How do etiological factors can explain the different clinical features of patients with differentiated thyroid cancer and their histopathological findings?

Abstract The aim was to retrospectively analyse the clinical–histopathological characteristics of patients with newly diagnosis of differentiated thyroid cancer (DTC) referred to two Italian centres, one in Northern and the other in Southern Italy, between 2000 and 2013. 1081 patients were included and subdivided into two groups: group A (474 patients from Novara) and group B (607 patients from Naples). The group A came from the industrial area of Novara, while the Group B came from the areas around Vesuvius and Campi Flegrei. The two groups were comparable for iodine levels, body mass index, diagnostic timing and clinical procedures. For all patients, demographic and clinical data were collected. No difference was found in gender, whereas the age at diagnosis was later in the group A (group A 53.1 ± 15.16 years, group B 41.9 ± 14.25 years, p < 0.001). In both groups, the most frequent histotype was papillary thyroid cancer (PTC) with prevalence of follicular variant in group A (p < 0.0001) and classical variant in group B (p < 0.0001). Aggressive histological features were mainly seen in group A (bilaterality p < 0.0001, multifocality p < 0.0001 and thyroid capsular invasion p < 0.0001). Microcarcinomas were more frequent in group A (p < 0.0001) but mostly characterized by bilaterality (p < 0.001) and multifocality (p < 0.04). In both groups, tumour-associated thyroiditis showed a significant increase over the years (group A p < 0.05, group B p < 0.04). Environmental factors could justify the differences found in our study. These preliminary data should stimulate the need for an Italian Cancer Registry of DTC in order to allow an epidemiological characterization, allowing the identification of specific etiological factors and an improvement in the management of the diseas

Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients’ and caregivers’ judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients’ home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients’ home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.Peer ReviewedPostprint (published version

Deep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-59147-6_30Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.Peer ReviewedPostprint (author's final draft

Optimal aircraft traffic flow management at a terminal control area during disturbances

AbstractThis work addresses the real-time problem of managing take-off and landing operations in presence of traffic disturbances at a busy Terminal Control Area (TCA). The possible aircraft conflict detection and resolution actions are aircraft timing and routing decisions. An important objective of traffic controllers is the minimization of delay propagation, which may reduce the aircraft travel times and their energy consumption. To improve the effectiveness of air traffic monitoring and control in a busy TCA, we present an optimization-based decision support system based on a rolling horizon framework. The problem is modelled via an alternative graph formulation, i.e. a detailed model of air traffic flows in the TCA, and solved by aircraft rescheduling and rerouting algorithms. We compare a truncated branch and bound algorithm for aircraft rescheduling with fixed routes, a tabu search scheme for combined aircraft rescheduling and rerouting, and the first in first out (FIFO) rule that we use as a surrogate for the dispatchers behaviour. Computational experiments are based on practical size instances from the Milan Malpensa airport, in Italy. Disturbed traffic situations are generated by simulating various sets of delayed landing/departing aircraft and a temporarily blocked runway. We evaluate different parameters of the rolling horizon framework, such as the frequency of aircraft retiming and rerouting and the time horizon of prediction, i.e., the extension of the current traffic flow forecast, including roll and look-ahead periods. The roll period is the time shift between the start of successive traffic predictions. A detailed analysis of the experimental results demonstrate that the solutions produced by the optimization algorithms are of better quality compared to FIFO, in terms of delay and travel time minimization. However, the optimization approaches require frequent re-timing and re-routing in consecutive time horizons

A variable neighbourhood search for fast train scheduling and routing during disturbed railway traffic situations

This paper focuses on the development of metaheuristic algorithms for the real-time traffic management problem of scheduling and routing trains in complex and busy railway networks. This key optimization problem can be formulated as a mixed integer linear program. However, since the problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY optimization solver in order to improve the possibility of finding good quality solutions quickly. The optimization solver manages trains at the microscopic level of block sections and at a precision of seconds. The solver outcome is a detailed conflict-free train schedule, being able to avoid deadlock situations and to minimize train delays. The proposed algorithmic framework starts from a good initial solution for the train scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. Variable neighbourhood search or tabu search algorithms are then applied to improve the solution by re-routing some trains. The neighbourhood of a solution is characterized by the set of candidate trains to be re-routed and the available routes. Computational experiments are performed on railway networks from different countries and various sources of disturbance. The new algorithms often outperform a state-of-the-art tabu search algorithm and a commercial solver in terms of reduced computation times and/or train delays. © 2016 Elsevier Ltd.This paper focuses on the development of metaheuristic algorithms for the real-time traffic management problem of scheduling and routing trains in complex and busy railway networks. This key optimization problem can be formulated as a mixed integer linear program. However, since the problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY optimization solver in order to improve the possibility of finding good quality solutions quickly. The optimization solver manages trains at the microscopic level of block sections and at a precision of seconds. The solver outcome is a detailed conflict-free train schedule, being able to avoid deadlock situations and to minimize train delays. The proposed algorithmic framework starts from a good initial solution for the train scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. Variable neighbourhood search or tabu search algorithms are then applied to improve the solution by re-routing some trains. The neighbourhood of a solution is characterized by the set of candidate trains to be re-routed and the available routes. Computational experiments are performed on railway networks from different countries and various sources of disturbance. The new algorithms often outperform a state-of-the-art tabu search algorithm and a commercial solver in terms of reduced computation times and/or train delays