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Biomedical subjects

M F Wilkins

Publications and source records attributed to M F Wilkins.

10 recordsLinked to original sources
PubMed2001

Pattern recognition in flow cytometry.

BACKGROUND: Analytical flow cytometry (AFC), by quantifying sometimes more than 10 optical parameters on cells at rates of approximately 10(3) cells/s, rapidly generates vast quantities of multidimensional data, which provides a considerable challenge for data analysis. We review the application of multivariate data analysis and pattern recognition techniques to flow cytometry. METHODS: Approaches were divided into two broad types depending on whether the aim was identification or clustering. Multivariate statistical approaches, supervised artificial neural networks (ANNs), problems of overlapping character distributions, unbounded data sets, missing parameters, scaling up, and estimating proportions of different types of cells comprised the first category. Classic clustering methods, fuzzy clustering, and unsupervised ANNs comprised the second category. We demonstrate the state of the art by using AFC data on marine phytoplankton populations. RESULTS AND CONCLUSIONS: Information held within the large quantities of data generated by AFC was tractable using ANNs, but for field studies the problem of obtaining suitable training data needs to be resolved, and coping with an almost infinite number of cell categories needs further research.

Flow Cytometry↗
PubMed2001

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

BACKGROUND: Artificial neural networks (ANNs) have been shown to be valuable in the analysis of analytical flow cytometric (AFC) data in aquatic ecology. Automated extraction of clusters is an important first stage in deriving ANN training data from field samples, but AFC data pose a number of challenges for many types of clustering algorithm. The fuzzy k-means algorithm recently has been extended to address nonspherical clusters with the use of scatter matrices. Four variants were proposed, each optimizing a different measure of clustering "goodness." METHODS: With AFC data obtained from marine phytoplankton species in culture, the four fuzzy k-means algorithm variants were compared with each other and with another multivariate clustering algorithm based on critical distances currently used in flow cytometry. RESULTS: One of the algorithm variants (adaptive distances, also known as the Gustafson--Kessel algorithm) was found to be robust and reliable, whereas the others showed various problems. CONCLUSIONS: The adaptive distances algorithm was superior in use to the clustering algorithms against which it was tested, but the problem of automatic determination of the number of clusters remains to be addressed.

Algorithms↗
PubMed2000

Proportion estimation with confidence limits.

A common task in microbiology involves determining the composition of a mixed population of individuals by drawing a sample from the population and using some procedure to identify the individuals in the sample. There may be a significant probability that the identification procedure misidentifies some members of the sample (for example, because the available data are insufficient unambiguously to identify an individual) which makes finding the proportions in the underlying population non-trivial. A further complication arises where individuals are present in the population that do not belong to any of the subpopulations recognised by use of the identification procedure. A simple algorithm is presented to address these problems and construct a maximum likelihood estimate of the proportions, together with confidence limits. The technique is illustrated using an example drawn from flow cytometry in which phytoplankton cells are identified from flow cytometry data by an RBF neural network, and the limitations of the approach are discussed.

Confidence Intervals↗
PubMed1999

Identification of phytoplankton from flow cytometry data by using radial basis function neural networks.

We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91. 5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from "novel" species (species not present in the training data) were analyzed.

Flow Cytometry↗
PubMed1996

A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data.

Four artificial neural network paradigms (multilayer perceptron networks, learning vector quantization networks, and radial and asymmetric basis function networks) and two statistical methods (parametric statistical classification by modelling each class with Gaussian distributions, and non-parametric density estimation via the K-nearest neighbour method) were compared for their ability to identify seven freshwater and five marine phytoplankton species from flow cytometric data. Kohonen self-organizing maps were also used to examine similarities between species. Optimized networks and statistical methods performed similarly, correctly identifying between 86.8% and 90.1% of data from freshwater species, and between 81.3% and 84.1% of data from marine species. Choice of identification technique must therefore be made on the basis of other criteria. We highlight the way each method partitions the data space and thereby separates the data clusters, and discuss the relative merits of each with reference to complexity of data boundaries, training time, analysis time and behaviour when presented with 'novel' data.

Biometry↗
PubMed1995

A novel environment disrupts copulation in sexually naive but not experienced male rats: reversal with naloxone.

Relative to sexually experienced male rats, sexually naive males typically display a pattern of copulatory behavior that is characterized by a larger proportion of rats that do not copulate: those that do copulate during their first exposure to a sexually receptive female have longer mount, intromission, and ejaculation latencies, and longer postejaculatory intervals. A male rat's first sexual experience often coincides with the first exposure to handling and to the novel testing apparatus. Because handling or exposure to novel environments is stressful and can induce the release of endogenous opioids, and because the opioid antagonist naloxone can facilitate the copulatory behavior of sexually naive male rats, we examined whether preexposure to handling alone, or to handling and a novel testing chamber, would diminish the magnitude of the relative disruption observed in sexually naive male rats. Handling alone did not increase the proportion of males that mounted or intromitted, whereas preexposure to the chamber increased these proportions significantly and decreased the mount and intromission latencies, and the number of mounts without intromission. Naloxone hydrochloride (10 mg/kg) facilitated the display of mounts and intromissions in rats that were preexposed to handling alone, but had no effect in rats that had been additionally preexposed to the testing chambers. In contrast, the copulatory behavior of rats with extensive sexual experience was not adversely affected in a novel testing chamber. Novelty disrupts the copulatory behavior of sexually naive males and may do so by increasing endogenous opioid activity. However, sexual experience appears to diminish or eliminate the disruptive effect of a novel environment.

Animals↗
PubMed1994

Neural network analysis of flow cytometric data for 40 marine phytoplankton species.

Flow cytometry data (time of flight, horizontal and vertical forward light scatter, 90 degrees light scatter, and "red" and "orange" integral fluorescence) were collected for laboratory cultures of 40 species of marine phytoplankton, from the following taxonomic classes, the Dinophyceae, Bacillariophyceae, Prymnesiophyceae, Cryptophyceae, and other flagellates. Single-hidden-layer "back-propagation" neural networks were trained to discriminate between species by recognising patterns in their flow cytometric signatures, and network performance was assessed using an independent test data set. Two approaches were adopted employing: (1) a hierarchy of small networks, the first identifying to which major taxonomic group a cell belonged, and then a network for that taxonomic group identified to species, and (2) a single large network. Discriminating some of the major taxonomic groups was successful but others less so. With networks for specific groups, cryptophyte species were all identified reliably (probability of correct classification always being > 0.75); in the other groups half of the species were identified reliably. With the large network, dinoflagellates, cryptomonads, and flagellates were identified almost as well as by networks specific for these groups. The application of neural computing techniques to identification of such a large number of species represents a significant advance from earlier studies, although further development is required.

Flow Cytometry↗
PubMed1994

A comparison of Radial Basis Function and backpropagation neural networks for identification of marine phytoplankton from multivariate flow cytometry data.

Two artificial neural network classifiers, the well-known Multi-layer Perception (MLP) (also known as the 'backpropagation network'), and the more recently developed Radial Basis Function (RBF) network, were evaluated and compared for their ability to identify multivariate flow cytometric data from five North Sea plankton groups (Dinoflagellidae, Bacillariophyceae, Prymnesiomonadida, Cryptomonadida, and other flagellates). RBF networks generally performed similarly to MLPs, and slightly better in cases where the data were markedly multimodal; RBF networks also have much shorter training times. The performance of MLPs was improved greatly by the use of a symmetrical bipolar 'transfer function' as opposed to the commonly-used asymmetric form. The issues of network optimisation and computational efficiency in use are discussed.

Evaluation Studies as Topic↗
PubMed1987

Hemi-body radiotherapy in the management of metastatic carcinoma.

Hemi-body radiotherapy has been used extensively during the last 14 years to provide effective palliation for patients with widespread metastatic carcinoma. A series of 125 consecutive assessable treatments using this technique is reported. The morbidity was minimal. The response rate overall was 82% and a response rate of 85% was obtained in patients with carcinoma of the prostate.

Bone Neoplasms↗