dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors are indebted to all farmers and veterinarians for their participation to influenza virus monitoring. They gratefully acknowledge Résavip, the  French national network  for surveillance of influenza A viruses in pigs (https://www. plateforme-esa. fr/page/thematique-virus-influenza-chez-le-porc), Mérial© (Lyon, France)  and  Ceva Santé Animale (Libourne, France ; formerly  IDT Biologika GmbH, Dessau-Roßlau, Germany ). They thank colleagues from the  Swine Epidemiological and Welfare Unit ,  ANSES (Ploufragan, France)  for their contribution. They are also grateful to colleagues from the  SPF Pig Production and Experimentation Unit ,  ANSES (Ploufragan, France)  for antiserum production in SPF pigs.
Roughly speaking, for single layers these techniques take full advantage of the location of extrema that can be seen in the wavelength variations of reflection and transmission, and of the contrast between these extrema. For more complex organizations, a specific RET is required. To summarize, thin film RET is used in this paper to analyse the leaf multilayer through THz pulsed spectroscopy data measured by reflection and transmission. By comparison with optical thin film techniques, we must stress on a number of key differences (in addition to the different wavelength ranges and associated technologies for emitters, detectors and components in the optics and THz regimes). The first difference is related to the high absorption level of plants in specific spectral regions (which are mainly due to water and pigments), which does not exist in high precision multi-dielectric coatings; such difference will give more complexity to the inverse problem to solve in leaves.
Experimental set-up In this study we performed the measurements campaign on a Terapulse 4000 spectrometer with external sample chamber compatible with a specular reflectance and transmission module (TeraView Ltd. , Cambridge, UK). THz radiation is produced by ultra-short pulse fibre laser (wavelength 790 nm, pulse duration <100 fs). The pulse is separated into a pump and probe beam, where the pump beam generates THz radiation and the probe beam detects THz pulses using laser-gated photoconductive emitters and detectors. The incident THz pulse was sampled with a spacing of the data points in the time-domain Δ of 0. 02 ps over an optical delay extend of 1000 ps, enabling a high spectral resolution of 1. 0 GHz. Spectra were the average of 100 scans (approximately 10-min measuring time). Parabolic mirrors focus the THz radiation onto the sample and the set-up can be used in transmission mode or in reflection mode.
In their Fig. 5 , the phylogeny using the D1-D2 regions of rRNA, which are generally ~600 bp in length, the proposed Alexandrium s. s. and Protogonyaulax are polyphyletic. In their Fig. 4 , the phylogeny using (presumably) partial SSU regions of rRNA, of an unknown length, Protogonyaulax, Episemicolon and Alexandrium s. s. are all para-or polyphyletic. In other words, based on their own analysis, the authors propose the replacement of a single paraphyletic taxon (Alexandrium) with several non-monophyletic ones. The generic concepts are not substantiated by the molecular data. Thus, rather than solving the taxonomic problems, they exacerbate them. 2. There is very little genetic difference between species of Centrodinium and Episemicolon. In a comparison of sequences of C. punctatum in the NCBI database, a difference of < 1% and < 5% was found between it and A.
Conclusions The data presented by  Gómez and Artigas (2019)  are insufficiently robust to form the basis on which to subdivide species of the genus Alexandrium into four different genera, and maintain Centrodinium. Resolving consistent generic-level clusters within the genus Alexandrium and across the gonyaulacoids more generally, would require additional detailed morphological re-investigations and more extensive multigene phylogenies, with careful attention to rigorous testing of taxon sampling effects, branching order stability, long-branch effects, and careful selection of appropriate multiple outgroups for rooting local versus global dinoflagellate phylogenies. A secondary structure analysis of a more expanded dataset could also be beneficial. Inclusion of cyst morphology and chemotaxonomic information should also be strived for.
In addition, 1 H-NMR analysis revealed also the characteristic signals of a fucose (≤0. 2% (w/w)) at 1. 295-1. 148 ppm for FSSA, and at 1. 293-1. 152 ppm for BBSA, indicating the weak traces of fucoidans as neutral polysaccharides co-extracted from brown algae. All these NMR data are conformed to commercial alginate (Figure  1C ) and reveal the high purity of our alginates from Fucus spiralis and Bifurcaria bifurcata. Taking into account the Grasdalen  [18]  method described in Section 4. 5, we have calculated frequencies of structural blocks of alginates (Table  2 ). The values of F MM , F GG , F GM (or F MG ) blocks and the M/G ratio were respectively evaluated at 0. 09, 0. 45, 0. 23 and 0. 47, indicating that BBSA was mainly composed of 68% guluronic acid and 32% mannuronic acid with abundance of homogenous doublet F GG.
Study species and dietary data Analyses were based on the dataset published by  Lunghi et al. (2018a)  providing quantitative information on the stomach contents of cave salamanders belonging to six Hydromantes species distributed in allopatry in peninsular Italy and in the Sardinia Island. Here we focused on species located in Sardinia; surveyed sites (i. e. , caves and other subterranean environments) were located throughout the island over an area of about 5,800 km 2 (Fig. S1  in supporting online information). Their elevations varied remarkably by approximately 870 m, ranging between 164 and 1033 m a. s. l. (Tab. S1); bioclimatic conditions and vegetation cover varied accordingly, with thermomediterranean thermotypes and Mediterranean shrubs dominating at low elevations, and mesomediterranean thermotypes and evergreen broad-leaved woodland characterizing higher elevations (Tab.
S1 and Fig. S1  for two contrasting examples; see also Results and Tab. S5). Stomach flushing was used to assess the diet of individual salamanders captured from 2015 to 2017. During this period, we surveyed each site twice in fall (2015 and 2016) and twice in spring  (2016 and 2017). For each salamander the ontogenetic stage (juvenile vs. adult), sex, and body size (snout-vent length, in mm) were assessed. Prey found in stomachs were classified to the lowest possible taxonomic level, discriminating between life stages when relevant, and identifying a total of 41 prey categories plus unidentified items. For each season, we pooled data from different years, and we considered only those populations where at least 15 individuals with recognizable prey in their stomachs were analyzed, with a minimum of three individuals per season.
Bioclimatic variables Bioclimatic variables obtained from the WorldClim v2 dataset (http://www. worldclim. org/) were used to predict the variation in the V measure among Hydromantes populations. Together with annual mean temperature, temperature seasonality, annual precipitation, and precipitation seasonality used in  Lunghi et al. (2018c) , we included five supplementary temperature and precipitation variables characterizing the hottest/driest quarter of the year, since dietary data were collected in spring and fall (Table  S3 ). In addition, two vegetation variables -i. e. , percent tree covered habitats and percent non-tree covered habitats -were extracted from the Terra MODIS Vegetation Continuous Field (VCF) product (available as MOD44B v006 at https://lpdaac. usgs. gov/products/mod44bv006/).
Statistical analyses We preliminary assessed whether individual diet specialization within populations was affected by seasonality or by other intra-population characteristics related to sex, body size and ontogeny (e. g. ,  Bolnick et al. 2003 , de Camargo et al. 2014). Individual PSi values were calculated using the R package RInSp (see further in this section) and analyzed using a two-way permutational ANOVA with Factor 1 = season (fixed, two levels, "spring" and "autumn"), Factor 2 = ontogenetic stage (fixed, two levels, "adult" and "juvenile") and the snout-vent length of individual salamanders as a covariate (Factor 3). We repeated the analyses for adults only, verifying the influence of sex. In addition, for each population spring and fall distributions of PSi values were smoothed using a kernel density estimation with a normal optimal bandwidth, and compared by a permutation test of equality  (Bowman and Azzalini 1997).
Comparisons were limited to populations with > 8 individuals per season, twice the minimum sample size required for mono-dimensional datasets  (Silverman 1998). We used regression analysis to verify the correlation between populations' V and TNW values. A Monte Carlo resampling procedure (described in  Bolnick et al. 2007 ) was adopted to test whether the slope observed for the V-TNW relationship could be generated by an artefact determined only by stochastic sampling. Briefly, the procedure requires each individual having n prey items in its stomach to sample n items randomly from the diet frequencies of the population it belongs via multinomial sampling. After 9,999 resampling replicates, a mean ISnull measure was estimated, and used to calculate a Vnull value. Populations' Vnull values were regressed against the observed TNW to evaluate the null hypothesis of a randomly-generated negative relationship between the two metrics, considering that the NVH hypothesis is supported if the empirical slope of the V-TNW relationship is significantly steeper than the null slope  (Bolnick et al.
Model selection adopted an Information Theoretic criterion  (Burnham and Anderson 2002, Hegyi and Garamszegi 2011). The second-order Akaike Information Criterion AICc  (Akaike 1974 , Sugiura 1978)  corrected for small sample sizes was calculated for each combination of n explanatory variables (max n = 2 given the relatively low number of populations) and used to identify the best MAM among the alternative regression models generated by the procedure. For models comparison, AICc values were used to estimate a set of positive Akaike weights wi summing 1. The model containing predictors significant at P < 0. 05 and showing the highest wi was considered the best candidate (e. g. ,  Mancinelli 2010). Model building and statistical analyses were performed following  Fox and Weisberg (2011). All statistical analyses were performed in the R statistical environment (R Development Core Team 2019); specifically, the package RInSp (v.
(B) Two contrasting examples of bipartite individualprey networks of the two Hydromantes populations characterized by the highest [V = 0. 69, H. genei (pop2)] and lowest [V = 0. 16, H. flavus (pop3)] degrees of individual diet specialization, respectively. Link thickness represents the number of prey items found in the stomach of each salamander, whose numerical code refers to that assigned in the original dataset (see text for further details).
Acknowledgements Fieldwork was authorized by the  Italian Ministry of Environment  ( Prot. 9384/PNM -12/05/2015  and  Prot. N. 20624/PNM -30/09/2016 ) and by the  Regione Autonoma della Sardegna  (n° 6312 of  27/03/2017 ). E. Lunghi is supported by the  Chinese Academy of Sciences President's International Fellowship Initiative  for postdoctoral researchers.
The gels were rinsed in distilled water for 90 min and scanned using an Image Scanner III LabScan 6. 0 (GE Healthcare Europe GbmH, Velizy-Villacoublay, France). The protein structures within IMFs were characterized in our previous study (22). Data are means ± SD (n = 3). Proteins within the unheated IMFs were considered as 100% native. The protein structures for each IMF are ordered according to their amount within the IMFs. n. d. : non determined.
Food & Function Paper Bands on SDS-PAGE gels were quantified by densitometry using the software Image Quant TL™ (GE Healthcare Europe183 GbmH, Velizy-Villacoublay, France) to study the kinetics of hydrolysis of α-LA, β-LG, LF and caseins during in vitro digestion. Intact proteins remaining in the samples were quantified following the eqn (1) after normalization of the protein peak area by the protein amount loaded in each well.
Statistical analysis All data are reported as means ± standard deviation (SD). Data statistical analysis was performed using the R software (version 3. 6. 1). Residual intact protein and DH variables were analyzed within the gastric or intestinal phase using a mixed linear model for repeated measures (nlme package) with "time" (4 levels: 5 min -15 min -30 min -60 min), "formula" (2 levels: control IMF -LF + α-LA + IMF) and "treatment" nested within "formula" (3 levels: unheatedheated at 67. 5 °Cheated at 80 °C) as fixed factors, and digestion replicates as random factor. Residual normality and variance homogeneity for each factor were tested for all variable with Shapiro-Wilk test and Levene test, respectively (lawstat package). When data did not conform to the previous conditions, BoxCox-transformation was performed (MASS package).
Acknowledgements We would like to thank  Ingredia Dairy Experts  and  Agropur Inc  in providing LF and α-LA powders used for the study. We would also like to thank  G. Henry ,  O. Ménard  and  J. Ossemond  for the technical support. This work was supported by the  Brittany Region, France  (Grant  ARED 17-032 ) and the  Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE, France).
Acknowledgments We are grateful to the PhD students who contributed to these studies ( C. Antoine ,  E. Chichti ,  V. Greffeuille ,  K. Heinze ,  P. Lasme ). We thank  F-X. Oury  ( INRA, UMR GEDEC, Clermont-Ferrand ) and  C. Michelet  ( UFS, Paris ) for their gift of near-isogenic grains differing in Pinb-D1 alleles. Special thanks to  F-X. Oury  for his careful reading and comments on this manuscript. We would like to thank  Dr Craig F. Morris  ( USDA-ARS, Pullman, WA, USA ) and their colleagues for the gift of durum wheat in which both wild-type puroindoline alleles were introduced and for growing them in the  USA , and  A. Massi  ( Syngeta ) for the gift of Svevo durum wheat, as well as  J. C. Dusautoir  ( INRA, retired ) for cultivating the wheat.
Study occurrences and climatic data The distributional range of L. pomponium extends from the Neva Valley in northwest Italy to the Verdon Valley in southwest France (Fig. 1 ). Species occurrences were obtained from field surveys (performed by the authors) and from regional databases: SILENE (Conservatoire botanique national méditerranéen de Porquerolles; http://flore. silen e. eu/) and LiBiOss (Regione Liguria; http://www. carto grafi arl. regio ne. ligur ia. it/Biodi v/Biodi v. aspx). Occurrences were spatially filtered and those closer than 1 km to each other were removed, resulting in a final data set of 809 occurrences. To study climatic conditions of populations, we downloaded nineteen bioclimatic variables representative of the period 1979-2013 from the CHELSA climate database website (http://chels a-clima te.
Definition of climatically central and marginal populations To distinguish ecologically marginal and central populations we first characterized the climatic niche of L. pomponium carrying out a principal component analysis (PCA) of the bioclimatic variables using the 'ade4' package implemented in R (R Core Team 2018). We considered "central" and "marginal" the populations falling into and outside the 70% of confidence ellipse, respectively. Then we grouped the marginal populations in different climate groups according to the PCA quadrants where they fall. Moreover, we tested differences among groups of populations because of mean annual temperature and annual precipitation using the non-parametric Kruskal-Wallis test. Kernel density plots were used to visualize the distribution of each variable.
Statistical analyses To test whether the groups of populations differed significantly in number of flowers per scape, flower size, and pollen and ovules production, we applied the non-parametric Tukey-Kramer-Nemenyi post hoc test using the R 'PCM-MRplus' package  (Pohlert 2014) , implemented in R (R Core Team 2018). To test whether the groups of populations differed significantly in the percentage of flowers belonging to different groups of populations, we used chi-squared or exact Fisher test when the expected frequencies was less than five in some cells. Because seed production follows a binomial distribution, lacking the property of linearity and additivity, the effects of marginality of populations on plant fitness were analysed by fitting factorial generalized linear mixed models (GLMMS, logit link function, binomial distribution) to the seed set data with group of populations as fixed predictors, and populations as random factor.
Statistical analyses were performed using the 'lme4' package  (Bates et al. 2015)  implemented in R (R Core Team 2018). Post hoc tests were conducted to evaluate pair-wise differences in measured traits between treatments using the 'glht' function in the 'multicomp' package  (Hothorn et al. 2008)  implemented in R (R Core Team 2018). Moreover, to test the relationship between elevation and flower size, number of flowers per scape, seed set and degree of pollen limitation, we calculated Spearman rank correlation coefficients.
The semi-quantitative E-FFQ entailed socio-economic characteristics and questions related to selected food that comprise frequency of selected items (on daily, weekly and monthly bases). In order to calculate food consumption, the conversion of frequency followed the study model of the European Prospective Investigation into Cancer & Nutrition (EPIC). Therefore, a frequency factor was used as follows: 0 to less than once a month; 0. 07 to 1 to 3 times a month; 0. 29 to 1 to 3 times a week; 0. 71 to 4 to 6 times a week; 1 to once a day  [32]. Portion size and food items were grouped in assembled photos to ease recognition of items and minimize biases  [33]. The E-FFQ was pilot tested before being used, targeting various population groups aged between the ages of 18 and 64. A customized web-based platform registered under Curve ® was created, tested and used to ease data collection and analysis.
This platform was used to deliver the E-FFQ, hosted the participants' answers, and allowed the rapid conversion of coded questions to analytical software, minimizing coding errors  [34]. Dissemination of the survey link was done prior to food sampling by a media agency through a messenger service message sent to smartphones. Messages were proportionally segmented by age to cover the official provinces (Beirut, Mount Lebanon, South (including Nabatiyeh), North (including Akkar) and Bekaa (including Hermel))  [35]. The collected information was exported after one week to an excel spreadsheet and thereafter was analyzed using IBM SPSS software version 25.
For the estimation of Al exposure from selected food, and compared with data reported by international agencies and based on a body weight of 60 kg, DWI of Al in the study population expressed in mg/kg bw/day was estimated to 0. 50 (50th DWI) and 1. 01 (95th DWI); mg/kg body weight per week. DWI was statistically significant compared with the international standards of the EFSA 1mg/kg, p<0. 001 -Table  2. While we found an escalation of DWI means from 0. 48 to 0. 57 respectively between the age groups of 18-30 and 41 to 50 years. Participants from South and North regions had lower DWI means (0. 43 and 0. 42). The highest means were found in Bekaa (0. 56). Females showed a slightly higher mean of DWI compared to males (0. 51 vs 0. 49), but there was no statistical difference among gender, age groups and region for DWI (Table  2 ).
Methods A literature search on PubMed and Scopus databases was performed on March 28, 2020, including articles with the following terms as keywords, title or abstract terms: (pig OR swine) AND (virus OR "viral infection" OR "viral disease") AND (simulation OR mathematical OR stochastic OR estimation OR inference) AND (model OR modeling OR modelling). In the present review, being dedicated to viral infectious diseases modelling, we decided to swipe relatively large, to include studies presenting mechanistic formulation of viral spread at different scales. This led to the preselection of 907 records (Pubmed: 458; Scopus: 449) from which 273 were duplicates. The titles and abstracts of 634 articles published between 1994 and 2020 were screened to select articles presenting dynamic models of viral diseases in domestic pig production system.
All mechanistic modelling approaches representing the transmission of pathogens in swine populations were first selected. Therefore, papers on virological or immunological aspects were excluded (169 papers), as well as statistical studies for risk factors analysis, quantitative risk analysis (98 papers) or models aiming at economic evaluation of interventions in infected premises (21 papers). Finally, 147 studies focusing on bacteria or alternative host species were discarded as well as 82 studies not considering mathematical approaches (e. g. experimental infection, in vitro models or field study analysis). Prisma flow diagram is displayed in Fig. 1. A bibliographic analysis was first performed to have a global overview of our selection using bibliometric package from R software  [16]. This analysis allowed for disentangling the studies in regards with the modelling frameworks and objectives.
For all these approaches on parameter inference in an experimental context, the underlying model is a stochastic Susceptible-Infectious-Recovered model where the infection pressureor force of infection (FoI)exerted on naïve animals is proportional to the prevalence of infected pigs in the population. The proportionality factor, denoted β, represents the transmission rate, i. e. the average number of new cases produced by one infectious animal per time unit  [9]. This parameter, along with the duration of the infectious period, are the key components for the evaluation of the basic reproduction number (R 0 ). Parameter estimations are usually based on likelihood approaches: the parameter space is scanned to find the optimal values that maximize the probability of observing the whole dataset given the model structure.
The protocol was nevertheless effective in fattening units where CSF-mortality could be disentangled from natural death, shortening the detection delay from 2 days. Costard et al. [61]  integrated data on farmer's behaviours to a within-herd ASF transmission model to assess their risk on virus spread due to release of infected animals. This study highlighted the importance of backyard and smallsize herds and suggested those as potential target for surveillance purpose since they may deliver infected animals through their trade contact network. Assessing surveillance and control measures at large geographical scale (Orange highlight in Supp. Mat. 1) Based on the selection process, between-herd infection spread was assessed in 50 research studies, along with evaluation of surveillance and control measures.
g. NAADSM, InterSpread, DTU-DADS or Be-FAST), mostly to study the spread of regulated diseases. A homogeneous mixing assumption is generally used to represent within-herd infection dynamics coupled with between-herd transmission module  [43, 56, 98] , but Kinsley et al. showed that the incorporation of accurate population dynamics in herds might shed new light on the actual infectious process  [99]. A recent study on HEV spread integrated the compartment structure in pig herds in the SimInf framework  [100] , a modelling framework originally developed to study VTEC-O157 spread among cattle herds in Sweden  [101]. Although more complex; this model accounted for realistic representation of the population demographics, together with the commercial network between production sites based on batch-rearing system.