dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter was done for a subset of attack sequences in which this was possible to determine (sailfish n = 28, striped marlin n = 28 attack sequences; see electronic supplementary material, videos for examples of each-video S1: horizontal slash, video S2: vertical tap). To test for a difference between species in each of these five behaviours, we ran Bayesian multilevel regression models, using the R package 'brms'  [17]. We ran separate models for each behaviour, fitting 'species' as population-level and prey school as group-level effect, using the binary distribution (family Bernoulli). We ran three chains of 2000 iterations, discarding the first 1000 as warm-up. For statistical inference, we determined whether the 95% credibility interval of the population-level effect (i. e. species) overlapped  (i) Micro-teeth length, type, position and density (d) ( e) (h) ( i) ( j ) ( f ) ( g) (b) (k) We used high-resolution computed tomography to characterize the length, type, position and density of all micro-teeth on the rostra tips (up to the first 55 mm of the rostra) (figure  1 ; electronic supplementary material, figure  S1  and details of micro-CT, and also for details on micro-teeth chemical composition).
Fourier transform infrared (FT-IR) spectroscopy FT-IR spectroscopy is widely used for preliminary detection of the functional groups of EPSs. FT-IR spectrum of the EPSs was recorded on a Perkin Elmer spectrum two FT-IR system (USA). About 3 mg of the EPSs were dispersed on the universal attenuated total reflectance (UATR) and were analyzed using FT-IR spectrum measurement in wave number range of 400 to 4000 cm -1 (the IR spectra were recorded at room temperature). The obtained spectra were resulted of averaging 50 scans. The analysis of IR spectra was carried out by using spectrum ES software.
The antiproliferative actions of EPSs were determined by using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) test. MTT assay is a colorimetric assay to assess cell viability and could reflect the dose-related toxicity. 20 he cells (4 × 10 3 cells/well) were seeded in 96-well plates with 100 µL of the media supplemented with 10% FBS and were allowed to attach overnight. Then, culture media were removed and cells were treated (triplicate) with EPSs solutions (20-1000 µg/mL) for 24 and 48 hours. Thereafter, the media were discarded and the MTT solution (0. 5 mg/ mL RPMI-1640 or DMEM) was added to each as well. After 4 hours of incubation, MTT solution was removed and the 100 µL of dimethyl sulphoxide (DMSO) solution was added to dissolve the formazan crystals. The optical density (OD) was measured on an ELISA plate reader (Bio Tech Instruments, USA) at 570 nm and data were normalized to a control group (cells that were only treated with the media).
The following data were taken: latency to reach the stimulus corners (both forelegs in the contact zone), time spent in the contact zones near the own or alien partner during the1 st min, and the 5 min of the test, and the number of bleats during the 5 min of the test. In addition, ewes and lambs were considered to make a correct choice if they spent more than 60 % of the time near the two stimulus subjects and at least twothirds of this time near its familiar partner  (Chniter et al. , 2017).
Statistical analyses All the data were statistically analyzed and graphically represented using GraphPad Prism 6. 0 software. Tests of normality (Shapiro-Wilk test) were performed and showed non-normal distributions. Consequently, two-tailed nonparametric tests were used to study the season effect on maternal and neonatal behavior, and on lamb vigor. Overall, the effect of the season was performed by Kruskal-Wallis test with Dunn's correction for multiple comparisons. Inter-season comparisons were performed using the Mann-Whitney test. Preference toward a partner (own vs alien) was analyzed by the Wilcoxon signed-rank test. In all cases, P-values less than 0. 05 were considered as statistically significant. Results for behavioral traits and vigor are presented in terms of median and lower and upper quartiles [LQ-HQ].
We cannot conclude that winter or any specific season (nor a temperature range) has detrimental consequences on mother-young relationships. The literature shows that lambs are at risk in winter when environmental conditions combine wind, rain and cold which prevents them from maintaining homeothermy and normal behavior (review:  Pollard, 2006). This was not the case in our study even though we must keep in mind the limitations of the recording method concerning meteorological data. The database was based on average temperatures, minima and maxima for each lambing season, it did not provide specific local conditions for each birth. We cannot exclude however that some very cold winter nights (or even very hot summer days) may have influenced the expression of early behavior patterns; this should be taken into account in future work.
Acknowledgements The present work was carried out in the  "Laboratoire d'Elevage et Faune Sauvage  ( LR16IRA04 ),  Institut des Régions Arides de Medenine ". It was funded by the  Tunisian Ministry of Higher Education and Scientific Research, Gabès University. The analysis of the data was performed during  Amel Dhaoui's internship at the "Unité Mixte de Recherche de Physiologie de la Reproduction & des Comportements, INRA-PRC Val de Loire-France " and gave birth to the present manuscript. The first author thanks all  INRA-PRC team  for the warm welcome.
Pulse Program. Single pulse, without carbon decoupling ('zg' with 90° pulse). Data Points (acquired): 64 K. NS=64. Relaxation delay: D1=60s. Acquisition Time: 4s. Spectral window for proton: SW=30ppm and O1: 7. 5 ppm (the sample Temperature is xed at 19 °C ± 0. 1 K). Post-Acquisition Processing. Performed with ACDLABS software (1. 2, academic version). Zero Filling: to 256K. Line Broadening: LB = 0. 1 Hz. Phasing: manually. Baseline Correction: 6th order polynomial (for each signal measured a ratio signal/noise>100 is veri ed).
In this paper, we provide a comprehensive empirical analysis of the effects of agricultural suitability and market accessibility on the allocation of cultivated areas in 15 world regions (see region map in  Fig 3). Our analysis is based on global datasets including suitability values and accessibility indices and cropland fractions on a 30 arc-minute grid. Agricultural suitability is proxied by an index synthetizing the climatic, pedologic and topographic properties of land  [29]. Market accessibility is represented by an index reflecting the travel time using different types of infrastructure to medium (>50khab) and large (>750khab) cities and large maritime ports  [14]. In this paper, we refer to the areas with the highest suitability and/or accessibility indices as "best land". Using these indices, we display cropland distributions over quartiles of agricultural suitability and market accessibility in each of the 15 regions.
Patterns of cropland allocation The spatially explicit datasets that are currently available provide information on the cultivated fraction at the grid cell scale. However, we cannot directly infer from the formulation of rent theories their implications on this variable. To clarify this point, we show four contrasted patterns of cropland allocation on Fig 1. Four hypothetical regions are considered including four land classes covering an increasing gradient of land quality (Q1 = lowest quality, Q4 = highest quality). Here, the quality of land can correspond to either agricultural suitability or market accessibility (see Method for details). Each of the four land classes is further divided into four parcels of homogenous quality on which cropland can be allocated and cover from 0 to 100% of the areas (red areas).
Materials and methods The analysis is carried out at global scale using datasets describing cropland fractions, agricultural suitability and market accessibility at the beginning of the 20th century at a 30 arc-min resolution. Data are scaled to values ranging between 0 and 1 where this is not already their native format. The fraction of cropland, infrastructure and other areas (including grassland and forest) in each grid cell comes from historical data based on HYDE version 3. 2. 1  [1]  for the year 2017 without any distinction between crop types. HYDE 3. 2. 1 combines country statistics for different land use categories from FAO for the period 1960-2015, subnational levels statistics and spatially explicit depiction of land cover from the ESA Land Cover consortium maps for the year 2010.
Data can be found athttps://easy. dans. knaw. nl/ui/datasets/id/easydataset:74467/tab/2. Global agricultural suitability is measured using an index reflecting the climatic, soil and topographical conditions necessary to grow the 16 most important food and energy crops  [29]. This index represents for each pixel the maximum suitability value across the 16 crop species. This index is strongly correlated with the Global Agro-Ecological Zones index  [31]. As the Zabel's index is more recent, this index was chosen in our study. Data can be found at https://zenodo. org/record/3748350#. XzpPjegzbIU. Market accessibility of land is measured on the basis of the travel time using different types of infrastructure to mediumsized (>50khab) and large (>750khab) cities and large maritime ports integrated into a single index accounting for travel behavior  [14].
Data can be found at http://www. ivm. vu. nl/en/ Organisation/departments/spatial-analysis-decision-support/Market_Influence_Data/index. aspx. Maps of cropland fraction, agricultural suitability and market accessibility are shown on In order to assess the robustness of our conclusions as to the origin of the data, we compare our results to those obtained using cropland fraction from the Erb et al. land-use dataset  [32]. We also use the market influence index, which incorporates population density data as well as national level per capita GDP values in addition to travel time to cities and ports  [14]. Results, shown on S11-S14 Figs of S1 File, are consistent with those obtained with the default datasets.
Quantification of the effects of agricultural suitability and market accessibility on cropland fraction Quantile regression models were fitted for each of the 15 relevant regions to estimate the effects of a one-unit increase in suitability and accessibility on the cropland fraction. For each region, two models were fitted separately: one for the 25% most cultivated land, i. e. areas with crop fraction above the third quartile of the grid cells in our dataset, representing densely cropped areas; and one for the 25% least cultivated areas, i. e. areas with crop fractions lower than the first quartile of the grid cells, representing sparsely cropped areas (see details in Method). The estimated coefficients obtained for these two quantiles are plotted in Fig 6. Their comparison makes it possible to analyze the response of the fraction of cultivated land to a one-unit increase in suitability and accessibility, taking into account the intensity of agricultural land-use.
There are several limitations to our work. First, we use a static dataset that does not provide an analysis of land-use transitions. This implies that our analysis spans the entire history of agricultural settlement in a given region, and may not detect recent changes in the spatial allocation of crops. Moreover, we consider total cultivated area without distinguishing the different crop types and their associated agronomic constraints. This could be a source of inaccuracy in certain regions, for example Oceania and South-Eastern Asia where the dominant crop is oil palm for which suitability is mainly dependent upon regular rainfall. In this study, we assume that the actual crop mix is close to the optimal one (i. e. , the crop mix showing the highest suitability in a given grid cell). This seems a reasonable assumption because farmers generally tend to grow the species that are best adapted to their environment, i.
All data files are available at the following URLs: -HYDE 3. 2. 1: https://easy. dans. knaw. nl/ui/datasets/id/easydataset:74467/tab/2 -Agricultural Suitability: https://zenodo. org/record/3748350#. XzpPjegzbIU -Market Accessibility: http://www. ivm. vu. nl/en/ Organisation/departments/spatial-analysisdecision-support/Market_Influence_Data/index. aspx.
1C  and Table  1 ). All K d measurements were performed on untagged proteins, unlike the PBM experiments. The PBM result indicating a lower affinity of LUX MYB for DNA is thus likely due to the N-terminal MBP fusion, which may occlude the DNA binding site and suggests that large protein fusions close to the N terminus of the MYB domain negatively impact DNA binding. As the LUX DBD is embedded within the full-length protein with both N-and C-terminal unstructured regions adjacent to the DBD, the DNA binding affinity is likely affected by the accessibility of the DBD. Potential additional amino acid contributions present in the fulllength protein may also tune the specificity of the DBD as the PBM motifs varied for the full-length and DBD constructs. Overall, these data demonstrate that LUX is able to bind with high affinity to its cognate sites in the low-nanomolar range and that this high-affinity binding is likely sufficient to target the entire EC to these sites genome wide.
Protein Crystallization and Data Collection. Single-strand DNA oligomers were annealed and mixed with LUX MYB at a 1. 2:1 ratio, and then, they were used without further purification. Protein-DNA complexes were crystallized using the hanging drop method as previously described  (46). Diffraction data were collected at 100 Kelvin (K) at the European Synchrotron Radiation Facility, Grenoble, France. Data collection and refinement statistics are given in Table  2. The structures are deposited under Protein Data Bank ID codes 5LXU and 6QEC. EMSAs. A 36-bp DNA oligonucleotide (5′-ATG ATG TCT TCT CAA GAT TCG ATA AAA ATG GTG TTG-3′) from the PRR9 promoter containing a LUX DNA binding site (bold and underlined in Table  3 ) was used for EMSAs, and the core LBS was mutated to yield different sequences (Table  3 ).
ITC Measurements for LUX FL -PRR9 DNA. ITC titration experiments were performed on a Microcal iTC200 instrument in 20 mM Tris, pH 7. 0, 50 mM NaCl, and 1 mM MgCl 2 buffer. PRR9 promoter LBS 10-mer DNA (5′-AAGATTCGAT-3′, 300 μM) was titrated into 20 μM LUX FL at 4 °C, 12 °C, and 27 °C. LUX FL concentration was determined from absorbance at 280 nm. Data were processed with Origin to extract the thermodynamic parameter K d (1/K a ). All titrations fit the single-binding site mechanism. Plasmid Construction and Generation of Transgenic Plants. For the lux-4 pLUX::LUX R146A , lux-4 pLUX::LUX constructs, an ∼800-bp upstream fragment of LUX was PCR amplified from genomic DNA. Full-length coding sequence (CDS) of LUX and LUX R146A was PCR amplified from the pESPRIT002 expression vector containing the respective CDS with an N-terminal FLAG tag (DYKDDDDK) added (SI Appendix, Fig.
ACKNOWLEDGMENTS. We thank  Francois Parcy  for helpful discussions,  Elodie Pierre  and  Caroline Mas  for technical assistance, the  European Synchrotron Radiation Facility beamline  staff on ID29 and  ID23-2 , and  Darren Hart  and  Philippe Mas  for the ESPRIT002 vector. This work used the platforms of the Grenoble Instruct-European  Research Infrastructure Consortium Center within the Grenoble Partnership for Structural Biology , supported by the  French Infrastructure for Integrated Structural Biology  ( Agence National de la Recherche  grant  ANR-10-INBS-05-02 ) and the  Grenoble Alliance for Integrated Structural and Cell Biology  (grant  ANR-10-LABX-49-01 ). Access to the  European Molecular Biology Laboratory High-Throughput Crystallisation Laboratory (EMBL-HTX)  was provided by the  European Community 's  Seventh Framework Program  ( FP7)  under grant agreement  283570.
Additional platform support was financed within the  Chemistry-Biology-Health (CBH) ,  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  (grant  ANR-17-EURE-0003 ). This work was supported by the  French National Research Agency programs Tempsens  (grant  ANR-19-CE20-0021-01  to C. Z. ), the  Commissariat à l' Énergie Atomique et aux Énergies Alternatives thesis student program  and  Raman Charpak Fellowship  (to A. N. ) and a  Spanish Ministry of Science and Innovation  (grant  BIO2017-86651-P  to J. M. F. -Z. ).
Bacterial diversity was monitored using two types of amplicon sequencing (16S rRNA and GyrB encoding genes) at different time points for the different conditions (576 samples for gyrB and 436 samples for 16S rDNA). Sequencing data were generated by using Illumina MiSeq. The sequencing data have been deposited in the bioproject PRJNA522361. Samples accession numbers vary from SAMN10964863 to SAMN10965438 for gyrB amplicon and from SAMN10970131 to SAMN10970566 for 16S. © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. ( http://creativecommons. org/licenses/by/4. 0/ ).
Specifications table Subject area Applied Microbiology and Biotechnology More specific subject area Microbial ecology of food spoilage during industrial production. Type of data The type of meat used for production of sausage was the first parameter analyzed (raw pork sausages and raw poultry sausages). The second parameter was the time of storage from the raw meat material up to sausages at the end of storage (primary cuts, gut casing, spices and fat). The third parameter was meat batch variability along ten sampling campaigns which were conducted for each meat product. Finally, we analyzed two process parameters: one was the influence of three doses of potassium lactate (complete dose, half dose or zero dose) used as preservative as well as three packaging atmospheres (air, and two modified atmospheres i.
Value of the data • The data provide a link between meat spoilage (bacterial counts, pH, color, volatile metabolic spoilage compounds non-targeted quantification further referred as "volatilome" and sensorial profiles), the packaging atmosphere, and the use of lactate and microbiota composition. • Sequencing data can be used to understand the variation of bacterial community dynamics, abundance and diversity in these two meat products according to the type of meat and packaging atmosphere, and the use of lactate. • Sequencing data can be used to identify biomarkers of spoilage. Accessibility to 16S rDNA and gyrB OTU (Operational Taxonomic Unit) data and detailed associated metadata allows researchers to perform new analyses with their own research purposes. • Ten independent sampling campaigns were conducted for two meat products.
The objective of this project was thus to provide such dataset based on a large collaborative project between academic partners, technical centers, and two industrial producers. We collected over a six months production period, much comprehensive information on the dynamics of bacterial communities and physico-chemical variables associ- ated with meat spoilage along the processing steps. Processed pork and poultry meat (sausages) were chosen as two examples of spoilage sensitive meat products that are among the most consumed in France. Products included all sausages ingredients (meat cuts, gut casing, spice mixes, and fat) to cover the whole meat process. The experimental work consisted in determining the diversity and the dynamics of bacterial communities during food processing and storage, and correlating them with spoilage occurrence.
In the frame of this project, sensory and nontargeted metabolic volatilome analyses were performed in order to characterize the meat product spoilage in relation with food processing factors like concentration of chemical preservatives (potassium lactate) and gas composition of storage packaging. The originality of this project was to integrate biotic (microbial ecology), abiotic (sensory attributes and storage conditions) and temporal factors (monitoring along processing steps) to generate heterogeneous multivariate data for used-by-date predictive mathematical models. Fig. 1  illustrates the global experimental design of this study. Links to supplementary data (available as spreadsheets) presented in Table  1  include all the metadata characterizing each sample, as well as the samples that were selected for the different analyses (16S rDNA sequencing, gyrB sequencing, volatilome and sensorial profile).
Sequence read processing, OTU clustering and taxonomic assignments Paired-end sequences were merged and trimmed as described previously  [5]. Data were subsequently imported into the FROGS (Find Rapidly OTUs with Galaxy Solution) pipeline  [6]  to be cleaned, filtered, clustered into Operational Taxonomic units and taxonomically assigned using the Silva 128 SSU database  [7]  and a homemade gyrB database  [5].
The HS-GC-MS conditions were as follows: capillary column: DB-624 UI (30 m x 0. 25 mm I. D × 1. 4 mm film thickness) (Agilent Technologies, USA); Carrier gas: Helium with a flow rate of 1. 4 mL. min -1 ; Injection port mode: splitless; Needle temperature: 60 °C; Injection temperature: 220 °C. The oven temperature was programmed from an initial temperature of 40 °C (7 min holding), rising to 50 °C at 4 °C/min (1 min holding), to 70 °C at 4 °C/min (1 min holding), to 120 °C at 3 °C/min (2 min holding) and to 245 °C at 30 °C/min (4 min holding). Transfer line temperature: 250 °C. The temperatures of the manifold and the ion trap were kept constant at 150 °C and 40 °C respectively. Data were obtained in scan mode at four scans/s in the mass range ( m/z ) of 35-350 atomic mass units. VOC were identified by comparison of GC retention times and mass spectra with those of the standard compounds.
Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. This work was supported by the ANR RedLosses Project, Grant ANR-16-CE21-0 0 06 , operated by the French Agence Nationale de la Recherche. SP and NDML were granted for postdoctoral and PhD study respectively through this agency. The funding body did not play any role neither in the design of the study nor in collection, analysis, and interpretation of data.
The sensorgrams for the different PINA concentrations were overlaid and aligned to the baseline determined in the absence of PINA (Fig. S3 ). Using raw data, the binding kinetic parameters of PINA to gliadins were determined: k a , k d , K D =k d /k a , i. e. , association rate, dissociation rate and equilibrium dissociation constant, respectively. They were determined by fitting the experimental data with the 1:1 Langmuir model (BIA-evaluation software 4. 1, GE Healthcare). The binding constants were calculated by two methods: affinity analysis (steady-state fit) and association/dissociation analysis (kinetic fit)  [29].
In recent years, the analysis of spectral measurements by ASCA has often been performed for the resolution of food-related issues  (Bevilacqua, Bucci, Materazzi, & Marini, 2013; De Luca et al. , 2016; Mazerolles, Boccard, Hanafi, & Rudaz, 2011). Keeping this in mind, the present article aims at providing a comprehensive and meaningful description of cheddar cheese ripening by means of low-level data fusion of Raman and MIR spectroscopic data. More specifically, ASCA will be used to evaluate the influence of storage temperature and ageing time (as well as their interaction) on the physico-chemical phenomena driving such a process and captured in the Raman and MIR domain. Even though these two experimental factors are probably the most relevant parameters affecting cheese ageing  (Adhikari et al.