calculations without knowing "a priori" the endpoint of the titration. F5BC also allows the determination of the First and Second Apparent Dissociation Constant of the carbonate system of saline and high ionic strength waters.

Problems and potentialities of e-Learning for regular undergraduate courses in emergency medicine

BACKGROUND: E-learning techniques are spreading at great speed in medicine, raising concerns about the impact of adopting them. Websites especially designed to host courses are becoming more common. There is a lack of evidence that these systems could enhance student knowledge acquisition. GOAL: To evaluate the impact of using dedicated-website tools over cognition of medical students exposed to a first-aid course. METHODS: Prospective study of 184 medical students exposed to a twenty-hour first-aid course. We generated a dedicated-website with several sections (lectures, additional reading material, video and multiple choice exercises). We constructed variables expressing the student's access to each section. The evaluation was composed of fifty multiple-choice tests, based on clinical problems. We used multiple linear regression to adjust for potential confounders. RESULTS: There was no association of website intensity of exposure and the outcome - beta-coeficient 0.27 (95%CI - 0.454 - 1.004). These findings were not altered after adjustment for potential confounders - 0.165 (95%CI -0.628 - 0.960). CONCLUSION: A dedicated website with passive and active capabilities for aiding in person learning had not shown association with a better outcome.

Spatio-temporal modelling of the duration of the cotton cycle in the State Of Goiás, Brazil

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.