Pose Estimation from Electromyographical Data using Convolutional Neural Networks.

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.

Sharing the Story of the Cardiac Rehab Patient Experience: A QUALITATIVE STUDY.

Purpose: To provide a prototypical patient narrative of the cardiac rehabilitation (CR) experience for providers and prospective patients using narrative analysis.

Methods: Qualitative interviews with 17 CR patients from a previous study regarding their experiences, reasons, and motivations related to engagement in CR were analyzed using narrative inquiry. Interviews were previously analyzed and coded for recurring themes, and these themes were implemented in an exploratory narrative inquiry to craft a CR patient "story.

The complete family of epidermal growth factor receptors and their ligands are co-ordinately expressed in breast cancer.

The levels of expression of the four receptors and eleven ligands composing the epidermal growth factor family were measured using immunohistochemical staining in one hundred cases of breast cancer. All of the family were expressed to some degree in some cases; however, individual cases showed a very wide range of expression of the family from essentially none to all the factors at high levels. The highest aggregate level of expression of a receptor was HER2 followed by HER1, then HER3, then HER4.

Message-passing algorithms for the prediction of protein domain interactions from protein-protein interaction data.

Motivation: Cellular processes often hinge upon specific interactions among proteins, and knowledge of these processes at a system level constitutes a major goal of proteomics. In particular, a greater understanding of protein-protein interactions can be gained via a more detailed investigation of the protein domain interactions that mediate the interactions of proteins. Existing high-throughput experimental techniques assay protein-protein interactions, yet they do not provide any direct information on the interactions among domains.

Simulating complex intracellular processes using object-oriented computational modelling.

The aim of this paper is to give an overview of computer modelling and simulation in cellular biology, in particular as applied to complex biochemical processes within the cell. This is illustrated by the use of the techniques of object-oriented modelling, where the computer is used to construct abstractions of objects in the domain being modelled, and these objects then interact within the computer to simulate the system and allow emergent properties to be observed. The paper also discusses the role of computer simulation in understanding complexity in biological systems, and the kinds of information which can be obtained about biology via simulation.