Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery

A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of an overarching electromechanical model for a servo-axis. Every loss source is taken into account, be it mechanical or electrical. The mechanical transmission is modeled by means of a sequence of lumped-parameter blocks. The electric model of the motor and the inverter takes into account winding losses, iron losses and controller switching losses. No experimental characterizations are needed to implement the electric model, since the parameters are inferred from the data available in commercial catalogs. With the global model at disposal, a second objective of this work is to perform the optimization analysis, in particular, the selection of the motor-reducer unit. The optimal transmission ratios that minimize several objective functions are found. An optimization process is carried out and repeated for each candidate motor. Then, we present a novel method where the discrete set of available motor is extended to a continuous domain, by fitting manufacturer data. The problem becomes a two-dimensional nonlinear optimization subject to nonlinear constraints, and the solution gives the optimal choice for the motor-reducer system. The presented electromechanical model, along with the implementation of optimization algorithms, forms a complete and powerful simulation tool for servo-controlled automatic machines. The tool allows for determining a wide range of electric and mechanical parameters and the behavior of the system in different operating conditions.

Dissecting the Heat-Stress Tolerance QTLome in Durum Wheat

Heat stress negatively affects wheat performance during its entire cycle, particularly during the reproductive stage. In view of the climate change and the prediction of a continued increase in temperature in the new future, it is urgent to concentrate efforts to discover novel genetic sources able to improve the resilience of wheat to heat stress. In this direction, this study addressed two different experiments in durum wheat to identify novel QTLs suitable to be applied in marker-assisted selection for heat tolerance. Chlorophyll fluorescence (ChlF) is a valuable indicator of plant response to environmental changes allowing a detailed assessment of PSII activity in view of its non-invasive measurement and high-throughput phenotyping. In the first study (Chapter 2), the Light-Induced Fluorescence Transient (LIFT) method was used to access ChlF data to map QTLs for ChlF-related traits during the vegetative growth stage in durum wheat under heat stress condition. Our results provide evidence that LIFT consistently measures ChlF at the level of high-throughput phenotyping combined with high accuracy which is required for Genome-Wide Association Study (GWAS) aimed at identifying genomic regions affecting PSII activity. The 50 QTLs identified for ChlF-related traits under heat stress mostly clustered into five chromosomes hotspots unrelated to phenology, a feature that makes these QTLs a valuable asset for marker-assisted breeding programs across different latitudes. In the second study (Chapter 3), a set of 183 accessions suitable for GWAS, was exposed to optimal and high temperature during two crop seasons under field conditions. Important agronomic traits were evaluated in order to identify valuable QTLs for GY and its components. The GWAS analysis identified several QTLs in the single years as well as in the joint analysis. From the total QTLs identified, 13 QTL clusters can be highlighted to be affecting heat tolerance across different years and/or different traits.