Tim J Hutton’s research while affiliated with University College London and other places

The search for life-like processes that can be simulated on computer has led to many types of proposed systems. Of these, cellular automata are one of the most venerable and one of the most visually appealing.

Edgar Codd's 1968 design for a self-replicating cellular automaton has never been implemented. Partly this is due to its enormous size, but we have also identified four problems with the original specification that would prevent it from working. These problems potentially cast doubt on Codd's central assertion, that the eight-state space he presents supports the existence of machines that can act as universal constructors and computers. However, all these problems were found to be correctable, and we present a complete and functioning implementation after making minor changes to the design and transition table. The body of the final machine occupies an area that is 22,254 cells wide and 55,601 cells high, composed of over 45 million nonzero cells in its unsheathed form. The data tape is 208 million cells long, and self-replication is estimated to take at least 1.7 x 10(18) time steps.

This was a retrospective cephalometric study in patients undergoing mandibular advancement surgery. Our aim was to provide a more precise estimation of the postsurgical soft-tissue outcomes than can be achieved by using simple ratios of the hard and soft tissues. The lateral cephalograms of 64 patients undergoing mandibular advancement, from before and near the end of treatment, were scanned and digitized with a customized software program. Multivariable regression analyses were used to create prediction equations for soft-tissue changes at pogonion, inferior labial sulcus, labrale inferius, and stomion inferius (all in the horizontal plane). This method of using multiple explanatory variables appears to be useful in the prediction of soft-tissue changes. At least 96% of the variation of each dependent variable was explained by its relationship with the explanatory variables in the relevant multivariable regression equation, and the results appeared to be clinically useful.

We describe some results submitted by users of the Organic Builder, a Java applet where the rules of an artificial chemistry can be chosen in order to achieve a desired behavior. Though it was initially intended as a set of challenges to be tackled as a game, the users experimented with the system far beyond this and discovered several novel forms of self-replicators. When searching for a system with certain properties such asself-replication, making the system accessible to the public through a Web site is an unusual but effective way of making scientific discoveries, credit for which must go to the users themselves for their tireless experimentation and innovation.

This thesis describes and evaluates Dense Surface Models (DSMs), a new technique for building point distribution models of surfaces, from raw input data. DSMs can be used on data from a wide range of surface acquisition systems without preprocessing since they do not require that the surfaces be closed or even locally manifold, and can cope well with holes and spikes in the surfaces. This is an advantage over comparable techniques, which impose such constraints on the input. The core of the DSM algorithm is as follows. Adense correspondence is made between the surfaces using thin-plate spline warping guided by means of a small set of hand-placed landmarks. The area of interest is automatically defined by a threshold on a measure of the closeness of the correspondence at each point. Apoint distribution model is then built using the vertices from the trimmed and densely-corresponded surfaces. The key benefit of using models of the whole surface is illustrated by the large improvement in classification on face shape that is obtained when using DSMs as compared

The heterogeneity of autism spectrum disorders (ASDs) confounds attempts to identify causes and pathogenesis. Identifiable endophenotypes and reliable biomarkers within ASDs would help to focus molecular research and uncover genetic causes and developmental mechanisms. We used dense surface-modelling techniques to compare the facial morphology of 72 boys with ASD and 128 first-degree relatives to that of 254 unrelated controls. Pattern-matching algorithms were able to discriminate between the faces of ASD boys and those of matched controls (AUC=0.82) and also discriminate between the faces of unaffected mothers of ASD children and matched female controls (AUC=0.76). We detected significant facial asymmetry in boys with ASD (P<0.01), notably depth-wise in the supra- and periorbital regions anterior to the frontal pole of the right hemisphere of the brain. Unaffected mothers of children with ASD display similar significant facial asymmetry, more exaggerated than that in matched controls (P<0.03) and, in particular, show vertical asymmetry of the periorbital region. Unaffected fathers of children with ASD did not show facial asymmetry to a significant degree compared to controls. Two thirds of unaffected male siblings tested were classified unseen as more facially similar to unrelated boys with ASD than to unrelated controls. These unaffected male siblings and two small groups of girls with ASD and female siblings, all show overall directional asymmetry, but without achieving statistical significance in two-tailed t-tests of individual asymmetry of ASD family and matched control groups. We conclude that previously identified right dominant asymmetry of the frontal poles of boys with ASD could explain their facial asymmetry through the direct effect of brain growth. The atypical facial asymmetry of unaffected mothers of children with ASD requires further brain studies before the same explanation can be proposed. An alternative explanation, not mutually exclusive, is a simultaneous and parallel action on face and brain growth by genetic factors. Both possibilities suggest the need for coordinated face and brain studies on ASD probands and their first-degree relatives, especially on unaffected mothers, given that their unusual facial asymmetry suggests an ASD susceptibility arising from maternal genes.

We present a novel unit of evolution: a self-reproducing cell in a two-dimensional artificial chemistry. The cells have a strip of genetic material that is used to produce enzymes, each catalyzing a specific reaction that may affect the survival of the cell. The enzymes are kept inside the cell by a loop of membrane, thus ensuring that only the cell that produced them gets their benefit. A set of reaction rules, each simple and local, allows the cells to copy their genetic information and physically divide. The evolutionary possibilities of the cells are explored, and it is suggested that the system provides a useful framework for testing hypotheses about self-driven evolution.

This study evaluated the reproducibility of 24 soft tissue landmarks on six three-dimensional (3D) facial scans. The scans were taken on a DSP400 facial scanner and were viewed using a customized software program. Intraoperator data were obtained by one researcher placing the 24 landmarks on all six scans a total of 30 times. Thirty different orthodontists of varying experience were then asked to place all 24 landmarks on each of the six facial scans in order to establish interoperator reproducibility. The standard deviations (SDs) from the mean were calculated from the data for each individual landmark in the x-, y-, and z-axes. For the intraoperator data, 12 of the 24 landmarks were found to be reproducible to within a 1 mm SD for each plane of space. The interoperator data showed lower reproducibility with just two landmarks showing less than a 1 mm SD in all three planes of space. Familiarity with 3D facial scans and associated software programs is important in improving reproducibility. In addition, the landmarks investigated in this study included those not often used. It is suggested that landmarks showing poor reproducibility for both inter- and intraoperator data should be avoided, if at all possible, or at least used with caution.

Many genetic syndromes involve a facial gestalt that suggests a preliminary diagnosis to an experienced clinical geneticist even before a clinical examination and genotyping are undertaken. Previously, using visualization and pattern recognition, we showed that dense surface models (DSMs) of full face shape characterize facial dysmorphology in Noonan and in 22q11 deletion syndromes. In this much larger study of 696 individuals, we extend the use of DSMs of the full face to establish accurate discrimination between controls and individuals with Williams, Smith-Magenis, 22q11 deletion, or Noonan syndromes and between individuals with different syndromes in these groups. However, the full power of the DSM approach is demonstrated by the comparable discriminating abilities of localized facial features, such as periorbital, perinasal, and perioral patches, and the correlation of DSM-based predictions and molecular findings. This study demonstrates the potential of face shape models to assist clinical training through visualization, to support clinical diagnosis of affected individuals through pattern recognition, and to enable the objective comparison of individuals sharing other phenotypic or genotypic properties.

Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image. The models provide dramatic visualizations of 3D face-shape variation with potential for training physicians to recognize the key components of particular syndromes. We demonstrate their use to visualize and recognize shape differences in a collection of 3D face images that includes 280 controls (2 weeks to 56 years of age), 90 individuals with Noonan syndrome (NS) (7 months to 56 years), and 60 individuals with velo-cardio-facial syndrome (VCFS; 3 to 17 years of age). Ten-fold cross-validation testing of discrimination between the three groups was carried out on unseen test examples using five pattern recognition algorithms (nearest mean, C5.0 decision trees, neural networks, logistic regression, and support vector machines). For discriminating between individuals with NS and controls, the best average sensitivity and specificity levels were 92 and 93% for children, 83 and 94% for adults, and 88 and 94% for the children and adults combined. For individuals with VCFS and controls, the best results were 83 and 92%. In a comparison of individuals with NS and individuals with VCFS, a correct identification rate of 95% was achieved for both syndromes. This article contains supplementary material, which may be viewed at the American Journal of Medical Genetics website at http://www.interscience.wiley.com/jpages/0148-7299/suppmat/index.html.