dataesr/scientific-paragraphs-categorization huggingface dataset:
High performance size-exclusion chromatography coupled to multiangle laser light scattering (HPSEC-MALLS) Molar mass and size distribution of soluble pectins were determined by HPSEC-MALLS, coupled to a differential refractive index detector (Shimadzu, Tokyo, Japan). The equipment, detectors and sample preparation were the same as reported previously  (Buergy et al. , 2020). Serum pectins (2. 5 mg AIS/mL eluent, 100 μL) were eluted at 40 • C with an acetate buffer (0. 2 mol/L, pH 3. 6) at a flow rate of 0. 6 mL/min and separated by three PolySep-GFC columns (P3000, P5000 and P6000, 300 × 7. 8 mm) and a guard column, all from Phenomenex (Le Pecq, France). Data were treated by ASTRA® software (Wyatt Technology Corporation, version 7. 3. 2. 19 for PC) as described before  (Buergy et al. , 2020).
Statistical analysis Fruit texture was determined 10 times on three different apples of the same cultivar. All other measurements were conducted once for each of the three technological replications, except starch determination and particle size distribution that were analysed twice for each of the three replications. At least two of three serum pectins were injected on the HPSEC-MALLS system. As the Shapiro-Wilk test indicated that not all results were normally distributed, they were compared with Kruskal-Wallis non-parametric test  (Kruskal & Wallis, 1952)  at the 95% level of significance. The XLSTAT package  (Addinsoft, 2020)  for Microsoft Excel was used. Pooled standard deviations were calculated for each series of replicated measurement using the sum of individual variances weighted by the individual degrees of freedom  (Box, Hunter, & Hunter, 1978).
Acknowledgements This work was carried out as part of " Interfaces " flagship project, publicly funded through  ANR  (the  French National Agency)  under the " Investissements d'avenir" program  with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Fondation  under the reference ID  1603-001. Studies conducted with the phytotron and the HPSEC-MALLS system were supported by the various CPER Platform 3A funders:  European Union ,  European Regional Development Fund , the  French Government , the  Sud Provence-Alpes-Côte d'Azur Region , the  Departmental Council of Vaucluse  and the  Urban Community of Greater Avignon.
1 ). Because of these active compounds, A. integrifolia has intensively been used as an antimicrobial, anti-inflammatory, antiarthritic, cardiotonic, antimalarial, antitumor and antioxidant agent  (Abbasi et al. 2020). Isolation and analysis of plant secondary metabolites has gained huge attention in the last three decades due to their marvellous pharmacological activities  (Kabera et al. 2014). It is declared as endangered species in the entire region cited in the International Union for Conservation of Nature Red List Database  (Hussain et al. 2016; Saeed et al. 2017). To conserve endangered species, secondary metabolite production and develop diseases free plants, the tissue culture technique is a promising alternative irrespective of environmental limitations  (Oseni et al. 2018). For instance, in vitro shoot organogenesis is a considerable method to conserve the plant by producing clones in a short time period  (Verma et al.
Statistical analyses All statistical analyses were done using the software R version 3. 4. 3 and the nlme package for mixed model fitting  [52]. We fitted statistical models separately to data from each experiment even though they were run with the same animals since we were primarily interested in testing the effects of environmental conditions on thermoregulation behavior. Given that experiments had to be run in sequence, we acknowledge that differences between values of each experiment might represent genuine effects of time passing and acclimation to experimentation. First, we calculated for each individual and each daily trial the PBT, the SD of PBT (an index of thermal precision) and the body mass change (difference in body mass between the end of the start of each trial, our proxy of daily water loss).
The PBT was calculated as the mean T b of each daily trial and its SD was calculated from the 12 data points of each daily trial to provide summary statistics for the thermal preferences and the precision of thermoregulation. We then analyzed the variation of each of these three variables with a linear mixed model accounting for repeated measurements on the same lizards over consecutive trials. In the full linear mixed models, we included the main fixed effects of sex, treatment groups and their interactions to take into account the factorial ANOVA design. We further included individual identity as a random intercept factor. These analyses were based on 192 records in Experiment 1 and 96 records records in Experiments 2 and 3. We did these analyses because this is the classical approach to quantify thermal preferences statistics in lizards.
This work was suppported by an  Agence Nationale de la Recherche  grant ( ANR-17-CE02-0013 , 'AQUATHERM') to  J. -F. Le Galliard. David Rozen-Rechels ' PhD grant is funded by the  Ministère de l'Enseignement Superieur et de la Recherche. This work has benefited from technical and human resources provided by the  CNRS IR ECOTRONS and CEREEP-Ecotron IleDeFrance  ( CNRS/ENS UMS 3194 ) as well as financial support from the  Regional Council of Ile-de-France  under the DIM Program R2DS bearing the references I- 05-098/R  and  2011-11017735  and from the  European Union FEDER  program  2007-2013. It has received a support under the program " Investissements d'Avenir " launched by the  French government  and implemented by  ANR  with the reference  ANR-11-INBS-0001   AnaEE France.
Software details and resources Our proposal has been implemented in several programming languages-C++, Python and R-although the results that are shown in this article have been obtained with Python. We optimized the parameters using library pycma  [2]  , and numpy has been used for mathematical operations. In the different performed statistical analyses, we have used R. Plots have been made both in R with ggplot2 library and in Python with matplotlib. Finally, the training data used to fit the models in the first wave can be downloaded at  [67]  , and  [18]  , that represent the daily Spanish statistics of COVID-19 fatalities. In the most extensive analysis of the overall Spanish population, we use the excess of mortality as a source of information. The raw data to estimated excess of mortality can be obtained in the public web interface related to MoModaily Spanish mortality surveillance system ( https://momo.
In general, our results agree with different epidemiological studies in Spain and several parts of the world ( https://covid19-modeling. github. io ). In case of discrepancies, these can be explained as a consequence of the lack of appropriate parameter tuning. Also, there are essential discrepancies between the results of national survey studies versus more local ones such as the one carried out in Torrejón de Ardoz  [4]. This might be explained by the fact that the effect of the pandemic was worse in Torrejón de Ardoz than in the majority of the population of Madrid. Another critical factor to highlight is the limitations in the statistical survey methodology employed in the national seroprevalence study. Concretely, there are more refined ways to carry on statistical inference modeling than the standard application of the Inverse Probability weight estimator.
Another significant contribution could be the use of several sources of information simultaneously to obtain more accurate estimations. In a recent paper  [35]  , the authors used the results of seroprevalence surveys of the U. S. population to explore this idea. Finally, using machine learning approaches to accelerate parameter fitting is a promising area that may be necessary for more complex simulation models  [6]. In preliminary tests with our approach, speed gains are considerable. As for the new library for simulation that we developed, we fill some gaps in the literature. There are few computational approaches to reconstruct infection dynamics from mortality reports  [36, 62]  , and most of the existing models are not dynamic. In this direction, we provide a well-built and reliable python library that allows estimation of seroprevalence, with interpretable and easily comparable parameters across countries.
Acknowledgments We want to thank the reviewers for their thoughtful comments that undoubtedly helped improve our initial manuscript's quality. The authors also thank  Oscar Hernán Madrid Padilla  for their helpful suggestions. Finally, we are also grateful to  Felipe Muñoz López  for his help in the computer implementations at the early stages of this research. This work has received financial support from the  Spanish Ministry of Science, Innovation, and Universities  under Grant  RTI2018-099646-B-I00 , the  Consellería de Educación, Universidade e Formación Profesional  and the  European Regional Development Fund  under Grant  ED431G-2019/04.
Evaluation of the DCT in simulation In order to evaluate the performances of the DCT and its impact in the quality of transmission, we carried out a series of simulations with and without DCT for the WLAN IEEE. 802. 11y standard, through the block scheme presented in Fig. (2). The simulation block is implemented in MATLAB software platform according to the parameters presented in Table  (1). The standard used adopts OFDM technique for Up-Link (UL) and Down-Link (DL) with a frequency band of 3,7 GHz. The method evaluation is performed as follows: preliminary results study the performances in term of PAPR reduction at the transmitter using Complementary Cumulative Distribution Function (CCDF) and using some communication criteria like EVM and BER. The CCDF of the PAPR is one of the most frequently used performance evaluation for PAPR reduction techniques.
INTRODUCTION mRNA vaccines have reached a new milestone with clinical efficacy demonstrated for COVID-19 vaccines  1, 2  and dozens more vaccine candidates have entered the clinical stage of development  [3] [4] [5] [6] [7] [8]. In vitro transcribed mRNA packaged in LNPs can produce complex antigens in vivo, via engagement of endogenous ribosomal machinery, with proper post-translational modifications including assembly, glycosylation and trafficking resembling the native antigens. This vaccination modality conveys the advantages of live attenuated vaccines by inducing adaptive immune responses of both humoral and T cell-mediated immunity  9. Unlike DNA vaccines, mRNA incurs minimal risk of host genome integration  10. The unprecedented rapid development and approval of COVID-19 mRNA vaccines under Emergency Use Authorization, and the growing safety database in millions of human populations, support the exploration of the mRNA platform to address both global pandemic and seasonal viral pathogens.
A total of 3141 69frame movie exposures were collected at a dose per frame of 1. 045 e/Å2 with defocus between -0. 5 and -1. 7 um. For each movie exposure, patchbased motion correction, binning of super-resolution pixels, and frame dose-weighting was performed using RELION-3. 1. 34. From corrected images, over 700 candidate particle coordinates were extracted. Subsequent data analysis was done with Matlab R2019a with image processing toolbox.
Following the challenge procedure, mice were placed in dorsal recumbency and observed until recovery from anesthesia. Daily body weights were taken following H1N1 challenge. Any individual animal with a single observation > 20% body weight loss was euthanized. The weight measurements were either recorded daily post challenge until euthanasia in the online database, Pristima® (Version 7. 5. 0 Build 8), or written on study specific working sheets.
For weight change over time, descriptive statistical analysis was used. Medians and ranges of each group of the maximum %body weight loss from baseline (day 0) over time were reported to evaluate the worse scenarios; medians and ranges of each group of the %body weight change from baseline at the last observation were reported to evaluate the body weight recovery. Statistical analyses were performed using SAS version 9. 4. All statistical tests were two-sided with significant level of 0. 05.
ACKNOWLEDGEMENTS We appreciate the support from the  Flu  project team at  Sanofi Pasteur ,  Drs. Tim Alefantis ,  Guadalupe Cortes-Garcia , and  Maryann Giel-Moloney  for generously providing us with critical reagents and viral stocks for performing HAI and NAI assays. We also want to thank exceptional support from veterinary research staff, especially  Dr. Emily Romero  and  Jason E Goetzman , for their work of the in-vivo phase of the NHP study at  New Iberia Research Center, LA. We would like also to thank the entire  Covance Antibody Reagents & Vaccines division in Denver, PA  for their support of the in-vivo phase of the mouse study and subsequent sample analysis of mouse and primate samples. We also appreciate contribution of  Bioqual Inc. staff, particularly  Swagata Kar ,  Laurent Pessaint ,  Tatyana Orekov ,  Maciel Porto ,  John Rathmann ,  Tarek AE El-Maghrabi ,  Shanai Browne ,  Kelvin Blade ,  Anne Thomas ,  Daniel Valentin , and  Zack Flinchbaugh  for their support of mice studies and sample analysis.
Index cases contacted unrelated unaffected friends or colleagues with years of birth matched to ±3 years and invited them to participate and referred those who accepted to participate to the coordinating center. The coordinating center organized the enrollment of index cases and their unrelated controls, collection of questionnaires, family, and clinical data of participants (Fig. 1 ). All women completed a questionnaire on environmental, lifestyle and reproductive factors, and family history of cancer. Blood samples were collected at participation (see Supplementary Methods Section in Additional file 1. doc). We considered only women reporting European ancestry (i. e. , over 95% of the study population) for this evaluation.
To minimize potential survival bias, we also conducted an analysis restricted to cases diagnosed at most 5 years before enrollment in GENESIS. Finally, as the DNA repair gene groups were defined using a priori bounds for ORs, we performed sensitivity analyses using different bounds (i. e. , Group "Low": OR< 0. 8 or <1. 0; Group "No Effect": 0. 8≤OR≤1. 2 or OR=1. 0; Group "High": OR>1. 2 or >1. 0). To evaluate the effect of missing information on the observed results, we performed multiple imputations using the chained equations method (MICE)  [21, 22]  as implemented in STATA  [23]. This method uses a Gibbs-like algorithm  [24]  to obtain 100 imputed datasets with complete observations for each outcome. ORs estimated on the imputed data sets were pooled together using Rubin's rules to obtain valid statistical inferences  [25].
Acknowledgements We wish to thank the genetic epidemiology platform (the  PIGE, Plateforme d'Investigation en Génétique et Epidémiologie :  S. Eon-Marchais ,  M. Marcou ,  D. Le Gal ,  L. Toulemonde ,  J. Beauvallet ,  N. Mebirouk ,  E. Cavaciuti ,  A. Fescia ), the biological resource center ( C. Verny-Pierre ,  L. Barjhoux ,  V. Sornin ,  N. Mebirouk ,  F. Lesueur ), and all the  GENESIS collaborating cancer clinics (Clinique Sainte Catherine, Avignon :  H. Dreyfus ;  Hôpital Saint Jacques, Besançon: M-A. Collonge-Rame ;  Institut Bergonié, Bordeaux :  M. Longy ,  A. Floquet ,  E. Barouk-Simonet ;  CHU, Brest: S. Audebert ;  Centre François Baclesse , Caen:  P. Berthet ;  Hôpital Dieu , Chambéry:  S. Fert-Ferrer ;  Centre Jean Perrin , Clermont-Ferrand:  Y-J. Bignon ;  Hôpital Pasteur ,  Colmar: J-M.
Funding The  Brazilian National Council for the Improvement of Higher Education -CAPES , for the fellowship (process number:  BEX 5852/15-3 ) to M. R. Guerra. Financial support for GENESIS was provided by the  Ligue Nationale contre le Cancer  (3 grants:  PRE05/DSL  and  PRE07/DSL  to  D. Stoppa-Lyonnet ;  PRE11/NA  to  N. Andrieu ), the  French National Institute of Cancer (INCa , Grant  b2008-029/LL-LC ), and the comprehensive cancer center  SiRIC (Site de Recherche Intégrée sur le Cancer: Grant INCa-DGOS-4654)  to  N. Andrieu.
Climatic and hydrological parameters In our study, we use the gridded daily T and P observations provided by Safran on a regular projected grid of 8 km x 8 km for the period 1959-2018 (Fig. 1 ). Safran is a mesoscale atmospheric system developed by the French meteorological agency Meteo-France  (Habets et al. , 2008; Soubeyroux et al. , 2008). We computed pixel-wise monthly and seasonal averages of each variable. Watershed and cluster averages (see below) were computed by calculating the mean of all grid points falling within each spatial entity. Boundaries for each watershed were provided by the Carthage database (BD Carthage Métropole, 2021). Although potential evapotranspiration (PET) can be directly extracted from Safran, we reconstructed series of this parameter from temperature data alone according to the formula proposed by  Folton and Lavabre (2007)  that was validated in our area by previous studies  (Labrousse et al.
, 2020; Lespinas et al. , 2014). The reason for this is that PET is not available in simulations of future climate conditions (see below) and we intended to use a uniform approach for this parameter both for past and future climate conditions. Data of water discharge for each river were taken from the HYDRO database hosted at the French Ministry of Ecology, Sustainable Development and Energy (hydroweb, 2020) which provides daily records for the most downstream gauging stations in the studied river basins. We used the same data series as in  Labrousse et al. , (2020). Contrarily to our P and T observations, water discharge series do not cover the entire 1959-2018 period, as monitoring of the water gauging stations only started around 1970, and some of time series are affected by monitoring gaps.
Both datasets also agree on the seasonal patterns of each cluster, which remain preserved in the future simulations. For all season, the eastern cluster depicts slightly higher temperatures than the western cluster, in agreement with its vicinity to the Mediterranean Sea and greater coverage of lowland terrains. For precipitation however, the relative importance of each cluster is variable according to the season. The western cluster always shows higher precipitation values during spring and summer compared to the eastern one, whereas the opposite is the case during autumn. During winter, both clusters have about equal values. Interestingly, in the future simulations, these differences between the western and eastern clusters slightly decrease during spring and summer, but increase during autumn.
8 Papers in the literature deal with the collective decision dimension only. 9 See the discussion on probabilistic voting models in Section 4. 1. On empirical grounds, the literature on the link between income inequality and environmental policy is sparse. 10  Its main contribution lies in the validation of the negative link between inequality and indicators of environmental policy, though there seems to be a dependence of the results on the level of income. Magnani (2000) measures inequality by the Gini index while environmental policy is captured by public R&D expenditure to protect the physical environment. Working with a panel dataset for OECD countries over the period 1980-1991, the author shows the existence of a negative correlation between income inequality and environmental policy.
11  Vona and Patriarca (2011) follow the leads of Magnani (2000). They consider a more recent and longer period (1985-2005, also on OECD countries) and focus on environmental innovations like green R&D and the production of environmental patents, especially by the public sector. Their empirical results highlight that inequality negatively influences the diffusion of innovations in countries with high percapita income. The dependance over GDP result can be explained by the methodology used in these papers. The regressions include a second order polynomial in the GDP and an interaction term between GPD and the Gini index. This typically falls within the EKC empirical literature tradition. 12   Our contribution to the empirical literature is two-fold. First, we use a more recent and broader dataset, focusing on European countries, and a more exhaustive variable to capture environmental public expenditure.
Data description We build our dataset using data from Eurostat, the statistical office of the European Union. Values for environmental protection expenditures include a large set of items and ensure a high degree of international comparability. The database captures all government expenditures in terms of waste management, waste water management, pollution abatement, protection of biodiversity and landscape, R&D environmental protection, and others. 31 In the coming analysis, we consider two main dependent variables that are extracted from the category government expenditure in environmental protection. 32  As first dependent variable, we take general government environmental protection expenditure. To account for different types of political systems, we also deal with a second dependent variable: environmental protection expenditure by local governments.
33  In Section 5. 4, we finally consider a third dependent variable as robustness test: total environmental taxation. 34  We express the dependent variable in per-capita terms since the variable Q, introduced in the model, also represents the average environmental quality when population size is normalized to 1. The main explanatory variables are the GDP per capita in current euros  31  According to the European System of Accounts (ESA), examples of environmental protection expenditures are: "investments in clean technologies, restoring the environment after it has been polluted, recycling, the production of environmental goods and services, conservation and the management of natural assets and resources". 32 This dataset provides the total government expenditure by functions and by type of institution.
In this dataset there are data for 31 European countries over the period 1996-2019. List of countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, UK. Variables are extracted from gov 10a exp. 33 According to the ESA, general governments are "institutional units which are non-market producers whose output is intended for individual and collective consumption, and are financed by compulsory payments made by units belonging to other sectors, and institutional units principally engaged in the redistribution of national income and wealth". Local government are: "public administration whose competence extends to only a local part of the economic territory, apart from local agencies of social security funds".
34  Even though the theoretical model does not consider environmental taxation, we look at this outcome variable because we expect that taxation and public environmental spending move in the same directions. There are 34 countries in the latter dataset, with Liechtenstein, Serbia and Turkey in the sample. and the Gini index, taken from the European Union Statistics on income and living conditions survey (EU-SILC). In Section 4. 2, we assessed the impact of income inequality on environmental policy by considering a mean preserving spread -that is a change in the standard deviation for given average income -which boils down to measuring inequality via the coefficient of variation (CV). Here we use the Gini index because it is strongly (positively) correlated with the CV, and this is the measure of inequality the most commonly used in the literature.