Deciphering lipid structures based on platform-independent decision rule sets

We developed decision rule sets for Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2), enabling automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry. Platform independence was proven in various mass spectrometric experiments, comprising low- and high-resolution instruments and several collision energies. We propose that this independence and the capability to identify novel lipid molecular species render current state-of-the-art lipid libraries now obsolete

Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed

Quantification of the link between timed up-and-go test subtasks and contractile muscle properties

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive tool to assess the function of skeletal muscles. In a clinical study, we evaluated the correlation between the TMG parameters of the skeletal muscle contraction of 23 elderly participants (22 f, age 86.74 % 7.88) and distance-based TUG test subtask times. TUG tests were recorded with an ultrasonic-based device. The sit-up and walking phases were significantly correlated to the contraction and delay time of the muscle vastus medialis (% = 0.55%0.80, p < 0.01). In addition, the delay time of the muscles vastus medialis (% = 0.45, p = 0.03) and gastrocnemius medialis (% = %0.44, p = 0.04) correlated to the sit-down phase. The maximal radial displacements of the biceps femoris showed significant correlations with the walk-forward times (% = %0.47, p = 0.021) and back (% = %0.43, p = 0.04). The association of TUG subtasks to muscle contractile parameters, therefore, could be utilized as a measure to improve the monitoring of elderly people%s physical ability in general and during rehabilitation after a fall in particular. TUG test subtask measurements may be used as a proxy to monitor muscle properties in rehabilitation after long hospital stays and injuries or for fall prevention

Deciphering lipid structures based on platform-independent decision rules

10.1038/NMETH.4470NATURE METHODS14121171-117

Deciphering lipid structures based on platform-independent decision rules.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete