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Spinal muscular atrophy (SMA) refers to a group of genetic neuromuscular disorders affecting lower motor neurons causative of numerous phenotypes. To date, according to the age of onset, maximum muscular activity achieved, and life expectation four types of SMA are recognized, all caused by mutations in the SMN1 gene with SMN2 copy number influencing disease severity. Herein, we describe the case of a 31-year-old young male with normal psychomotor development who has experienced fatigue, cramps, and muscle fasciculations in the lower limbs for a period of 2 months. Based on electrophysiological and clinical findings we performed SMA genetic, clinical exome and RNA expression of candidate genes which led us to suggest SMN1-SMN2 genes [(2+0) and (0+0)] combination as possibly being implicated in the phenotype.

none 25 no Abstract OBJECTIVES: To evaluate the incidence and dose-dependency of mitoxantrone (MTX)-associated acute myelocytic leukemia (AML) in the network of Italian multiple sclerosis (MS) clinics. METHODS: We performed a multicenter retrospective cohort study of patients treated with MTX in MS centers under the Italian national health care system between 1998 and 2008. Demographic, disease, treatment, and follow-up information were collected using hospital records. RESULTS: Data were available for 3,220 patients (63% women) from 40 Italian centers. Follow-up (mean ± SD) was 49 ± 29 months (range 12-140 months). We observed 30 cases of AML (incidence 0.93% [95% confidence interval 0.60%-1.26%]). The mean cumulative dose was higher in patients with AML (78 vs 65 mg/m(2), p = 0.028). The median interval from the start of therapy to AML diagnosis was longer than expected at 33 months (range 13-84 months); 8 patients (27%) developed AML 4 years or more after the first MTX infusion. The rate of mortality associated with AML was 37%. CONCLUSIONS: This higher than expected risk of AML and related mortality requires that treatment decisions must be made jointly between clinicians and patients who understand their prognosis, treatment options, and treatment-related risks. The now large exposed MS population must be monitored for hematologic abnormalities for at least 6 years from the end of therapy, to ensure the rapid actions needed for early diagnosis and treatment of AML. none Martinelli V; Cocco E; Capra R; Salemi G; Gallo P; Capobianco M; Pesci I; Ghezzi A; Pozzilli C; Lugaresi A; Bellantonio P; Amato MP; Grimaldi LM; Trojano M; Mancardi GL; Bergamaschi R; Gasperini C; Rodegher M; Straffi L; Ponzio M; Comi G; For The Italian Mitoxantrone Group; De Luca G; Di Tommaso V; Farina D Martinelli V; Cocco E; Capra R; Salemi G; Gallo P; Capobianco M; Pesci I; Ghezzi A; Pozzilli C; Lugaresi A; Bellantonio P; Amato MP; Grimaldi LM; Trojano M; Mancardi GL; Bergamaschi R; Gasperini C; Rodegher M; Straffi L; Ponzio M; Comi G; For The Italian Mitoxantrone Group; De Luca G; Di Tommaso V; Farina D

In the last few years, the use of gestural data has become a key enabler for human-computer interaction (HCI) applications. The growing diffusion of low-cost acquisition devices has thus led to the development of a class of middleware aimed at ensuring a fast and easy integration of such devices within the actual HCI applications. The purpose of this paper is to present a modular middleware for gestural data and devices management. First, we describe a brief review of the state of the art of similar middleware. Then, we discuss the proposed architecture and the motivation behind its design choices. Finally, we present a use case aimed at demonstrating the potential uses as well as the limitations of our middleware.

To evaluate the efficacy and safety of natalizumab in patients with active relapsing-remitting multiple sclerosis (MS). We included 285 MS patients receiving natalizumab. Clinical, neuroradiological and safety data were registered every 6 months. Neutralizing antibodies (NABs) were tested after 6 months of treatment. After 1 year, the annualized relapse rate decreased to 0.26, with a significant reduction compared to the previous year (2.13). At 24 months the proportion of "relapse free" patients was 78% while that of "MRI free" patients was 69%. Considering clinical and MRI cumulative activity, "disease free" patients were 63% at 24 months. A total of 18 patients showed NABs positivity. We reported 34 cases of treatment interruptions. In conclusion, our data confirm the remarkable efficacy of natalizumab in a group of patients with higher disease activity than that of pivotal studies. © Springer-Verlag 2010.

Background Thrombosed external haemorrhoids are one of the most frequent anorectal emergencies. They are associated with swelling and intense pain. Internal sphincter hypertonicity plays a role in the aetiology of the pain. This study evaluated the efficacy and safety of an intrasphincteric injection of botulinum toxin for pain relief in patients with thrombosed external haemorrhoids. Methods Thirty patients with thrombosed external haemorrhoids who refused surgical operation were randomized into two groups. Patients received an intrasphincteric injection of either 0·6 ml saline or 0·6 ml of a solution containing 30 units botulinum toxin. Anorectal manometry was performed before treatment and 5 days afterwards. Results After 5 days of treatment, the maximum resting pressure fell in both groups, but was significantly lower in the botulinum toxin group (P = 0·004). Pain intensity was significantly reduced within 24 h of botulinum toxin treatment (P < 0·001), but only after 1 week in the placebo group (P = 0·019). Conclusion A single injection of botulinum toxin into the anal sphincter seems to be effective in rapidly controlling the pain associated with thrombosed external haemorrhoids, and could represent an effective conservative treatment for this condition. Registration number: NCT00717782 (http://www.clinicaltrials.gov).

The way people access resources, data and services, is radically changing using modern mobile technologies. In this scenario, biometry is a good solution for security issues even if its performance is influenced by the acquired data quality. In this paper, a novel embedded automatic fingerprint authentication system (AFAS) for mobile users is described. The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures. The system is focused on the quality evaluation of the raw acquired fingerprint, identifying areas of poor quality. Using this approach, no image enhancement process is needed after the fingerprint acquisition phase. The Agility RC2000 board has been used to prototype the embedded device. Due its different image resolution and quality, the experimental tests have been conducted on both PolyU and FVC2002 DB2-B free databases. Experimental results show an interesting trade-off between used resources, authentication time, and accuracy rate. The best achieved false acceptance rate (FAR) and false rejection rate (FRR) indexes are 0% and 6.25%, respectively. The elaboration time is 62.6 ms with a working frequency of 50 MHz.

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using a lot of training examples, the proposed approach requires only 18 directional images per class. Experimental results, conducted on a consistent subset of the free downloadable PolyU database, show a classification rate of 87.59%.

Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancement phase. Experimental trials, conducted on a subset of the free downloadable PolyU database, show a classification rate of 91% over a 100 images test database using only 12 training examples.

The aim of the present study was to explore further the effects of light deprivation (LD) on visual cortex excitability. Healthy subjects reporting reliable induction of phosphenes by occipital transcranial magnetic stimulation (TMS) underwent 60 min of complete LD. Phosphene threshold (PT) was measured before (T0), after 45 min (T1) and 60 min (T2) of LD, and then every 10 min after light re-exposure until recovery to T0 values. Repetitive TMS (rTMS) (at 1 or 10 Hz) was applied in separate sessions during the last 15 min of LD. PTs significantly decreased after 45 min of LD. rTMS differentially modified the effects of 60 min LD on PTs depending on stimulation frequency. One hertz rTMS did not change the decreasing of PT values as observed in baseline condition, but significantly prolonged the time to recover T0 PT values after light re-exposure. By contrast, 10 Hz rTMS significantly increased PT and the time to recover T0 PT values after light re-exposure was shortened. The results of this study show that the modulatory effects of different rTMS frequencies on visual cortex critically depend on the pre-existing excitability state of inhibitory and facilitatory circuits, and provide novel insights into the neurophysiological changes that take place in the visual cortex following functional visual deafferentation.

During the research activity in my Ph.D. course, I thoroughly studied the biometric systems and the relevant design and implementation techniques allowing the employment of such systems in embedded devices. I focused my attention on the fingerprint-based recognition and classification systems, and on their implementation on Field Programmable Gate Array (FPGA) devices. I was prompted to study biometric systems mainly because these systems may play a key role in the new emerging market of mobile devices (for example, they are recently available in the new generation of Apple and Samsung smart phones). Such market is rapidly growing and influencing the way people use network resources and functionalities (such as commercial, banking, and government services), requiring a security level higher than in the past. Consequently, novel design techniques and technologies for user recognition and are required to be investigated, in order to provide a secure services and resources access. The traditional authentication systems based on username and password are not able to guarantee a suitable protection level. Unlike password, instead, user biometric information is unique and unchangeable; therefore the biometric identity has the advantage to guarantee that only the authorized users have access to available resources and services. However, traditional biometric approaches involves interactions among a large number of entities: passive access points for user biometric trait acquisition, networked databases for user biometric identity storing, and trusted servers running the user recognition systems. So, traditional systems usually undergo several types of attacks, such as Communication Attack (attacking the channel between the server and the database), Replay Attack (replication of user biometric trait processed during the acquisition phase), and Database Attack (manipulation of the stored user biometric identity). Embedded architectures, instead, provide a more secure and flexible infrastructure, since all elaboration steps are performed on board, so biometric identities are securely managed and stored inside the system without any data leaking out. The goal of this thesis is to illustrate the analysis and results of my research activity focused on the design and development of new fingerprint-based recognition systems for embedded devices. The study of the state-of-the-art about biometric systems led me to realize novel approaches to improve the performance of standard systems in order to enable their employment in embedded devices architectures. Most common literature approaches used to implement fingerprint-based recognition and classification systems are reported to provide a starting-point for understanding the contribution of this work. There are many literature approaches to deal with software systems, but few on design and implementation of embedded hardware prototypes. Referring to the developed and proposed fingerprint-based systems, this thesis represents an advancement of embedded biometrics respect to state-of-the-art. The step-over proposed in this work is focused on: 1. a heuristic fingerprint classification technique, requiring only a little set of images as training dataset; 2. an advanced matching technique for personal recognition based on partial fingerprint, able to enhance the system accuracy; 3. the design and implementation of an efficient fingerprint features extractor; 4. the design and implementation of a quality evaluator of raw fingerprint images (able to identify poor quality areas, such as dry and moist portions), allowing to define a novel flow of image processing steps for user recognition. This thesis is divided into two parts, creating a path connecting the state-of-the-art about biometric systems and the novel implemented approaches. The knowledge of the state-of-the-art about biometrics is fundamental to understand the step over presented in this work. For this reason, in the first part, general characteristics of biometric systems are presented with particular reference to fingerprint-based approaches used in literature to realize embedded systems. The second part proposes the developed innovative sensor. A novel flow of image processing steps for user recognition is outlined. Successively, an efficient micro and macro fingerprint features extractor is illustrated. Then, an advanced matching technique for personal recognition using partial fingerprints is presented. Finally, an innovative fingerprint classification approach based on the fusion of Fuzzy C-Means and Naive-Bayes technique is detailed. Experimental results and comparisons with analogous literature systems show the effectiveness on the proposed sensor. All the innovative approaches proposed in this thesis have been published in international conferences and journals.