INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Data fusion and visualization towards city disaster management: Lisbon case study

INTRODUCTION: Due to the high level of unpredictability and the complexity of the information requirements, disaster management operations are information demanding. Emergency response planners should organize response operations efficiently and assign rescue teams to particular catastrophe areas with a high possibility of surviving. Making decisions becomes more difficult when the information provided is heterogeneous, out of date, and often fragmented. OBJECTIVES: In this research work a data fusion of different information sources and a data visualization process was applied to provide a big picture about the disruptive events in a city. This high-level knowledge is important for emergency management authorities. This holistic process for managing, processing, and analysing the seven Vs (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value) in order to generate actionable insights for disaster management. METHODS: A CRISP-DM methodology over smart city-data was applied. The fusion approach was introduced to merge different data sources. RESULTS: A set of visual tools in dashboards were produced to support the city municipality management process. Visualization of big picture based on different data available is the proposed work. CONCLUSION: Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the most affected area.info:eu-repo/semantics/publishedVersio

Anterior Cervical Infection: Presentation and Incidence of an Uncommon Postoperative Complication.

STUDY DESIGN: Retrospective multi-institutional case series. OBJECTIVE: The anterior cervical discectomy and fusion (ACDF) affords the surgeon the flexibility to treat a variety of cervical pathologies, with the majority being for degenerative and traumatic indications. Limited data in the literature describe the presentation and true incidence of postoperative surgical site infections. METHODS: A retrospective multicenter case series study was conducted involving 21 high-volume surgical centers from the AOSpine North America Clinical Research Network, selected for their excellence in spine care and clinical research infrastructure and experience. Medical records for 17 625 patients who received cervical spine surgery (levels from C2 to C7) between January 1, 2005, and December 31, 2011, inclusive, were reviewed to identify the occurrence of 21 predefined treatment complications. Patients who underwent an ACDF were identified in the database and reviewed for the occurrence of postoperative anterior cervical infections. RESULTS: A total of 8887 patients were identified from a retrospective database analysis of 21 centers providing data for postoperative anterior cervical infections (17/21, 81% response rate). A total of 6 postoperative infections after ACDF were identified for a mean rate of 0.07% (range 0% to 0.39%). The mean age of patients identified was 57.5 (SD = 11.6, 66.7% female). The mean body mass index was 22.02. Of the total infections, half were smokers (n = 3). Two patients presented with myelopathy, and 3 patients presented with radiculopathic-type complaints. The mean length of stay was 4.7 days. All patients were treated aggressively with surgery for management of this complication, with improvement in all patients. There were no mortalities. CONCLUSION: The incidence of postoperative infection in ACDF is exceedingly low. The management has historically been urgent irrigation and debridement of the surgical site. However, due to the rarity of this occurrence, guidance for management is limited to retrospective series

STRATEGIC MULTIPLE SENSOR DATA FUSION FOR TIME-CRITICAL NATURAL DISASTER RESPONSE

Recently, large-scale natural disasters have been occurred in the various areas of the world. The super-sized multi-hazards over the world have required more and more scientific and systematic measurement technologies in the fields of management and response of natural disaster. The purpose of this study is to suggest a multi-sources data fusion approach like LiDAR, aerial images, and satellite imagery and evaluate its applicability for timely natural disaster response. In order to achieve a high-accurate mapping in time in disaster situation, we proposed strategic approach using multi-sensors data fusion in this paper. The data fusion approach using the satellite imagery, low altitude aerial imagery from the mini-UAV and the small manned helicopter, and LiDAR point data simultaneously is expected to enhance the capability for more accurate damage analysis and the faster hazard mapping

Flexible data input layer architecture (FDILA) for quick-response decision making tools in volatile manufacturing systems

This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments’ Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant’s Key Performance Factors

Interoperability and information sharing

Communication and information sharing are two of the most pressing issues facing the public safety community today. In previous chapters of this volume, authors have made note of the changing public safety landscape as it relates to the need for enhanced information and intelligence sharing among a broad cross-section of organizations. Public safety organizations, particularly law enforcement agencies, have been quick to adopt emerging technologies that have allowed for greater communication and information sharing capacities. While substantial improvements have been made over the decades that enhanced communication and information sharing, many challenges remain in the move to seamlessly integrated communication capacities. The key challenge in the upcoming decades relates to the technical and cultural changes necessary to achieve integrated communication systems. There is no shortage of resources given to increasing the communications capacity of the public safety community, yet serious challenges remain in the degree of interoperability within and across public safety domains. Interoperability has in many ways become the defining issue in the arenas of communications and information sharing. This chapter will provide an overview of critical historical events that placed questions of interoperability and information sharing on the national agenda. The chapter will also provide an overview of national models for information sharing

Conceptual design study for heat exhaust management in the ARC fusion pilot plant

The ARC pilot plant conceptual design study has been extended beyond its initial scope [B. N. Sorbom et al., FED 100 (2015) 378] to explore options for managing ~525 MW of fusion power generated in a compact, high field (B_0 = 9.2 T) tokamak that is approximately the size of JET (R_0 = 3.3 m). Taking advantage of ARC's novel design - demountable high temperature superconductor toroidal field (TF) magnets, poloidal magnetic field coils located inside the TF, and vacuum vessel (VV) immersed in molten salt FLiBe blanket - this follow-on study has identified innovative and potentially robust power exhaust management solutions.Comment: Accepted by Fusion Engineering and Desig

Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains

Emergency management in the highways of the future. A performance-based multilayered ITS architecture design proposal

Emergency management is one of the key aspects within the day-to-day operation procedures in a highway. Efficiency in the overall response in case of an incident is paramount in reducing the consequences of any incident. However, the approach of highway operators to the issue of incident management is still usually far from a systematic, standardized way. This paper attempts to address the issue and provide several hints on why this happens, and a proposal on how the situation could be overcome. An introduction to a performance based approach to a general system specification will be described, and then applied to a particular road emergency management task. A real testbed has been implemented to show the validity of the proposed approach. Ad-hoc sensors (one camera and one laser scanner) were efficiently deployed to acquire data, and advanced fusion techniques applied at the processing stage to reach the specific user requirements in terms of functionality, flexibility and accuracy