dataesr/scientific-paragraphs-categorization huggingface dataset:
Raman measurements Raman measurements were carried out by means of a Horiba Scientific Labram HR Evolution spectrophotometer in a spectral range between 900 and 3600 cm -1 with a nominal resolution of 1. 53 cm -1. Spectra were acquired at random positions on the sample surface using a green light laser (Nd:YAG, 532 nm) and a 600 lines/mm grating with a 50x objective. Acquisition time was 10 s and 5 scans were collected and averaged to improve signal-to-noise ratio. The spectroscopic platform was equipped with a Linkam THM600 sample holder to keep the cheese at a constant temperature during the experiments and, therefore, to avoid sample degradation due to laser heating. Raman spectra were recorded by LabSpec 6 Spectroscopy Suite Software (Horiba Scientific) and afterwards imported in Matlab (The MathWorks, Natick, MA, USA, version R2015a).
MIR measurements MIR measurements were carried out by means of a Bruker Vertex 70 FTIR equipped with a Bruker Hyperion 3000 IR microscope and an ATR system carrying a Germanium crystal in the wavenumber range between 900 and 3850 cm -1 with a nominal resolution of 8 cm -1. A series of spectral profiles was acquired at room temperature by means of a 20x objective on a sample area of 160μmx160μm (64x64 pixels). 1000 scans were performed which resulted in a mean recording time of about 15 minutes. Spectra were registered by OPUS Spectroscopy Software (Bruker), imported in Matlab and afterwards averaged to obtain a single representative profile per acquisition run  (Islam et al. , 2018). Baseline drifts were here corrected by a Savitzky-Golay first-order derivation  (Savitzky & Golay, 1964 ) encompassing a 9-data point moving window and a second-order polynomial function.
ANOVA-Simultaneous Component Analysis (ASCA) As detailed before, ASCA combines the advantages of both design of experiments and multivariate exploratory analysis permitting to study whether the recorded data are significantly affected by certain experimental parameters or factors (or by the interaction of multiple of them) and to assess how they actually vary under their influence. Mathematically speaking, ASCA decomposes the centred data matrix ( ) into the sum of several arrays according to the ANOVA scheme. In this work, as the effect of storage temperature (factor A) and ageing time (factor B) on cheddar ripening will be investigated, the partition of is carried out as: = + + + (1) where , and account for the variability induced by the effect of factor A, factor B, and factor A/factor B interaction, respectively, while carries the residuals not explained by the ANOVA model.
ASCA will be here utilized in a data fusion context. Despite data fusion has been widely exploited in, e. g. , classification or regression problems  (Biancolillo, Boqué, Cocchi, & Marini, 2019) , there is no reported application of ASCA for the analysis of fused data sets in literature. Given the complementary nature of the information provided by Raman and MIR spectroscopy, the individual spectroscopic data obtained by these two modalities will be integrated at a low-level for a more comprehensive description of the ageing process of cheddar cheese.
Conclusion In this article, the effect of storage temperature and ripening time on the chemical composition of cheddar cheese during ageing were investigated by means of two complementary spectroscopic modalities, Raman and MIR, coupled to ANOVA-Simultaneous Component Analysis. The spectral data were acquired following an upstream experimental protocol designed and fused at low-level to provide more comprehensive description of the aforementioned process, which highlighted the occurrence of both proteo-and lypolisis phenomena in a non-destructive, relatively cheap, rapid and green fashion. Altogether, the reported outcomes showed the proposed strategy is an easily implementable tool that could possibly be widely applied for food quality control in both laboratory and industrial scenarios.
Parentage assignment of experimental stocks Parentage assignment of challenged fish was done on 1500 fish by SNP genotype data  (12, 311  SNPs grouped in 50 linkage groups) using R software version 3. 6. 1 (R Core Team, 2019) with hsphase package version 2. 0. 2  (Ferdosi et al. , 2014). Parentage assignment of the second and the third year fish (production traits) was based on the analysis of 12 microsatellite loci and performed using the AccurAssign software, applying a maximum-likelihood method  (Boichard et al. , 2012). For more details see  Palaiokostas et al. (2018)  and  Prchal et al. (2018b).
1a )  [37, 38, 51, 52]. Next, we generated a non-redundant allele database. We observed a variable number of distinct CDSs alleles (Fig. 1b ), resulting in high proportion of unique allelic profiles (234 out of 353 isolates, Additional file 1: Figure  S1 ). We used these non-redundant allelic profiles as a proxy to investigate the mitochondrial genome distribution in the population. Globally, we observed a poor overlap between the mitochondrial and nuclear genome phylogenetic lineages  [47]  with few exceptions that include near-to-clonal nuclear genome lineages, having specific mitochondrial profiles. These exceptions include a Sake subclade, the two clinical Wine/European subclades (Y′ amplification and S. boulardii), the North American and the reproductively isolated Malaysian clades  [53].
Extensive admixture of mitochondrial genomes We investigated the mitochondrial genome population structure using the eight concatenated CDS to calculate the phylogenetic network using SPLITSTREE  [55]. The dataset comprises 239 non-redundant CDS profiles, with 234 S. cerevisiae isolates with complete CDS sequences and five S. paradoxus representatives  [56]  as outgroups. The resulting intertwined network shows a strong interconnectivity of the sequences, underlying frequent historical recombination (Fig. 2a ). In contrast, classical phylogenetic trees are unable to consistently group the isolates (Fig. 2b ). Using ADMIXTURE  [57] , we observed that the opposite edges of the trees fall in the same population for low K values (K = 2-3), further underlying a poor grouping. Mitochondrial population structure appears to poorly reflect the clustering obtained from the nuclear genome.
6b ). All petite isolates appear to be rho 0 , with the exception of two rho -isolates: ABM which retained COB gene and AHV which kept ATP9 and VAR1 genes (Additional file 2: Table  S4 ). These rho -isolates, with other five rho 0 petite, were laboratory-derived haploid (HO deleted), and since strain manipulation could have caused their mitochondrial condition, they were excluded from further analyses. We examined a selection of four strains for mitochondrial activity (measured as membrane potential) and volume. We included as controls a wild-type rho + strain and two derived rho 0 variants with one wearing an additional mutation (ATP2 G1099T), which partially restore growth in rich media (Michael Breitenbach, unpublished data). As expected, activity data show inability to grow on non-fermentable carbon sources (YPEG).
Genes and coding sequences The sequences of the eight protein-coding genes (ATP6, ATP8, ATP9, COB, COX1, COX2, COX3 and VAR1) were retrieved from the 1011 assemblies by performing BLAST similarity searches. The boundaries of the coding regions of COX1 and COB genes were identified through pairwise global alignments between the gene sequences and the reference CDS, constructed with stretcher  [72] , and manually refined. The average pairwise diversity π was calculated with Variscan  [73]  while sequence divergence was calculated as the percentage of SNPs. Presences of omega intron and RF1, RF2 and RF3 endonuclease were identified by blast search using a cutoff of 90% of identity on 90% of the sequences. Query sequence were the following: omega intron SGD: S000007279, RF1 accession number KP263414.
Population structure A non-redundant database of profiles of mitochondrial CDSs was built for the isolates with complete CDS sequences. Variant Call Format (VCF) file was created running snp-sites v2. 3. 3  [75]  (snp-sites -v -o mitochondria. vcf ConcatenatedCDS. fasta) on the concatenation of the CDS genes multiple alignment. Plink v1. 90  [76]  (plink --vcf mitoCDS_conc. vcf --make-bed -out plink. out) was used to prepare the data to run ADMIXTURE v1. 3. 0  [57]  software (admixture plink. out. bed K, with K from 2 to 15). The concatenations of CDS were also used to run SplitsTree v4. 16. 6  [55]  and produce the phylogenetic network while the bootstrap of the NJ phylogenetic tree was produced by MEGA v7. 0. 26  [77].
Funding This work was supported by the  France Génomique  ( ANR-10-INBS-09-08 ). GL is supported by the  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-13-BSV6-0006-01 ,  ANR-15-IDEX-01 ,  ANR-16-CE12-0019  and  ANR-18-CE12-0004 ). JS is supported by a  European Research Council (ERC)   Consolidator grant  ( 772505 ). JS is a Fellow of the  University of Strasbourg Institute for Advanced Study (USIAS)  and a member of the  Institut Universitaire de France.
Leaf nitrogen content was assessed using an N-tester on 30 leaves per plot in August of each year. Temperature probes, Tinytag Talk2-TK-4023 (Gemini Data Loggers, United Kingdom) were installed on eight representative plots in 2017 and in the remaining plots in 2018 to record maximum, minimum and daily average temperature once an hour during the vegetative period (April to October). The data loggers were set up on vine posts in the vineyard, with probes installed inside solar radiation shields (Type RS3), at a height of approximately 1. 8 m. Precipitation and temperature data were retrieved from the Agrometeo (http://www. agrometeo. ch/) station of Bourg-en-Lavaux (BeL) (which corresponds to the location of plot 19). The Winkler degree day summation  (Winkler et al. , 1974)  was calculated, as it is well-adapted to studying the influence of temperature on vine development.
Acknowledgements: We are grateful to  Sandrine Belcher  for performing the FTIR analysis, and to the students  Alexia Henny ,  Pierre Durand  and  Axel Vacheron  for their help during the data acquisition in the field. We would also like to thank the viticulturists (particularly  Jacques Joly ) of the studied area for providing their plots, and to "la Fédération vaudoise des vignerons (FVV)" for their financial support.
Arable Weeds and Management in Europe" is a collection of weed vegetation records from arable fields in Europe, initiated within the Working Group Weeds and Biodiversity of the European Weed Research Society (EWRS). Vegetation-plot data from this scientific community was not previously contributed to databases. We aim to prove the usefulness of collection for large scale studies through some first analyses. We hope to assure other weed scientists who have signalled willingness to share data, and plan to construct a full data base, making the data available for easy sharing. Presently, the collection has over 60,000 records, taken between 1996 and 2015. Many more studies for potential inclusion exist. Data originate mostly from studies exploring the effect of agricultural management on weed vegetation.
The database is accompanied with extensive meta-data on crop and weed management on the surveyed fields. The criteria for inclusion were a minimum amount of information on the cultivated crop, and a georeference. Most fields were surveyed repeatedly, i. e. transects, multiple random plots, or repeated visits. All surveys aimed to record the complete vegetation on the plots. Sometimes, taxa were identified only to genus level, due to survey dates very early in the vegetation period. Plant taxonomy is standardized to the Euro+Med PlantBase.
Database manager(s): Jana Bürger (jana. buerger@uni-rostock. de) Owner: Members of the EWRS Working Group Weeds and Biodiversity Scope: Weed vegetation survey data, complemented with data on agricultural management of the surveyed fields. Abstract: Arable Weeds and Management in Europe is a collection of weed vegetation records from arable fields in Europe, collected through an initiative within the Working Group Weeds and Biodiversity of the European Weed Research Society (EWRS). Plot-vegetation data from this particular scientific community has not previously been collected in a data base. The initiators aim to prove the usefulness of collection for large scale studies through some first analyses, to assure other weed scientists who have signaled willingness to share data. Presently, the collection has 59839 records, from approx.
5000 fields, starting from the early 1990s. Many more studies for potential inclusion exist. Data originate mostly from studies exploring the effect of agricultural management on weed vegetation. It is accompanied with extensive meta-data on crop and weed management on the surveyed fields, provided by the farmers. The criteria for inclusion were a minimum requirement of meta data on the cultivated crop, and a georeference to the location. Plot sizes range mainly from 1m² to 2000m², but sometimes a larger area of the field was surveyed (0. 5-1ha). Most fields were surveyed repeatedly, for example with 10 plots per fields, transects from edge to interior, or with multiple visits per cropping season. All surveys aimed to record the complete vegetation on the plots. Sometimes, taxa were identified only to genus level, due to survey dates very early in the vegetation period.
To identify and quantify the relative abundances of the peptides present in the samples, a hybrid ion mobility quadrupole time of flight mass spectrometer 6560, IM-Q-TOF, Agilent, Santa Clara, CA, USA) was used. All LC-MS/MS experiments were acquired using Q-TOF. A positive mode at Extended Dynamic Range, 2 Ghz, 3200 m/z with a scan range of 100-3200 m/z was used to record the MS/MS signals. The drying gas was nitrogen at 13. 0 L/min and 150 • C and was also used as nebulizer gas at 30 psig. The capillary voltage was set at 3500 V while the nozzle voltage at 300 V and the one of the fragmentors at 400 V. The instrument calibration was carried out by using an ESI-L low concentration tuning mix (G1969-85000, Agilent Technologies, Santa Clara, CA, USA). Data acquisition and analysis were performed with the Agilent Mass Hunter Software package (LC/MS Data Acquisition, Version B.
The analysis was performed in sensitivity, positive ion and data-dependent analysis (DDA) modes. The source temperature was at 150 • C and the capillary and cone voltages were fixed, respectively, at 3000 and 60 V. Data for m/z values were collected in the range of 100 to 2000 Da with a scan time of 0. 2 s. A maximum of 10 precursor ions were chosen for MS/MS analysis with an intensity threshold of 100,000. A collision-induced dissociation (CID) fragmentation mode and a scan time of 0. 1 s were chosen for MS/MS data collection. PEAKS Studio (version 8. 5, Bioinformatics Solutions Inc. , Waterloo, ON, Canada) software and its database search tools were used for peptide identification. The UniProt database restricted to Bos Taurus was used. A mass tolerance of 35 ppm, 3 missing cleavage sites and an MS/MS tolerance of 0.
2 Da were allowed while a variable methionine oxidation was considered. Protein and peptide identity relevance was judged according to their score in the research software (p value of 0. 05 (p < 0. 05), False Discovery Rate < 1%). For the search of peptides from the pepsic hydrolysate by Peaks ® software, the Bos taurus bovine hemoglobin alpha and beta chain sequences were taken as model proteins. In addition, heat maps were generated to visualize peptide abundance. The results were expressed using a color code where high, low and no occurrence of an individual amino acid were represented in red, yellow and white, respectively. Progenesis QI for proteomics (Version 4. 0, Nonlinear Dynamics, Newcastle upon Tyne, England) was also used to analyze mass spectrometry data. These data were subjected to the following successive processing steps: (i) alignment of the peptide maps with a quality control corresponding to an equivolume mixture of each of the analyzed samples, (ii) peak picking, detection of isotopic massif passing the software intensity threshold, (iii) data standardization to carry out a statistical principal component analysis (PCA).
50 min, from 1% to 20% solvent B over 8. 50 min, from 20% to 50% solvent B over 1 min followed by a washing step and an equilibrating procedure, respectively, with 95% and 1% solvent B for 2 min. The eluate was directed as previously and the MS analysis performed in sensitivity, with positive ions. As previously for the identification and characterization of the peptide population, the source temperature, capillary, cone voltages m/z value range and scan time of 0. 2 s were set as the same values. MassLynx V4. 1 software (Waters Corporation) was used to analyze the LC-MS data and, in these conditions, the retention time for α137-141 was 3. 20 min. Extracted chromatograms from α137-141 ((M + H 2 ) 2+ 327. 7) were generated at a mass precision of 0. 1 Da. After integration, quantification of α137-141 was performed according to peak areas after validation of isotopic clusters and elution time.
RP-UPLC Statistical Analyses All analyses were performed in triplicate and three independent repetitions were carried out for each condition. Data were subjected to one-way or two-way analyses of variance (ANOVA). Tukey tests were also performed on data using SigmaPlot software (Version 14. 0, Systat Software, San Jose, CA, USA) to determine which treatment was statistically different from the others at a probability level p of 0. 05.
Characterization of the Peptide Populations Principal Component Analysis (PCA) and Heat Maps In order to investigate the different hydrolysis conditions, hydrolysates obtained following 3 h of hydrolysis were selected for peptide identification by LC-MS/MS. PCA was performed on the mass spectrometry data after importation of raw data in Progenesis QI for proteomics software (version 4. 1). Peptic maps, representing retention time of the ions according to the m/z ratio of the ions and the intensity of the ions, obtained for all hydrolysates, were automatically and manually aligned thanks to the quality control used and peak picking of mass signals was constrained (Section 2). The PCA was done using the detected peptides and showed the peptide population obtained by considering all the mass signals (Figure  9A ).
The first two dimensions in the PCA explained 54. 4% of the variance (detected ions corresponding to variables). The more distant the groups were, the more different they were in terms of ion population. The PCA clearly showed a similar ion population and thus a similar peptide population between the three repetitions of each condition, meaning that it was reproducible at the condition level. Additionally, PCA showed a slight difference in ion population and thus a slight difference in peptide population between control, EDBM-MCP and EDBM-AEM. Moreover, the divergence between the different identified peptides in control, EDBM-MCP and EDBM-AEM were subsequently presented in the form of a heat map via Peaks Studio Software (Version 8. 5, Bioinformatics Solutions Inc. , Waterloo, ON, Canada)) (Figure  9B ).
Acknowledgments: The  Natural Sciences and Engineering Research Council of Canada (NSERC)  financial support is acknowledged. This work was supported by the  NSERC Industrial Research Chair METABIOLAC  (Grant IRCPJ  499946-15  to Ismail Fliss), the  NSERC  strategic partnership grants for projects Program (Grant  SPGP 5212082  to  Laurent Bazinet ) and the  NSERC Discovery Grants Program  (Grant  SD 210829409  to Laurent Bazinet). This work was realized within the  French-Canadian International Associated Laboratory on «Bioproduction  of natural antimicrobials» (LIAAN). The authors thank  Véronique Perreault  and  Diane Gagnon , research professionals at  Laval University  and  Rémi Przybylski , doctor at  Charles Violette Institute  for their technical assistance and for their help at the lab.
The authors are grateful to the  Advanced High Throughput Technologies Platform for Biorefineries Catalysts Design "REALCAT " funded by a  French  governmental subsidy managed by the  French National Research Agency (ANR)  within the frame of the " Future Investments program  ( ANR-11-EQPX-0037 )". The  Hauts-de-France region  and the  FEDER , the  Ecole Centrale de Lille  and the  Centrale Initiatives Foundation  are also warmly acknowledged for their financial contributions to the acquisition of REALCAT platform equipment.
1 mmol dNTPs, 2 µmol of primer, 1% bovine serum albumin, 1U of Taq polymerase (Thermo Fisher Scientific), 13. 4 µL of water. PCRs were ran in a T3000 Thermocycler by Biometra (Goettingen, Germany) at 94 • C for 3 min, followed by 35 cycles of 1 min at 94 • C, 1 min at 50 • C and 2 min at 72 • C, with a final extension at 72 • C for 5 min. We visualized PCR templates on 1. 4% Agar gels stained with ethidium bromide in TAE 0. 5× buffer at 80 V for 100 min. We used the peq-GOLD 100 bp plus ladder by VWR (Radnor, USA) as fragment size marker. We visualized gels and acquired images with a Gel Doc™ EZ system by BioRad (Basel, Switzerland). We defined bands size with the help of the software Image Lab Software developed by BioRad and further manually checked by two independent operators. Due to the high number of bands obtained from each marker, five SCoT primers were enough to obtain a significant number of loci to perform genetic population analysis.
Fingerprinting first output was a loci presence-absence matrix (Table  S4 ). Except when otherwise stated, we performed genetic analyses in R version 3. 6. 2 (R Core Team, 2019) and the package "poppr"  [47]. We used Table  S4  to perform the following analysis. We calculated a genotype accumulation curves for determining the minimum number of loci necessary to discriminate between individuals in a population, using the genotype_curve function with 1000 resamples. We applied the clonecorrect function to identify duplicated multilocus genotypes into the original dataset and the informloci function to remove phylogenetically uninformative loci. We performed Analysis of Molecular Variance (AMOVA) to estimate molecular variance among and between samples and populations, using the poppr. amova function and the randtest.
Occurrence Data We assembled a dataset of 277 occurrence localities of T. balbisiana based on literature data, herbarium collections, and original data [Silene-Flore (http://flore. silene. eu), exported on December 2018]. Occurrences distribution was heterogeneous, with several proximate localities at a distance lower than to the spatial resolution of our environmental rasters, and a disproportion of occurrences in the French side of the distribution range of the species (13 records in Italy vs. 264 in France). This disproportion is partly originated by the different accession data policies in force in the two countries, but to some extent it is also coherent with species distribution. In order to minimize spatial bias, we performed a thinning of the occurrences using the thin function in the R package "red"  [65] , setting the resolution at 0.
Calibration Area We calibrated species distribution models within the accessible area  [66, 67] , namely the geographical extent hypothesized to fall within the long-term dispersal and colonization potential for T. balbisiana over its evolutionary history. In lack of precise information on the dispersal ability of this species, we used the maximum linear distance between any occurrences in our dataset (ca. 250 km) as a general indication of the maximum dispersal potential. We buffered thinned occurrence records by this distance and combined all individual buffers in a single envelope that we used to mask the environmental predictors. Given that the species does not occur at low altitudes, we further excluded from the accessible area areas below 550 m a. s. l. (Figure  1a ).