dataesr/scientific-paragraphs-categorization huggingface dataset:
Position and contribution Our study focuses on the French cattle trade network from 2005 to 2015, as it appears in the BDNI. Other works have described previous versions of this database:  [24]  described year 2005, and  [30]  described the 5-years period from 2005 to 2009. Here, we do not aim at modeling the propagation of a specific disease, with a precise compartment model or a very finegrained description of the agents of the system-in the spirit of  [33]  for example. We rather consider this study in the line of  [34] : we explore the structure of the dataset using a simple epidemic model, which is a deterministic SI model, with an infection probability during a contact set to 1. This model can be understood as a proxy to represent a worst case scenario. Besides that, many conclusions on spreading characteristics remain valid for more complex propagation models in spite of its simplicity.
For example, if we are looking for potential super-spreaders of an infection, there is a high probability that a node identified as such with a SI model would also be a super-spreader using a more elaborate model. [35]  also describes the SI model as a good mean to explore the first stages of an infection spreading. From our point of view, a decisive argument to use such a model is that one may consider that it is rather a measurement probe of the structure of the data, as it is deterministic. We seek properties of the temporal structure of the dataset which have an important impact on spreading processes in the specific case of this cattle trade dataset. Indeed, cattle trade networks exhibit specificities that we aim at pointing out, and in that sense our work is close to studies such as  [30, 32].
• Finally, based on the former observations and definitions, we propose suited target control protocols. In particular, we propose to use as a cost unit the actual atom of information in a link stream, which is an interaction. We will see that it deeply affects the intuition that a user can have of the efficiency of a target control strategy. And consequently, we bring into light that link-based strategies, which directly use the temporal nature of the data are more efficient than usual node-based strategies in the literature. In order to reach these goals, we first present some characteristics of the dataset, focusing on properties which have a direct impact on the spreading dynamics examined later. Then, we discuss the infection model at the heart of this work and the sequences of infections that ensue in a static and a dynamic context, comparing the number of reachable nodes in both cases.
Materials and methods Throughout this work, we deal with the BDNI, which records bovine trade movements in France. We have access to eleven years of data from 2005 to 2015. The access to the BDNI is not public, but can be obtained through a specific agreement with the French ministry of agriculture. It contains approximately 148 million animal transfers. Animals are often traded in batches, and as we do not model an infection at the animal level, we focus on batch transfers between holdings, which are dated with a daily granularity. Batch sizes are not investigated in this work. Moreover, different types of holdings feature the data: farms, markets, assembly centers, slaughterhouses and knackery premises. Movements to slaughterhouses and knackeries are dead-ends concerning disease propagation, therefore, we exclude them from our data.
Other formalisms exist with similar benefits, like for instance TVG  [38] , but we use here the link stream formalism that emphasizes the streaming nature of data. It also allows to describe the temporal network with continuous time, though we do not use this property of the formalism in this work. In a few words, a link stream L is a set of nodes V, a set of temporal links or interactions E, and a set of interaction times T. An interaction is a triplet (t, i, j) 2 E, with t 2 T and (i, j) 2 V × V. Here, a directed edge from holding i to holding j on day t corresponds to a triplet (t, i, j) in the data. In most experiments conducted in this work, we focus on temporal parts of the dataset. We mainly use year 2015, and check that the results are consistent for other time periods.
Implementation Throughout this work, our computer implementations have been achieved in python. Specific graph-based computations (such as betweenness centrality) are realized using the NetworkX library (https://networkx. github. io/). To give the reader an idea of the order of magnitude of the computational times, a typical batch of 1000 spreading runs (such as the ones described later in the paper, with Δ = 4 weeks) takes approximately 1 hour. Consequently, it may be useful to use a lower level, more efficient programming language in order to scale up the simulation parameters.
Characteristics of the BDNI In the rest of this section, we report a few features of this dataset, which have a direct impact on the spreading dynamics, as we will see later on. Temporal variations of active nodes. A holding is said to be active during a given period if it sent or received at least one batch during this period. We show in Fig  1  the number of active holdings (or nodes) and the number of batches exchanged over several aggregation periods. Of course, only active nodes can relay an infection, so it gives insight on how important is the risk of spreading a disease in case of an outbreak depending on the period considered. We can see that the number of active holdings as well as the number of transfers is fluctuating: the overall trend is a decrease over the years. It is consistent with the observations on years 2005 to 2009 in  [30] , and it is probably a consequence of the holding merging phenomenon in France.
We have reported a few characteristics of the dataset which have a direct impact on spreading simulations. A more detailed description of the BDNI is not the topic of this work, however we report that the dataset exhibits characteristics which are consistent with measurements made on previous analyses of the BDNI  [24, 30]. Moreover, other bovine trade networks exhibit similar structural properties, for example the dataset has qualitatively similar distributions of inactivity periods as the one observed in the Italian cattle trade network  [27]. Even other animal trade networks have common traits with the one that we have used, such as pig trade in Germany  [32] , which suggests that results presented in the following might be general to animal trade networks.
Propagation profiles and modeling The number of individuals reached by an epidemic-like process in a population can be schematically represented by a S-shape curve. First, there is often a slow initial growth, then it takes off and grows rapidly, in a way which is typically superlinear (e. g. ,  [22] ) and finally saturates when all reachable nodes are indeed reached. Of course, we expect that a SI spreading model behaves similarly on the BDNI dataset. However, this may correspond to very different spreading scenarios which depends on the duration of each of these schematic phases and on the timescale of observation. Differentiating profiles with speed. To discriminate between various spreading scenarios, such as the ones represented in Fig  6 , the size of the outbreaks is not sufficient.
This simple model is then sufficient to understand the salient features of the spreading cascade. Note that the model still fits well the data at a quarterly timescale, but the fit is less convincing on larger timescales. We plot in Fig  12  the distributions of waiting times and growth rates according to the model. Waiting times are heterogeneously distributed, with a large majority of cascades having small waiting times. By contrast, growth rates are distributed rather homogeneously: almost 90% of growth rates fall in the range from 150 to 400 infected nodes per day. The distribution is centered around a peak at about 280. Note also that there is a peak at very low growth rates (a few units), which correspond to cascades that do not reach their tipping point. To conclude, we underline the fact that the growth phase on the BDNI is well fitted by a linear approximation.
Adapting the protocol to temporal networks. We have previously mentioned that a same cascade size could correspond to very different spreading scenarios. Consequently, we have defined the AUIC in order to account for the spreading speed. In the following, we use this measurement to assess the epidemic risk. Note that in the case of the BDNI, analyzing the sizes of the cascades or the AUIC leads to the same conclusions on target control strategies, certainly because most large cascades follow the same 2-phases scenario that we have described earlier. However, we suggest that with other datasets or other spreading contexts, this measurement should complete an analysis based on outbreak sizes. Indeed, the AUIC plays a role which can be compared to the measurement of the epidemic peak in the case of more elaborate epidemic models (such as SIS or SIR).
Another important point is that the atom of information in a link stream is a temporal link (t, i, j). Thus, it makes more sense to evaluate the cost of a strategy using the number of triplets removed rather than the number of nodes or links. Moreover, it makes possible to compare strategies which target different elements of the temporal network structure. Retrospective analysis. We analyze the efficiency of target control strategies in the following way: first we rank nodes, links or triplets according to some centrality measurement. Then, we delete a given fraction of the top-ranked triplets and run the SI spreading model on the remaining structure. We evaluate the efficiency of the strategy by measuring the AUIC (and the size, not reported here) of the cascades. This analysis is retrospective in the sense that the data which is used to rank elements of the temporal network structure is also the data on which we run the SI cascades.
Discussion In this work, we gave support to the idea that it is crucial to take into account the temporal dimension of data in order to model spreadings in animal trade networks. This point of view is well-spread in the literature, and previous works put forward several arguments in its favor. In the case of the BDNI, we confirmed several aspects of these works, in particular the fact that snapshot-based representations tend to overestimate the sizes of outbreaks, and give a distorted view of the distribution of potential outbreak sizes. Furthermore, we investigated the spreading scenarios encountered, using not only measurements of the outbreak size but also of its speed. We pointed out that our spreading process leads essentially to a unique kind of large-size cascades in the case of the BDNI, well approximated by a two phases model.
In the second case, we would never observe the saturation phenomenon, and if the renewal rate is high enough, the linear growth could continue forever. The truth lies in between these two extreme cases. A few additional experiments indicate that it is closer to the former hypothesis: indeed, after a few years the renewal rate per day seems to stabilize around a few dozens of new nodes appearing in the dataset per day, which is insufficient to account for the typical growth rate of the cascades. However, a question to solve for future works is to determine what part of the growth rate is explained by new nodes entering the system and what part comes from old nodes which have not been reached yet.
Protein insolubilisation during resting of freshly extruded pasta The impact of resting for 120 min at different temperatures (20, 30 or 40°C) on protein solubility and pasta characteristics was investigated. Soluble gluten proteins were quantified for time related to resting and HT drying. Impact on protein solubility in SDS buffer -Resting led to specific solubility decrease of protein fractions F 1 and F 2 (figure  3 ) while fractions F 3 , F 4 and F 5 were slightly impacted (data not shown). Resting decreased the extractability of protein in SDS buffer by about 6 to 10% as the temperature rose from 20 to 40°C. In accordance with  Joubert et al. (2018) , only glutenin polymers were impacted, and large polymers from fraction F 1 reacted faster than those from fraction F 2 , suggesting that solubility loss resulted from an increase in the size of glutenin polymers.
Acknowledgments The authors kindly acknowledge  L. Lhomond ,  G. Maraval ,  F. Rodriguez , and  A. Reau  for their help in pasta making and characterization,  J. Bonicel ,  C. Fabre  and  T-M. Lasserre  for biochemichal analyses,  Panzani (Marseille, France)  for providing durum wheat semolina. This work was supported by  BPI   France through the research program  " Defi Ble Dur ".
In order to avoid biases due to non-bacterial DNA (e. g. from the host or the parasite), QPCR data were normalized to the total bacterial DNA content estimated by the mean of the Cycle Quantification (CQ) obtained with 341F/534R and BAC338F/BAC805R primer pairs. The log2 of the ratio of a taxonomic group in a treated condition relative to the untreated one were obtained by subtracting the normalized CQ of control from the normalized CQ of the treated sample at the corresponding season and sampling day. Statistical analyses were performed using PAST software  [38]. Non-normal data gave no significant difference using paired Wilcoxon signed rank or Mann & Whitney tests. Paired t-test was performed for normal data and differences were considered significant for p-values below 0. 05. When a difference was significant, a similar positive or negative effect was observed in all of the five colony replicates.
The amplification products were cloned into pGEM-T Vector (Promega). Following transformation and blue/white screening, colonies were cultured and their DNA extracted. The amplicon sequences were obtained using T7 and M13 primers (Eurofins Genomics) and deposited, after the trimming of vector and primer sequences, in GenBank database (accessions MH782109 to MH782121). Cultures of seven selected isolates were diluted 50 times in fresh HIB medium, but supplemented MRS medium for Lactobacillus strains (S2 Table ). 750 μL of the dilution were deposited in wells of a 24-well plate containing various quantities of glyphosate and AMPA. Six independent cultures were performed for each herbicide concentration. After 48h of incubation as above, the optical density (OD) of the cultures were measured at 600 nm, using sterile medium as a blank.
Summer and winter honeybees were chronically exposed to 1. 5 mM or 7. 5 mM of glyphosate (GLY) and/or AMPA in their feeding sugar syrup. Some bees were also infected with spores of N. ceranae (Ncer). Control bees (Ct) were not exposed to any stressor. 15 days after treatment initiation, the DNA from guts was extracted and the 16S rDNA of the major taxa of the microbiota was quantified by QPCR and normalized to the total bacterial 16S content. The data are represented as the log2 of the ratio of the abundance in treated condition relative to untreated controls. Data were gathered from five independent colony-replicates. Bars represent the 95% confidence intervals. Stars and hash marks indicate significant differences (paired t-test, p<0. 05 and p<0. 005 respectively). When a difference was significant, a similar effect was observed in all the five colony replicates.
It has also been proposed that AMPA could act as an alanine analog that would inhibit enzymes involved in peptidoglycan biosynthesis, explaining its antimicrobial properties  [58]. Genes encoding all these enzymes are present in the genomes of the species discussed in this work, including G. apicola, S. alvi and L. kunkeei. The comparison of protein sequences with those available in the UniProt database did not allow formulating hypotheses about variation in their sensitivity and the higher sensitivity of G. apicola to AMPA may as well be linked to other mechanisms. The absence of bacterial response to AMPA in vivo demonstrated that glyphosate itself is the active component affecting the microbiota. Its degradation to AMPA may contrariwise reduce its effects, although the toxicity of AMPA on the honeybee physiology is unknown.
(A) amino acid sequences were taken from a previously reported phylogenetic tree  [1] , and sequences of species phylogenetically related to the isolated strains were selected in GenBank. E. coli MurA sequence was used as an outgroup belonging to the EPSPS family. Sequences were aligned using Muscle and trimmed to 726 sites (amino acids 4 to 419 of V. cholerae reference sequence). Le and Gascuel model with discrete Gamma distribution and allowance for invariant sites (LG+I+G) was selected as best-fit model of protein evolution using ProtTest  [2]. The Bayesian phylogenetic tree was inferred using MrBayes V3. 2. 6 software  [3] , with branch probabilities evaluated from 865 000 simulations and 15% burn-in. The consensus phylogenetic tree was built by majority greedy clustering with � 0. [1] , the latter being indicated with genome accession numbers.
5 support probability. The glyphosate-sensitive Class Iα and Class Iβ and the glyphosate-resistant Class II EPSPS are indicated in blue, green and red respectively. (B) Presence of the conserved amino acid residues characteristic of Classes Iα (blue), Iβ (green) and II (red) according to Light et al. [1] in the representative species mentioned in A. Positions are given for reference sequences from V. cholerae (Class I) and C. burnetii (Class II). Stars ( � ) indicate non-conserved amino acids. (PDF) S6 Fig. Evolutionary relationships within Snodgrassella alvi (A) and Gilliamella apicola (B) species. 16S nucleotide sequences were taken from GenBank and from Motta et al.
Acknowledgments The authors are thankful to the  UCA Microbiology Master  students ( M. Allain ,  E. Barbe ,  F. Bazi ,  O. Benyamna ,  J. Billy ,  H. Botchorichvili ,  S. Brahmi ,  I. Canihac ,  T. Capela ,  M. Cheymol ,  J. Crochepeyre ,  K. Dahmani ,  A. Dahuron ,  C. Deschamps ,  S. Etienne ,  M. Garofalo ,  L. Gaudon ,  A. J. Guardia Gonzalez ,  J. Hamet ,  F. Idjer ,  K. Kasmi ,  T. Kesraoui ,  N. S. Guerram ,  T. Merciecca ,  M. I. Moreira de Gouveia ,  D. Nadhir ,  A. Nouichi ,  S. Qasmi ,  H. L. Sow ,  O. Talouh ,  F. Tamzait ,  C. Touche  and  N. Vialle ) for their help in the winter experiment. The authors are also thankful to  I. Mary  and  E. Cano  for their technical support and to  Clermont Auvergne Metropole  for lending a beehive site.
The project was funded by the  CNRS   EC2CO  ( ECosphère COntinentale et COtière )  CALAMiTI  project (NB) and by the  ANR (Agence Nationale de la Recherche)   MIXTRESS  project ( ANR-15-CE34-0004 ) (LV, RR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In general, it is now assumed that if anthocyanins have a beneficial effect on human health, this effect is mediated by what is called breakdown metabolites, formed by the different bacteria existing in the microbiota. However, when searching in electronic databases (Medline PubMed and Web of Science) for anthocyanins and microbiota, less than 200 articles were initially selected. After removal of duplicates and an initial screening, 43 articles were selected for further reading. After detailed analysis of the full text, 12 articles were rejected, due to lack of relevant outcomes, aspects of the study design, and so forth. Finally, articles published between 2005 and 2018 were incorporated in this review (see Tables  3 4 5 ). Other articles were not selected because, for example, the polyphenol composition of the dietary sources of anthocyanins was too complex or because anthocyanins did not represent a big proportion with respect to the total polyphenol content (i.
Funding: The authors thank the financial support of the  COST Action FA1403-POSITIVe  "Interindividual variation in response to consumption of plant food bioactives and determinants involved" supported by  COST (European Cooperation in Science and Technology , http://www. cost. eu/) to conduct a short-term scientific mission (STSM) to M. E. E. at  ICTAN-CSIC (S. d. P. -T. ) during which most of the data collection was performed.
E. lathyris Isoprenoids All latex triterpenes, sitosterol (by far the major phytosterol) and phytol were extracted from freeze-dried plant material and isolated by preparative thin layer chromatography (TLC) and isolated after acetylation as acetate derivatives, which were further purified by TLC on silica gel or on AgNO 3 impregnated silica gel when this was required. Identification was made by GC, GC-MS, 1 H-and 13 C-NMR spectroscopy and comparison of the data with those of authentic reference material. In order to determine the best plant material and the most suitable labeling conditions for outdoor grown adult plants, latex from such plants, plants grown indoor in pots and plantlets grown in axenic conditions were separately analyzed. Adult outdoor grown plants contained the highest triterpene concentration, the triterpenes of isolated latex representing 20% of the mass of the dry latex, suggesting that the size and the volume of the laticifers were enhanced as compared to those of other plant tissues.
Project analysis The EJT steering committee, consisting of the general directors of the members of the EJT consortium, asked for an assessment of the potential ways to achieve a semantically enhanced XML version of EJT's publications. In 2015, the Naturalis Biodiversity Centre (an EJT consortium member) was tasked with investigating the different methods of obtaining and utilizing an XML version of EJT content. The report submitted by Naturalis in March 2017 concluded that, while the field of taxonomy provided exciting opportunities for interlinking data to the wider biodiversity community, elaborating an in-house workflow operated by the EJT production team was not advisable. Given the journal's independent position (i. e. , entirely managed, produced and published directly by the consortium members), the development of a tailor-made XML production platform was considered too costly, and performing manual encoding would be too time-consuming and technical for the desk editors to undertake.
Plazi and GoldenGATE Based on its findings, the Naturalis team carried out a proof-of-concept using GoldenGATE to apply mark-up to EJT articles published in PDF (Fig. 1 ). GoldenGATE is a semi-automatic retro-conversion tool for encoding taxonomic literature  (Sautter et al. 2007 ). The open-source program was developed by the Swiss NGO Plazi (http://plazi. org) to support and promote the interoperability of taxonomic treatments with other relevant cyber infrastructure components (name servers, biodiversity resources, etc. ). It was specifically conceived to process "born-digital" PDFs (as opposed to digitised paper publications) by recovering the text from the rendering instructions embedded in the file and discovering structural elements such as figures, tables and textual sub-sections e.
The EJT-Plazi workflow The solution of a collaboration between EJT and Plazi was chosen owing to the latter's valuable expertise in domain-specific mark-up for taxonomic literature  (Agosti et al. 2019a) , as well as for their established distribution network for the harvested content; both essential points for EJT. Furthermore, Plazi's data warehouse Treatment Bank (https://treatmentbank. org), combined with the group's active status as a trusted data provider for the Global Biodiversity Infrastructure Facility (GBIF) and the Biodiversity Literature Repository (BLR; https://zenodo. org/communities/biosyslit/; Agosti 2019b) offered further scope for the distribution of the taxonomic treatments, images, specimens and other data published by EJT. Using GoldenGATE and TaxPub, the taxonomic treatments and specimen data featured within EJT articles are converted into Darwin Core (DwC) archives  (Wieczorek et al.