http://hdl.handle.net/10379/5750 2017-10-29T23:45:50Z http://hdl.handle.net/10379/6704 Detection of ROS induced proteomic signatures by mass spectrometry McDonagh, Brian Reversible and irreversible post-translational modifications ( PTMs) induced by endogenously generated reactive oxygen species (ROS) in regulatory enzymes and proteins plays an essential role in cellular signaling. Almost all cellular processes including metabolism, transcription, translation and degradation have been identified as containing redox regulated proteins. Specific redox modifications of key amino acids generated by ROS offers a dynamic and versatile means to rapidly alter the activity or functional structure of proteins in response to biochemical, environmental, genetic and pathological perturbations. How the proteome responds to these stimuli is of critical importance in oxidant physiology, as it can regulate the cell stress response by reversible and irreversible PTMs, affecting protein activity and protein-protein interactions. Due to the highly labile nature of many ROS species, applying redox proteomics can provide a signature footprint of the ROS species generated. Ideally redox proteomic approaches would allow; (1) the identification of the specific PTM, (2) identification of the amino acid residue that is modified and (3) the percentage of the protein containing the PTM. New developments in MS offer the opportunity of a more sensitive targeted proteomic approach and retrospective data analysis. Subsequent bioinformatics analysis can provide an insight into the biochemical and physiological pathways or cell signaling cascades that are affected by ROS generation. This mini-review will detail current redox proteomic approaches to identify and quantify ROS induced PTMs and the subsequent effects on cellular signaling. 2017-01-01T00:00:00Z http://hdl.handle.net/10379/6425 Inertial sensor technology for elite swimming performance analysis: a systematic review Mooney, Robert; Corley, Gavin; Godfrey, Alan; Quinlan, Leo R.; Ó Laighin, Gearóid Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers' performance and may serve to inform both applied and research practices. 2015-12-25T00:00:00Z http://hdl.handle.net/10379/6424 Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer Rodríguez-Martín, Daniel; Samà, Albert; Pérez-López, Carlos; Català, Andreu; Moreno Arostegui, Joan M.; Cabestany, Joan; Bayés, Àngels; Alcaine, Sheila; Mestre, Berta; Prats, Anna; Cruz Crespo, M.; Counihan, Timothy J.; Browne, Patrick; Quinlan, Leo R.; Ó Laighin, Gearóid; Sweeney, Dean; Lewy, Hadas; Azuri, Joseph; Vainstein, Gabriel; Annicchiarico, Roberta; Costa, Alberto; Rodríguez-Molinero, Alejandro Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy. 2017-02-15T00:00:00Z http://hdl.handle.net/10379/6423 Evaluation of the Finis Swimsense (R) and the Garmin Swim (TM) activity monitors for swimming performance and stroke kinematics analysis Mooney, Robert; Quinlan, Leo R.; Corley, Gavin; Godfrey, Alan; Osborough, Conor; Ó Laighin, Gearóid Aims The study aims were to evaluate the validity of two commercially available swimming activity monitors for quantifying temporal and kinematic swimming variables. Methods Ten national level swimmers (5 male, 5 female; 15.3 +/- 1.3years; 164.8 +/- 12.9cm; 62.4 +/- 11.1kg; 425 +/- 66 FINA points) completed a set protocol comprising 1,500m of swimming involving all four competitive swimming strokes. Swimmers wore the Finis Swimsense and the Garmin Swim activity monitors throughout. The devices automatically identified stroke type, swim distance, lap time, stroke count, stroke rate, stroke length and average speed. Video recordings were also obtained and used as a criterion measure to evaluate performance. Results A significant positive correlation was found between the monitors and video for the identification of each of the four swim strokes (Garmin: X-2 (3) = 31.292, p 2017-02-08T00:00:00Z