CRC Robotics

Conditional correlation networks, within Gaussian Graphical Models (GGM), are widely used to describe the direct interactions between the components of a random vector. In the case of an unlabelled Heterogeneous population, Expectation Maximisation (EM) algorithms for Mixtures of GGM have been proposed to estimate both each sub-population's graph and the class labels. However, we argue that, with most real data, class affiliation cannot be described with a Mixture of Gaussian, which mostly groups data points according to their geometrical proximity. In particular, there often exists external co-features whose values affect the features' average value, scattering across the feature space data points belonging to the same sub-population. Additionally, if the co-features' effect on the features is Heterogeneous, then the estimation of this effect cannot be separated from the sub-population identification. In this article, we propose a Mixture of Conditional GGM (CGGM) that subtracts the heterogeneous effects of the co-features to regroup the data points into sub-population corresponding clusters. We develop a penalised EM algorithm to estimate graph-sparse model parameters. We demonstrate on synthetic and real data how this method fulfils its goal and succeeds in identifying the sub-populations where the Mixtures of GGM are disrupted by the effect of the co-features.

RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem. Using 41 nucleotide long DNA sequences, we show that novel A-to-I RNA editing events can be predicted from known A-to-I RNA editing events intra- and interspecies. The validity of the proposed method was verified in an independent experimental dataset. Using our approach, 203 202 putative A-to-I RNA editing events were predicted in the whole human genome. Out of these, 9% were previously reported. The remaining sites require further validation, e.g., by targeted deep sequencing. In conclusion, the approach described here is a useful tool to identify potential A-to-I RNA editing events without the requirement of extensive RNA sequencing.

Efforts are underway to design improved “second generation” distress beacons for the Cospas Sarsat system. Relative to existing (first generation) beacons, these devices will transmit a greater variety of more precise information as well as improve geolocation and detection performance, helping to make SAR operations more effective. This paper describes a proposal for second generation beacon design that maintains substantial interoperability with the existing system before MEOSAR becomes operational while simultaneously offering substantial performance improvements.

Uterine fibroids are benign tumors that are highly prevalent among reproductive-aged women. Hysterectomy remains the only definitive treatment for symptomatic uterine fibroids; however, uterine-sparing treatment options are needed for women desiring future fertility or uterine preservation. Several alternative treatments exist, but these therapies are limited by significant fibroid recurrence within a few years of treatment or significant side effects. Interventional radiology procedures now extend to the management of fibroids and many medical therapies for treatment of symptomatic fibroids are available or are under investigation in clinical trials. Long-term outcome data for newer medical therapies are needed to determine which therapies offer a definitive advantage over traditional therapies. Future investigations into the molecular biology of fibroids will pave the way for the development of innovative therapies in the treatment and prevention of these highly prevalent gynecologic tumors.

Vierling, Lewis E. MS, NCC, NCCC, CRC, CCMEditor(s): Snowden, Frances BS, RN, CRRN, CCM Author Information

Due to infrequent and seasonal consumption of specific foods rich in carotenoids, and intra individual variability in fruit and vegetable consumption it has been assumed that 24 hour recalls are inadequate for measuring carotenoid consumption in individuals. We examined the increase in correlations with each additional day of intake in the Energetics study, an NIH funded validation DietDay, of a Web based, self administered 24 hour recall among African Americans and Caucasians. 90% of the subjects conducted 6 or more repeat 24 hour recalls online on non‐adjacent days. Intakes from supplements were included. Due to the expected right tailed distribution, plasma and dietary intake values were log normalized. For alpha and beta carotene and lycopene, adjustment for LDL cholesterol levels proved beneficial. Comparisons of Pearsons' correlations for the means of 2,3,4,5 and 6 days of recall started at 0.30 and rose to 0.5 at 4 days for beta cryptoxanthin. Across the carotenoids, the improvement with additional days was greatest from 2 to 4 days and continued in general to increase with additional days. In conclusion‐ a few days of 24 hour recalls provide stable estimates for most carotenoid levels as measured against plasma levels. The largest correlations in this population were seen with beta cryptoxanthin and the combined lutein and zeaxanthin measures. Supported by NIH R01CA105048.

Watson, Elizabeth MS, CRC, CCM, LCPCEditor(s): Snowden, Frances BS, RN, CRRN, CCM Author Information

This paper presents a parametric stiffness analysis of the Orthoglide, a 3-DOF translational Parallel Kinematic Machine. First, a compliant modeling of the Orthoglide is conducted based on an existing method. Then stiffness matrix is symbolically computed. This allows one to easily study the influence of the geometric design parameters on the matrix elements. Critical links are displayed. Cutting forces are then modeled so that static displacements of the Orthoglide tool during slot milling are symbolically computed. Influence of the geometric design parameters on the static displacements is checked as well. Other machining operations can be modeled. This parametric stiffness analysis can be applied to any parallel manipulator for which stiffness is a critical issue.