Energy Conversion Using New Thermoelectric Generator

During recent years, microelectronics helped to develop complex and varied technologies. It appears that many of these technologies can be applied successfully to realize Seebeck micro generators: photolithography and deposition methods allow to elaborate thin thermoelectric structures at the micro-scale level. Our goal is to scavenge energy by developing a miniature power source for operating electronic components. First Bi and Sb micro-devices on silicon glass substrate have been manufactured with an area of 1cm2 including more than one hundred junctions. Each step of process fabrication has been optimized: photolithography, deposition process, anneals conditions and metallic connections. Different device structures have been realized with different micro-line dimensions. Each devices performance will be reviewed and discussed in function of their design structure.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Left ventricular ejection time, not heart rate, is an independent correlate of aortic pulse wave velocity.

Salvi P, Palombo C, Salvi GM, Labat C, Parati G, Benetos A. Left ventricular ejection time, not heart rate, is an independent correlate of aortic pulse wave velocity. J Appl Physiol 115: 1610–1617, 2013. First published September 19, 2013; doi:10.1152/japplphysiol.00475.2013.— Several studies showed a positive association between heart rate and pulse wave velocity, a sensitive marker of arterial stiffness. However, no study involving a large population has specifically addressed the dependence of pulse wave velocity on different components of the cardiac cycle. The aim of this study was to explore in subjects of different age the link between pulse wave velocity with heart period (the reciprocal of heart rate) and the temporal components of the cardiac cycle such as left ventricular ejection time and diastolic time. Carotid-femoral pulse wave velocity was assessed in 3,020 untreated subjects (1,107 men). Heart period, left ventricular ejection time, diastolic time, and early-systolic dP/dt were determined by carotid pulse wave analysis with high-fidelity applanation tonometry. An inverse association was found between pulse wave velocity and left ventricular ejection time at all ages (25 years, r2 0.043; 25–44 years, r2 0.103; 45–64 years, r2 0.079; 65–84 years, r2 0.044; 85 years, r2 0.022; P 0.0001 for all). A significant (P 0.0001) negative but always weaker correlation between pulse wave velocity and heart period was also found, with the exception of the youngest subjects (P0.20). A significant positive correlation was also found between pulse wave velocity and dP/dt (P 0.0001). With multiple stepwise regression analysis, left ventricular ejection time and dP/dt remained the only determinant of pulse wave velocity at all ages, whereas the contribution of heart period no longer became significant. Our data demonstrate that pulse wave velocity is more closely related to left ventricular systolic function than to heart period. This may have methodological and pathophysiological implications

Skeleton and fractal scaling in complex networks

We find that the fractal scaling in a class of scale-free networks originates from the underlying tree structure called skeleton, a special type of spanning tree based on the edge betweenness centrality. The fractal skeleton has the property of the critical branching tree. The original fractal networks are viewed as a fractal skeleton dressed with local shortcuts. An in-silico model with both the fractal scaling and the scale-invariance properties is also constructed. The framework of fractal networks is useful in understanding the utility and the redundancy in networked systems.Comment: 4 pages, 2 figures, final version published in PR

Validity of Italian adaptation of the Dizziness Handicap Inventory (DHI) and evaluation of the quality of life in patients with acute dizziness

The impact of dizziness on Quality of Life (QoL) can be assessed by the Dizziness Handicap Inventory (DHI), which is used as a discriminative and evaluative tool. Although the DHI is available in several languages, an equivalent version for the Italian population is not yet available. Aim of this study was to translate the DHI into the Italian language (DHI-I), assess its correlation to the Italian version of the Short Form-36 Health Survey and to investigate its reliability in evaluating the QoL of patients with acute dizziness. The study population consisted of 50 patients (76% females and 24% males), mean age 51.6 years, range 25-85 years (SD = 14.5). A cross-sectional design was used to examine the internal consistency (Cronbach’s α) and concurrent validity (Pearson’s product moment correlation r). The application followed the stages of translation from English to Italian and linguistic adaptation, grammatical and idiomatic equivalence review. To confirm the external validity of DHI-I, the Pearson correlation test between the total score and single subscales of DHI-I and the 8 scales of the Short Form Health Survey (SF-36) was performed. The Cronbach α coefficients for internal consistency were 0.92 for the DHI-I and 0.82, 0.84 and 0.75 for the sub-scale functional, emotional and physical, respectively. The frequency distribution of no one item showed a percentage higher than 75% in a single possible answer (0, 2, 4), excluding a ceiling or floor effect. Correlations with the total score of DHI-I were consistent and the correlation between total score of DHI-I and total score on SF-36 was -0.593. Of the single subscales, the emotional scale showed a closer correlation with almost all scales of the SF-36. The correlation between the total score of SF-36 and the single sub-scale of DHI-I (functional, emotional, physical) were respectively -0.599, -0.563, -0.398. The DHI was culturally and linguistically adapted for its application in the Italian population. The DHI-I demonstrated a good reliability and is recommended as a measure of disability in patients with dizziness and unsteadiness. According to the DHI-I, patients with acute dizziness and with a clinical diagnosis of vestibular syndrome presented a decreased QoL; the physical aspects were the most compromised

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single image showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling