Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

Body mass estimates of an exceptionally complete Stegosaurus (Ornithischia: Thyreophora): comparing volumetric and linear bivariate mass estimation methods

© 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. The file attached is the published version of the article

Maps and atlases of cancer mortality : a review of a useful tool to trigger new questions

In this review we illustrate our view on the epidemiological relevance of geographically mapping cancer mortality. In the first part of this work, after delineating the history of cancer mapping with a view on interpretation of Cancer Mortality Atlases, we briefly illustrate the 'art' of cancer mapping. Later we summarise in a non-mathematical way basic methods of spatial statistics. In the second part of this paper, we employ the 'Atlas of Cancer Mortality in the European Union and the European Economic Area 1993-1997' in order to illustrate spatial aspects of cancer mortality in Europe. In particular, we focus on the cancer related to tobacco and alcohol epidemics and on breast cancer which is of particular interest in cancer mapping. Here we suggest and reiterate two key concepts. The first is that a cancer atlas is not only a visual tool, but it also contain appropriate spatial statistical analyses that quantify the qualitative visual impressions to the readers even though at times revealing fallacy. The second is that a cancer atlas is by no means a book where answers to questions can be found. On the contrary, it ought to be considered as a tool to trigger new questions

Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-onesubject- out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness

Objective assessment of walking impairments in myotonic dystrophy by means of a wearable technology and a novel severity index

Myotonic dystrophy type 1 (DM1) is a genetic inherited autosomal dominant disease characterized by multisystem involvement, including muscle, heart, brain, eye, and endocrine system. Although several methods are available to evaluate muscle strength, endurance, and dexterity, there are no validated outcome measures aimed at objectively evaluating qualitative and quantitative gait alterations. Advantageously, wearable sensing technology has been successfully adopted in objectifying the assessment of motor disabilities in different medical occurrences, so that here we consider the adoption of such technology specifically for DM1. In particular, we measured motor tasks through inertial measurement units on a cohort of 13 DM1 patients and 11 healthy control counterparts. The motor tasks consisted of 16 meters of walking both at a comfortable speed and fast pace. Measured data consisted of plantar-flexion and dorsi-flexion angles assumed by both ankles, so to objectively evidence the footdrop behavior of the DM1 disease, and to define a novel severity index, termed SI-Norm2, to rate the grade of walking impairments. According to the obtained results, our approach could be useful for a more precise stratification of DM1 patients, providing a new tool for a personalized rehabilitation approach

Hepatitis C Virus Drives the Unconstrained Monoclonal Expansion of VH1–69-Expressing Memory B Cells in Type II Cryoglobulinemia: A Model of Infection-Driven Lymphomagenesis

AbstractChronic hepatitis C virus infection causes B cell lymphoproliferative disorders that include type II mixed cryoglobulinemia and lymphoma. This virus drives the monoclonal expansion and, occasionally, the malignant transformation of B cells producing a polyreactive natural Ab commonly encoded by the VH1–69 variable gene. Owing to their property of producing natural Ab, these cells are reminiscent of murine B-1 and marginal zone B cells. We used anti-Id Abs to track the stages of differentiation and clonal expansion of VH1–69+ cells in patients with type II mixed cryoglobulinemia. By immunophenotyping and cell size analysis, we could define three discrete stages of differentiation of VH1–69+ B cells: naive (small, IgMhighIgDhighCD38+CD27−CD21highCD95−CD5−), "early memory" (medium-sized, IgMhighIgDlowCD38−CD27+CD21lowCD95+CD5+), and "late memory" (large-sized, IgMlowIgDlow-negCD38−CD27lowCD21low-negCD5−CD95−). The B cells expanded in cryoglobulinemia patients have a "memory" phenotype; this fact, together with the evidence for intraclonal variation, suggests that antigenic stimulation by hepatitis C virus causes the unconstrained expansion of activated VH1–69+ B cells. In some cases, these cells replace the entire pool of circulating B cells, although the absolute B cell number remains within normal limits. Absolute monoclonal VH1–69+ B lymphocytosis was seen in three patients with cryoglobulinemia and splenic lymphoma; in two of these patients, expanded cells carried trisomy 3q. The data presented here indicate that the hepatitis C virus-driven clonal expansion of memory B cells producing a VH1–69+ natural Ab escapes control mechanisms and subverts B cell homeostasis. Genetic alterations may provide a further growth advantage leading to an overt lymphoproliferative disorder

Ulnar dimensions and fossoriality in armadillos

Ulnar dimensions were measured in 14 species of armadillos (Xenarthra: Dasypodidae). An index of fossorial ability (IFA) was constructed, relating the length of the olecranon process to the remaining length of the ulna. For comparative purposes, the same measurements were taken in 14 other species of mostly South American mammals belonging to 3 orders and 11 families. The fossorial habits of these mammals were classified into 3 categories: (1) species mostly cursorial and non-digging; (2) species that often dig, but to which digging plays no essential part in their alimentary strategy and are not burrowers; and (3) species that are burrowers. IFA means of the studied mammal orders were compared using one-way analysis of variance on log-transformed data. Bivariate size allometry between ulnar dimensions and body mass was assessed by fitting (least squares and geometric mean) linear regressions of log-transformed data. It is concluded that the IFA discriminates among the species according to their fossorial habits within orders, but it is not equally useful in distinguishing fossorial species between orders. In armadillos, the relationships between ulnar dimensions and body mass are isometrical. Finally, the IFA is independent of body size.Facultad de Ciencias Naturales y Muse

Microwave driven synthesis of narrow bandgap alpha-tin nanoparticles on silicon

This work proposes a microwave-based synthetic route for the preparation of tin nanospheres with a diamond-like a-phase structure on silicon. The main characteristics of the synthesized material are an extraordinarily narrow (around 50 meV) direct bandgap and an improved thermal stability (up to 200° C). Structural and compositional characterizations showed a core–shell structure comprised of an outer amorphous oxide shell and inner core containing a-phase tin domains. Microwaves turned out to be instrumental in achieving the specific nanostructures reported, due to their peculiar heating characteristics. Low pressure, low temperature and compatibility with integrated circuits manufacturing represent the most innovative features of the present synthetic process