MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

Development of a Coupling Model for Fluid-Structure Interaction using the Mesh-free Finite Element Method and the Lattice Boltzmann Method

In the presented thesis work, the meshfree method with distance fields was coupled with the lattice Boltzmann method to obtain solutions of fluid-structure interaction problems. The thesis work involved development and implementation of numerical algorithms, data structure, and software. Numerical and computational properties of the coupling algorithm combining the meshfree method with distance fields and the lattice Boltzmann method were investigated. Convergence and accuracy of the methodology was validated by analytical solutions. The research was focused on fluid-structure interaction solutions in complex, mesh-resistant domains as both the lattice Boltzmann method and the meshfree method with distance fields are particularly adept in these situations. Furthermore, the fluid solution provided by the lattice Boltzmann method is massively scalable, allowing extensive use of cutting edge parallel computing resources to accelerate this phase of the solution process. The meshfree method with distance fields allows for exact satisfaction of boundary conditions making it possible to exactly capture the effects of the fluid field on the solid structure.

Variations développementales chez les poissons hybrides clonaux Chrosomus eos-neogaeus : transgressions phénotypiques en 3D

Le succès écologique des organismes dépend principalement de leur phénotype. Une composante important du phénotype est la morphologie fonctionnelle car elle influence la performance d’un organisme donné dans un milieu donné et donc reflète son écologie. Des disparités dans la morphologie fonctionnelle ou dans le développement entre espèces peuvent donc mener à des différences écologiques. Ce projet évalue le rôle des mécanismes de variation morphologique dans la production de différences écologiques entre espèces au sein des poissons hybrides du complexe Chrosomus eos-neogaeus. En utilisant la microtomodensitométrie à rayons X et la morphométrie géométrique 3D, la forme des éléments des mâchoires est décrite pour comparer la variation morphologique et les différences développementales entre les membres du complexe C. eos neogaeus. Les hybrides présentent autant de variation phénotypique que les espèces parentales et présentent des phénotypes nouveaux, dit transgressifs. Les hybrides présentent aussi des différences marquées avec les espèces parentales dans leur allométrie et dans leur intégration phénotypique. Finalement, ceux-ci semblent être plastiques et en mesure de modifier leur phénotype pour occuper plusieurs environnements. L’entièreté de ces résultats suggère que des changements dans le développement des hybrides entraînent une différenciation phénotypique et écologique avec les espèces parentales.

The Influence of Healed Intrablock Rockmass Structure on the Behaviour of Deep Excavations in Complex Rockmasses

Conventional rockmass characterization and analysis methods for geotechnical assessment in mining, civil tunnelling, and other excavations consider only the intact rock properties and the discrete fractures that are present and form blocks within rockmasses. Field logging and classification protocols are based on historically useful but highly simplified design techniques, including direct empirical design and empirical strength assessment for simplified ground reaction and support analysis. As modern underground excavations go deeper and enter into more high stress environments with complex excavation geometries and associated stress paths, healed structures within initially intact rock blocks such as sedimentary nodule boundaries and hydrothermal veins, veinlets and stockwork (termed intrablock structure) are having an increasing influence on rockmass behaviour and should be included in modern geotechnical design. Due to the reliance on geotechnical classification methods which predate computer aided analysis, these complexities are ignored in conventional design. Given the comparatively complex, sophisticated and powerful numerical simulation and analysis techniques now practically available to the geotechnical engineer, this research is driven by the need for enhanced characterization of intrablock structure for application to numerical methods. Intrablock structure governs stress-driven behaviour at depth, gravity driven disintegration for large shallow spans, and controls ultimate fragmentation. This research addresses the characterization of intrablock structure and the understanding of its behaviour at laboratory testing and excavation scales, and presents new methodologies and tools to incorporate intrablock structure into geotechnical design practice. A new field characterization tool, the Composite Geological Strength Index, is used for outcrop or excavation face evaluation and provides direct input to continuum numerical models with implicit rockmass structure. A brittle overbreak estimation tool for complex rockmasses is developed using field observations. New methods to evaluate geometrical and mechanical properties of intrablock structure are developed. Finally, laboratory direct shear testing protocols for interblock structure are critically evaluated and extended to intrablock structure for the purpose of determining input parameters for numerical models with explicit structure.

Design and Synthesis of Novel Kinase Inhibitors for the Treatment of Chronic Respiratory Diseases

In 2017, Chronic Respiratory Diseases accounted for almost four million deaths worldwide. Unfortunately, current treatments are not definitive for such diseases. This unmet medical need forces the scientific community to increase efforts in the identification of new therapeutic solutions. PI3K delta plays a key role in mechanisms that promote airway chronic inflammation underlying Asthma and COPD. The first part of this project was dedicated to the identification of novel PI3K delta inhibitors. A first SAR expansion of a Hit, previously identified by a HTS campaign, was carried out. A library of 43 analogues was synthesised taking advantage of an efficient synthetic approach. This allowed the identification of an improved Hit of nanomolar enzymatic potency and moderate selectivity for PI3K delta over other PI3K isoforms. However, this compound exhibited low potency in cell-based assays. Low cellular potency was related to sub optimal phys-chem and ADME properties. The analysis of the X-ray crystal structure of this compound in human PI3K delta guided a second tailored SAR expansion that led to improved cellular potency and solubility. The second part of the thesis was focused on the rational design and synthesis of new macrocyclic Rho-associated protein kinases (ROCKs) inhibitors. Inhibition of these kinases has been associated with vasodilating effects. Therefore, ROCKs could represent attractive targets for the treatment of pulmonary arterial hypertension (PAH). Known ROCK inhibitors suffer from low selectivity across the kinome. The design of macrocyclic inhibitors was considered a promising strategy to obtain improved selectivity. Known inhibitors from literature were evaluated for opportunities of macrocyclization using a knowledge-based approach supported by Computer Aided Drug Design (CADD). The identification of a macrocyclic ROCK inhibitor with enzymatic activity in the low micro molar range against ROCK II represented a promising result that validated this innovative approach in the design of new ROCKs inhibitors.

Studio e Realizzazione del Modello RVE (Representative Volume Element) per componenti polimerici realizzati in stampa 3D FDM

A fianco ai metodi più tradizionali, fin ora utilizzati, le tecnologie additive hanno subito negli ultimi anni una notevole evoluzione nella produzione di componenti. Esse permettono un ampio di range di applicazioni utilizzando materiali differenti in base al settore di applicazione. In particolare, la stampa 3D FDM (Fused Deposition Modeling) rappresenta uno dei processi tecnologici additivi più diffusi ed economicamente più competitivi. Gli attuali metodi di analisi agli elementi finiti (FEM) e le tecnologie CAE (Computer-Aided Engineering) non sono in grado di studiare modelli 3D di componenti stampati, dal momento che il risultato finale dipende dai parametri di processo e ambientali. Per questo motivo, è necessario uno studio approfondito della meso struttura del componente stampato per estendere l’analisi FEM anche a questa tipologia di componenti. Lo scopo del lavoro proposto è di creare un elemento omogeneo che rappresenti accuratamente il comportamento di un componente realizzato in stampa 3D FDM, questo avviene attraverso la definizione e l’analisi di un volume rappresentativo (RVE). Attraverso la tecnica dell’omogeneizzazione, il volume definito riassume le principali caratteristiche meccaniche della struttura stampata, permettendo nuove analisi e ottimizzazioni. Questo approccio permette di realizzare delle analisi FEM sui componenti da stampare e di predire le proprietà meccaniche dei componenti a partire da determinati parametri di stampa, permettendo così alla tecnologia FDM di diventare sempre di più uno dei principali processi industriali a basso costo.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Tolerance optimization with statistical approach in the CAT environment

Nowadays, product development in all its phases plays a fundamental role in the industrial chain. The need for a company to compete at high levels, the need to be quick in responding to market demands and therefore to be able to engineer the product quickly and with a high level of quality, has led to the need to get involved in new more advanced methods/ processes. In recent years, we are moving away from the concept of 2D-based design and production and approaching the concept of Model Based Definition. By using this approach, increasingly complex systems turn out to be easier to deal with but above all cheaper in obtaining them. Thanks to the Model Based Definition it is possible to share data in a lean and simple way to the entire engineering and production chain of the product. The great advantage of this approach is precisely the uniqueness of the information. In this specific thesis work, this approach has been exploited in the context of tolerances with the aid of CAD / CAT software. Tolerance analysis or dimensional variation analysis is a way to understand how sources of variation in part size and assembly constraints propagate between parts and assemblies and how that range affects the ability of a project to meet its requirements. It is critically important to note how tolerance directly affects the cost and performance of products. Worst Case Analysis (WCA) and Statistical analysis (RSS) are the two principal methods in DVA. The thesis aims to show the advantages of using statistical dimensional analysis by creating and examining various case studies, using PTC CREO software for CAD modeling and CETOL 6σ for tolerance analysis. Moreover, it will be provided a comparison between manual and 3D analysis, focusing the attention to the information lost in the 1D case. The results obtained allow us to highlight the need to use this approach from the early stages of the product design cycle.

Progetto e implementazione di software di segmentazione assistita di immagini mediche e predizione di biomarkers

La segmentazione prevede la partizione di un'immagine in aree strutturalmente o semanticamente coerenti. Nell'imaging medico, è utilizzata per identificare, contornandole, Regioni di Interesse (ROI) clinico, quali lesioni tumorali, oggetto di approfondimento tramite analisi semiautomatiche e automatiche, o bersaglio di trattamenti localizzati. La segmentazione di lesioni tumorali, assistita o automatica, consiste nell’individuazione di pixel o voxel, in immagini o volumi, appartenenti al tumore. La tecnica assistita prevede che il medico disegni la ROI, mentre quella automatica è svolta da software addestrati, tra cui i sistemi Computer Aided Detection (CAD). Mediante tecniche di visione artificiale, dalle ROI si estraggono caratteristiche numeriche, feature, con valore diagnostico, predittivo, o prognostico. L’obiettivo di questa Tesi è progettare e sviluppare un software di segmentazione assistita che permetta al medico di disegnare in modo semplice ed efficace una o più ROI in maniera organizzata e strutturata per futura elaborazione ed analisi, nonché visualizzazione. Partendo da Aliza, applicativo open-source, visualizzatore di esami radiologici in formato DICOM, è stata estesa l’interfaccia grafica per gestire disegno, organizzazione e memorizzazione automatica delle ROI. Inoltre, è stata implementata una procedura automatica di elaborazione ed analisi di ROI disegnate su lesioni tumorali prostatiche, per predire, di ognuna, la probabilità di cancro clinicamente non-significativo e significativo (con prognosi peggiore). Per tale scopo, è stato addestrato un classificatore lineare basato su Support Vector Machine, su una popolazione di 89 pazienti con 117 lesioni (56 clinicamente significative), ottenendo, in test, accuratezza = 77%, sensibilità = 86% e specificità = 69%. Il sistema sviluppato assiste il radiologo, fornendo una seconda opinione, non vincolante, adiuvante nella definizione del quadro clinico e della prognosi, nonché delle scelte terapeutiche.

An Idea of a Computer Knowledge Bank on Medical Diagnostics

The paper is a description of information and software content of a computer knowledge bank on medical diagnostics. The classes of its users and the tasks which they can solve are described. The information content of the bank contains three ontologies: an ontology of observations in the field of medical diagnostics, an ontology of knowledge base (diseases) in medical diagnostics and an ontology of case records, and also it contains three classes of information resources for every division of medicine – observation bases, knowledge bases, and data bases (with data about patients), that correspond to these ontologies. Software content consists of editors for information of different kinds (ontologies, bases of observations, knowledge and data), and also of a program which performs medical diagnostics.

Forming Knowledge Bases in the Computer Knowledge Bank on Medical Diagnostics

* This paper was made according to the program of FEBRAS, project "Creating methods and tools for developing intellectual information systems in medicine and public health".

On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

An evaluation of active lecturing in a computer networks course

The article describes an attempt to improve student learning outcomes in a computer networks course by making lectures more active learning experiences. Quick quizzes, group and individual exercises, the review of student questions, as well as multiple breaks, were incorporated into the weekly three-hour lectures. Student responses to the modified lectures was overwhelmingly positive: over 85% of respondents agreed that the lectures aided understanding, with large majorities of the respondents finding the individual activities useful to their learning. Although student examination performance improved over the previous year, performance on an examination question that was designed to examine deep understanding remained unchanged.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.