My Rose

https://digitalcommons.library.umaine.edu/mmb-vp/2224/thumbnail.jp

Modeling Movement Disorders in Parkinson's Disease using Computational Intelligence

Parkinson's is the second most common neurodegenerative disease after Alzheimer's Disease and affects 127,000 people in the UK alone. Providing the most appropriate treatment pathway can prove challenging owing to the difficulty in obtaining an accurate diagnosis; due to its similarity in symptoms with other neurodegenerative diseases, it is estimated that in the United Kingdom around 24% of cases are misdiagnosed by general neurologists. A means of providing an accurate and early diagnosis of Parkinson's Disease would thereby enable a more effective management of the disease, increased quality of life for patients, and reduce costs to the healthcare system. The work described in this thesis details progress towards this goal by modeling movement disorders in the form of positional data recorded from simple movement tasks, building towards a fully objective diagnostic system without requiring any specialist domain knowledge. This is accomplished by modeling established movement disorder markers using Evolutionary Algorithms to train ensembles, before implementing feature design strategies with both Genetic Programming and Echo State Networks. The findings of this study make an important contribution to the area of data mining, including: the demonstration that Computational Intelligence-based feature design strategies can be competitive to conventional models using features extracted with expert domain knowledge; a thorough survey of evolutionary ensemble research; and the development of a novel evolutionary ensemble approach comprising traditional single objective Evolutionary Algorithm. Furthermore, an extension to a Genetic Programming feature design strategy for periodic time series is detailed, in addition to demonstrating that Echo State Networks can be directly applied to time series classification as a feature design method. This research was carried out in the context of building an applied diagnostic aid and required developing models with means of indicating the most discriminatory aspects of the sequence data, thereby facilitating inference of the precise mechanics of movement disorders to clinical neurologists

EGL-9 Controls C. elegans Host Defense Specificity through Prolyl Hydroxylation-Dependent and -Independent HIF-1 Pathways

Understanding host defense against microbes is key to developing new and more effective therapies for infection and inflammatory disease. However, how animals integrate multiple environmental signals and discriminate between different pathogens to mount specific and tailored responses remains poorly understood. Using the genetically tractable model host Caenorhabditis elegans and pathogenic bacterium Staphylococcus aureus, we describe an important role for hypoxia-inducible factor (HIF) in defining the specificity of the host response in the intestine. We demonstrate that loss of egl-9, a negative regulator of HIF, confers HIF-dependent enhanced susceptibility to S. aureus while increasing resistance to Pseudomonas aeruginosa. In our attempt to understand how HIF could have these apparently dichotomous roles in host defense, we find that distinct pathways separately regulate two opposing functions of HIF: the canonical pathway is important for blocking expression of a set of HIF-induced defense genes, whereas a less well understood noncanonical pathway appears to be important for allowing the expression of another distinct set of HIF-repressed defense genes. Thus, HIF can function either as a gene-specific inducer or repressor of host defense, providing a molecular mechanism by which HIF can have apparently opposing roles in defense and inflammation. Together, our observations show that HIF can set the balance between alternative pathogen-specific host responses, potentially acting as an evolutionarily conserved specificity switch in the host innate immune response

Using Echo State Networks for Classification : A Case Study in Parkinson's Disease Diagnosis

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input-output mappings. Our results show that ESNs can carry out effective classification and are competitive with existing approaches that have significantly longer training times, in addition to performing similarly with models employing conventional feature extraction strategies that require expert domain knowledge. This suggests that ESNs may prove beneficial in situations where predictive models must be trained rapidly and without the benefit of domain knowledge, for example on high-dimensional data produced by wearable medical technologies. This application area is emphasized with a case study of Parkinson’s Disease patients who have been recorded by wearable sensors while performing basic movement tasks

Using multi-state modelling to facilitate informed personalised treatment planning in Follicular Lymphoma

Follicular lymphoma is an incurable haematological cancer that tends to follow a remitting relapsing course; with treatment options ranging from “watch and wait” (active monitoring), chemotherapy, and radiotherapy. The treatment decision is typically dependent upon patient characteristics and disease stage. Using routine data collected by the Haematological Malignancy Research Network (www.hmrn.org), we are undertaking research into improving understanding of the treatment pathways and the impact of these decisions not only on the patient, but also on the cost to the healthcare provider. The aim is to facilitate informed decision making by providing a personalised prognostic tool that identifies a suitable treatment option at each stage of the treatment pathway, as part of a shared decision-making process between patient and clinician. Multi-state modelling provides an appropriate toolkit for this work, owing to its ability to predict the movement of a person through a discrete state-space captured by individual-level characteristics. This work details implementation considerations of the modelling stage, including the design of the statediagram, the fitting of transition-specific hazard functions, and the use of Discrete Event Simulation to provide estimated transition probabilities. Other considerations include how to present the results of a complex model into a deployable clinical tool

Application of the LymphGen classification tool to 928 clinically and genetically-characterised cases of diffuse large B cell lymphoma (DLBCL).

We recently published results of targeted sequencing applied to 928 unselected cases of DLBCL registered in the Haematological Malignancy Research Network (HMRN) registry (1). Clustering allowed us to resolve five genomic subtypes. These subtypes shared considerable overlap with those proposed in two independent genomic studies(2, 3), suggesting the potential to use genetics to stratify patients by both risk and biology. In the original studies, clustering techniques were applied to sample cohorts to reveal molecular substructure, but left open the challenge of how to classify an individual patient. This was addressed by the LymphGen classification tool (4). LymphGen assigns an individual case to one of six molecular subtypes. The tool accommodates data from exome or targeted sequencing, either with or without copy number variant (CNV) data. Separate gene expression data allows classification of a seventh, MYC-driven subtype defined by a double hit (DHL) or molecular high-grade (MHG) gene expression signature(5-7).HR was funded by a studentship from the Medical Research Council. DH was supported by a Clinician Scientist Fellowship from the Medical Research Council (MR/M008584/1). The Hodson laboratory receives core funding from Wellcome and MRC to the Wellcome-MRC Cambridge Stem Cell Institute and core funding from the CRUK Cambridge Cancer Centre. HMRN is supported by BCUK 15037 and CRUK 18362

Activation of caspase-1 by the NLRP3 inflammasome regulates the NADPH oxidase NOX2 to control phagosome function

Phagocytosis is a fundamental cellular process that is pivotal for immunity as it coordinates microbial killing, innate immune activation and antigen presentation. An essential step in this process is phagosome acidification, which regulates a number of functions of these organelles that allow them to participate in processes essential to both innate and adaptive immunity. Here we report that acidification of phagosomes containing Gram-positive bacteria is regulated by the NLRP3-inflammasome and caspase-1. Active caspase-1 accumulates on phagosomes and acts locally to control the pH by modulating buffering by the NADPH oxidase NOX2. These data provide insight into a mechanism by which innate immune signals can modify cellular defenses and establish a new function for the NLRP3-inflammasome and caspase-1 in host defense

The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition

Approaches for temporal analysis and quantitative characterisation of single cell morphology and dynamics remain in high demand. Here authors present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos

Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report.

Based on the profile of genetic alterations occurring in tumor samples from selected diffuse large B-cell lymphoma (DLBCL) patients, 2 recent whole-exome sequencing studies proposed partially overlapping classification systems. Using clustering techniques applied to targeted sequencing data derived from a large unselected population-based patient cohort with full clinical follow-up (n = 928), we investigated whether molecular subtypes can be robustly identified using methods potentially applicable in routine clinical practice. DNA extracted from DLBCL tumors diagnosed in patients residing in a catchment population of ∼4 million (14 centers) were sequenced with a targeted 293-gene hematological-malignancy panel. Bernoulli mixture-model clustering was applied and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1, and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2, and MYD88 recapitulated recent studies showing good, intermediate, and poor prognosis, respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. Our findings confirm the existence of molecular subtypes of DLBCL, providing evidence that genomic tests have prognostic significance in non-selected DLBCL patients. The identification of both good and poor risk subtypes in patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) clearly show the clinical value of the approach, confirming the need for a consensus classification.Bloodwise (grant number 15037) funded the majority of this study. Genetic sequencing was funded by 14M Genomics, a start-up company that ceased trading February 2016. DJH was funded by a clinician scientist fellowship from the MRC and receives core funding from Wellcome and MRC to the Wellcome-MRC Cambridge Stem Cell Institute. Some of the analysis in this study was performed on the “Viking” high performance computing cluster at the University of York.1