Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ∼50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

MOTIVATION: The 3D structure of chromatin in the nucleus is important for gene expression and regulation. Chromosome conformation capture techniques, such as Hi-C, generate large amounts of data showing interaction points on the genome but these are hard to interpret using standard tools. RESULTS: We have developed CSynth, an interactive 3D genome browser and real-time chromatin restraint-based modeller to visualize models of any chromosome conformation capture (3C) data. Unlike other modelling systems, CSynth allows dynamic interaction with the modelling parameters to allow experimentation and effects on the model. It also allows comparison of models generated from data in different tissues/cell states and the results of third-party 3D modelling outputs. In addition, we include an option to view and manipulate these complicated structures using Virtual Reality (VR) so scientists can immerse themselves in the models for further understanding. This VR component has also proven to be a valuable teaching and a public engagement tool. AVAILABILITYAND IMPLEMENTATION: CSynth is web based and available to use at csynth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper demonstrates HOPE, an efficient and effective database partitioning system that is designed for OLTP workloads. HOPE is built on top of a novel tuple-group based database partitioning model, which is able to minimize the number of distributed transactions as well as the extent of partition and workload skews during the workload execution. HOPE conducts the partitioning in an iterative manner in order to achieve better partitioning quality, save the user's time spent on partitioning design and increase its application scenes. HOPE is also highly interactive, as it provides rich opportunities for the user to help it further improve the partitioning quality by passing expertise and indirect domain knowledge.

A novel biological software approach to define and evolve 3D computer art forms is described based on a re-implementation of the FormGrow system produced by Latham and Todd at IBM in the early 1990’s. This original work is extended by using DNA sequences as the input to generate complex organic-like forms. The translation of the DNA data to 3D graphic form is performed by two contrasting processes, one intuitive and one informed by the biochemistry. The former involves the development of novel, but simple, look-up tables to generate a code list of functions such as the twisting, bending, stacking, and scaling and their associated parametric values such as angle and scale. The latter involves an analysis of the biochemical properties of the proteins encoded by genes in DNA, which are used to control the parameters of a fixed FormGrow structure. The resulting 3D data sets are then rendered using conventional techniques to create visually appealing art forms. The system maps DNA data into an alternative multi-dimensional space with strong graphic visual features such as intricate branching structures and complex folding. The potential use in scientific visualisation is illustrated by two examples. Forms representing the sickle cell anaemia mutation demonstrate how a point mutation can have a dramatic effect. An animation illustrating the divergent evolution of two proteins with a common ancestor provides a compelling view of an evolutionary process lost in millions of years of natural history.

A major drawback of the set-theoretic solid model is the global and non-unique way in which it is defined. In a recent paper, Rossignac and Voelcker5 introduced the concept of the active zone of a primitive or sub-model, which provides a localization of a set-theoretic model which is useful for a number of purposes. We present a more general, but simplified, definition of the active zone which exploits the properties of the symmetric difference operator. We also outline a novel application of the active zone to the interactive graphical editing of set-theoretic solid models.

Abstract A hybrid analytic and experimental approach to the analysis of large systems is described. The approach is iterative under the assumption that a correct analysis will not be made first time. It is primarily aimed at the analysis of the performance effect of proposed changes prior to coding them. The requirement is for a fairly approximate analysis that is easy to make. The approach is illustrated by an experiment on a relational database system but we believe it to be applicable to many kinds of large systems. The different reasons for analysing performance of a system are discussed and then the ways of approaching the analysis, with an outline of our approach. The details of our approach are given, and illustrated by our experiment.

This paper extends the relational algebra of data bases, presented by Codd [4] and others, in four areas. The first is the use of selector names to remove order dependencies from the columns of a relation. This use of selector names enables us to define a more general class of operations, which include the normal relational operations of union, equi-join etc., as special cases. Thirdly we introduce relations represented algorithmically as well as by a stored set of tuples. Such computed relations cannot always be effectively realised as a finite set of tuples. Finally we consider relational expressions as algorithmic representations of relations and characterize their effectiveness.

PRTV [6] is a research prototype interactive data base. It is intended for use on its own or as part of a specialist system. The principle objectives are to provide: a) high level flexible data support, b) functional extensibility and c) reasonable efficiency. The prototype is also designed as a method of generating and testing ideas in a wider data base research programme.