dataesr/scientific-paragraphs-categorization huggingface dataset:
stearic acid (S), arachidic acid (Ar), behenic acid (B), myristoleic acid (Mo), palmitoleic acid (Po), oleic acid (O), linoleic acid (L), α-linolenic acid (ALA), γ-linolenic acid (GLA), arachidonic acid (AA), EPA, or DHA. The structural vectors of the datasets selected are provided in SM-A. The Boston dataset contained the fragmentation data of regioisomers of TGs ionized in ESI as [M+NH 4 ] + adducts were acquired on a ThermoFinnigan LCQ Advantage iontrap mass spectrometer with ESI (Sunnyvale, CA) at the University of Massachusetts Boston [31-34]. The fragmentations were performed at relative collision energy of 30 (unitless scale). Calibration curves for the regioisomers of TG(rac-O/S/O)-TG(rac-S/O/O); TG(rac-S/O/S)-TG(rac-S/S/O); and TG(rac-P/S/O)-TG(rac-S/P/O)-TG(rac-P/O/S) were also acquired at the University of Massachusetts Boston.
The Pardubice dataset contained regioisomers of TGs with P, S, O, L, ALA, and GLA that were ionized by APCI as [M+H] + in five different instruments: orbitrap, Q-ToF, iontrap, single quadrupole, and triple quadrupole [35]. In all five instruments the [M+H] + adduct fragmented spontaneously to suffer the neutral loss of the fatty acids. The Nantes dataset was built by using the regioisomers TG(rac-P/O/P), TG(rac-P/P/O), TG(rac-P/L/P), TG(rac-S/P/O), TG(rac-O/P/O), TG(rac-O/O/P), TG(rac-P/L/S), TG(rac-P/O/L), TG(rac-L/L/P), TG(rac-M/O/B), TG(rac-O/L/O), TG(rac-O/O/L), TG(rac-L/S/L), TG(rac-L/O/L), TG(rac-L/L/O), TG(rac-O/O/ALA), and TG(rac-L/L/GLA) purchased from Larodan, Solna, Sweden. The regioisomers were analyzed on an Acquity-Synapt G2S Q-ToF (Waters.
Identification of the qualitative fragmentation patterns of pure regioisomers of TGs when ionized as [M+NH 4 ] + in ESI and [M+H] + in APCI. Influence of the type of analyzer and adduct on the fragmentation patterns from a qualitative point of view. To identify trends in the neutral losses of fatty acids in TGs as [M+NH 4 ] + adduct in ESI, we analyzed qualitatively the datasets from Boston [31-34] and from Nantes. To study the trends of fragmentation of TGs ionized as [M+H] + adduct in APCI, we analyzed qualitatively the Pardubice dataset [35]. By exploratory data analysis of the structural parameters (SM-A), we found four patterns of TG fragmentation in CID to yield the neutral loss of the fatty acids: I. Species with only saturated fatty acids. A) The structural parameters comparing two saturated fatty acids in external positions were close to zero.
13  (SM-A, Boston dataset). As PUFAs show a globular-like structure, we attribute this behavior to an interaction among PUFAs via intramolecular London dispersion forces. These forces would be weaker for species with saturated fatty acids, which present a linear structure. As the neutral loss of these PUFAs is inhibited, these forces would increase the energy of the transition state of the reaction of fragmentation. From a modeling point of view, this behavior suggests the introduction of a fragmentation parameter for the inhibition of the neutral loss of PUFAs when more than one PUFA is present in the TG species. If we compare the ionization type in the three datasets, the [M+H] + adduct in APCI (Pardubice dataset) showed the same qualitative fragmentation patterns as the [M+NH 4 ] + adduct in ESI, but the quantitative values differed.
For example, SP P/O in TG(rac-P/O/P) was, on average, 0. 89 for the adduct [M+NH 4 ] + in the Boston dataset, while the value was 0. 99 in the Nantes dataset (SM-A). On the other hand this structural parameter was, on average, 0. 57 for the adduct [M+H] + in the Pardubice dataset (SM-A). Consequently, the neutral loss of P was favored over O in the three datasets for TG(rac-P/O/P), but the degree of this difference depended on the instrument and adduct. These facts point to the existence of homogeneous qualitative trends of fragmentation in the three datasets. However, the quantitative trends were adduct and instrument specific. Further comparisons among adducts and instruments are provided in the next Section, after modeling these fragmentation patterns.
3: A) γ ext for the external position; B) γ MU for monounsaturated fatty acids with the double bond in position 9 (Mo, Po, O); C) γ L , γ ALA , γ GLA , γ EPA , and γ DHA for respective PUFAs; and D) γ IN for the interaction among PUFAs. As the model was based on the differences of the parameters Γ, the origin of the scale was fixed for the saturated fatty acids in the internal position (i. e. Γ sat,int , γ int , and γ sat equaled zero). Formulae and further details can be found in SM-C. The fitted fragmentation parameters γ reproduced and quantified the trends observed in Section 3. 3 (Table2). All other factors equal: 1) the loss of an external fatty acid was favored in comparison with the internal fatty acids (γ ext < 0); 2) the neutral loss of a fatty acid increased with the number of unsaturations and the closer the position of the first unsaturation was to the carboxylic group (γ MU , γ L , γ ALA , and γ GLA , in all datasets with also γ EPA , and γ DHA in the Boston dataset); and 3) the presence of more than one PUFA inhibited the tendency of a PUFA to suffer a neutral loss (γ IN > 0).
As example of a TG with all regioisomers in the training dataset, we can consider the quantification of the regioisomers of TG(O/O/P) in both sunflower and olive oils. In both cases, our quantification yielded the pure regioisomer TG(rac-O/O/P). This quantification agrees previous studies, which had reported an abundance of this regioisomer of at least 91% in sunflower oil, and 95% in olive oil.
Acknowledgements We thank  Michelle Viau ,  Karinne Pouponneau , and  Fabrice Monteau  for expert technical assistance and advice. We also thank  Prof. Michal Holčapek  for providing guidance about the fragmentation patterns reported in the Pardubice dataset. We also thank  Dr. Carlos Palo-Nieto  for advice in the discussion of the reactivity of the TGs in gas phase.
3. 3. 3 Identification of the qualitative fragmentation patterns of pure regioisomers of TGs when ionized as [M+NH 4 ] + in ESI and [M+H] + in APCI. Influence of the type of analyzer and adduct on the fragmentation patterns from a qualitative point of view. To identify trends in the neutral losses of fatty acids in TGs as [M+NH 4 ] + adduct in ESI, we analyzed qualitatively the datasets from Boston [31-34] and from Nantes. To study the trends of fragmentation of TGs ionized as [M+H] + adduct in APCI, we analyzed qualitatively the Pardubice dataset [35]. By exploratory data analysis of the structural parameters (SM-A), we found four patterns of TG fragmentation in CID to yield the neutral loss of the fatty acids: I. Species with only saturated fatty acids. A) The structural parameters comparing two saturated fatty acids in external positions were close to zero.
Quantitative Proteomics and Correlation Analysis Each fraction was analyzed by shotgun mass spectrometry using 300 mg of starch. Starch granules were washed with 2% SDS to remove unspecifically bound proteins prior to gelatinization and isolation of internally trapped proteins. Peptides were obtained by the eFASP (enhanced filteraided sample preparation) method and analyzed by UPLC-nano-ESI-MS/MS. Protein quantification was performed with the Maxquant and Perseus 1. 6. 0. 2 software and a custom database to avoid quantification biases because of the redundant gene fragments present in the PGSC (potato genome sequencing consortium) databank (Supplementary File 1). This database included proteins that we.
Quantitative Proteomics and Correlation Analysis Each fraction was analyzed by shotgun mass spectrometry using 300 mg of starch. Starch granules were washed with 2% SDS to remove unspecifically bound proteins prior to gelatinization and isolation of internally trapped proteins. Peptides were obtained by the eFASP (enhanced filter-aided sample preparation) method and analyzed by UPLC-nano-ESI-MS/MS. Protein quantification was performed with the Maxquant and Perseus 1. 6. 0. 2 software and a custom database to avoid quantification biases because of the redundant gene fragments present in the PGSC (potato genome sequencing consortium) databank (Supplementary File 1). This database included proteins that we previously identified in association with potato starch and comprised most of the starch metabolizing enzymes as well Plants 2019, 8, 324 8 of 18 as a thioredoxin (THRx), a gluthatione peroxidase (GPx), and a peptidyl-propyl-cis-trans-isomerase (CYP20-20)  [16].
MS data was acquired on Q Exactive plus Orbitrap mass spectrometer (Thermo Fisher Scientific) using a data-dependent top 20 method, dynamically choosing the most abundant precursor ions from the survey scan (350-1600 m/z) for HCD fragmentation. Dynamic exclusion duration was 60 s. Isolation of precursors was performed with a 1. 6 m/z window and MS/MS scans were acquired with a starting mass of 80 m/z. Survey scans were acquired at a resolution of 70,000 at m/z 400 (AGC set to 10 6 ions with a maximum fill time of 100 ms). Resolution for HCD spectra was set to 70,000 at m/z 200 (AGC set to 10 5 ions with a maximum fill time of 200 ms). Normalized collision energy was 28 eV. The underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.
Proteins Label Free Quantification with Maxquant and Perseus Proteins were quantified with Maxquant software 1. 5. 3. 30 with custom starch bound protein databank (Supplementary file 1) regrouping proteins identified as bound to starch in our previous study  [16] , in order to obtain iBAQ and LFQ (label-free quantitation) score for each identified proteins. iBAQ score is used on Perseus 1. 6. 0. 2 software to obtain the abundance of each protein. Retained parameter for the quantification is "Concentration [nM] iBAQ" (Supplementary file 2). The concentration score is then divided by the total concentration of protein in µg. mg of starch -1 in order to obtain a result in fmol. mg of starch -1. Fold change values are obtained by normalizing the concentration of one protein on the concentration of this protein in the fraction A.
Supplementary Materials: The following are available online at http://www. mdpi. com/2223-7747/8/9/324/s1, Figure  S1 : X-ray diffraction (XRD) profiles of size-fractionated Monalisa starch granules, Table  S1 : Pearson paired-samples correlation analysis of starch-bound protein concentrations, Table  S2 : Pearson paired-samples correlation analysis between starch-bound protein concentrations and the relative proportion of each DP from chain length distribution (CLD) analysis, Table  S3 : Pearson paired-samples correlation analysis between starch-bound protein concentrations and phosphate contents or granule morphology, Supplementary file 1: Custom protein database, Supplementary file 2: Intensity based absolute quantification of proteins, Supplementary file 3: Statistical analysis of protein quantification.
Acknowledgments: We thank the  NanoBio-ICMG Platform (FR 2607, Grenoble)  for granting access to the electron microscopy facility and  Christine Lancelon-Pin  ( CERMAV ) for the SEM observations. The proteomic platform was supported by the  IBiSA network. The mass spectrometers were funded by the  University of Lille , the  CNRS , the  Région Hauts-de-France , and the  European Regional Development Fund.
The aim of this review is to give an overview of the available data about lactose and galactose metabolism in order to facilitate expert annotation with a special focus on LAB. To do so, the litterature was reviewed as well as the data available in 237 accessible genomes (complete or draft, see Table  S1  for accession numbers) of LAB and some Gram-positive bacteria of the low GC subdivision, like Bacillus, Listeria and Staphylococcus  (Axelsson, 2004). Genome analyses were mainly based on sequence alignment and synteny analyses which were performed by using the MicroScope platform as previously described  (Iskandar et al. , 2016; Vallenet et al. , 2013).
The low values of K 232 , K 270 , and ∆K also confirmed the good overall quality of control and leaf olive oils. The established health benefits of olive oils, due to their biological activities, required their quali-quantitative characterization with regard to their bioactive compound content. The total phenol content of all the olive oils was determined by Folin-Ciocalteau assay. As evidenced in data reported in Table  3 , orange and olive leaf olive oils, together with the control, have the highest total phenols content, followed by the lemon leaf olive oil. Table  3. Total phenolic content, intensity of bitterness, Free-radical scavenging capacity, total carotenoid and total chlorophylls of the control, orange leaf olive oil (Orange LOO), lemon leaf olive oil (Lemon LOO), and olive leaf olive oil (Olive LOO).
0 s; threshold: 1 count. The identification of the constituents was based on the comparison of their retention times (t R ) with those of pure reference samples and their linear retention indices (LRIs), which were determined relatively to the t R of a series of n-alkanes. The detected mass spectra were compared with those listed in the commercial libraries NIST 14 and ADAMS, as well as in a homemade mass-spectral library, built up from pure substances and components of known oils and in MS literature data [19 and literature therein].
Extraction and Detection of Tocopherols (Vitamin E) Tocopherol extractions were performed in the dark as previously reported by Sgherri et al. [65]  and according to the method of Gimeno et al. [78]. Tocopherol isoforms (α-, β-, γ-, and δ-) were determined by isocratic RP-HPLC using a Shimadzu apparatus (model LC-20AD) with an electrochemical detector (Metrohm model 791, Varese, Italy) equipped with a glassy carbon electrode and LC Solution software (Shimadzu) for the integration of peaks, as reported by Tarchoune et al. [18]. Detection was performed according to Galatro et al. [79]  at +0. 6 V and at 25 • C, with a Nova Pak C-18 4 µm column (3. 9 × 150 mm), as reported by Tarchoune et al. [18]. The extracts were eluted with 95% methanol containing 20 mM LiClO 4 at a flow rate of 1 mL/min.
To determine the presence of PrP res , 20 μl of the different samples were analyzed by WB as previously described  53. For immunoblotting, membranes were incubated with Sha31 mAb. Immunocomplexes were detected with horseradish peroxidase-conjugated anti-mouse IgG (Amersham Pharmacia Biotech) after incubating the membranes for 1 hour. Immunoreactivity was visualized by chemiluminescence with ECL Select (GE Healthcare Amersham Biosciences). When necessary, WB images were quantified using ImageLab software v5. 2. 1(BioRad). Measurement of prion infectivity by mouse bioassay. The relative infectivity in the unheated and heated BSE, RML or 22L samples (10% brain homogenates) was calculated as a function of the survival times (ST) observed after their inoculation in Tga20 mice. Briefly, groups of 6-9 individually identified (six to seven weeks old) Tga20 mice were anesthetized with isoflurane and intracerebrally inoculated with 20 µl of the inocula (ten-fold serial dilution of each unheated and heated sample) in the right parietal lobe using a 25-gauge disposable hypodermic needle.
Statistical information. Mann-Whitney's t test was applied to the ST data in order to find statistically significant differences between heated and un-heated inoculated animals. Significance level was adjusted to 0. 05 and the test had two tails. prp res detection by Western blot in the brain of transgenic mice after mouse bioassay. A total of 175 mg of whole brain tissue was homogenized in 5% glucose in distilled water in grinding tubes (Bio-Rad) and adjusted to 10% (w/v) by using a TeSeE TM Precess 48 TM homogenizer (Bio-Rad) following the manufacturer's instructions. Homogenates were pressed through 0. 4 mm needles of a calibration syringe and immediately frozen at -20 °C. To determine the presence of PrP res , 20 μl of 10% brain homogenates were analyzed by WB as described above. For immunoblotting, membranes were incubated with Sha31 mAb  19.
Acknowledgements This work was supported by grants from the  Spanish Ministerio de Economía y Competitividad  ( AGL2016-78054-R (AEI/FEDER , UE), the  French Fondation pour la Recherche Médicale (FRM)  ( DEQ. 20150331689 ) and  European Community  ( FP7 222887 "Priority" ). A. M. M. was supported by a fellowship from the  INIA  ( FPI-SGIT-2015-02 ) and P. A. C. was supported by a fellowship from the  Spanish Ministerio de Economía  y.
The signals were digitized using a Digidata1440A convertor (Axon Instruments, United States). pCLAMP1 0. 0. 1. 10 (Axon Instruments, United States) or Axoscope software version 8. 1 (Axon Instruments) and MiniAnalysis 6. 03 (Synaptosoft, Decatur, CA, United States) programs were used for the acquisition and analysis.
After validation of the libraries on a Bioanalyzer DNA1000 chip (Agilent) and QuBit (Invitrogen) quantification, sequencing was performed on a HiSeq 2500 (Illumina). Reads were 65 bp-long in single mode. Count data were analyzed using R version 3. 3. 1  38  and the Bioconductor package DESeq2 version 1. 12. 4. 39 Normalization and the estimation of dispersion were performed with DESeq2, using the default parameters, and statistical tests for differential expression were performed, applying the independent filtering algorithm. A generalized linear model was used to test for the differential expression between the three biological conditions: PAO1 without metals, PAO1 in the presence of Co 2+ or Fe 3+. For each pairwise comparison, raw p-values were adjusted for multiple testing according to the Benjamini and Hochberg (BH) procedure  40  and genes with an adjusted p-value lower than 0.
05 were considered to be differentially expressed. Reads were cleaned of adapter sequences and low-quality sequences using an in-house program (https://github. com/baj12/clean_ngs). Only sequences at least 25 nt in length were considered for further analysis. Bowtie version 0. 12. 7  41  , with default parameters, was used for alignment on the reference genome (Pseudomonas aeruginosa PAO1 from NCBI). Genes were counted using featureCounts version 1. 4. 6-p3  42 from Subreads package (parameters: -t gene -s 1).
22 µm syringe filter unit. The samples were then analyzed with an ICP-AES apparatus (Varian 720 ES) at the following wavelengths (nm): Co (228. 62), Fe (238. 20), Ca (393. For the data in Figure  4 , bacteria were grown in CAA medium in the presence of 5 µM CoCl2. At the end of the exponential phase, samples were centrifuged, and the bacterial pellets treated as described above for the experiment with PCH-Co or PCH-Ni complexes. 55 Fe 3+ uptake. 55 FeCl3 was obtained from Perkin Elmer Life and Analytical Sciences (Billerica, MA, USA), in solution, at a concentration of 71. 1 mM, with a specific activity of 10. 18 Ci/g. Siderophore-  55  Fe complexes were prepared at  55  Fe concentrations of 20 µM, with a siderophore:  55  Fe 3+ (mol:mol) ratio of 20:1. 55 Fe 3+ uptake assays were carried out as previously described,  43  except that, after growth, bacteria were incubated with 0.
48 During bacterial growth, in the absence of any metals, two different variation of mCHERRY fluorescence were observed depending on the strains. For pchA-mCherry, pchE-mcherry and fptX-mCherry strains a strong increase of fluorescence is observed corresponding to an increase of PchA-mCHERRY, PchE-mCHERRY and FptX-mCHERRY fusion protein expression (Figure  3B ). On the opposite, for mcherry-pchR in the same growth conditions, mCHERRY fluorescence decreased slightly after 6 hours culture (Figure  3C ). As expected, the presence of 5 µM Fe 3+ repressed strongly PchA-mCHERRY, PchE-mCHERRY, mCHERRY-FptX and mCHERRY-PchR fusion protein expression during bacterial growth. Consistent with the RT-qPCR data (Figure  3A ), Co  2+  repressed the expression of PchA-mCHERRY, PchE-mCHERRY and mCHERRY-FptX but not of mCHERRY-PchR.
QP realized the spectral characterization of PCHmetal complexes. PF realized the RT-qPCR experiment and participated in the design of all experiments and the analysis of all data. IJS directed the project, analyzed data and wrote the paper. All authors discussed the results and contributed to the writing of the manuscript. Aliquots were removed at various times and the radioactivity in the cells monitored. The data are expressed as moles per volume as described previously. 13 e absence of columns for some metals corresponds to the detection limits for low-abundance elements under these experimental conditions. For all experiments, bacteria were grown in CAA, harvested at the end of the exponential phase, and prepared for ICP-AES measurements as described in Materials and Methods.
05 were considered to be differentially expressed. Reads were cleaned of adapter sequences and low-quality sequences using an in-house program (https://github. com/baj12/clean_ngs). Only sequences at least 25 nt in length were considered for further analysis. Bowtie version 0. 12. 7  41  , with default parameters, was used for alignment on the reference genome (Pseudomonas aeruginosa PAO1 from NCBI). Genes were counted using featureCounts version 1. 4. 6-p3  42 from Subreads package (parameters: -t gene -s 1). For the data in Figure  4 , bacteria were grown in CAA medium in the presence of 5 µM CoCl 2. At the end of the exponential phase, samples were centrifuged, and the bacterial pellets treated as described above for the experiment with PCH-Co or PCH-Ni complexes.
48 During bacterial growth, in the absence of any metals, two different variation of mCHERRY fluorescence were observed depending on the strains. For pchA-mCherry, pchE-mcherry and fptX-mCherry strains a strong increase of fluorescence is observed corresponding to an increase of PchA-mCHERRY, PchE-mCHERRY and FptX-mCHERRY fusion protein expression (Figure  3B ). On the opposite, for mcherry-pchR in the same growth conditions, mCHERRY fluorescence decreased slightly after 6 hours culture (Figure  3C ). As expected, the presence of 5 µM Fe 3+ repressed strongly PchA-mCHERRY, PchE-mCHERRY, mCHERRY-FptX and mCHERRY-PchR fusion protein expression during bacterial growth. Consistent with the RT-qPCR data (Figure  3A ), Co 2+ repressed the expression of PchA-mCHERRY, PchE-mCHERRY and mCHERRY-FptX but not of mCHERRY-PchR.
Conclusion We show for the first time, how an excess of biological metals, other than iron, can interfere with transcriptional regulators that control the transcription of genes of the PCH pathway. Aliquots were removed at various times and the radioactivity in the cells monitored. The data are expressed as moles per volume as described previously. 13 e absence of columns for some metals corresponds to the detection limits for low-abundance elements under these experimental conditions. For all experiments, bacteria were grown in CAA, harvested at the end of the exponential phase, and prepared for ICP-AES measurements as described in Materials and Methods. 3SM  in Supplemental Materials. "B. PchA-mCherry, PchE-mCHERRY and FptX-mCHERRY expression in P. aeruginosa cells grown in CAA medium with or without 5 µM FeCl 3 , CoCl 2 , NiCl 2 or ZnCl 2.
Acknowledgments This work was partly funded by the  Centre National de la Recherche Scientifique. O. Cunrath  held a fellowship from the  French Ministère de la Recherche et de la Technologie  (3 years) and the  FRM (Fondation pour la Recherche Médical , 6 months). A. Y Carballido Lopez  held a fellowship from the  University of Strasbourg (IDEX)  and  B. Pesset  from the  DGA (Direction Générale des l'Armement). J. P. and I. M. S. thank the  Laboratory of Excellence GRAL  (grant  ANR-11-LABX-49-01 ) and the  Labex ARCANE  (grant  ANR-11-LABX-0003-01 ). The Transcriptome and  Epigenome Platform  is a member of the  France Génomique consortium  ( ANR10-NBS-09-08 ). We thank  Dr A. Boos  and  P. Ronot  ( Institut Pluridisciplinaire Hubert Curien, Strasbourg ) for their help in the ICP-AES measurements.