Operators with Polynomial Coefficients and Generalized Gelfand-Shilov Classes

2010 Mathematics Subject Classification: Primary 35S05, 35J60; Secondary 35A20, 35B08, 35B40.We study the problem of the global regularity for linear partial differential operators with polynomial coefficients. In particular for multi-quasi-elliptic operators we prove global regularity in generalized Gelfand-Shilov classes. We also provide counterexamples of globally regular operators which are not multi-quasi-elliptic

Currants and strawberries as bioactive compounds source: determination of antioxidant profile with HPLC-DAD/MS system

Berry fruits show high antioxidant capacity among plant foods. Medical research has uncovered medicinal properties of pigmented polyphenols, such as flavonoids, anthocyanins, tannins and other phytochemicals, mainly localized in berry skin and seeds. The aim of this work was to contribute to the study of nutraceutical features of some berry fruits (currants, gooseberries and strawberries). The content in different antioxidant compounds  in fresh fruits of different cultivars and selections of Ribes spp. and Fragaria x ananassa Duch was analyzed  .  Fruits of 29 cultivars of 3 different species of Ribes spp. and 5 cultivars of strawberry were analysed by High Performance Liquid Chromatograph coupled to a UV/Vis detector and a mass detector (MS) to identify and quantify the main antioxidant compounds. Regarding the cultivars of Ribes spp., it was confirmed the presence of a high content in phenolic compounds, representing, therefore, an important source of antioxidant compounds. Moreover, results showed that the considered cultivars and selections of strawberries are a good source of bioactive compounds, especially phenolics. The results of this study contributed to give some new insights  into the  nutraceutical aspects of the considered berry fruit species

Predicting respiratory failure in patients infected by SARS-CoV-2 by admission sex-specific biomarkers

Background: Several biomarkers have been identified to predict the outcome of COVID-19 severity, but few data are available regarding sex differences in their predictive role. Aim of this study was to identify sex-specific biomarkers of severity and progression of acute respiratory distress syndrome (ARDS) in COVID-19. Methods: Plasma levels of sex hormones (testosterone and 17β-estradiol), sex-hormone dependent circulating molecules (ACE2 and Angiotensin1-7) and other known biomarkers for COVID-19 severity were measured in male and female COVID-19 patients at admission to hospital. The association of plasma biomarker levels with ARDS severity at admission and with the occurrence of respiratory deterioration during hospitalization was analysed in aggregated and sex disaggregated form. Results: Our data show that some biomarkers could be predictive both for males and female patients and others only for one sex. Angiotensin1-7 plasma levels and neutrophil count predicted the outcome of ARDS only in females, whereas testosterone plasma levels and lymphocytes counts only in males. Conclusions: Sex is a biological variable affecting the choice of the correct biomarker that might predict worsening of COVID-19 to severe respiratory failure. The definition of sex specific biomarkers can be useful to alert patients to be safely discharged versus those who need respiratory monitoring

Improving multiple removal using least-squares dip filters and independent component analysis

International audienceThe suppression of multiple events is a crucial task in seismic data processing, and the adaptive subtraction of the predicted multiples is recognized as one of the main challenges for the success of the surface-related multiple elimination technique. The traditional least-squares matching approach can affect the primary events because the estimated multiples tend to adapt to the primaries under the minimum energy condition. We investigate two filtering techniques for improving the multiple removal results. In the first proposed method, we combine the advantages of the least-squares and pattern dip-based subtraction methods. Doing so, we exploit the separation of primaries and multiples in the dip domain, and then we apply the least-squares adaptive subtraction in each dip band before recomposing the data to obtain the final subtraction result. As a result of the dip decomposition, the primary-multiple interferences are reduced, allowing for a more reliable least-squares filtering. In the second method, we propose to replace the multiple subtraction step by a separation step using independent component analysis (ICA) methods. We employ the ICA method after least-squares adaptive filtering. Because of the non-Gaussian distributions of the involved signals, primaries and multiples can be separated by computing the optimal rotation between these two signals. We apply the ICA method in local 2D time-space windows to better compensate the space and time variant character of the data. Two-dimensional synthetic and field data examples demonstrate that the multiple subtraction results of both methods are indeed improved with respect to the classical least-squares method

Replication data for: Elections and Democratization in Authoritarian Regimes

When do elections in authoritarian regimes lead to democracy? Building from the distinction between competitive and hegemonic authoritarian regimes, I argue that presence of relatively weaker incumbents renders competitive authoritarian elections more prone to democratization, but only when domestic and international actors choose to actively pressure the regime. The effects of two forms of pressure—opposition electoral coalitions and international conditionality— are theorized. Propositions are tested using a comprehensive dataset of elections in authoritarian regimes, from 1990-2007. Results support two core claims: that the effect of electoral pressure is conditional on the type of authoritarianism; and that this greater vulnerability to pressure is the reason why competitive authoritarian elections are more likely to lead to democracy. In contrast, several alternative explanations—that differences across regime type are explained by alternation in power, better electoral conduct, or ongoing processes of liberalization—are not supported by the evidence

Curvelet-based multiple prediction

International audienceThe suppression of multiples is a crucial task when processing seismic reflection data. Using the curvelet transform for surface-related multiple prediction is investigated. From a geophysical point of view, a curvelet can be seen as the representation of a local plane wave and is particularly well suited for seismic data decomposition. For the prediction of multiples in the curvelet domain, first it is proposed to decompose the input data into curvelet coefficients. These coefficients are then convolved together to predict the coefficients associated with multiples, and the final result is obtained by applying the inverse curvelet transform. The curvelet transform offers two advantages. The directional characteristic of curvelets allows for exploitation of Snell's law at the sea surface. Moreover, the possible aliasing in the predicted multiple is better managed by using the curvelet multiscale property to weight the prediction according to the low-frequency part of the data. 2D synthetic and field data examples show that some artifacts and aliasing effects are indeed reduced in the multiple prediction with the use of curvelets, thus allowing for an improved multiple subtraction result