Investigating haptic distance-to-break using linear and nonlinear materials in a simulated minimally invasive surgery task

Accurate detection of mediated haptic information in minimally invasive surgery (MIS) is critical for applying appropriate force magnitudes onto soft tissue with the aim of minimising tissue trauma. Force perception in MIS is a dynamic process, with surgeons’ administration of force into tissue revealing information about the remote surgical site which further informs the surgeons’ haptic interactions. The relationship between applied force and material deformation rate provides biomechanical information specifying the deformation distance remaining until a tissue will fail: which is termed distance-to-break (DTB). The current study demonstrates that observers can detect DTB while deforming simulated tissues and stop before reaching the tissues’ failure points. The design of training simulators, control devices and automated robotic systems for applications outside of MIS is discussed.

Practitioner Summary: In MIS, haptic information is critical for applying appropriate forces onto soft tissue to minimise tissue trauma. Observers used force information to detect how far they could deform a virtual tissue before it would break. The design of training simulators, control devices and automated robotic systems is discussed.     

How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.     

Promoting traits into model-driven development

Traits, as sets of behaviors, can provide a good mechanism for reusability. However, they are limited in important ways and are not present in widely used programming and modeling languages and hence are not readily available for use by mainstream developers. In this paper, we add UML associations and other modeling concepts to traits and apply them to Java and C++ through model-driven development. We also extend traits with required interfaces so dependencies at the semantics level become part of their usage, rather than simple syntactic capture. All this is accomplished in Umple, a textual modeling language based upon UML that allows adding programming constructs to the model. We applied the work to two case studies. The results show that we can promote traits to the modeling level along with the improvement in flexibility and reusability.     

Pattern matching in the Hamming distance with thresholds

It has long been known that pattern matching in the Hamming distance metric can be done in time, where n is the length of the text, m is the length of the pattern, and Σ is the alphabet. The classic algorithm for this is due to Abrahamson and Kosaraju. This paper considers the following generalization, motivated by the situation where the entries in the text and pattern are analog, or distorted by additive noise, or imprecisely given for some other reason: in any alignment of the pattern with the text, two aligned symbols a and b contribute +1 to the similarity score if they differ by no more than a given threshold θ, otherwise they contribute zero. We give an time algorithm for this more general version of the problem; the classic Hamming distance matching problem is the special case of θ=0.     

Analysis of biofluids for biomarker research

While many of the developments made in high-throughput proteomics were originally applied to procaryotic and simple eucaryotic organisms, the analysis of biofluids became increasingly important as the prospect of using proteomics to discover novel biomarkers became realized. Biofluids have represented a unique challenge to proteomics as they are often present only in small amounts and, particularly in the cases of serum and plasma, can have protein concentration ranges that differ by ten orders of magnitude. While the discovery of authentic, clinically useful biomarkers in the proteomics era has been lacking, there have been a number of significant developments in the ability to more comprehensively characterize biofluid proteomes. The rapid pace of these developments suggest that the eventual discovery of clinically validate biomarkers using proteomic technologies still has a bright future.     

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Statistical topic models for multi-label document classification

Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A?drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.     

Fringe—A Java-based finite fringe analysis package

A package for analysing two-dimensional finite fringe interferograms is described. Through a combination of automatic and interactive routines, an interferogram can be processed to extract the phase shift imparted on the recording light by a transparent object. The package consists of routines to condition and pad the original image for Fourier transform analysis, to filter the image and obtain the phase, to unwrap the phase, and to remove the background phase ramp. A sample image recorded using holographic interferometry is successfully analysed.Program summaryProgram title: FRINGECatalogue identifier: AEMM_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMM_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 134006No. of bytes in distributed program, including test data, etc.: 4029801Distribution format: tar.gzProgramming language: Java.Computer: Personal Computers.Operating system: Mac OS X, Windows XP, Linux and any other system that can run Java Jar files.RAM: 1GB recommendedClassification: 18.Nature of problem: A standalone multi-platform program to perform analysis of finite fringe interferograms.Solution method: Fourier filtering approach with phase unwrapping and background subtraction.Restrictions: Designed to analyse square images.Running time: Interactive processing takes several minutes. Minimal cpu time.