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Accurate vehicle localization at the level of individual lanes is crucial to ensure the safe and efficient operation of autonomous vehicles, serving as a cornerstone for the development of future Advanced Driving Assistance Systems (ADAS). Contemporary localization methods relying on Global Navigation Satellite Systems (GNSS) often fall short of achieving the necessary precision, necessitating the involvement of additional systems. These supplementary systems frequently depend on the output of road line detectors, whose performance can be hindered by various factors, including adverse weather conditions and heavy traffic, resulting in noisy or sporadically missing data. This study introduces a probabilistic algorithm designed to precisely estimate the actual lane positioning of a vehicle in the specific context of multi-lane roads, such as highways, without relying on GNSS data. The proposed algorithm is built upon a Hidden Markov Model that exploits the output of a generic line detector, a common component of contemporary driving assistance systems. This model ensures consistent lane localization estimates even when faced with noisy or intermittently missing data. Experiments demonstrate the algorithm's effectiveness, providing a reliable estimate of the vehicle in-lane position in challenging datasets containing highway scenarios with hundreds of lane changes. This contributes to the enhancement of existing literature, achieving an accuracy of 86.71% over a segment exceeding 50 km. These results, improving by almost 10% over our previous efforts, suggest that our approach has the potential to enable new ADAS functionalities and offer a robust localization scheme for use in the context of autonomous driving scene understanding.

Deferasirox (DFX) is used for the management of iron overload (IOL) in many haematological malignancies including myelofibrosis (MF). The ‘RUX-IOL’ study retrospectively collected 69 MF patients treated with ruxolitinib (RUX) and DFX for IOL to assess: safety, efficacy in term of iron chelation response (ICR) and erythroid response (ER), and impact on overall survival of the combination therapy. The RUX–DFX therapy was administered for a median time of 12.4 months (interquartile range 3.1–71.2). During treatment, 36 (52.2%) and 34 (49.3%) patients required RUX and DFX dose reductions, while eight (11.6%) and nine (13.1%) patients discontinued due to RUX- or DFX-related adverse events; no unexpected toxicity was reported. ICR and ER were achieved by 33 (47.8%) and 32 patients (46.4%) respectively. Thirteen (18.9%) patients became transfusion-independent. Median time to ICR and ER was 6.2 and 2 months respectively. Patients achieving an ER were more likely to obtain an ICR also (p = 0.04). In multivariable analysis, the absence of leukocytosis at baseline (p = 0.02) and achievement of an ICR at any time (p = 0.02) predicted improved survival. In many MF patients, the RUX–DFX combination provided ICR and ER responses that correlated with improved outcome in the absence of unexpected toxicities. This strategy deserves further clinical investigation.

Objective: Alterations in voice intensity and quality may constitute a social life limitation in people with multiple sclerosis (MS), but only 2% of cases receive speech therapy. Especially the Lee Silverman Voice Treatment (LSVT)-Loud is a highly effective intensive method for voice intensity, requiring subjects’ repeated attendance at the clinic. Telerehabilitation may represent a feasible solution to bypass potential barriers related to speech therapy attendance, scaling up the beneficial effects of the treatment to a broader population. The proposed protocol aims to test the feasibility and the pilot efficacy of the LSVT-Loud delivered in telerehabilitation (Tele-LSVT-Loud), compared to the same treatment delivered in the clinic (LSVT-Loud). Methods: A single-blinded, parallel, two-arm, pilot randomized (1:1 ratio) controlled trial will be performed involving 20 people with MS. Patients will be allocated to 4 weeks of Tele-LSVT-Loud by accessing a telerehabilitation platform at home or LSVT-Loud conventionally delivered in the clinic. Feasibility and pilot effectiveness will be evaluated three times: before (T0), after the treatment (T1), and 3-month follow-up (T2). Feasibility measures will include adherence, adverse events, user experience, motivation, engagement, and acceptability. Vocal intensity during a 1-minute monologue will be the primary outcome measure. Secondary outcome measures will be the vocal quality during a 1-minute monologue, sustained /a/ voice intensity, quality and stability, voice use in daily life, voice subjective perception in daily life, and quality of life. Results: Expected results will be (1) high feasibility of Tele-LSVT-Loud and (2) a non-inferiority effect of Tele-LSVT-Loud compared with face-to-face treatment delivery on voice intensity and quality outcomes. Conclusions: Tele-LSVT-Loud may be a feasible intervention for MS alteration in voice intensity and quality with a non-inferior effect compared to LSVT-Loud.

BACKGROUND: Vocal disorders are frequent in people with multiple sclerosis (MS). Cognitive impairment, fatigue, depression, and other clinical characteristics can be associated with treatment effectiveness in rehabilitation. Finding baseline characteristics that identify those who are responding to treatment can help the clinical decision-making process, which can then help improve the effectiveness of voice treatment. We developed a model to identify factors associated with treatment-related improvement on voice intensity in people with MS. METHODS: Data are from a randomized controlled trial of the effects of voice therapy. Forty-four people with MS were enrolled and randomized to receive Lee Silverman Voice Treatment LOUD, specifically addressing voice intensity, or conventional speech-therapy group. Voice intensity (dB) was measured during monologue before and after treatment and was used to differentiate those who responded (posttreatment voice intensity?> 60 dB) from those who did not. Possible associated factors were cognitive impairment, fatigue, depression, disability, and disease duration. Associations were assessed by univariate logistic regression and univariate and multivariate linear regressions. RESULTS: Mean ± SD monologue voice intensity is improved in the whole sample (before rehabilitation: 51.8 ± 4.2 dB; and after rehabilitation 57.0 ± 6.5 dB; P <.001), and 11 people with MS (27.5%) responded to treatment. Specificity of treatment was associated with the return to normal voice intensity (OR, 14.28; 95% CI, 12.17-309.56) and we found a linear association between voice improvement and the specificity of treatment (6.65 [SE = 1.54] dB; P <.05). Moreover, the analysis revealed a nonlinear association between improvement and fatigue, suggesting increased benefits for people with MS with moderate fatigue. Other factors were not significantly associated with treatment effectiveness. CONCLUSIONS: Moderate fatigue and the specificity of the inter

Background: Coronavirus disease 2019 (COVID-19) broke out in China in December 2019 and now is a pandemic all around the world. In Italy, Northern regions were hit the hardest during the first wave. We aim to explore the prevalence and the exposure characteristics of physiotherapists (PTs) working in different Italian regions during the first wave of COVID-19. Methods: Between April and May 2020 a structured anonymous online survey was distributed to all PTs registered in the National Professional Registry to collect prevalence data of a confirmed diagnosis of COVID-19 (i.e., nasopharyngeal swab and/or serological test). A bottom-up agglomerative nesting hierarchical clustering method was applied to identify groups of regions based on response rate. Multivariable logistic regression was used to explore personal and work-related factors associated with a confirmed diagnosis of COVID-19. Results: A total of 15,566 PTs completed the survey (response rate 43.3%). The majority of respondents (57.7%) were from Northern regions. Considering all respondents, the number of confirmed COVID-19 cases in Northern and Central Italy, was higher compared to those in Southern Italy (6.9% vs. 1.8%, P < 0.001); focusing the analysis on respondents who underwent nasopharyngeal swab and/or serological test led to similar findings (14.1% vs. 6.4%, P < 0.001). Working in Northern and Central regions was associated with a higher risk of confirmed diagnosis of COVID-19 compared to Southern regions (OR 3.4, 95%CI 2.6 to 4.3). PTs working in Northern and Central regions were more likely to be reallocated to a different unit and changing job tasks, compared to their colleagues working in the Southern regions (10.5% vs 3.7%, P < 0.001). Conclusions: Work-related risk factors were differently distributed between Italian regions at the time of first pandemic wave, and PTs working in the Northern and Central regions were more at risk of a confirmed diagnosis of COVID-19, especially when w

Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot be generalized. Public datasets proposed until now, taken individually, cover only a few kinds of environment and mostly a single sensor. For these reasons, we developed a benchmark, for localization and mapping applications, using multiple publicly available datasets. In this way, we are able to cover many kinds of environment and many kinds of sensor that can produce point clouds. Furthermore, the ground truth has been thoroughly inspected and evaluated to ensure its quality. For some of the datasets, the accuracy of the ground truth measuring system was not reported by the original authors, therefore we estimated it with our own novel method, based on an iterative registration algorithm. Along with the data, we provide a broad set of registration problems, chosen to cover different types of initial misalignment, various degrees of overlap, and different kinds of registration problems. Lastly, we propose a metric to measure the performances of registration algorithms: it combines the commonly used rotation and translation errors together, to allow an objective comparison of the alignments. This work aims at encouraging authors to use a public and shared benchmark, instead of data collected ad-hoc, to ensure objectivity and repeatability, two fundamental characteristics in any scientific field.

Successful discontinuation of tyrosine kinase inhibitors has been achieved in patients with chronic-phase chronic myeloid leukemia (CML). Careful molecular monitoring after discontinuation warrants safe and prompt resumption of therapy. We retrospectively evaluated how molecular monitoring has been conducted in Italy in a cohort of patients who discontinued tyrosine kinase inhibitor (TKI) treatment per clinical practice. The outcome of these patients has recently been reported—281 chronic-phase CML patients were included in this subanalysis. Median follow-up since discontinuation was 2 years. Overall, 2203 analyses were performed, 17.9% in the first three months and 38.4% in the first six months. Eighty-six patients lost major molecular response (MMR) in a mean time of 5.7 months—65 pts (75.6%) during the first six months. We evaluated the number of patients who would experience a delay in diagnosis of MMR loss if a three-month monitoring schedule was adopted. In the first 6 months, 19 pts (29.2%) would have a one-month delay, 26 (40%) a 2-month delay. Very few patients would experience a delay in the following months. A less intense frequency of monitoring, particularly after the first 6 months off treatment, would not have affected the success of treatment-free remission (TFR) nor put patients at risk of progression.

Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps remains fairly unexplored, even though the path toward high definition 3D maps, produced mainly from LiDARs, is clear. In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition. We trained a 2D and a 3D DNN that create embeddings, respectively from images and from point clouds, that are close to each other whether they refer to the same place. An extensive experimental activity is presented to assess the effectiveness of the approach w.r.t. different learning paradigms, network architectures, and loss functions. All the evaluations have been performed using the Oxford Robotcar Dataset, which encompasses a wide range of weather and light conditions.

Aim: The rehabilitation of voice disorders is an unmet need in multiple sclerosis (MS). The Lee Silverman Voice Treatment (LSVT LOUD) is a well-documented and effective speech treatment, developed to treat voice disorders in Parkinson Disease. The purpose of the present study was to examine the viability of applying the LSVT LOUD to individuals with MS and verify short- and long-term improvements in acoustic and perceptual voice parameters. Methods: A single subject design was performed in a consecutive sample of 8 subjects with MS. The subjects’ voice was recorded with PRAAT software for 5 days at baseline during the 16 treatment sessions, and at follow-up (FU) 6/12 months later. PRAAT provided data on sustained /a/ (SPL/a/) voice intensity and maximum phonation time (MPT/a/) of sustained /a/, and on functional sentences voice intensity. In addition, self-assessment questionnaire Voice Handicap Index, the perceptual GIRBAS scale and intensity of monologue were collected at first day of baseline, post-treatment and at FU. In the treatment phase each subject received treatment according to LSVT LOUD protocol. Visual analysis calculated for daily acoustic variables was used to determine baseline stability and analyse changes following treatment. The Wilcoxon test was used to assess statistically significant differences between baseline and post treatment. Results: All participants completed the LSVT LOUD programme; one participant dropped out at FU. Improvements in acoustic analysis were found: SPL/a/ improved on average (± standard deviation) 11.64 ± 4.19 dB with 7 subjects showing statistically significant improvement (P < 0.05); MPT/a/ improved on average 1.2 ± 1.53seconds, while intensity of functional sentences improved on average 8.11 ± 3.46 dB with 4 and 5 subjects showed statistically significant improvement, respectively. Intensity of monologue improved 14.90 ± 3.33 dB. Acoustic values are maintained or increased at FU respect to baseline. All subjects imp

Background: Hypophonia is a prevailing problem in people with multiple sclerosis (PwMS). However, evidence supporting the effectiveness of voice rehabilitation is lacking. Objective: The aim of this study was to identify the most effective method to reduce hypophonia. Methods: In this randomized controlled trial, 44 PwMS were randomized to intensive and high-effort voice treatment groups, the LSVT-LOUD®, and conventional treatment group. Subjects received 16 treatments (4 sessions/week) lasting 45 minutes. The primary outcome was voice intensity (dB) in monologue, vocalization, and sentences while voice handicap index (VHI) measured voice self-perception. Outcomes were assessed by a blinded observer at baseline, post-treatment, and 15-month follow-up (FU). Results: Linear models revealed a significant post-intervention between-group mean difference in favor of LSVT-LOUD for monologue: +6.3 dB (95% CI: 2.5 to 10.1); vocalization: +7.4 dB (95% CI: 2.3 to 12.5); and sentences: +9.5 dB (95% CI: 4.7 to 14.3). However, 43.7% PwMS in the LSVT-LOUD and 10% in the conventional treatment group obtained a full recovery of voice intensity (>60 dB) post-treatment, Fisher’s test = 13.3, p < 0.01. However, these improvements were not maintained at FU. Between-group differences at VHI were −10.8 (95% CI: −21.2 to −0.4) and −11.3 (95% CI: −24.3 to −1.7) in favor of LSVT-LOUD at post and FU. Conclusion: LSVT-LOUD can be a valid treatment to increase voice intensity in PwMS. However, results suggest the need for FU interventions targeting maintenance.

Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task; however, their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this article, we introduce the novel loop closure detection network (LCDNet) that effectively detects loop closures in light detection and ranging (LiDAR) point clouds by simultaneously identifying previously visited places and estimating the six degrees of freedom relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin, especially while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city.

It is judged safe to discontinue treatment with tyrosine kinase inhibitors (TKI) for chronic myeloid leukemia (CML) in experimental trials on treatment-free remission (TFR). We collected a total of 293 Italian patients with chronic phase CML who discontinued TKI in deep molecular response. Seventy-two percent of patients were on treatment with imatinib, and 28% with second generation TKI at the time of discontinuation. Median duration of treatment with the last TKI was 77 months [Interquartile Range (IQR) 54;111], median duration of deep molecular response was 46 months (IQR 31;74). Duration of treatment with TKI and duration of deep molecular response were shorter with second generation TKI than with imatinib (P<0.001). Eighty-eight percent of patients discontinued as per clinical practice, and reasons for stopping treatment were: toxicity (20%), pregnancy (6%), and shared decision between treating physician and patient (62%). After a median follow up of 34 months (range, 12-161) overall estimated TFR was 62% (95%CI: 56;68). At 12 months, TFR was 68% (95%CI: 62;74) for imatinib, 73% (95%CI: 64;83) for second generation TKI. Overall median time to restart treatment was six months (IQR 4;11). No progressions occurred. Although our study has the limitation of a retrospective study, our experience within the Italian population confirms that discontinuation of imatinib and second generation TKI is feasible and safe in clinical practice

This paper deals with the task of ego-vehicle localization at intersections, a significant task in autonomous road driving. We propose an online vision-based method that can hence be applied if the intersection is visible. It relies on stereo images and on a coarse street-level pose estimate, used to retrieve intersection data from a digital map service. Pixel-level semantic segmentation, and 3D reconstruction from state-of-the art Deep Neural Networks are coupled with an intersection model; this allows good positioning accuracy compared to the state-of-the-art in this task. To demonstrate the effectiveness of the method and make it possible to compare it with other methods, an extensive activity has been conducted in order to set up a dataset of approaches to an intersection, which has then been used to benchmark the proposed method. The dataset is made available to the community, and it currently includes more than forty intersection approaches, from KITTI. Another important contribution of the paper is the definition of criteria for the comparison of different methods, on recorded datasets. The proposed method achieves nearly sub-meter accuracy in difficult real conditions.

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in Open-StreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

Background: Therapeutic drug monitoring (TDM) is a tool to personalize and optimize dosing by measuring the drug concentration and subsequently adjusting the dose to reach a target concentration or exposure. The evidence to support TDM is however often ranked as expert opinion. Limitations in study design and sample size have hampered definitive conclusions of the potential added value of TDM. Objectives: We aim to give expert opinion and discuss the main points and limitations of available data from antibiotic TDM trials and emphasize key elements for consideration in design of future clinical studies to quantify the benefits of TDM. Sources: The sources were peer-reviewed publications, guidelines and expert opinions from the field of TDM. Content: This review focuses on key aspects of antimicrobial TDM study design: describing the rationale for a TDM study, assessing the exposure of a drug, assessing susceptibility of pathogens and selecting appropriate clinical endpoints. Moreover we provide guidance on appropriate study design.

Purpose: The aim of this study was to develop an exercise protocol to improve maximal mouth opening (MMO), tongue protrusion (Tprot), tongue strength (Tstren), and lip strength (Lstren), and to assess its effects on subjects with scleroderma. Methods: We performed four replicated single-system studies in a consecutive sample of subjects with scleroderma. An instrumented assessment measured MMO, Tprot, Tstren, and Lstren. Each day, subjects were assessed and performed orofacial exercises conducted by speech therapists. Treatments were first aimed at improving mouth physical characteristics by impairment-oriented exercises and then to improve skills with function-oriented exercises. Results: The mean phase differences between assessment and treatment phases across subjects were from 0.88 to 9.56 mm in MMO, from 2.03 to 12.3 mm in Tprot, from-0.12 to 5.35 N in Tstren, and from-0.84 to 5.19 N in Lstren. After treatment, 3 subjects crossed the 5th percentile discriminating normal from abnormal performances for both Tstren and Tprot, while this occurred in 2 subjects for MMO and Lstren. Conclusions: The results of this study suggest that rehabilitation appears to be useful in reducing tongue and lip impairments and in improving oral functions in subjects with scleroderma.

In questa tesi presento il mio lavoro di dottorato, che ha riguardato la localizzazione di un veicolo stradale in ambito urbano. In particolare, ho fatto uso di tecniche di Machine Learning per l'elaborazione delle immagini provenienti dalle camere a bordo di un veicolo. I sistemi sviluppati hanno lo scopo di produrre una stima della posa del veicolo e quindi, nel caso di deep neural networks, si tratta di reti che effettuano una pose regression. Al meglio della mia conoscenza, alcuni dei miei sviluppi sono i primi in letteratura in grado di effettuare visual pose regression basandosi su mappe tridimensionali. Queste mappe tridimensionali sono usualmente ottenute mediante dispositivi LIDAR da parte di grosse aziende specializzate che costituiscono il mondo delle aziende che producono mappe (HERE, TOM-TOM, etc.). Questo consente di attendersi uno sviluppo commerciale delle mappe ad altissima definizione, che risulteranno quindi utilizzabili per la localizzazione da parte del veicolo. Da nostri contatti con produttori industriali di sistemi di guida autonoma per veicoli stradali, ci risulta che la presenza di LIDAR a bordo dei veicoli sia a tutt'oggi osteggiata, in quanto non sono oggi disponibili LIDAR privi di apparati di scansione meccanica che risultano quindi inusabili a causa delle accelerazioni e vibrazioni presenti su un veicolo stradale. Per questo motivo, essendo inoltre i veicoli usualmente attrezzati di diverse camere già oggi, il fatto di riuscire a svolgere una localizzazione visuale su mappe ad alta definizione costituisce una prospettiva molto significativa, non solo sul piano della ricerca ma anche dell'applicazione. La localizzazione è un aspetto essenziale per ogni robot mobile, specialmente per veicoli stradali a guida autonoma, dove una cattiva stima di posizione può a portare ad incidenti anche fatali di utenti della strada. Non si può fare affidamento solo sui Global Navigation Satellite Systems, come il GPS, a causa della accuratezza e affi, In this thesis, we present different approaches which dealt with the localization of a road vehicle in urban settings. In particular, we made use of machine learning techniques to process the images coming from onboard cameras of a vehicle. The developed systems aim at computing a pose and therefore in case of deep neural networks, they are referred to as pose regression networks. To the best of our knowledge, some of the developed approaches are the first deep neural networks in the literature capable of computing visual pose regression basing on 3D maps. Such 3D maps are usually built by means of LIDAR devices, and this is done from large specialized companies, which make the world of commercial map makers. It is therefore likely to expect a commercial development of very high definition maps, which will make it possible to use them for the localization of vehicles. From our contacts with industrial makers of autonomous driving systems for road vehicles, we know that LIDARs onboard the vehicles, as for today, are not well accepted, mainly because of the state-of-the-art of LIDARs, which are based on mechanical scanning systems and therefore are not capable of sustaining the accelerations and vibrations of a road vehicle. For this reason, as today's vehicles already include many cameras, to be able to visually localize a vehicle on high-definition maps is a very significant perspective, not only under a research point of view, but also for real applications. The localization is an essential task for any mobile robot, especially for self-driving cars, where a wrong position estimate might lead to accidents and even fatal injuries for other road users. We cannot rely only on Global Navigation Satellites Systems, such as the Global Positioning System, because the accuracy and reliability of these systems are often inadequate for autonomous driving applications. This is even truer in urban environments, where buildings may block or deflect the satellites' signals, lead

Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to make a sizeable impact on improving capabilities of metric visual localization. One of the major hindrances has been the inability of existing Convolutional Neural Network (CNN)- based pose regression methods to generalize to previously unseen places. Our recently introduced CMRNet effectively addresses this limitation by enabling map independent monocular localization in LiDAR-maps. In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters. We enable this capability by combining deep learning with geometric techniques, and by moving the metric reasoning outside the learning process. In this way, the weights of the network are not tied to a specific camera. Extensive evaluations of CMRNet++ on three challenging autonomous driving datasets, i.e., KITTI, Argoverse, and Lyft5, show that CMRNet++ outperforms CMRNet as well as other baselines by a large margin. More importantly, for the first-time, we demonstrate the ability of a deep learning approach to accurately localize without any retraining or fine-tuning in a completely new environment and independent of the camera parameters.

Patients with Philadelphia-negative myeloproliferative neoplasm (MPN) are prone to the development of second cancers, but the factors associated with these events have been poorly explored. In an international nested case-control study, we recruited 647 patients with carcinoma, nonmelanoma skin cancer, hematological second cancer, and melanoma diagnosed concurrently or after MPN diagnosis. Up to 3 control patients without a history of cancer and matched with each case for center, sex, age at MPN diagnosis, date of diagnosis, and MPN disease duration were included (n 5 1234). Cases were comparable to controls for MPN type, driver mutations and cardiovascular risk factors. The frequency of thrombosis preceding MPN was similar for cases and controls (P 5 .462). Thrombotic events after MPN and before second cancer were higher in cases than in controls (11.6% vs 8.1%; P 5 .013), because of a higher proportion of arterial thromboses (6.2% vs 3.7%; P 5 .015). After adjustment for confounders, the occurrence of arterial thrombosis remained independently associated with the risk of carcinoma (odds ratio, 1.97; 95% confidence interval, 1.14-3.41), suggesting that MPN patients experiencing arterial events after MPN diagnosis deserve careful clinical surveillance for early detection of carcinoma.

N/A

One out of ten patients with Philadelphia-negative myeloproliferative neoplasms (MPN) develop a second cancer (SC): in such patients we aimed at assessing the survival impact of SC itself and of MPN-specific therapies. Data were therefore extracted from an international nested case-control study, recruiting 798 patients with SC diagnosed concurrently or after the MPN. Overall, 2995 person-years (PYs) were accumulated and mortality rate (MR) since SC diagnosis was 5.9 (5.1-6.9) deaths for every 100 PYs. A “poor prognosis” SC (stomach, esophagus, liver, pancreas, lung, ovary, head-and-neck or nervous system, osteosarcomas, multiple myeloma, aggressive lymphoma, acute leukemia) was reported in 26.3% of the patients and was the cause of death in 65% of them (MR 11.0/100 PYs). In contrast, patients with a “non-poor prognosis” SC (NPPSC) incurred a MR of 4.6/100 PYs: 31% of the deaths were attributed to SC and 15% to MPN evolution. At multivariable analysis, death after SC diagnosis was independently predicted (HR and 95% CI) by patient age greater than 70 years (2.68; 1.88-3.81), the SC prognostic group (2.57; 1.86-3.55), SC relapse (1.53; 10.6-2.21), MPN evolution (2.72; 1.84-4.02), anemia at SC diagnosis (2.32; 1.49-3.59), exposure to hydroxyurea (1.89; 1.26-2.85) and to ruxolitinib (3.63; 1.97-6.71). Aspirin was protective for patients with a NPPSC (0.60; 0.38-0.95). In conclusion, SC is a relevant cause of death competing with MPN evolution. Prospective data are awaited to confirm the role of cytoreductive and anti-platelet drugs in modulating patient survival after the occurrence of a SC.

Nel campo della robotica mobile la localizzazione dell’osservatore nel suo ambiente operativo costituisce da sempre un problema chiave. La ricerca condotta nel lab. IRA (Informatica e Robotica per l’Automazione) del Dipartimento di Informatica, Sistemistica e Comunicazione dell’Università degli Studi di Milano - Bicocca si colloca nel settore dell’intelligenza artificiale per Sistemi di Trasporto e “Smart Cities”.

Nel campo della robotica mobile la localizzazione dell’osservatore nel suo ambiente operativo costituisce da sempre un problema chiave. La ricerca condotta nel lab. IRA (Informatica e Robotica per l’Automazione) del Dipartimento di Informatica, Sistemistica e Comunicazione dell’Università degli Studi di Milano - Bicocca si colloca nel settore dell’intelligenza artificiale per Sistemi di Trasporto e “Smart Cities”.

Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps remains fairly unexplored, even though the path toward high definition 3D maps, produced mainly from LiDARs, is clear. In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition. We trained a 2D and a 3D DNN that create embeddings, respectively from images and from point clouds, that are close to each other whether they refer to the same place. An extensive experimental activity is presented to assess the effectiveness of the approach w.r.t. different learning paradigms, network architectures, and loss functions. All the evaluations have been performed using the Oxford Robotcar Dataset, which encompasses a wide range of weather and light conditions.

In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network (CNN) to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i. e., CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07° median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17°. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.

We conducted a large international nested case-control study including 1881 patients with Philadelphia-negative myeloproliferative neoplasms (MPN). Cases (n = 647) were patients with second cancer (SC: carcinoma, non-melanoma skin cancer, hematological second cancer, and melanoma) and controls (n = 1234) were patients without SC, matched with cases for sex, age at MPN diagnosis, date of MPN diagnosis, and MPN disease duration. The aim was to evaluate the risk of SC after exposure to cytoreductive drugs. Patients exposed to hydroxyurea (HU) (median: 3 years) had a risk of SC similar to unexposed patients (OR = 1.06, 95% CI 0.82–1.38). In contrast, in cancer-specific stratified multivariable analysis, HU had two-fold higher risk of non-melanoma (NM) skin cancer (OR = 2.28, 95% CI 1.15–4.51). A significantly higher risk of NM-skin cancer was also documented for pipobroman (OR = 3.74, 95% CI 1.00–14.01), ruxolitinib (OR = 3.87, 95% CI 1.18–12.75), and for drug combination (OR = 3.47, 95% CI 1.55–7.75). These three drugs did not show excess risk of carcinoma and hematological second cancer compared with unexposed patients. Exposure to interferon, busulfan, and anagrelide did not increase the risk. In summary, while it is reassuring that no excess of carcinoma was documented, a careful dermatologic active surveillance before and during the course of treatments is recommended.

In this paper we present a probabilistic lane-localization algorithm for highway-like scenarios designed to increase the accuracy of the vehicle localization estimate. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The idea behind the proposed approach is to exploit the availability of OpenStreetMap road properties in order to reduce the localization uncertainties that would result from relying only on a noisy line detector, by leveraging consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing a line detection algorithm and showing we could achieve a much more usable, i.e., stable and reliable, lane-localization over more than 100Km of highway scenarios, recorded both in Italy and Spain. Moreover, as we could not find a suitable dataset for a quantitative comparison of our results with other approaches, we collected datasets and manually annotated the Ground Truth about the vehicle ego-lane. Such datasets are made publicly available for usage from the scientific community.

In this report we present the work performed in order to build a dataset for benchmarking vision-based localization at intersections, i.e., a set of stereo video sequences taken from a road vehicle that is approaching an intersection, altogether with a reliable measure of the observer position. This report is meant to complement our paper “Vision-Based Localization at Intersections using Digital Maps” submitted to ICRA2019. It complements the paper because the paper uses the dataset, but it had no space for describing the work done to obtain it. Moreover, the dataset is of interest for all those tackling the task of online localization at intersections for road vehicles, e.g., for a quantitative comparison with the proposal in our submitted paper, and it is therefore appropriate to put the dataset description in a separate report. We considered all datasets from road vehicles that we could find as for the end of August 2018. After our evaluation, we kept only sub-sequences from the KITTI dataset. In the future we will increase the collection of sequences with data from our vehicle.

Once-daily (od), low-dose aspirin (75–100 mg) is recommended to reduce the thrombotic risk of patients with essential thrombocytemia (ET). This practice is based on data extrapolated from other high-risk patients and an aspirin trial in polycythemia vera, with the assumption of similar aspirin pharmacodynamics in the two settings. However, the pharmacodynamics of low-dose aspirin is impaired in ET, reflecting accelerated renewal of platelet cyclooxygenase (COX)-1. ARES is a parallel-arm, placebo-controlled, randomized, dose-finding, phase II trial enrolling 300 ET patients to address two main questions. First, whether twice or three times 100 mg aspirin daily dosing is superior to the standard od regimen in inhibiting platelet thromboxane (TX)A2 production, without inhibiting vascular prostacyclin biosynthesis. Second, whether long-term persistence of superior biochemical efficacy can be safely maintained with multiple vs. single dosing aspirin regimen. Considering that the primary study end point is serum TXB2, a surrogate biomarker of clinical efficacy, a preliminary exercise of reproducibility and validation of this biomarker across all the 11 participating centers was implemented. The results of this preliminary phase demonstrate the importance of controlling reproducibility of biomarkers in multicenter trials and the feasibility of using serum TXB2 as a reliable end point for dose-finding studies of novel aspirin regimens.

The MOSCAB experiment (Materia OSCura A Bolle) uses the “geyser technique”, a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector and technical solutions of the device are reported in detail. First results obtained in a series of test runs taken in laboratory demonstrate that we have successfully built and tested a geyser-concept bubble chamber that can be used in particle physics, especially in dark matter searches, and that we are ready to move underground for extensive data taking.

The MOSCAB experiment (Materia OSCura A Bolle) uses the “geyser technique”, a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector and technical solutions of the device are reported in detail. First results obtained in a series of test runs taken in laboratory demonstrate that we have successfully built and tested a geyser-concept bubble chamber that can be used in particle physics, especially in dark matter searches, and that we are ready to move underground for extensive data taking.

In this paper we propose a probabilistic approach for detecting and classifying urban road intersections from a moving vehicle. The approach is based on images from an onboard stereo rig; it relies on the detection of the road ground plane on one side, and on a pixel-level classification of the road on the other. The two processing pipelines are then integrated and the parameters of the road components, i.e., the intersection geometry, are inferred. As opposed to other state-of-the-art offline methods, which require processing of the whole video sequence, our approach integrates the image data by means of an online procedure. The experiments have been performed on well-known KITTI datasets, allowing for future comparisons.

[EN] The MOSCAB experiment (Materia OSCura A Bolle) uses the "geyser technique", a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector and technical solutions of the device are reported in detail. First results obtained in a series of test runs taken in laboratory demonstrate that we have successfully built and tested a geyser-concept bubble chamber that can be used in particle physics, especially in dark matter searches, and that we are ready to move underground for extensive data taking.

A 48-year-old woman with multiple sclerosis (MS), treated with natalizumab for more than one year without clinical and magnetic resonance imaging (MRI) signs of disease activity, was diagnosed with definite progressive multifocal leukoencephalopathy (PML). She presented with subacute motor deficit of the right upper limb (UL), followed by involvement of the homolateral leg and urinary urgency. The patient was treated with steroids and plasma exchange. On follow-up MRI scans, the PML lesion remained stable and no MS rebounds were observed, but the patient complained of a progressive worsening of the right UL motor impairment, becoming dependent in most activities of daily living. A cycle of multidisciplinary rehabilitation (MDR) was then started, including daily sessions of UL robot therapy and occupational therapy. Functional MRI (fMRI) was acquired before and at the end of the MDR cycle using a motor task which consisted of 2 runs: in one run the patient was asked to observe while the second one consisted of hand grasping movements. At the end of the rehabilitation period, both the velocity and the smoothness of arm trajectories during robot-based reaching movements were significantly improved. After MDR, compared with baseline, fMRI showed significantly increased functional activation within the sensory-motor network in the active, motor task, while no significant differences were found in the observational task. MDR in MS, including robot-assisted UL training, seems to be clinically efficacious and to have a significant impact on brain functional reorganization on a short-term, even in the presence of superimposed tissue damage provoked by PML.

The MOSCAB experiment (Materia OSCura A Bolle) uses the "geyser technique", a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector and technical solutions of the device are reported in detail. First results obtained in a series of test runs taken in laboratory demonstrate that we have successfully built and tested a geyser-concept bubble chamber that can be used in particle physics, especially in dark matter searches, and that we are ready to move underground for extensive data taking., Comment: 11pages, 7 figures, 1 table

[EN] The MOSCAB experiment (Materia OSCura A Bolle) uses the "geyser technique", a variant of the superheated liquid technique of extreme simplicity. Operating principles of the new dark matter detector and technical solutions of the device are reported in detail. First results obtained in a series of test runs taken in laboratory demonstrate that we have successfully built and tested a geyser-concept bubble chamber that can be used in particle physics, especially in dark matter searches, and that we are ready to move underground for extensive data taking.

OBJECTIVE - To assess the effects of inhibited gastrointestinal cholesterol absorption in statin-treated dyslipidemic patients. RESEARCH DESIGN AND METHODS - In a multicenter prospective randomized double-blind placebo-controlled trial, we primarily compared by ANCOVA the effect of 2-month ezetimibe (10 mg/day) or placebo therapy on LDL cholesterol serum levels in 108 type 2 diabetic patients with albuminuria [removed]135 mg/dl despite simvastatin treatment (40 mg/day). RESULTS - Unlike placebo, ezetimibe decreased LDL cholesterol from 99 ± 31 to 66 ± 22 mg/dl, total cholesterol from 162 ± 36 to 124 ± 30 mg/dl, and apolipoprotein B from 83 ± 22 to 64 ± 18 mg/dl (P < 0.0001 for all changes versus placebo). A total of 72 and 17% of patients on ezetimibe or placebo achieved LDL levels <70 mg/dl, respectively (P < 0.0001). Treatment was well tolerated. CONCLUSIONS - Adding ezetimibe to simvastatin therapy helps to improve the proatherogenic lipoprotein profile in type 2 diabetic patients who fail to reach recommended lipid targets with statin therapy alone.

In this paper we present a technique that takes advantage of detected building façades and OpenStreetMaps data to improve the localization of an autonomous vehicle driving in an urban scenario. The proposed approach leverages images from a stereo rig mounted on the vehicle to produce a mathematical representation of the buildings' façades within the field of view. This representation is matched against the outlines of the surrounding buildings as they are available on OpenStreetMaps. The information is then fed into our probabilistic framework, called Road Layout Estimation, in order to produce an accurate lane-level localization of the vehicle. The experiments conducted on the well-known KITTI datasets prove the effectiveness of our approach.

The optimal duration of treatment with vitamin K antagonists (VKA) after venous thromboembolism (VTE) in patients with Philadelphia-negative myeloproliferative neoplasms (MPNs) is uncertain. To tackle this issue, we retrospectively studied 206 patients with MPN-related VTE (deep venous thrombosis of the legs and/or pulmonary embolism). After this index event, we recorded over 695 pt-years 45 recurrences, venous in 36 cases, with an incidence rate (IR) of 6.5 per 100 pt-years (95% confidence interval (CI): 4.9-8.6). One hundred fifty-five patients received VKA; the IR of recurrent thrombosis per 100 pt-years was 4.7 (95% CI: 2.8-7.3) on VKA and 8.9 (95% CI: 5.7-13.2) off VKA (P=0.03). In patients receiving VKA, the IR of recurrent thrombosis per 100 pt-years was 5.3 (95% CI: 3.2-8.4) among 108 patients on long-term VKA and 12.8 (95% CI: 7.3-20.7) after discontinuation among the 47 who ceased treatment (P=0.008), with a doubled risk of recurrence after stopping VKA (hazard ratio: 2.21, 95% CI: 1.19-5.30). The IR of major bleeding per 100 pt-years was 2.4 (95%: CI: 1.1-4.5) on VKA and 0.7 (95% CI: 0.08-2.5) off VKA (P=0.08). In conclusion, in MPN patients with VTE recurrent thrombosis is significantly reduced by VKA and caution should be adopted in discontinuation; however, the incidence of recurrence on treatment remains high, calling for clinical trials aimed to improve prophylaxis in this setting.

We retrospectively studied 181 patients with polycythaemia vera (n=67), essential thrombocythaemia (n=67) or primary myelofibrosis (n=47), who presented a first episode of splanchnic vein thrombosis (SVT). Budd-Chiari syndrome (BCS) and portal vein thrombosis were diagnosed in 31 (17.1%) and 109 (60.3%) patients, respectively; isolated thrombosis of the mesenteric or splenic veins was detected in 18 and 23 cases, respectively. After this index event, the patients were followed for 735 patient years (pt-years) and experienced 31 recurrences corresponding to an incidence rate of 4.2 per 100 pt-years. Factors associated with a significantly higher risk of recurrence were BCS (hazard ratio (HR): 3.03), history of previous thrombosis (HR: 3.62), splenomegaly (HR: 2.66) and leukocytosis (HR: 2.8). Vitamin K-antagonists (VKA) were prescribed in 85% of patients and the recurrence rate was 3.9 per 100 pt-years, whereas in the small fraction (15%) not receiving VKA more recurrences (7.2 per 100 pt-years) were reported. Intracranial and extracranial major bleeding was recorded mainly in patients on VKA and the corresponding rate was 2.0 per 100 pt-years. In conclusion, despite anticoagulation treatment, the recurrence rate after SVT in myeloproliferative neoplasms is high and suggests the exploration of new avenues of secondary prophylaxis with new antithrombotic drugs and JAK-2 inhibitors.

Lo studio esplora il rapporto tra deficit dello stato di coscienza e processi semantici, e propone un'applicazione del biomarcatore ERP N400

This open-label, single centre pilot study was designed to evaluate safety and tolerability of the combination of the drugs isosorbide dinitrate, a nitric oxide donor, and ibuprofen, a non steroid anti-inflammatory drug, in a cohort of adult dystrophic patients (Duchenne, Becker and Limb-Girdle Muscular Dystrophy). Seventy-one patients were recruited: 35, treated with the drug combination for 12 months, and 36 untreated. Safety and adverse events were assessed by reported signs and symptoms, physical examinations, blood tests, cardiac and respiratory function tests. Exploratory outcomes measure, such as the motor function measure scale, were also applied. Good safety and tolerability profiles of the long-term co-administration of the drugs were demonstrated. Few and transient side effects (i.e. headache and low blood pressure) were reported. Additionally, exploratory outcomes measures were feasible in all the disease population studied and evidenced a trend towards amelioration that reached statistical significance in one dimension of the MFM scale. Systemic administration of ibuprofen and isosorbide dinitrate provides an adequate safety margin for clinical studies aimed at assessing efficacy.

Lo studio esplora il rapporto tra deficit dello stato di coscienza e processi semantici, e propone un'applicazione del biomarcatore ERP N400

Contains fulltext : 96886.pdf (publisher's version ) (Closed access)

Contains fulltext : 96886.pdf (publisher's version ) (Closed access)

Contains fulltext : 96886.pdf (publisher's version ) (Closed access)

The purpose of this study was to investigate retrospectively the mRNA expression of genes involved in different DNA repair pathways implicated in processing platinum-induced damage in 171 chemotherapy-naïve ovarian tumours and correlate the expression of the different genes with clinical parameters. The expression of genes involved in DNA repair pathways (PARP1, ERCC1, XPA, XPF, XPG, BRCA1, FANCA, FANCC, FANCD2, FANCF and PolEta), and in DNA damage transduction (Chk1 and Claspin) was measured by RT-PCR in 13 stage I borderline and 77 stage I and 88 III ovarian carcinomas. ERCC1, XPA, XPF and XPG genes were significantly less expressed in stage III than in stage I carcinoma; BRCA1, FANCA, FANCC, FANCD2 gene expressions were low in borderline tumours, higher in stage I carcinomas and lower in stage III samples. High levels of ERCC1, XPA, FANCC, XPG and PolEta correlated with an increase in Overall Survival (OS) and Progression Free Survival (PFS), whilst high BRCA1 levels were associated with PFS on univariate analysis. With multivariate analyses no genes retained an association when adjusted by stage, grade and residual tumour. A tendency towards a better PFS was observed in patients with the highest level of ERCC1 and BRCA1 after platinum-based therapy than those given both platinum and taxol. The expression of DNA repair genes differed in borderline stage I, stage I and stage III ovarian carcinomas. The role of DNA repair genes in predicting the response in ovarian cancer patients seems far from being established.

With the increasing use of mycophenolic acid (MPA) in solid organ transplantation, the need for more accurate drug dosing has become evident. Personalized immunosuppressive therapy requires better strategies for avoidance of drug-related toxicity while maintaining efficacy. Few studies have assessed the clinical usefulness of therapeutic drug monitoring (TDM) of MPA in solid organ transplantation in a prospective way, and they have produced opposing results. To provide clinicians with an objective and balanced clinical interpretation of the current scientific evidence on TDM of MPA, a consensus meeting involving 47 experts from around the world was commissioned by The Transplantation Society and held in Rome on November 20 to 21, 2008. The goal of this consensus meeting was to offer information to transplant practitioners on clinically relevant pharmacokinetic characteristics of MPA, to rationalize the basis for currently advised target exposure ranges for MPA in various types of organ transplantation, and to summarize available methods for application of MPA TDM in clinical practice. Although this consensus report does not evaluate the final role of MPA TDM in transplantation, it seeks to examine the current scientific evidence for concentration-controlled dosing of MPA. Copyright

Contains fulltext : 71444.pdf (publisher's version ) (Closed access), OBJECTIVE: The pharmacokinetics of mycophenolic acid (MPA) were compared in renal transplant patients receiving either mycophenolate mofetil (MMF) or enteric-coated mycophenolate sodium (EC-MPS). METHODS: MPA concentration-time profiles were included from EC-MPS- (n = 208) and MMF-treated (n = 184) patients 4-257 months after renal transplantation. Population pharmacokinetic analysis was performed using nonlinear mixed-effects modelling (NONMEM). A two-compartment model with first-order absorption and elimination was used to describe the data. RESULTS: No differences were detected in MPA clearance, intercompartmental clearance, or the central or peripheral volume of distribution. Respective values and interindividual variability (IIV) were 16 L/h (39%), 22 L/h (78%), 40 L (100%) and 518 L (490%). EC-MPS was absorbed more slowly than MMF with respective absorption rate constant values of 3.0 h(-1) and 4.1 h(-1) (p < 0.001) [IIV 187%]. A mixture model was used for the change-point parameter lag-time (t(lag)) in order to describe IIV in this parameter adequately for EC-MPS. Following the morning dose of EC-MPS, the t(lag) values were 0.95, 1.88 and 4.83 h for 51%, 32% and 17% of the population (IIV 8%), respectively. The morning t(lag) following EC-MPS administration was significantly different from both the t(lag) following MMF administration (0.30 h; p < 0.001 [IIV 11%]) and the t(lag) following the evening dose of EC-MPS (9.04 h; p < 0.001 [IIV 40%]). Post hoc analysis showed that the t(lag) was longer and more variable following EC-MPS administration (morning median 2.0 h [0.9-5.5 h], evening median 8.9 h [5.4-12.3 h]) than following MMF administration (median 0.30 h [0.26-0.34 h]; p < 0.001). The morning MPA predose concentrations were higher and more variable following EC-MPS administration than following MMF administration, with respective values of 2.6 mg/L (0.4-24.4 mg/L) and 1.6 mg/L (0.2-7.6 mg/L). The correlation between predose concentrations and the area