Discriminating signal from noise: recognition of a movement-based animal display by artificial neural networks

In this study, we investigated the feasibility of applying neural networks to understanding movement-based visual signals. Networks based on three different models were constructed, varying in their input format and network architecture: a Static Input model, a Dynamic Input model and a Feedback model. The task for all networks was to distinguish a lizard (Amphibolurus muricatus) tail-flick from background plant movement. Networks based on all models were able to distinguish the two types of visual motion, and generalised successfully to unseen exemplars. We used curves defined by the receiver-operating characteristic (ROC) to select a single network from each model to be used in regression analyses of network response and several motion variables. Collectively, the models predicted that tail-flick efficacy would be enhanced by faster speeds, greater acceleration and longer durations.     

Detection of heart murmurs using wavelet analysis and artificial neural networks

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.     

Incidence and predictors of morphometric vertebral fractures in patients with ankylosing spondylitis

Introduction

Ankylosing spondylitis (AS) is associated with an increased incidence of vertebral fractures (VFs); however the actual incidence and predictors of morphometric VFs are unknown. The present study examined the incidence and predictors of new VFs in a large AS cohort.

Methods

In total, 298 AS patients who fulfilled the modified New York criteria were enrolled and spinal radiographs were evaluated biennially. Clinical and laboratory data and radiographic progression were assessed according to the Bath AS Disease Activity Index, erythrocyte sedimentation rate, C-reactive protein (CRP), and the Stoke AS spine score (SASSS). VF was defined according to the Genant criteria. The incidence of VFs at 2 and 4 years was evaluated using the Kaplan-Meier method. The age-specific standardized prevalence ratio (SPR) for AS patients in comparison with the general population was calculated.

Results

Of 298 patients, 31 (10.8%) had previous VFs at baseline. A total of 30 new VFs occurred in 26 patients over 4 years. The incidence of morphometric VFs was 4.7% at 2 years and 13.6% at 4 years. Multivariate logistic regression analysis showed that previous VFs at baseline and increased CRP levels at 2 years were predictors of new VFs (odds ratio (OR) =12.8, 95% confidence interval (CI) = 3.6-45.3 and OR = 5.4, 95% CI = 1.4–15.9). The age-specific specific standardized prevalence ratio of morphometric VFs in AS was 3.3 (95% CI 2.1–4.5).

Conclusions

The incidence of morphometric VFs increased in AS. Previous VFs and increased CRP levels predicted future VFs. Further studies are needed to identify the effects of treatment interventions on the prevention of new VFs.     

Drawing inspiration from biological dendrites to empower artificial neural networks

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.     

Modeling of pain using artificial neural networks

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.     

Recognition of general patterns using neural networks

The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly orthogonal patterns. A new algorithm is put forth to permit the recognition of general (non-orthogonal) patterns. The algorithm specifies the construction of the new network's memory matrix T _ij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of T _ij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of T _ij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.     

Evaluation of gel-pad oligonucleotide microarray technology by using artificial neural networks

Past studies have suggested that thermal dissociation analysis of nucleic acids hybridized to DNA microarrays would improve discrimination among duplex types by scanning through a broad range of stringency conditions. To more fully constrain the utility of this approach using a previously described gel-pad microarray format, artificial neural networks (NNs) were trained to recognize noisy or low-quality data, as might derive from nonspecific fluorescence, poor hybridization, or compromised data collection. The NNs were trained to classify dissociation profiles (melts) into groups based on selected characteristics (e.g., initial signal intensity, area under the curve) using a data set of 21,044 profiles derived from 186 probes hybridized to a study set of RNA extracted from 32 microbes common to the human oral cavity. Three melt profile groups were identified: one group consisted mostly of ideal melt profiles; another group consisted mostly of poor melt profiles; and, the remainder were difficult to classify. Screening of melting profiles of perfect-match hybrids revealed inconsistencies in the form of melting profiles even for identical probes on the same microarray hybridized to same target rRNA. Approximately 18% of perfect-match duplex types were correctly classified as poor. Experimental variability and deviation from ideal melt behavior were shown to be attributable primarily to a method of local background subtraction that was very sensitive to displacement of the grid frames used for image capture (both determined by the image analysis system) and duplexes with low binding constants. Additional results showed that long RNA fragments limit the discriminating power among duplex types.     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

Assessment of respiratory system mechanics by artificial neural networks: an exploratory study.

We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.     

Constraints on adaptation: explaining deviation from optimal sex ratio using artificial neural networks

Determining processes constraining adaptation is a major challenge facing evolutionary biology, and sex allocation has proved a useful model system for exploring different constraints. We investigate the evolution of suboptimal sex allocation in a solitary parasitoid wasp system by modelling information acquisition and processing using artificial neural networks (ANNs) evolving according to a genetic algorithm. Theory predicts an instantaneous switch from the production of male to female offspring with increasing host size, whereas data show gradual changes. We found that simple ANNs evolved towards producing sharp switches in sex ratio, but additional biologically reasonable assumptions of costs of synapse maintenance, and simplification of the ANNs, led to more gradual adjustment. Switch sharpness was robust to uncertainty in fitness consequences of host size, challenging interpretations of previous empirical findings. Our results also question some intuitive hypotheses concerning the evolution of threshold traits and confirm how neural processing may constrain adaptive behaviour.     

PROSHIFT: protein chemical shift prediction using artificial neural networks

The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the ¹H, ¹³C, and ¹⁵N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 Å a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server: www.jens-meiler.de/proshift.html     

Prediction of fingers posture using artificial neural networks

Predicting the hand and fingers posture during grasping tasks is an important issue in the frame of biomechanics. In this paper, a technique based on neural networks is proposed to learn the inverse kinematics mapping between the fingertip 3D position and the corresponding joint angles. Finger movements are obtained by an instrumented glove and are mapped to a multichain model of the hand. From the fingertip desired position, the neural networks allow predicting the corresponding finger joint angles keeping the specific subject coordination patterns. Two sets of movements are considered in this study. The first one, the training set, consisting of free fingers movements is used to construct the mapping between fingertip position and joint angles. The second one, constructed for testing purposes, is composed of a sequence of grasping tasks of everyday-life objects. The maximal mean error between fingertip measured position and fingertip position obtained from simulated joint angles and forward kinematics is 0.99+/-0.76mm for the training set and 1.49+/-1.62mm for the test set. Also, the maximal RMS error of joint angles prediction is 2.85 degrees and 5.10 degrees for the training and test sets respectively, while the maximal mean joint angles prediction error is -0.11+/-4.34 degrees and -2.52+/-6.71 degrees for the training and test sets, respectively. Results relative to the learning and generalization capabilities of this architecture are also presented and discussed.     

Application of artificial neural networks to modelling of starch hydrolysis by glucoamylase

The applicability of neural networks to the dynamic modelling of starch hydrolysis by Aspergillus niger glucoamylase is studied. The advantage of this technique is the possibility of predicting the reaction curves without a detailed kinetic model. Two independent neural models were proposed to predict the concentration of the products and conversion degree of the substrate at the end of the reaction (Model 1) as well as the reaction courses in the first stage when the sharp changes in the reaction rate are observed (Model 2). The results of simulations prove the ability of neural-network models to describe the complex kinetics of starch hydrolysis by glucoamylase.     

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.     

Molecular evolution of a peptide GPCR ligand driven by artificial neural networks

Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back-propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.     

Anti-glycated activity prediction of polysaccharides from two guava fruits using artificial neural networks

High-efficiency ultrasonic treatment was used to extract the polysaccharides of Psidium guajava (PPG) and Psidium littorale (PPL). The aims of this study were to compare polysaccharide activities from these two guavas, as well as to investigate the relationship between ultrasonic conditions and anti-glycated activity. A mathematical model of anti-glycated activity was constructed with the artificial neural network (ANN) toolbox of MATLAB software. Response surface plots showed the correlation between ultrasonic conditions and bioactivity. The optimal ultrasonic conditions of PPL for the highest anti-glycated activity were predicted to be 256 W, 60 °C, and 12 min, and the predicted activity was 42.2%. The predicted highest anti-glycated activity of PPG was 27.2% under its optimal predicted ultrasonic condition. The experimental result showed that PPG and PPL possessed anti-glycated and antioxidant activities, and those of PPL were greater. The experimental data also indicated that ANN had good prediction and optimization capability.