Personalized Medicine in Epidemics

This reprint contains 11 chapters on a wide range of subjects related to the impacts of different types of epidemics on our ability to practice personalized medicine. Together, these chapters provide a broad overview with many different examples of epidemics. The personalization of medicine is present both despite and because of epidemics. Many more examples are possible, but this reprint offers a primary overview emphasizing the widely spread relevance of the topic

Multilayer network methodologies for brain data analysis and modelling

The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: brain data coming from Traumatic Brain Injury (TBI) patients and data collected for the study of neurocognitive healthy ageing. In both cases there was the need of applying computational techniques able to integrate different features. To do so we used multilayer networks. For two groups of TBI patients (adults and paediatrics), time series data were collected from the observations of IntraCranial Pressure (ICP) and Heart Rate (HR). We first detected events of simultaneous increase of HR and ICP, which we called brain-heart crosstalks. Subsequently time series were translated into graphs, and network measures, during brain-heart crosstalks, were obtained. These were then included as predictors in a mortality outcome model, with crosstalks. Causality measures were also investigated, using a Granger causality approach, to understand the dynamics of signals during these events. We further applied multilayer networks to study neurocognitive ageing. To do so, we implemented a pipeline for community detection, which we called NetRank, applying it to the Cam-CAN, a large cross-sectional cohort for the study of healthy neurocognitive ageing. Using multilayer networks modelling, we identified subgroups of individuals, with similar lifestyles, and we related them to structural and functional brain features. We believe that multilayer networks and their extensions represent a powerful tool to be used in integrative and cross modal neuroscience datasets. New insights on cognitive neuroscience and time series analysis, can in fact be gained trough multilayer network, possibly improving patients managements and allowing to develop new predictive tools.EPSR

Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.

Automated tracking of cells across timelapse microscopy image sequences typically employs complex segmentation routines and/or bio-staining of the tracking objective. Often accurate identification of a cell's morphology is not of interest and the accurate segmentation of cells in pursuit of non-morphological parameters is complex and time consuming. This thesis explores the potential of internalized quantum dot nanoparticles as alternative, bio- and photo-stable optical markers for tracking the motions of cells through time. CdTe/ZnS core-shell quantum dots act as nodes in moving light display networks within A549, epithelial, lung cancer cells over a 40 hour time period. These quantum dot fluorescence sources are identified and interpreted using simplistic algorithms to find consistent, non-subjective centroids that represent cell centre locations. The presented tracking protocols yield an approximate 91% success rate over 24 hours and 78% over the full 40 hours. The nanoparticle moving light displays also provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships enabling the construction of multi-parameter lineage trees. This principle is then developed further through inclusion of 3 different coloured quantum dots to create cell specific colour barcodes and reduce the number of time points necessary to successfully track cells through time. The tracking software and identification of parameters without detailed morphological knowledge is also demonstrated through automated extraction of DOX accumulation profiles and Cobalt agglomeration accruement statistics from two separate toxicology assays without the need for cell segmentation

The Utility of Applying Textual Analysis to Descriptions of Offender Modus Operandi for the Prevention of High Volume Crime

Police crime information systems contain modus operandi (MO) fields which provide brief text descriptions of the circumstances surrounding crime events and the actions taken by offenders to commit them. This Thesis aims to assess the feasibility of undertaking systematic analysis of these descriptions for high volume crimes. In particular, it seeks to ask the following three questions: 1) Are police recorded MO data a potential source of actionable intelligence to inform crime prevention? 2) Can techniques drawn from computer-aided text analysis be used to identify meaningful patterns in MO data for high volume crimes? 3) Do conceptual frameworks add value to the analysis and interpretation of patterns in MOs? The study focuses on a sample of theft from the person and robbery of personal property offences (n~30,000). Although existing studies have utilised similar data, they have tended to focus on crime detection and have been beset with problems of data quality. To explore these aims, it was first necessary to conduct a thorough review of MO fields to identify the challenges they present for analysis. Problems identified include various types of error but a more prominent challenge is the inherent flexibility found within natural language, i.e. human language as opposed to languages that are artificially constructed. Based on the data review, it was possible to select, and develop, appropriate techniques of computer-aided content analysis to process the data ready for further statistical investigation. In particular, a cluster analysis successfully identified and classified groups of offences based on similarities in their MO fields. The findings from the analysis were interpreted using two conceptual frameworks, the conjunction of criminal opportunity and crime scripts, both of which are informed by situational crime theories. The thesis identified that the benefits of these frameworks were twofold. As methods of analysis the frameworks ensure that the interpretation of results is systematic. As theoretical frameworks they provide an explicit link between patterns in the data, findings from previous literature, theories of crime causation and methods of prevention. Importantly, using the two frameworks together helps to build an improved understanding of offender's ability both to cope with and to exploit crime situations. The thesis successfully demonstrates that MO fields contain a potential source of intelligence relevant to both practical crime prevention and research, and that it is possible to extract this information using innovative computer-aided textual analysis techniques. The research undertaken served as a pathfinding exercise developing what amounts to a replicable technique applicable to datasets from other localities and other crime types. However, the analysis process is neither fully objective nor automated. The thesis concluded that criminological frameworks are a pre-requisite to the interpretation of this intelligence although the research questioned the strict categories and hierarchies imposed by the frameworks which do not entirely reflect the flexibilities of real-life crime commission

Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Investigation and interpretation of large mass spectrometry imaging datasets

Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabelled molecular species including drugs, metabolites, lipids and proteins in complex samples such as intact tissue. In this research, a new extensible software platform is presented, suitable for spectral preprocessing, multivariate analysis and visualisation of large MSI datasets from all major MSI vendors. Principal component analysis (PCA) has been widely used in the unsupervised processing of MSI data. Standard implementations of PCA require the entire dataset to be stored in memory, necessitating a compromise between the number of pixels and the number of peaks to include. In this research a new method which has no limitation on the number of pixels is developed. Hierarchical composition of data has been shown as an efficient method of capturing the information present within images in other fields. An adaptation of these ideas to MSI data is described. The way in which imaging data are presented can have a significant impact on the perceived structure, especially when using false colour to display images. The research presented in this thesis has resulted in new recommendations for presentation of MS images. Finally, the software and algorithms presented were used to analyse MSI data from a traumatic brain injury model. Manual exploration and use of multivariate analysis methods such as PCA did not reveal any differences between the injured hemisphere of the brain and the control hemisphere, however the hierarchical composition algorithms identified multiple ion images which appear elevated in the injured hemisphere

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Incorporating declared capacity uncertainty in optimizing airport slot allocation

Slot allocation is the mechanism used to allocate capacity at congested airports. A number of models have been introduced in the literature aiming to produce airport schedules that optimize the allocation of slot requests to the available airport capacity. A critical parameter affecting the outcome of the slot allocation process is the airport’s declared capacity. Existing airport slot allocation models treat declared capacity as an exogenously defined deterministic parameter. In this presentation we propose a new robust optimization formulation based on the concept of stability radius. The proposed formulation considers endogenously the airport’s declared capacity and expresses it as a function of its throughput. We present results from the application of the proposed approach to a congested airport and we discuss the trade-off between the declared capacity of the airport and the efficiency of the slot allocation process