Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

A general method for evaluation of functions and computations in a digital computer /

Vita.

Development and evaluation of an automated annotation pipeline and cDNA annotation system

Manual curation has long been held to be the gold standard for functional annotation of DNA sequence. Our experience with the annotation of more than 20,000 full-length cDNA sequences revealed problems with this approach, including inaccurate and inconsistent assignment of gene names, as well as many good assignments that were difficult to reproduce using only computational methods. For the FANTOM2 annotation of more than 60,000 cDNA clones, we developed a number of methods and tools to circumvent some of these problems, including an automated annotation pipeline that provides high-quality preliminary annotation for each sequence by introducing an uninformative filter that eliminates uninformative annotations, controlled vocabularies to accurately reflect both the functional assignments and the evidence supporting them, and a highly refined, Web-based manual annotation tool that allows users to view a wide array of sequence analyses and to assign gene names and putative functions using a consistent nomenclature. The ultimate utility of our approach is reflected in the low rate of reassignment of automated assignments by manual curation. Based on these results, we propose a new standard for large-scale annotation, in which the initial automated annotations are manually investigated and then computational methods are iteratively modified and improved based on the results of manual curation.

Enhancement of drug affinity for cell membranes by conjugation with lipoamino acids II. Experimental and computational evidence using biomembrane models

Lipoamino acids (LAAs) are promoieties able to enhance the amphiphilicity of drugs, facilitating their interaction with cell membranes. Experimental and computational studies were carried out on two series of lipophilic amide conjugates between a model drug (tranylcypromine, TCP) and LAA or alkanoic acids containing a short, medium or long alkyl side chain (C-4 to C-16). The effects of these compounds were evaluated by monolayer surface tension analysis and differential scanning calorimetry using dimyristoylphosphatidylcholine nnonolayers and liposomes as biomembrane models. The experimental results were related to independent calculations to determine partition coefficient and blood-brain partitioning. The comparison of TCP-LAA conjugates with the related series of TCP alkanoyl amides confirmed that the ability to interact with the biomembrane models is not due to the mere increase of lipophilicity, but mainly to the amphipatic nature and the kind of LAA residue. (C) 2005 Elsevier B.V. All rights reserved.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

Evaluation and comparison of mammalian subcellular localization prediction methods

Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.

Multi-scale approaches to Food Sciences: computational QM and MM explorations at the microscopic level

L'obiettivo principale della politica di sicurezza alimentare è quello di garantire la salute dei consumatori attraverso regole e protocolli di sicurezza specifici. Al fine di rispondere ai requisiti di sicurezza alimentare e standardizzazione della qualità, nel 2002 il Parlamento Europeo e il Consiglio dell'UE (Regolamento (CE) 178/2002 (CE, 2002)), hanno cercato di uniformare concetti, principi e procedure in modo da fornire una base comune in materia di disciplina degli alimenti e mangimi provenienti da Stati membri a livello comunitario. La formalizzazione di regole e protocolli di standardizzazione dovrebbe però passare attraverso una più dettagliata e accurata comprensione ed armonizzazione delle proprietà globali (macroscopiche), pseudo-locali (mesoscopiche), ed eventualmente, locali (microscopiche) dei prodotti alimentari. L'obiettivo principale di questa tesi di dottorato è di illustrare come le tecniche computazionali possano rappresentare un valido supporto per l'analisi e ciò tramite (i) l’applicazione di protocolli e (ii) miglioramento delle tecniche ampiamente applicate. Una dimostrazione diretta delle potenzialità già offerte dagli approcci computazionali viene offerta nel primo lavoro in cui un virtual screening basato su docking è stato applicato al fine di valutare la preliminare xeno-androgenicità di alcuni contaminanti alimentari. Il secondo e terzo lavoro riguardano lo sviluppo e la convalida di nuovi descrittori chimico-fisici in un contesto 3D-QSAR. Denominata HyPhar (Hydrophobic Pharmacophore), la nuova metodologia così messa a punto è stata usata per esplorare il tema della selettività tra bersagli molecolari strutturalmente correlati e ha così dimostrato di possedere i necessari requisiti di applicabilità e adattabilità in un contesto alimentare. Nel complesso, i risultati ci permettono di essere fiduciosi nel potenziale impatto che le tecniche in silico potranno avere nella identificazione e chiarificazione di eventi molecolari implicati negli aspetti tossicologici e nutrizionali degli alimenti.

Automated synthesis and evaluation of potential new anti-microbial agents

A series of N1-benzylideneheteroarylcarboxamidrazones was prepared in an automated fashion, and tested against Mycobacterium fortuitum in a rapid screen for antimycobacterial activity. Many of the compounds from this series were also tested against Mycobacterium tuberculosis, and the usefulness as M.fortuitum as a rapid, initial screen for anti-tubercular activity evaluated. Various deletions were made to the N1-benzylideneheteroarylcarboxamidrazone structure in order to establish the minimum structural requirements for activity. The N1-benzylideneheteroarylcarbox-amidrazones were then subjected to molecular modelling studies and their activities against M.fortuitum and M.tuberculosis were analysed using quantitative structure-analysis relationship (QSAR) techniques in the computational package TSAR (Oxford Molecular Ltd.). A set of equations predictive of antimycobacterial activity was hereby obtained. The series of N1-benzylidenehetero-arylcarboxamidrazones was also tested against a multidrug-resistant strain of Staphylococcus aureus (MRSA), followed by a panel of Gram-positive and Gram-negative bacteria, if activity was observed for MRSA. A set of antimycobacterial N1-benzylideneheteroarylcarboxamidrazones was hereby discovered, the best of which had MICs against m. fortuitum in the range 4-8μgml-1 and displayed 94% inhibition of M.tuberculosis at a concentration of 6.25μgml-1. The antimycobacterial activity of these compounds appeared to be specific, since the same compounds were shown to be inactive against other classes of organisms. Compounds which were found to be sufficiently active in any screen were also tested for their toxicity against human mononuclear leucocytes. Polyethylene glycol (PEG) was used as a soluble polymeric support for the synthesis of some fatty acid derivatives, containing an isoxazoline group, which may inhibit mycolic acid synthesis in mycobacteria. Both the PEG-bound products and the cleaved, isolated products themselves were tested against M.fortuitum and some low levels of antimycobacterial activity were observed, which may serve as lead compounds for further studies.

Sensemaking tools for understanding research literatures:design, implementation and user evaluation

This paper describes the work undertaken in the Scholarly Ontologies Project. The aim of the project has been to develop a computational approach to support scholarly sensemaking, through interpretation and argumentation, enabling researchers to make claims: to describe and debate their view of a document's key contributions and relationships to the literature. The project has investigated the technicalities and practicalities of capturing conceptual relations, within and between conventional documents in terms of abstract ontological structures. In this way, we have developed a new kind of index to distributed digital library systems. This paper reports a case study undertaken to test the sensemaking tools developed by the Scholarly Ontologies project. The tools used were ClaiMapper, which allows the user to sketch argument maps of individual papers and their connections, ClaiMaker, a server on which such models can be stored and saved, which provides interpretative services to assist the querying of argument maps across multiple papers and ClaimFinder, a novice interface to the search services in ClaiMaker.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Evaluation of wind-induced internal pressure in low-rise buildings: A multi scale experimental and numerical approach

Hurricane is one of the most destructive and costly natural hazard to the built environment and its impact on low-rise buildings, particularity, is beyond acceptable. The major objective of this research was to perform a parametric evaluation of internal pressure (IP) for wind-resistant design of low-rise buildings and wind-driven natural ventilation applications. For this purpose, a multi-scale experimental, i.e. full-scale at Wall of Wind (WoW) and small-scale at Boundary Layer Wind Tunnel (BLWT), and a Computational Fluid Dynamics (CFD) approach was adopted. This provided new capability to assess wind pressures realistically on internal volumes ranging from small spaces formed between roof tiles and its deck to attic to room partitions. Effects of sudden breaching, existing dominant openings on building envelopes as well as compartmentalization of building interior on the IP were systematically investigated. Results of this research indicated: (i) for sudden breaching of dominant openings, the transient overshooting response was lower than the subsequent steady state peak IP and internal volume correction for low-wind-speed testing facilities was necessary. For example a building without volume correction experienced a response four times faster and exhibited 30–40% lower mean and peak IP; (ii) for existing openings, vent openings uniformly distributed along the roof alleviated, whereas one sided openings aggravated the IP; (iii) larger dominant openings exhibited a higher IP on the building envelope, and an off-center opening on the wall exhibited (30–40%) higher IP than center located openings; (iv) compartmentalization amplified the intensity of IP and; (v) significant underneath pressure was measured for field tiles, warranting its consideration during net pressure evaluations. The study aimed at wind driven natural ventilation indicated: (i) the IP due to cross ventilation was 1.5 to 2.5 times higher for Ainlet/Aoutlet>1 compared to cases where Ainlet/Aoutlet<1, this in effect reduced the mixing of air inside the building and hence the ventilation effectiveness; (ii) the presence of multi-room partitioning increased the pressure differential and consequently the air exchange rate. Overall good agreement was found between the observed large-scale, small-scale and CFD based IP responses. Comparisons with ASCE 7-10 consistently demonstrated that the code underestimated peak positive and suction IP.

Computational Tracking of Mental Health in Youth: Latin American Contributions to a Low-Cost and Effective Solution for Early Psychiatric Diagnosis

The early onset of mental disorders can lead to serious cognitive damage, and timely interventions are needed in order to prevent them. In patients of low socioeconomic status, as is common in Latin America, it can be hard to identify children at risk. Here, we briefly introduce the problem by reviewing the scarce epidemiological data from Latin America regarding the onset of mental disorders, and discussing the difficulties associated with early diagnosis. Then we present computational psychiatry, a new field to which we and other Latin American researchers have contributed methods particularly relevant for the quantitative investigation of psychopathologies manifested during childhood. We focus on new technologies that help to identify mental disease and provide prodromal evaluation, so as to promote early differential diagnosis and intervention. To conclude, we discuss the application of these methods to clinical and educational practice. A comprehensive and quantitative characterization of verbal behavior in children, from hospitals and laboratories to homes and schools, may lead to more effective pedagogical and medical intervention

A methodology for hierarchical image segmentation evaluation

This paper proposes a method to evaluate hierarchical image segmentation procedures, in order to enable comparisons between different hierarchical algorithms and of these with other (non-hierarchical) segmentation techniques (as well as with edge detectors) to be made. The proposed method builds up on the edge-based segmentation evaluation approach by considering a set of reference human segmentations as a sample drawn from the population of different levels of detail that may be used in segmenting an image. Our main point is that, since a hierarchical sequence of segmentations approximates such population, those segmentations in the sequence that best capture each human segmentation level of detail should provide the basis for the evaluation of the hierarchical sequence as a whole. A small computational experiment is carried out to show the feasibility of our approach.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.