Is there an association between T102C polymorphism of the serotonin receptor 2A gene and urinary incontinence?

The regulation of bladder function is influenced by central serotonergic modulation. Several genetic polymorphisms related to serotonin control have been described in the literature. T102C polymorphism of the serotonin receptor 2A gene (5-HT2A) has been shown to be associated with certain diseases such as non-fatal acute myocardial infarction, essential hypertension, and alcoholism. In the present study, we examined the association between 5-HT2A gene polymorphism and urinary incontinence in the elderly. A case-control study was performed in 298 elderly community dwellers enrolled in the Gravataí-GENESIS Project, Brazil, which studies gene-environmental interactions in aging and age-related diseases. Clinical, physical, biochemical, and molecular analyses were performed on volunteers. 5-HT2A genotyping was determined by PCR-RFLP techniques using the HpaII restriction enzyme. The subjects had a mean age of 68.05 ± 6.35 years (60-100 years), with 16.9% males and 83.1% females. The C allele frequency was 0.494 and the T allele frequency was 0.506. The CC genotype frequency was 21.78%, the CT genotype frequency was 55.24% and the TT genotype frequency was 22.98%. We found an independent significant association between the TT genotype (35.7%) and urinary incontinence (OR = 2.06, 95%CI = 1.16-3.65). Additionally, urinary incontinence was associated with functional dependence and systolic hypertension. The results suggest a possible genetic influence on urinary incontinence involving the serotonergic pathway. Further investigations including urodynamic evaluation will be performed to better explain our findings.

Association of urinary 90 kDa angiotensin- converting enzyme with family history of hypertension and endothelial function in normotensive individuals

We described angiotensin-I-converting enzyme (ACE) isoforms with molecular masses of 190, 90, and 65 kDa in the urine of normotensive offspring of hypertensive subjects. Since they did not appear in equal amounts, we suggested that 90 kDa ACE might be a marker for hypertension. We evaluated the endothelial response in normotensive offspring with or without family history of hypertension and its association with the 90 kDa ACE in urine. Thirty-five normotensive subjects with a known family history of hypertension and 20 subjects without a family history of hypertension, matched for age, sex, body weight, and blood pressure, were included in the study. Endothelial function was assessed by ultrasound and a sample of urine was collected for determination of ACE isoforms. In the presence of a family history of hypertension and detection of 90 kDa ACE, we noted a maximal flow mediated dilation of 12.1 ± 5.0 vs 16.1 ± 6.0% in those without a previous history of hypertension and lacking urinary 90 kDa ACE (P < 0.05). In subjects with a family history of hypertension and presenting 90 kDa ACE, there were lower levels of HDL-cholesterol (P < 0.05) and higher levels of triglycerides (P < 0.05). Subjects with 90 kDa ACE irrespective of hypertensive history presented a trend for higher levels of triglycerides and HDL-cholesterol (P = 0.06) compared to subjects without 90 kDa ACE. Our data suggest that the 90 kDa ACE may be a marker for hypertension which may be related to the development of early atherosclerotic changes.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Effect of castration on renal glycosaminoglycans and their urinary excretion in male and female rats with chronic renal failure

Glycosaminoglycans (GAGs) participate in a variety of processes in the kidney, and evidence suggests that gender-related hormones participate in renal function. The aim of this study was to analyze the relationship of GAGs, gender, and proteinuria in male and female rats with chronic renal failure (CRF). GAGs were analyzed in total kidney tissue and 24-h urine of castrated (c), male (M), and female (F) Wistar control (C) rats (CM, CMc, CF, CFc) and after 30 days of CRF induced by 5/6 nephrectomy (CRFM, CRFMc, CRFF, CRFFc). Total GAG quantification and composition were determined using agarose and polyacrylamide gel electrophoresis, respectively. Renal GAGs were higher in CF compared to CM. CRFM presented an increase in renal GAGs, heparan sulfate (HS), and proteinuria, while castration reduced these parameters. However, CRFF and CRFFc groups showed a decrease in renal GAGs concomitant with an increase in proteinuria. Our results suggest that, in CRFM, sex hormones quantitatively alter GAGs, mainly HS, and possibly the glomerular filtration barrier, leading to proteinuria. The lack of this response in CRFMc, where HS did not increase, corroborates this theory. This pattern was not observed in females. Further studies of CRF are needed to clarify gender-dependent differences in HS synthesis.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Previsão de estabilidade de óleos vegetais através da rede de inteligência artificial

Redes Neurais Artificiais são técnicas computacionais que se utilizam de um modelo matemático capaz de adquirir conhecimentos pela experiência; esse comportamento inteligente da rede provém das interações entre unidades de processamento, denominadas de neurônios artificiais. O objetivo deste trabalho foi criar uma rede neural capaz de prever a estabilidade de óleos vegetais, a partir de dados de suas composições químicas, visando um modelo para a previsão da shelf-life de óleos vegetais, tendo como parâmetros apenas dados de suas composições químicas. Os primeiros passos do processo de desenvolvimento da rede consistiram na coleta de dados relativos ao problema e sua separação em um conjunto de treinamento e outro de testes. Estes conjuntos apresentaram como variáveis dados de composição química, que incluíram os valores totais em ácidos graxos, fenóis, tocoferóis e a composição individual em ácidos graxos. O passo seguinte foi a execução do treinamento, onde o padrão de entrada apresentado à rede como parâmetro de estabilidade foi o índice de peróxido, determinado experimentalmente por um período de 16 dias de armazenagem na ausência de luz, a 65ºC. Após o treinamento foi testada a capacidade de previsão adquirida pela rede, em função do parâmetro de estabilidade adotado, mas com um novo grupo de óleos. Seguindo o teste, foi determinada a correlação linear entre os valores de estabilidade previstos pela rede e aqueles determinados experimentalmente. Com os resultados obtidos, pode-se confirmar a viabilidade de previsão da estabilidade de óleos vegetais pela rede neural, a partir de dados de sua composição química, utilizando como parâmetro de estabilidade o índice de peróxido.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Inteligência artificial e teoria de resolução de problemas

Este artigo é uma tentativa de delinear as principais características da pesquisa numa nova área de estudos a chamada Inteligência Artificial (AI). Os itens 1 e 2 constituem um rápido histórico da AI e seus pressupostos básicos. O item 3 trata da teoria de resolução de problemas, desenvolvida por A. Newell e H. Simon. O item 4 procura mostrar a relevância da AI para a Filosofia, em especial para a filosofia da Mente e para a Teoria do Conhecimento.

Robots, intencionalidade e inteligência artificial

O artigo aborda problemas filosóficos relativos à natureza da intencionalidade e da representação mental. A primeira parte apresenta um breve histórico dos problemas, percorrendo rapidamente alguns episódios da filosofia clássica e da filosofia contemporânea. A segunda parte examina o Chinese Room Argument (Argumento do Quarto do Chinês) formulado por J. Searle. A terceira parte desenvolve alguns argumentos visando mostrar a inadequação do modelo funcionalista de mente na construção de robots. A conclusão (quarta parte) aponta algumas alternativas ao modelo funcionalista tradicional, como, por exemplo, o conexionismo.