Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research

Prediction of exercise adherence with goal orientations and motivational climate

2012 Summer.Includes bibliographical references.Decreasing physical activity levels across the nation have aided in the rise of obesity. One reason for this decline in activity is the lack of adherence to exercise programs. Psychological factors such as goal orientations and motivational climates may provide insight into the adherence of exercise. The collegiate population (18-25 years old) at the campus of Colorado State University was sampled in the present study. Individual goal orientations of the subjects were measured using the Task and Ego Orientation in Sport Questionnaire (TEOSQ). Participants were categorized by their individual goal orientations, high task/high ego, high task/low ego, or low task/high ego. Subjects were randomly placed into two groups where they underwent a six-week exercise program with varying motivational climates. The number of attended sessions was greater in the Task/Mastery (T/M) climate compared to the Ego/Performance (E/P) climate (8.84 ± 2.48 to 6.16 ± 2.52, respectively), while the number of missed sessions following exposure to the environment was lesser in the T/M climate, comparatively (3.00 ± 2.43 to 5.53 ± 2.44). Further, task orientation scores were positively correlated with attendance and negatively correlated with missed sessions. Ego orientation scores were in direct contrast revealing negative correlation with attendance and positive correlation with missed sessions. Additionally, individuals with high task/low ego orientation had better adherence outcomes and were the most motivationally adapted group. Lastly, ego scores increased in the E/P climate (3.29 ± 0.92 to 3.7 ± 1.1), while they decreased in the T/M climate (3.33 ± 0.76 to 2.97 ± 0.82). These data provide a greater understanding of the relationship between not only motivational climates and exercise adherence, but also between goal orientations and motivational climates. Task-oriented individuals inherently adhere to exercise programs more easily regardless of the motivational climate compared to ego-oriented individuals. Also, it has become clear that a T/M climate improves exercise adherence outcomes regardless of individual goal orientation based on the finding that dispositional orientations might be altered by the climate provided

Knowledge evaluation for knowledge management implementation : : the case study of the radio-pharmaceutical center of IPEN

In recent years organizations are using multiple methods and approaches to design their strategic and action plans. In this context, Resource-based View (RBV) and Knowledge-based View (KBV) frameworks are receiving increased attention as instrumental to strategy formulation. The synergy of these approaches with Knowledge Management initiatives is intuitive and their use are in a common framework is discussed here to show the importance of methods and instruments to mapping and assessing the knowledge assets of the organization. The application of such methods to the Radio-pharmaceutical Center of IPEN is discussed in this paper.Knowledge management, Nuclear knowledge management

Contractual networks: an organizational model to reduce the competitive disadvantage of small and medium enterprises (SMEs) in Europe's less developed regions. A survey in southern Italy

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Random Lift of Set Valued Maps and Applications to Multiagent Dynamics

We introduce an abstract framework for the study of general mean field games and mean field control problems. Given a multiagent system, its macroscopic description is provided by a time-depending probability measure, where at every instant of time the measure of a set represents the fraction of (microscopic) agents contained in it. The trajectories available to each of the microscopic agents are affected also by the overall state of the system. By using a suitable concept of random lift of set valued maps, together with fixed point arguments, we are able to derive properties of the macroscopic description of the system from properties of the set valued map expressing the admissible trajectories for the microscopical agents. The techniques used can be applied to consider a broad class of dependence between the trajectories of the single agent and the state of the system. We apply the results in the case in which the admissible trajectories of the agents are the minimizers of a suitable integral functional depending also from the macroscopic evolution of the system

Multi-agent quality of experience control

In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents

Assessment and structuring knowledge of the organization for the strategic alignment of knowledge management: an application to the radiopharmacy center of ipen

International audienc

On the optimization of energy storage system placement for protecting power transmission grids against dynamic load altering attacks

In this paper a power system protection scheme based on energy storage system placement against closed-loop dynamic load altering attacks is proposed. The protection design consists in formulating a non-convex optimization problem, subject to a Lyapunov stability constraint and solved using a two-step iterative procedure. Simulation results confirm the effectiveness of the approach and the potential relevance of using energy storage systems in support of primary frequency regulation services

Chief Medical Officers meeting on implementing a public health genomics approach

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