Implementing interactive computing in an object-oriented environment

Statistical computing when input/output is driven by a Graphical User Interface is considered. A proposal is made for automatic control ofcomputational flow to ensure that only strictly required computationsare actually carried on. The computational flow is modeled by a directed graph for implementation in any object-oriented programming language with symbolic manipulation capabilities. A complete implementation example is presented to compute and display frequency based piecewise linear density estimators such as histograms or frequency polygons.

Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009

Different types of cell death are often defined by morphological criteria, without a clear reference to precise biochemical mechanisms. The Nomenclature Committee on Cell Death (NCCD) proposes unified criteria for the definition of cell death and of its different morphologies, while formulating several caveats against the misuse of words and concepts that slow down progress in the area of cell death research. Authors, reviewers and editors of scientific periodicals are invited to abandon expressions like 'percentage apoptosis' and to replace them with more accurate descriptions of the biochemical and cellular parameters that are actually measured. Moreover, at the present stage, it should be accepted that caspase-independent mechanisms can cooperate with (or substitute for) caspases in the execution of lethal signaling pathways and that 'autophagic cell death' is a type of cell death occurring together with (but not necessarily by) autophagic vacuolization. This study details the 2009 recommendations of the NCCD on the use of cell death-related terminology including 'entosis', 'mitotic catastrophe', 'necrosis', 'necroptosis' and 'pyroptosis'.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Saving our science from ourselves: the plight of biological classification

Saving our science from ourselves: the plight of biological classification. Biological classification ( nomenclature, taxonomy, and systematics) is being sold short. The desire for new technologies, faster and cheaper taxonomic descriptions, identifications, and revisions is symptomatic of a lack of appreciation and understanding of classification. The problem of gadget-driven science, a lack of best practice and the inability to accept classification as a descriptive and empirical science are discussed. The worst cases scenario is a future in which classifications are purely artificial and uninformative.

A data-dependent skeleton estimate and a scale-sensitive dimension for classification

The classical binary classification problem is investigatedwhen it is known in advance that the posterior probability function(or regression function) belongs to some class of functions. We introduceand analyze a method which effectively exploits this knowledge. The methodis based on minimizing the empirical risk over a carefully selected``skeleton'' of the class of regression functions. The skeleton is acovering of the class based on a data--dependent metric, especiallyfitted for classification. A new scale--sensitive dimension isintroduced which is more useful for the studied classification problemthan other, previously defined, dimension measures. This fact isdemonstrated by performance bounds for the skeleton estimate in termsof the new dimension.

Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound.

The differentiation between benign and malignant focal liver lesions plays an important role in diagnosis of liver disease and therapeutic planning of local or general disease. This differentiation, based on characterization, relies on the observation of the dynamic vascular patterns (DVP) of lesions with respect to adjacent parenchyma, and may be assessed during contrast-enhanced ultrasound imaging after a bolus injection. For instance, hemangiomas (i.e., benign lesions) exhibit hyper-enhanced signatures over time, whereas metastases (i.e., malignant lesions) frequently present hyperenhanced foci during the arterial phase and always become hypo-enhanced afterwards. The objective of this work was to develop a new parametric imaging technique, aimed at mapping the DVP signatures into a single image called a DVP parametric image, conceived as a diagnostic aid tool for characterizing lesion types. The methodology consisted in processing a time sequence of images (DICOM video data) using four consecutive steps: (1) pre-processing combining image motion correction and linearization to derive an echo-power signal, in each pixel, proportional to local contrast agent concentration over time; (2) signal modeling, by means of a curve-fitting optimization, to compute a difference signal in each pixel, as the subtraction of adjacent parenchyma kinetic from the echopower signal; (3) classification of difference signals; and (4) parametric image rendering to represent classified pixels as a support for diagnosis. DVP parametric imaging was the object of a clinical assessment on a total of 146 lesions, imaged using different medical ultrasound systems. The resulting sensitivity and specificity were 97% and 91%, respectively, which compare favorably with scores of 81 to 95% and 80 to 95% reported in medical literature for sensitivity and specificity, respectively.

Natural classification of knots

The principal objective of the knot theory is to provide a simple way of classifying and ordering all the knot types. Here, we propose a natural classification of knots based on their intrinsic position in the knot space that is defined by the set of knots to which a given knot can be converted by individual intersegmental passages. In addition, we characterize various knots using a set of simple quantum numbers that can be determined upon inspection of minimal crossing diagram of a knot. These numbers include: crossing number; average three-dimensional writhe; number of topological domains; and the average relaxation value

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 2 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Wechsel von einem mechanischen Mikrokeratom auf einen Femtosekunden-Laser zur Korrektur von Astigmatismus mittels LASIK: klinische Ergebnisse einer retrospektiven, konsekutiven Vergleichsstudie Moving from a Mechanical Microkeratome to a Femtosecond Laser for LASIK to Correct Astigmatic Patients: Clinical Outcomes of a Retrospective, Consecutive, Comparative Study.

Background: To evaluate outcomes after optimized laser in situ keratomileusis (LASIK) for astigmatism correction with flap created by a mechanical microkeratome or a femtosecond laser. Patients and Methods: In this retrospective study, a total of 102 eyes of 71 consecutive patients were enrolled undergoing optimized LASIK treatments using the Allegretto laser system (WaveLight Laser Technologie AG, Erlangen, Germany). A mechanical microkeratome for flap creation was used (One Use, Moria®) in 46 eyes (31 patients, spherical equivalent [SE] -4.44 D ± 2.4) and a femtosecond laser was used (LDV, Ziemer®) in 56 eyes (40 patients, spherical equivalent [SE] -3.07 D ± 3.3). The two groups were matched for inclusion criteria and were operated under similar conditions by the same surgeon. Results: Overall, the preoperative spherical equivalent was -9.5 diopters (D) to +3.37 D; the preoperative manifest astigmatism was between -1.5 D and -3.5 D. At 6 months postoperatively, the mean postoperative uncorrected distance visual acuity (UDVA) was 0.93 ± 0.17 (range 0.4 to 1.2) in the Moria group and 1.0 ± 0.21 (range 0.6 to 1.6) in the Femto group, which was statistically significant (p = 0.003). Comparing the cylinder power there was a statistical difference between the two groups (p = 0.0015). Conclusions: This study shows that the method of flap creation has a significant impact on postoperative astigmatism with a significantly better postoperative UDVA in the Femto group. These findings suggest that the femtosecond laser provides a better platform for LASIK treatment of astigmatism than the commonly used microkeratome.

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.