The effects of a revised 7^7Be e−^--capture rate on solar neutrino fluxes

The electron-capture rate on $^7$Be is the main production channel for $^7$Li in several astrophysical environments. Theoretical evaluations have to account for not only the nuclear interaction, but also the processes in the plasma where $^7$Be ions and electrons interact. In the past decades several estimates were presented, pointing out that the theoretical uncertainty in the rate is in general of few percents. In the framework of fundamental solar physics, we consider here a recent evaluation for the $^7$Be+e$^-$ rate, not used up to now in the estimate of neutrino fluxes. We analysed the effects of the new assumptions on Standard Solar Models (SSMs) and compared the results obtained by adopting the revised $^7$Be+e$^-$ rate to those obtained by the one reported in a widely used compilation of reaction rates (ADE11). We found that new SSMs yield a maximum difference in the efficiency of the $^7$Be channel of about -4\% with respect to what is obtained with the previously adopted rate. This fact affects the production of neutrinos from $^8$B, increasing the relative flux up to a maximum of 2.7\%. Negligible variations are found for the physical and chemical properties of the computed solar models. The agreement with the SNO measurements of the neutral current component of the $^8$B neutrino flux is improved.Comment: 7 pages, 3 figures, 4 tables. Accepted for the publication on A&

On the Origin of the Early Solar System Radioactivities. Problems with the AGB and Massive Star Scenarios

Recent improvements in stellar models for intermediate-mass and massive stars are recalled, together with their expectations for the synthesis of radioactive nuclei of lifetime $\tau \lesssim 25$ Myr, in order to re-examine the origins of now extinct radioactivities, which were alive in the solar nebula. The Galactic inheritance broadly explains most of them, especially if $r$-process nuclei are produced by neutron star merging according to recent models. Instead, $^{26}$Al, $^{41}$Ca, $^{135}$Cs and possibly $^{60}$Fe require nucleosynthesis events close to the solar formation. We outline the persisting difficulties to account for these nuclei by Intermediate Mass Stars (2 $\lesssim$ M/M$_\odot \lesssim 7 - 8$). Models of their final stages now predict the ubiquitous formation of a $^{13}$C reservoir as a neutron capture source; hence, even in presence of $^{26}$Al production from Deep Mixing or Hot Bottom Burning, the ratio $^{26}$Al/$^{107}$Pd remains incompatible with measured data, with a large excess in $^{107}$Pd. This is shown for two recent approaches to Deep Mixing. Even a late contamination by a Massive Star meets problems. In fact, inhomogeneous addition of Supernova debris predicts non-measured excesses on stable isotopes. Revisions invoking specific low-mass supernovae and/or the sequential contamination of the pre-solar molecular cloud might be affected by similar problems, although our conclusions here are weakened by our schematic approach to the addition of SN ejecta. The limited parameter space remaining to be explored for solving this puzzle is discussed.Comment: Accepted for publication on Ap

Physical Activity Classification for Elderly People in Free Living Conditions

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). Support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single-sensor or multi-sensor) to correctly classify the ADLs of older people in free living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. On the other hand, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications

s-Processing in the Galactic Disk. I. Super-Solar Abundances of Y, Zr, La, Ce in Young Open Clusters

In a recent study, based on homogeneous barium abundance measurements in open clusters, a trend of increasing [Ba/Fe] ratios for decreasing cluster age was reported. We present here further abundance determinations, relative to four other elements hav- ing important s-process contributions, with the aim of investigating whether the growth found for [Ba/Fe] is or not indicative of a general property, shared also by the other heavy elements formed by slow neutron captures. In particular, we derived abundances for yttrium, zirconium, lanthanum and cerium, using equivalent widths measurements and the MOOG code. Our sample includes 19 open clusters of different ages, for which the spectra were obtained at the ESO VLT telescope, using the UVES spectrometer. The growth previously suggested for Ba is confirmed for all the elements analyzed in our study. This fact implies significant changes in our views of the Galactic chemical evolution for elements beyond iron. Our results necessarily require that very low-mass AGB stars (M < 1.5M\odot) produce larger amounts of s-process elements (hence acti- vate the 13 C-neutron source more effectively) than previously expected. Their role in producing neutron-rich elements in the Galactic disk has been so far underestimated and their evolution and neutron-capture nucleosynthesis should now be reconsidered.Comment: ApJ accepte

Methylglyoxal-dependent glycative stress and deregulation of SIRT1 functional network in the ovary of PCOS mice

Advanced glycation end-products (AGEs) are involved in the pathogenesis and consequences of polycystic ovary syndrome (PCOS), a complex metabolic disorder associated with female infertility. The most powerful AGE precursor is methylglyoxal (MG), a byproduct of glycolysis, that is detoxified by the glyoxalase system. By using a PCOS mouse model induced by administration of dehydroepiandrosterone (DHEA), we investigated whether MG-dependent glycative stress contributes to ovarian PCOS phenotype and explored changes in the Sirtuin 1 (SIRT1) functional network regulating mitochondrial functions and cell survival. In addition to anovulation and reduced oocyte quality, DHEA ovaries revealed altered collagen deposition, increased vascularization, lipid droplets accumulation and altered steroidogenesis. Here we observed increased intraovarian MG-AGE levels in association with enhanced expression of receptor for AGEs (RAGEs) and deregulation of the glyoxalase system, hallmarks of glycative stress. Moreover, DHEA mice exhibited enhanced ovarian expression of SIRT1 along with increased protein levels of SIRT3 and superoxide dismutase 2 (SOD2), and decreased peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC1 alpha), mitochondrial transcriptional factor A (mtTFA) and translocase of outer mitochondrial membrane 20 (TOMM20). Finally, the presence of autophagy protein markers and increased AMP-activated protein kinase (AMPK) suggested the involvement of SIRT1/AMPK axis in autophagy activation. Overall, present findings demonstrate that MG-dependent glycative stress is involved in ovarian dysfunctions associated to PCOS and support the hypothesis of a SIRT1-dependent adaptive response

FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

Background: About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. Objective: The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. Methods: FRAT-up is based on the assumption that a subject\u2019s fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. Results: The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration. Conclusions: FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface. Trial Registration: ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR)

Machine Learning Based Fall Detector with a Sensorized Tip

Fall detection has become an area of interest in recent years, as quick response to these events is critical to reduce the morbidity and mortality rate. In order to ensure proper fall detection, several technologies have been developed, including vision system, environmental detection systems, and wearable sensor based systems. However, in elderly or impaired people, it has been shown that the implementation of sensors in Assistive Devices for Walking, such as crutches or canes, can also be a promising alternative. In this work, a Support Vector Machine (SVM) based Fall Detection system is proposed, which uses the data provided by a Sensorized Tip which can be attached to different Assistive Devices for Walking (ADW). Unlike other approaches, the developed one is able to differentiate the fall of the ADW from the fall of the user. For that purpose, the developed Fall Detector uses two modules connected in series. The first one detects all falls, while the second differentiates between user and ADW falls. The proposed approach is validated in a set of experimental tests carried out by healthy volunteers that have simulated different falls. In addition, a comparative analysis is carried out by comparing the performance of the Sensorized Tip based Fall Detector and a state-of-the-art commercial accelerometer system. Results demonstrate that the proposed approach provides high Fall Detection Ratios (over 90%), similar or higher to wearable-sensor based approaches

Physical Activity Classification for Elderly People in Free-Living Conditions

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free-living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). A support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single sensor or multisensor) to correctly classify the ADLs of older people in free-living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. By contrast, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.<br/

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced