Ground reaction forces during locomotion in simulated microgravity

The role of ET and KATP channel in hypoxia-induced negative chronotropic effect of pacemaker cells in rabbit sinoatrial node was studied with intracellular microelectrode technique. The results obtained were as follows: (1) Hypoxia produced a progressive decrease in the velocity of diastolic depolarization (VDD) of pacemaker cells resulting in a reduced rate of pacemaker firing (RPF), and induced a decrease in APD, especially APD50. (2) KATP channel opener cromakalim markedly induced a negative chronotropic effect in a concentration-dependent manner and significantly shortened APD50. KATP channel blocker glibenclamide alleviated the effects of hypoxia on pacemaker cells, thereby suggesting the involvement of KATP channel in the hypoxia-induced effects. (3) By superfusion of ET-1, the hypoxia-induced decrease in RPF was remarkably potentiated and the occurrence of pacemaker arrest was shifted to an earlier time. The hypoxia-induced effects could be effectively attenuated after pretreatment with BQ-123, implying the role of endogenous ET-1 release in hypoxia-induced effects. It is concluded that the negative chronotropic effect and the decrease in APD induced by hypoxia may be attributed to the activation of KATP channel and the release of endogenous ET.     

Evaluation of complications of kidney transplantation using artificial neural networks

The aim of this study was to develop an artificial neural network (ANN) to differentiate between rejection, acute tubular necrosis (ATN) and normally functioning kidneys in a group of patients with renal transplants. The performance of ANN was compared with that of an experienced observer using a database of 35 patients' records, each of which included 12 quantitative parameters derived from renograms and clinical data as well as a clinical evaluation. These findings were encoded as features for a three-layered neural network to predict the outcome of biopsy or clinical diagnosis. The network was trained and tested using the jackknife method and its performance was then compared to that of a radiologist. The network was able to correctly classify 31 of the 35 original cases and gave a better diagnostic accuracy (88%) than the radiologist (83%), by showing an association between the quantitative data and the corresponding pathological results (r = 0.78, P < 0.001). We conclude that an ANN can be trained to differentiate rejection from acute tubular necrosis, as well as normally functioning transplants, with a reasonable degree of accuracy.     

Automated interpretation of myocardial SPECT perfusion images using artificial neural networks

The purpose of this study was to develop a computer-based method for automatic detection and localization of coronary artery disease (CAD) in myocardial bull's-eye scintigrams. METHODS: A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest-stress scintigraphy and coronary angiography within 3 mo was studied. Different image data reduction methods, including pixel averaging and two-dimensional Fourier transform, were applied to the bull's-eye scintigrams. After a quantitative and qualitative evaluation of these methods, 30 Fourier components were chosen as inputs to multilayer perceptron artificial neural networks. The networks were trained to detect CAD in two vascular territories, using coronary angiography as gold standard. A "leave one out" procedure was used for training and evaluation. The performance of the networks was compared to those of two human experts. RESULTS: One of the human experts detected CAD in one of two vascular territories, with a sensitivity of 54.4% at a specificity of 70.5%. The sensitivity of the networks was significantly higher at that level of specificity (77.2%, p = 0.0022). The other expert had a sensitivity of 63.2% at a specificity of 61.5%. The networks had a sensitivity of 77.2% (p = 0.038) at this specificity level as well. The differences in sensitivity between human experts and networks for the other vascular territory were all less than 6% and were not statistically significant. CONCLUSION: Artificial neural networks can detect CAD in myocardial bull's-eye scintigrams with such a high accuracy that the application of neural networks as clinical decision support tools appears to have significant potential.     

A medical expert system approach using artificial neural networks for standardized treatment planning

PURPOSE: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. METHODS AND MATERIALS: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. RESULTS: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. CONCLUSIONS: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.     

Prediction of dynamic tendon forces from electromyographic signals: an artificial neural network approach

Artificial neural networks (ANN) with a backpropagation algorithm were used to predict dynamic tendon forces from electromyographic (EMG) signals. To achieve this goal, tendon forces and EMG-signals were recorded simultaneously in the gastrocnemius muscle of three cats while walking and trotting at different speeds on a motor-driven treadmill. The quality of the tendon force predictions were evaluated for three levels of generalization. First, at the intrasession level, tendon force predictions were made for step cycles from the same experimental session as the step cycles which were used to train the ANN. At this level of generalization very good results were obtained. Second, at the intrasubject level, tendon force predictions were made for one cat walking at a given speed while the ANN was trained with data from the same animal walking at different speeds. For the intrasubject predictions, the quality of the results depended on the walking speed for which the predictions were made: for the speeds at the low and high extremes, the predictions were worse than for the intermediate speeds. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.78 to 0.91. Third, at the intersubject level, tendon forces were predicted for one animal walking at a given speed while the ANN was trained with data from the remaining two animals walking at the corresponding speed. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.72 to 0.98. It was concluded that the ANN-approach is a powerful technique to predict dynamic tendon forces from EMG-signals.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

Prediction of the measured temperature after the last finishing stand using artificial neural networks

In this report the development of an artificial neural network, capable of predicting the temperature after the last finishing stand of a hot strip mill for a certain class of steels, is described. Three neural networks with different numbers of hidden nodes (3, 5 and 7) were trained. The relative standard deviation in finish temperature as predicted by the best performing neural network model (7 hidden nodes) was just over 25% smaller than that of the linear Hoogovens model. This improved accuracy can be explained by the incorrect assumption in the Hoogovens model of linear dependence of the finishing temperature on some input parameters. With the trained neural network, the influence of the various input parameters on the finishing temperature could be examined. The dependencies predicted by the neural network can be approximated by a linear fit and are a factor 2 lower for all input parameters. It is conceivable that operation of the mill using an artificial neural network for the prediction of the finishing temperature would have resulted in smaller operational fluctuations.     

Prediction of shape defects over length of cold rolled sheet using artificial neural networks

Abstract

A model based on an artificial neural network (ANN) has been developed for prediction of flatness of cold rolled (CR) sheet in a tandem cold rolling mill for white goods applications. Various process parameters including roll bending, roll shifting, tensions between stands etc., which affect flatness of CR sheet are considered in the model. Substantial amounts of data are obtained from level II automation of PL-TCM of TATA Steel to develop the prediction model. The developed ANN model, based on back propagation algorithm, is able to predict the flatness defects like edge buckles, centre buckles for a given set of rolling parameters. The model involves a large number of process parameters and application of ANN to such kind of problems is successfully demonstrated in the present study. The model is in good agreement with the observed flatness values at different locations across the width. High coefficient of determination close to 0·919 is achieved for the prediction of flatness at edges.     

Digital perfusion, evaluated scintigraphically, and hoof wall growth in horses with chronic laminitis treated with egg bar-heart bar shoeing and coronary grooving

Nuclear scintigraphy was used to assess digital perfusion before and after treatment in 10 horses with clinical and radiographic evidence of chronic laminitis. Horses were evaluated for lameness, degree of distal phalanx rotation, and heel-toe hoof wall growth ratio, and randomly divided into two treatment groups. Group 1 horses received only egg bar-heart bar shoeing; Group 2 underwent egg bar-heart bar shoeing and coronary grooving. Horses were re-evaluated for digital perfusion, lameness, degree of distal phalanx rotation, and hoof wall growth at 6 week intervals over the 18 week follow-up period. Prior to treatment, relative scintigraphic activity at the dorsal laminar area was decreased and relative scintigraphic activity at the toe and adjacent solar area was increased. Egg bar-heart bar shoeing was associated with significantly increased dorsal laminar scintigraphic activity and significantly decreased solar scintigraphic activity over the 18 week period. Coronary grooving, in combination with egg bar-heart bar shoeing, resulted in a significantly lower heel-toe hoof wall growth ratio but did not enhance digital perfusion. Seven of 10 (70%) horses were responsive to treatment, defined as an improvement in lameness by at least one grade. Horses that were refractory to treatment had significantly lower dorsal laminar scintigraphic activity and higher palmar coronary scintigraphic activity prior to treatment than horses that responded to treatment. Our results are the first to demonstrate that egg bar-heart bar shoeing is associated with improved dorsal laminar perfusion, and support the use of this technique. In addition, we found that pre-treatment nuclear scintigraphy was predictive of clinical outcome in horses with chronic laminitis treated with corrective shoeing.     

神经网络方法在围岩稳定性分级评价中的应用

文中介绍了自适应调整学习率的改进BP算法,建立了适用于围岩稳定性分级评价和模式识别的BP神经网络模型,编写了相应的MATLAB计算程序,应用示例表明,该模型具有良好的评价识别效果。     

Comparison of hot rolled steel mechanical property prediction models using linear multiple regression,non-linear multiple regression and non-linear artificial neural networks

Abstract

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed. In the present paper a comparison of the application of linear multiple regression, non-linear multiple regression and non-linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non-linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non-linear multiple regression.     

Belt speed control in a sintering plant using neural networks

Sintering transforms fine-grained ore into lumped ore so that the latter can be used in a blast furnace. The fine-grained ore combined with coke and other materials is loaded into a sinter box and moved along by the sintering belt while the ignited coke burns. The speed by which the belt moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, a neural network-based sintering belt speed controller is proposed which copies human operator knowledge. Actual process data were collected from a sintering plant for eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a backpropagation learning algorithm. In on-line testing at the sintering plant, the proposed model reliably controlled the sintering belt speed during normal operation without the help of human operators. Moreover, the quality and productivity was as good as with human operators.     

Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with > 3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection of PyMS in conjunction with ANNs was 3 x 10(4) E. coli UB5201 cells in 1.6 x 10(7) E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.     

Quantitation of grey matter, white matter, and cerebrospinal fluid from spin-echo magnetic resonance images using an artificial neural network technique

An operator independent technique has been developed to quantitate the volume of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) using spin-echo magnetic resonance images. Using skull stripped spin-echo images, CSF was removed using an automated thresholding technique. The bimodal histogram of the remaining images was used to train a perceptron and a single hidden layer neural network to output the percentage of GM and WM in the image. The output values were compared with those of a semiautomated technique employing a least square fitting technique [graduated nonconvexity algorithm (GNC)] applied to the bimodal histogram. This semiautomated technique allowed for intervention by the radiologist. Fourteen normal volunteers with eight contiguous slices each were analyzed. The individual percentages of WM, GM, and CSF of 40 slices from five subjects not used in the training set as well as the total percentages of GM, WM, and CSF in each individual were predicted using the two artificial network architectures. GM, WM, and CSF percentages were predicted within 7% for individual slices while total percentages of WM, GM, and CSF were computed accurately with an absolute error of less than 5% when compared to the semiautomated technique involving a trained neuroradiologist.     

Comparison of artificial neural networks (ANN) with classical modelling techniques using different experimental designs and data from a galenical study on a solid dosage form

Artificial Neural Networks (ANN) methodology was used to analyse experimental data from a tabletting study and compared both graphically and numerically to classical modelling techniques (i.e. Response surface methodology, RSM). The aim of this investigation was to describe quantitatively the degree of data fitting achieved and the robustness of the developed models using two types of experimental design (i.e. a statistical, highly organised design and a randomised design). To compare goodness of fit, the R(2) coefficient was used, whereas for the robustness of the models the R(2) coefficient of an independent validation data set was computed. Comparable results were achieved for both ANN and RSM methodology when using the statistical plan. However, the robustness of the models when developed based on a randomised plan was clearly better for the ANN methodology. Based on the results of this study, it appears that the ANN methodology is much less sensitive to the organisational level of a trial design and is therefore better adapted to the data analysis of the results of historical or poorly organised trials. All tablet properties determined were largely influenced by the dwell time during compression as well as by concentration of silica aerogel and magnesium stearate, whereas the other factors showed very much weaker effects.     

Ovarian dysplasia in epithelial inclusion cysts. A morphometric approach using neural networks

BACKGROUND: Ovarian dysplasia has been described in the ovarian surface epithelium by histologic and morphometric studies. This study evaluates ovarian dysplasia in epithelial inclusion cysts adjacent to overt carcinoma and also incidentally found in ovaries removed for nonneoplastic diseases, including oophorectomies for family history of ovarian cancer, using an artificial neural network. METHODS: Histologic sections from 37 ovaries of which 26 were diagnosed with dysplasia in epithelial inclusion cysts (10 adjacent to carcinoma and 16 incidental) and 11 with benign epithelial inclusion cysts were evaluated by tracing nuclear profiles and assessing measures of nuclear area, shape, and texture. These sections were analyzed using artificial neural networks and also statistically using the Kruskal-Wallis test with the Dunn procedure to compare the morphologic similarity of dysplasia found incidentally in inclusion cysts unrelated to carcinoma from that in inclusion cysts adjacent to carcinoma. RESULTS: Neither statistical nor artificial neural network analysis was able to distinguish between incidental and adjacent dysplasia. Both types differed significantly from the control cases. CONCLUSIONS: Neural networks are powerful classification tools when applied to multiple variables extracted from individual cases. In this study, they helped to substantiate the similarity between dysplasia found incidentally and that adjacent to ovarian carcinoma. Because dysplasia represents a potential precancerous lesion, its incidental finding may help identify patients at risk for developing ovarian carcinoma.     

Elastic modulus of equine hoof horn, tested in wall samples, sole samples and frog samples at varying levels of moisture

In mammals and yeast, 5-aminolaevulinic acid dehydratase is a zinc-dependent enzyme that catalyses the synthesis of porphobilinogen-the pyrrole building block that is incorporated into all modified tetrapyrroles, including haem, chlorophyll and vitamin B12. The X-ray structure of this enzyme reveals how substitution of the catalytically important zinc ion by lead inactivates the enzyme and causes a form of pseudo-porphyria.