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At least 127 records · Page 7Linked to original sources
PubMed1999

Design of artificial neural network and its applications to the analysis of alcoholism data.

Artificial neural networks were applied to the alcoholism data to reveal nonlinear relationships between intermediate phenotypes, marker identity-by-descent sharing, and the affection status. A variable number of hidden units were considered to achieve a balance between the minimal mean-squared error and over-fitting of the data. The predictability of the affection status based on intermediate phenotype information (event-related potential 300, monoamine oxidase, and gender) was 65% to 75%, and sensitivity/specificity ranged around 50% to 80%. The IBD approach succeeded in identifying the same marker as previous studies, but also found additional peaks.

Alcoholism↗
PubMed1999

Microconidia of Neurospora crassa.

Neurospora crassa produces two types of vegetative spores-relatively small numbers of uninucleate microconidia and very large numbers of multinucleate macroconidia (blastoconidia and arthroconidia). The microconidia can function either as spermatia (male gametes) or as asexual reproductive structures or both. In nature they probably function exclusively in fertilization of protoperithecia. The environmental conditions favoring their formation and the pattern of their development are quite distinct from those of macroconidia. Mutants of N. crassa have been isolated in which macroconidiation is selectively blocked without affecting microconidiation, showing that these two types of conidial differentiation involve distinct developmental pathways. Unlike microconidia of some related ascomycetes, those of Neurospora are capable of germination, providing viable uninucleate haploid cells which are desired in several types of investigations. A technique of selectively removing macroconidia from culture initiated on cellophane overlying agar medium allows pure microconidia to be obtained even from the wild-type strains of Neurospora. The conditional microcyclic strain, mcm, allows either macroconidia or microconidia to be obtained at will, depending on the conditions of culture. The new methods of obtaining pure microconidia from normal laboratory strains will make it quick and easy to purify heterokaryotic transformants following introduction of DNA into multinucleate protoplasts. Moreover, these methods allow the detection of genetic variability that remains hidden within an individual fungus and the estimation of the frequency of nuclear types in laboratory-constructed heterokaryons. The discovery, function, and development of microconidia are described and their research applications are discussed in this review.

Culture Media↗
PubMed1996

Presence of vasomotor and respiratory rhythms in the discharge of single medullary neurons involved in the regulation of cardiovascular system.

We analyzed the discharges of 77 single neurons located in the rostral ventrolateral medulla (RVLM, n = 25), caudal ventrolateral medulla (CVLM, n = 18), lateral tegmental field (LTF, n = 19) and caudal raphe nuclei (n = 15). These recordings were made from 36 vagotomized and sinoaortic denervated cats that were either decerebrate (n = 27) or anesthetized with urethane (n = 9) and from 3 decerebrate cats with intact sinoartic and vagal nerves. These neurons were classified as sympathetic-related (n = 61) if spike triggered averaging showed that their naturally occurring discharges were correlated to either the cardiac related (2-6 Hz) or a faster (10 Hz) oscillation in inferior cardiac sympathetic nerve discharge. Neurons were classified as sympathetic-unrelated (n = 16) if they lacked these characteristics. We used autoregressive spectral techniques to detect additional slower oscillations hidden in the variability of neuronal discharge and possibly correlated to the oscillations of systolic arterial pressure (SAP). This analysis revealed the existence of a low frequency (LF) oscillation (0.12 +/- 0.02 Hz) in the discharges of 36 sympathetic-related and 9 sympathetic-unrelated neurons. In relation to 35 neurons in 21 animals there was also an LF component in SAP variability. In 29 instances the LF neuronal discharges and SAP variabilities were significantly correlated. In addition, there was a high frequency (HF) oscillation (0.34 +/- 0.06 Hz) in the discharges of 59 medullary neurons. In 56 cases the HF in neuronal discharge variability cohered to that in SAP variability. These data are the first to demonstrate the existence of an LF component in the discharges of individual medullary neurons, at least some of which were likely to be involved in the regulation of the cardiovascular system. Since these oscillations were evident in cats with section of sinoaortic and vagal nerves, they likely reflect central rhythmogenic properties.

Animals↗
PubMed2001

Inactivating one hippocampus impairs avoidance of a stable room-defined place during dissociation of arena cues from room cues by rotation of the arena.

Unilateral intrahippocampal injections of tetrodotoxin were used to temporarily inactivate one hippocampus during specific phases of training in an active allothetic place avoidance task. The rat was required to use landmarks in the room to avoid a room-defined sector of a slowly rotating circular arena. The continuous rotation dissociated room cues from arena cues and moved the arena surface through a part of the room in which foot-shock was delivered. The rat had to move away from the shock zone to prevent being transported there by the rotation. Unilateral hippocampal inactivations profoundly impaired acquisition and retrieval of the allothetic place avoidance. Posttraining unilateral hippocampal inactivation also impaired performance in subsequent sessions. This allothetic place avoidance task seems more sensitive to hippocampal disruption than the standard water maze task because the same unilateral hippocampal inactivation does not impair performance of the variable-start, fixed hidden goal task after procedural training. The results suggest that the hippocampus not only encodes allothetic relationships amongst landmarks, it also organizes perceived allothetic stimuli into systems of mutually stable coordinates. The latter function apparently requires greater hippocampal integrity.

Animals↗
PubMed2003

Fraser-Cryptophthalmos syndrome with cardiovascular malformations: a rare association.

Fraser-Cryptophthalmos syndrome is a multiple malformation disorder associated variably with cryptophthalmos (hidden eye), anomalies of the head, nose and ears; syndactyly, renal and genital malformations. In this report, we describe a case of Fraser syndrome with cardiovascular malformations: coarctation of aorta, an association not previously described.

Abnormalities, Multiple↗
PubMed2006

[The sleep staging based on HRV analysis].

In order to deduce the sleep stages from heart rate, we analyze the heart rate variability (HRV) with hidden Markov model (HMM) for the identification of different characters of HRV within different sleep stages. Special technique is used to compensate the individual diversity. The relationship between the sleep stage and the ultra-low frequency components of HRV is also considered. Since the detection of heart rate hardly disturbs the sleep, the proposed method provides a simple approach to evaluating the sleep stage without disturbing the sleep. Our experiments have proved that this method meets the requirements of wide applications, especially the requirement of routine use in monitoring the normal subjects' sleep.

Adult↗
PubMed1995

Neural networks as an aid in the diagnosis of lymphocyte-rich effusions.

Neural network (NN) technology was applied to digital image analysis data for 112 Papanicolaou-fixed and -stained smears of lymphocyte-rich effusions (LREs). The smears were analyzed with an inexpensive image analysis system assembled in our laboratory. Several models were developed using backpropagation NN development software in an effort to optimize classification of the LREs as reactive lymphocytosis or malignant lymphoma and to analyze the effects of various parameters on classification rates. The greatest specificity and sensitivity of LRE classification were achieved with NN models that consisted of 7 input neurons, including 5 morphometric and 2 densitometric variables, 10 hidden-layer neurons and 1 output neuron. This NN architecture with a sigmoidal transfer function provided a true cross-validation rate of 89.3% of testing data, with a sensitivity of 76.9%, specificity of 93.0% and shrinkage of 10.7%. The same NN architecture with a step transfer function provided a true cross-validation rate of 95.3%, sensitivity of 85.7%, specificity of 97.6% and shrinkage of 0%. The effects of various parameters, such as network size, shrinkage and ratio of sample size to input layer size, on NN accuracy are discussed.

Ascitic Fluid↗
PubMed2003

Identification of land use with water quality data in stormwater using a neural network.

To control stormwater pollution effectively, development of innovative, land-use-related control strategies will be required. An approach that could differentiate land-use types from stormwater quality would be the first step to solving this problem. We propose a neural network approach to examine the relationship between stormwater water quality and various types of land use. The neural network model can be used to identify land-use types for future known and unknown cases. The neural model uses a Bayesian network and has 10 water quality input variables, four neurons in the hidden layer, and five land-use target variables (commercial, industrial, residential, transportation, and vacant). We obtained 92.3 percent of correct classification and 0.157 root-mean-squared error on test files. Based on the neural model, simulations were performed to predict the land-use type of a known data set, which was not used when developing the model. The simulation accurately described the behavior of the new data set. This study demonstrates that a neural network can be effectively used to produce land-use type classification with water quality data.

Agriculture↗
PubMed1994

Protein secondary structure from circular dichroism spectroscopy. Combining variable selection principle and cluster analysis with neural network, ridge regression and self-consistent methods.

Different approaches to improve the analysis of protein secondary structure from circular dichroism spectra are compared. Grouping proteins based on the similarity of their circular dichroism spectra, using cluster analysis methods, was utilized as a new way of implementing variable selection. The performance of three basic methods (neural networks, ridge regression and singular value decomposition) was evaluated in combination with three approaches to improve the predictions; namely, variable selection, cluster analysis and the self-consistent method. Cluster analysis performed on the basis set proteins resulted in three clusters, subanalyses of which provide a new way of performing variable selection. The neural network with two hidden layers performed better than that with one hidden layer and was combined with variable selection. Inclusion of the variable selection principle improved the performance of all three basic methods. While the neural network method performed slightly better than the other two methods at the basic level, the inclusion of variable selection led to similar performance indices for all three methods.

Circular Dichroism↗
PubMed2002

Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production.

In this study, an artificial autoassociative neural network (AANN) was used online to detect deviations from normal antibiotic production fermentation using conventional process variables. To improve the efficiency of extracting hidden information contained in multidimensional process variables, and to finally render the AANN adequate for fault detection, we explored the following methods: selection of process variables; preprocessing of data that involved normalizing the training data of the AANN; and evaluation of data that involved assessing the output of the AANN. A method for fault detection in virginiamycin M and S production by Streptomyces virginiae was successfully developed based on these techniques.

Journal Article↗
PubMed2000

Client concealment and self-presentation in therapy: comment on Kelly (2000)

The authors disagree with A. E. Kelly's (2000) conclusions that clients conceal things from therapists primarily for self-presentational reasons and that client concealment is positively related to positive therapy process and outcome. They also disagree with A. E. Kelly regarding the implications of self-presentation theory for therapy. Their review of the research suggests that clients do not conceal much from therapists, that what they do conceal involves many different kinds of information hidden for many different reasons, that therapists have wide variability in being able to detect hidden client material, and that the relationship of client concealment and therapist awareness of client concealment with therapy process and outcome is not clear. Finally, the authors discuss their views about implications of client concealment and self-presentation for therapy.

Humans↗
PubMed2005

Layer-by-layer electrodeposition of copper in the presence of o-phenanthroline, caused by a new type of hidden NDR oscillation with the effective electrode surface area as the key variable.

Electrochemical deposition of copper (Cu) from aqueous acidic Cu2+ solutions with o-phenanthroline (o-phen) shows both potential and current oscillations, together with a (partially hidden) N-shaped negative differential resistance (N-NDR), indicating that the oscillations are classified into hidden N-NDR (or HN-NDR) oscillations. The color and the surface morphology of Cu deposits oscillate in synchronization with the potential and current oscillations. Microscopic inspection has shown that dense round Cu leaflets, which look gray, grow in the positive side of the potential oscillation or in the high-current state of the current oscillation, whereas thin Cu leaflets, which look black, grow in the opposite-side stages of the potential and current oscillations, thus finally resulting in a layered Cu deposit with the layer thickness of about 5 microm. The appearance of the NDR is explained to be due to adsorption of the reduced form of a [Cu(II)(o-phen)2]2+ complex, which suppresses the Cu electrodeposition. The increase in the effective electrode surface area by growth of thin Cu leaflets, on the other hand, causes a current increase that can hide the NDR. This NDR-hiding mechanism is of a new type and the present oscillation is regarded as a new-type of HN-NDR oscillator.

Copper↗
PubMed1997

Assessment of learning by the Morris water task and fear conditioning in inbred mouse strains and F1 hybrids: implications of genetic background for single gene mutations and quantitative trait loci analyses.

Genetic methods including the creation of transgenic or null mutant models and mapping studies using quantitative trait loci strategies can be used to identify candidate genes in mice that regulate learning processes. Interpretations as to the impact of single gene mutations for polygenic behaviours like learning will depend in part on the genetic background of the animals used for these manipulations. To address the issue of genetic variability, 12 inbred strains and seven different F1 hybrids were tested on multiple behavioural tasks, including two complex learning paradigms: the Morris water task and fear conditioning. Strain differences were found for all variables measured. In the hidden platform version of the Morris task, the albino animals performed poorly while overall the F1 hybrids showed the best selectivity for the trained quadrant as measured in a probe trial. In contrast, almost all genotypes performed well on the contextual fear conditioning task and learned to associate the test context with the pairing of a foot shock and auditory stimulus as demonstrated 24 h after training by increased freezing in the test environment compared to an altered context. Significant genetic correlations were obtained for behavioural measures suggesting that the same genes regulate various aspects of performance on behavioural tasks. Scores from these multiple inbred strains and F1 hybrids provide a baseline level of learning ability for fear conditioning and the Morris water task. The results of the present study confirm the importance of genetic background in the performance of various learning tasks. This variability should be considered when developing new transgenic or null mutant animal models.

Acoustic Stimulation↗
PubMed1993

Pattern recognition of the electroencephalogram by artificial neural networks.

A back-propagation network was trained to recognize high voltage spike-and-wave spindle (HVS) patterns in the rat, a rodent model of human petit mal epilepsy. The spontaneously occurring HVSs were examined in 137 rats of the Fisher 344 and Brown Norway strains and their F1, F2 and backcross hybrids. Neocortical EEG and movement of the rat were recorded for 12 night hours in each animal and analog data were filtered (low cut: 1 Hz; high cut: 50 Hz) and sampled at 100 Hz with 12 bit precision. A training data set was generated by manually marking durations of HVS epochs in 16 representative animals selected from each group. Training data were presented to back-propagation networks with variable numbers of input, hidden and output cells. The performance of different types of networks was first examined with the training samples and then the best configuration was tested on novel sets of the EEG data. FFT transformation of EEG significantly improved the pattern recognition ability of the network. With the most effective configuration (16 input; 19 hidden; 1 output cells) the summed squared error dropped by 80% as compared with that of the initial random weights. When testing the network with new patterns the manual and automatic evaluations were compared quantitatively. HVSs which were detected properly by the network reached 93-99% of the manually marked HVS patterns, while falsely detected events (non-HVS, artifacts) varied between 18% and 40%. These findings demonstrate the utility of back-propagation networks in automatic recognition of EEG patterns.

Animals↗
PubMed1998

Dose-response relationships in chemical carcinogenesis: superposition of different mechanisms of action, resulting in linear-nonlinear curves, practical thresholds, J-shapes.

The shape of a carcinogen dose-cancer incidence curve is discussed as the result of a superposition of dose-response relationships for various effects of the carcinogen on the process of carcinogenesis. Effects include direct DNA damage, e.g., by covalent binding, indirect DNA damage, e.g., by increased formation of reactive oxygen species or interaction with DNA replication or chromosome integrity. The 'fixation' of a DNA adduct as a heritable mutation depends on its pro-mutagenic potency and on the rates of DNA repair and DNA replication. Endogenous and unavoidable DNA damage is responsible for a background rate of the process of mutagenesis and carcinogenesis and forms the basis of spontaneous cancer incidence. For DNA-reactive carcinogens, linearity of the dose response at the low-dose end is expected. With increasing dose, saturation of DNA repair can introduce a sublinearity (example: dimethylnitrosamine). Stimulation of cell division as a result of high-dose toxicity and regenerative proliferation also results in a sublinear deviation from low-dose linearity. If the DNA-damaging potency of the carcinogen is low in comparison with the high-dose effects, the linear part of the low dose-cancer incidence curve might be hidden within the background variability. Under such conditions, 'practical thresholds' could be discussed (formaldehyde). If a carcinogen increases the rate of cell division or the level of oxidative stress at high dose but has an antimitogenic or antioxidative effect at low dose, a J-shaped (or: U-shaped) curve with a decrease of the spontaneous tumor incidence at low dose could result (caffeic acid; TCDD). This phenomenon has been observed even under conditions of a genotoxic contribution (ionizing radiation; diesel exhaust particles). For a mechanism-based assessment of a low-dose cancer risk, information on the various modes of action and modulations should be available over the full dose range, and models should be refined to incorporate the respective information.

Carcinogens↗
PubMed1998

Representations and rates of approximation of real-valued Boolean functions by neural networks.

We give upper bounds on rates of approximation of real-valued functions of d Boolean variables by one-hidden-layer perceptron networks. Our bounds are of the form c/n where c depends on certain norms of the function being approximated and n is the number of hidden units. We describe sets of functions where these norms grow either polynomially or exponentially with d.

Journal Article↗
PubMedDate not supplied

Structure-based predictions of 1H NMR chemical shifts using feed-forward neural networks.

Feed-forward neural networks were trained for the general prediction of 1H NMR chemical shifts of CH(n) protons in organic compounds in CDCl3. The training set consisted of 744 1H NMR chemical shifts from 120 molecular structures. The method was optimized in terms of selected proton descriptors (selection of variables), the number of hidden neurons, and integration of different networks in ensembles. Predictions were obtained for an independent test set of 952 cases with a mean average error of 0.29 ppm (0.20 ppm for 90% of the cases). The results were significantly better than those obtained with counterpropagation neural networks.

Hydrogen↗
PubMed1997

Relating formulation variables to in vitro dissolution using an artificial neural network.

The purpose of this paper was to investigate the effect of several experimental variables on the ability of a neural network to predict in vitro dissolution rate as a function of product formulation changes. Neural network software was trained with sets of hypothetical and experimental data consisting of 4-15 formulations with known in vitro drug dissolution profiles and the ability of the trained model to recognize patterns was validated against similar formations not used to train the neural network. The effect of selected variables, e.g., number of hidden-layer nodes and iterations, as well as the use of replicate or mean data on the accuracy of the predictions was investigated. The importance of optimizing the number of hidden-layer nodes and iterations was demonstrated. The prediction error increased for validation data sets that were outside the range of the training data set. Accurate predictions were obtained with as few as four formulations in the training set, provided the formulations were carefully chosen, and the number of formulation variables were small. Also, limiting the validation set to one formulation was not sufficient to validate the neural network model. Increasing the size of the training set, or replication of the input and output data, also provided more accurate predictions. The neural network accurately predicted in vitro drug release provided the neural network variables were optimized, and the training and validation data sets were appropriately selected.

Acetaminophen↗