Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Background: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. Methods and findings: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). Conclusions: TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients

Wireless Broadband Communications – Some Research Activities in Singapore

Almost a decade ago, Singapore started crafting and implementing its IT2000 master plan to transform the city-state into an information-technology-based intelligent island. Since 1997, the main infrastructure of a high-speed ATM-based backbone network, called SingaporeONE, has been in place along with a host of commercial and governmental application service sites providing a plethora of online services. Because of its small size and extensive wired infrastructure, broadband access to homes and offices is currently provided via ADSL and cable modems. There is, however, interest in the use of wireless broadband communication technologies to access SingaporeONE, motivated primarily by its lower cost and faster deployment. In this article we describe some of our R&D activities motivated by the above interest to provide wireless broadband access to SingaporeONE. Specifically, we describe our study of LMDS, and the design and development of a wireless ATM LAN

Health-related quality of life in children with cancer undergoing treatment: A first look at the Singapore experience

Annals of the Academy of Medicine Singapore39143-4

Conferencing spread spectrum radio

IEEE International Symposium on Spread Spectrum Techniques & Applications2372-3750019