Two-parameter neutrino mass matrices with two texture zeros

We reanalyse Majorana-neutrino mass matrices M_nu with two texture zeros, by searching for viable hybrid textures in which the non-zero matrix elements of M_nu have simple ratios. Referring to the classification scheme of Frampton, Glashow and Marfatia, we find that the mass matrix denoted by A1 allows the ratios (M_nu)_{mu mu} : (Mnu)_{tau tau} = 1:1 and (M_nu)_{e tau} : (Mnu)_{mu tau} = 1:2. There are analogous ratios for texture A2. With these two hybrid textures, one obtains, for instance, good agreement with the data if one computes the three mixing angles in terms of the experimentally determined mass-squared differences Delta m^2_21 and Delta m^2_31. We could not find viable hybrid textures based on mass matrices different from those of cases A1 and A2.Comment: 10 pages, no figures, minor changes, some references adde

Exact and Approximate Formulas for Neutrino Mixing and Oscillations with Non-Standard Interactions

We present, both exactly and approximately, a complete set of mappings between the vacuum (or fundamental) leptonic mixing parameters and the effective ones in matter with non-standard neutrino interaction (NSI) effects included. Within the three-flavor neutrino framework and a constant matter density profile, a full set of sum rules is established, which enables us to reconstruct the moduli of the effective leptonic mixing matrix elements, in terms of the vacuum mixing parameters in order to reproduce the neutrino oscillation probabilities for future long-baseline experiments. Very compact, but quite accurate, approximate mappings are obtained based on series expansions in the neutrino mass hierarchy parameter \eta \equiv \Delta m^2_{21}/\Delta m^2_{31}, the vacuum leptonic mixing parameter s_{13} \equiv \sin\theta_{13}, and the NSI parameters \epsilon_{\alpha\beta}. A detailed numerical analysis about how the NSIs affect the smallest leptonic mixing angle \theta_{13}, the deviation of the leptonic mixing angle \theta_{23} from its maximal mixing value, and the transition probabilities useful for future experiments are performed using our analytical results.Comment: 29 pages, 8 figures, final version published in J. High Energy Phy

Adaptivity Support for MPSoCs Based on Process Migration in Polyhedral Process Networks

987209Computer System

EUROnu-WP6 2010 Report

This is a summary of the work done by the Working Package 6 (Physics) of the EU project "EUROnu" during the second year of activity of the project.Comment: 82 pages, 51 eps figure

Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe.

BACKGROUND: The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. OBJECTIVE: In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system. METHODS: Influenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone. RESULTS: In summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time. CONCLUSIONS: Results from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases

Primary Care Staff's Views and Experiences Related to Routinely Advising Patients about Physical Activity. A Questionnaire Survey

Background: United Kingdom public health policy has recently re-emphasised the role of primary health care professionals in tackling increasing levels of physical inactivity within the general population. However, little is known about the impact that this has had in practice. This study explores Scottish primary care staff's knowledge, attitudes and experiences associated with advising patients about physical activity during routine consultations. Methods: A cross-sectional questionnaire survey of general practitioners (or family physicians), practice nurses and health visitors based in four health regions was conducted during 2004. The main outcome measures included: i) health professionals' knowledge of the current physical activity recommendations; (ii) practice related to routine physical activity advising; and (iii) associated attitudes. Results: Questionnaires were returned by 757 primary care staff (response rate 54%). Confidence and enthusiasm for giving advice was generally high, but knowledge of current physical activity recommendations was low. In general, respondents indicated that they routinely discuss and advise patients about physical activity regardless of the presenting condition. Health visitors and practice nurses were more likely than general practitioners to offer routine advice. Lack of time and resources were more likely to be reported as barriers to routine advising by general practitioners than other professional groups. However, health visitors and practice nurses were also more likely than general practitioners to believe that patients would follow their physical activity advice giving. Conclusion: If primary health care staff are to be fully motivated and effective in encouraging and supporting the general population to become more physically active, policymakers and health professionals need to engage in efforts to: (1) improve knowledge of current physical activity recommendations and population trends amongst frontline primary care staff; and (2) consider the development of tools to support individual assessment and advice giving to suit individual circumstances. Despite the fact that this study found that system barriers to routine advising were less of a problem than other previous research has indicated, this issue still remains a challenge

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34, 000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries. Author summary This study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting the temporal trends of influenza-like illness without relying on a specific case definition. The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, to broaden the scope of our approach, we applied it to the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a near-real-time flexible surveillance and prediction tool that is not constrained by any disease case definition