dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of the Organoleptic Quality Based on our dataset, sensory analyses were identified as the most used approaches for evaluating the organoleptic quality of meat products (in 45% of all studies), probably because sensorial profiles are close to spoilage perception by consumers. In general, these approaches consist to determine the acceptability of food products by a panel constituted of several members. The number of members in the panel can vary from one study to another  [39, 40]. Depending on the types of meat (raw or cooked meats, red or white meats), the panel generally trained during preliminary sessions, determines different possible hedonic scales in order to evaluate organoleptic descriptors to be scored (intensity of off-odors or visible discoloration in fresh meats or general appearance as well as off-flavor for cooked products)  [41, 42].
Preservation and Spoilage Control Strategies Active and Modified Atmosphere Packaging Figure  3  illustrates the preservation strategies which have been studied in the collected dataset. The improvement in the characteristics of packaging for meat products were identified as the most common used strategies for preventing spoilage during storage. According to our dataset, in more than 50% of publications, meat products were conditioned under MAP. Vacuum packaging was often the other type of packaging under investigation. MAP consists in the replacement of the atmosphere surrounding the product by mixed gas compositions of O 2 , CO 2 , N 2 and CO, as well as inert gases such as Ar. In general, the common gas compositions correspond to balances between O 2 or CO 2 , since high O 2 concentrations are useful for red meat color stabilization  [65] [66] [67] , and increasing CO 2 in MAP is useful for inhibiting microbial growth  [21, 68].
Preservation and Spoilage Control Strategies Active and Modified Atmosphere Packaging Figure  3  illustrates the preservation strategies which have been studied in the collected dataset. The improvement in the characteristics of packaging for meat products were identified as the most common used strategies for preventing spoilage during storage. According to our dataset, in more than 50% of publications, meat products were conditioned under MAP. Vacuum packaging was often the other type of packaging under investigation. MAP consists in the replacement of the atmosphere surrounding the product by mixed gas compositions of O2, CO2, N2 and CO, as well as inert gases such as Ar. In general, the common gas compositions correspond to balances between O2 or CO2, since high O2 concentrations are useful for red meat color stabilization  [65] [66] [67] , and increasing CO2 in MAP is useful for inhibiting microbial growth  [21, 68].
Spoilage Occurrence Time in Red and White Meat Products The second selection of publications providing quantitative data on spoilage over time resulted in 24 studies corresponding to data obtained from white (chicken or turkey) or red (beef, pork, ostrich, foal or lamb) meat stored under refrigerated temperatures between 0 and 12 • C. Several types of fresh products, such as raw fillets or fresh sausages, were included in the dataset (Table  2 ). In total, 84 initial experimental conditions (N = Σn i = 84) were considered from the 24 chosen studies. The whole dataset obtained provided a global distribution of spoilage occurrence time, which varied from 3 to 26 days under refrigerated temperatures, with a median spoilage time estimated at nine days (Figure  4 ). Since this distribution was asymmetrical, the median time of nine days was defined as a critical threshold characterizing two principal spoilage stages.
05). Therefore, the presence of O2 in packaging significantly impacted the spoilage occurrence time of fresh meat products. The spoilage occurrence time seemed to also be impacted by the "more than 5%" or "less than 5%" carbon dioxide groups, since its distribution behaved differently in these conditions (Figure  6 ). In packaging with less than 5% carbon dioxide, the asymmetrical distribution indicated that there were more early spoiled products than belated ones. With less than 5% of carbon dioxide, most of the cases indicated early spoilage before the fifth day. These cases corresponded exclusively to packaging under air. The effect of CO2 on spoilage time was also evaluated in the same way as described above for O2. Surprisingly, our dataset did not show any significant statistical difference between packaging containing more than 5% or less than 5% CO2.
Influence of Chilled Storage Temperature The spoilage occurrence time seemed to also be impacted by the "more than 5%" or "less than 5%" carbon dioxide groups, since its distribution behaved differently in these conditions (Figure  6 ). In packaging with less than 5% carbon dioxide, the asymmetrical distribution indicated that there were more early spoiled products than belated ones. With less than 5% of carbon dioxide, most of the cases indicated early spoilage before the fifth day. These cases corresponded exclusively to packaging under air. The effect of CO 2 on spoilage time was also evaluated in the same way as described above for O 2. Surprisingly, our dataset did not show any significant statistical difference between packaging containing more than 5% or less than 5% CO 2. Spoilage was significantly delayed in the absence of O 2 ; however, even under anaerobic conditions, the great variability and symmetrical distribution of spoilage occurrence time suggested that O 2 depletion from the packaging did not ensure a belated spoilage for all products.
In this study, the difficulties in analyzing effects of storage temperature on spoilage occurrence time might be mainly due to several statistical reasons. Since data were collected from experiments performed under different MAP, with various types of meat, temperatures, etc. , the data analyses are thus multifactorial. Although effects of temperature on microbial growth in meat products are incontestable  [167, 168] , the multifactorial data analyses may have allowed the effects of storage temperature to be covered up by other factors. Moreover, as seen in Figure  5 , very few data points corresponding to a spoilage time greater than 15-16 days were reported, which led to a bad estimation of the confidence interval. Finally, since most experiments were conducted at 4 °C, the non-Gaussian distribution of storage temperature led us to assess correlation between temperature and spoilage occurrence time using the inevitably appropriate non-parametric test, which usually has less statistical power than its parametric equivalent  [169].
Relationship between Spoilage Time and Microbiological Indicators Five principal microbiological indicators were considered, i. e. , total mesophilic aerobic counts, LAB, Brochothrix spp. , Pseudomonas spp. and Enterobacteriaceae. The CFU enumerations are summarized in Table  2. The distribution of the counts for each indicator is represented as a function of the spoilage stage (Figure  8 ). At the spoilage time and regardless of spoilage stage (early or belated), total mesophilic aerobic counts were observed as the most abundant group (6. 52 log CFU/g in average) and Enterobacteriaceae In this study, the difficulties in analyzing effects of storage temperature on spoilage occurrence time might be mainly due to several statistical reasons. Since data were collected from experiments performed under different MAP, with various types of meat, temperatures, etc.
The text-mining approach was also useful to identify relevant literature data, since it identified relevant keywords from articles provided by experts, thus enabling better targeting of further searches on scientific databases. Nevertheless, the quantitative analysis of spoilage-related data was, unfortunately, limited by the data available from research articles. Although the searches were performed on full texts to avoid omission of important studies, the thorough "manual" filtering and required selection criterion regarding the quantitative sensorial data over time led to the ultimate selection of only 24 studies to use for quantitative data analyses. The identification of influencing factors on spoilage occurrence time was consequently performed from quite a small dataset (84 initial experimental conditions in multifactorial dataset).
The effects of these factors could not all be evaluated by hypothesis testing because of statistical significance problems. Indeed, using a multitude of hypothesis tests (and p values) on the same dataset may lead to uninterpretable test statistics, increasing the "false positive" effect and otherwise covering up the effects of other factors  [170, 171]. Therefore, for some factors, we preferred avoiding the hypothesis significance testing and using only graph interpretation instead. We aimed to study the effects of several biological and physicochemical factors on spoilage occurrence time of meat products using a novel method for analyzing literature data. This method of combining experts' opinions and the text mining approach for extracting relevant information enabled us to give an overview of multiple different studies.
8 and 9. 8 log CFU/g with the lowest standard deviation. The remaining bacterial indicators, including LAB, Brochothrix spp. and Pseudomonas spp. , had overall average and standard deviation values of approximately the same level. Globally, the results from the whole dataset (84 initial experimental experiments) showed a significant difference in the bacterial counts of the five bacterial indicators observed at the spoilage occurrence time, regardless of the spoilage stage (p < 0. 05, ANOVA). Microorganisms 2020, 8, x FOR PEER REVIEW 18 of 30 as the least abundant group (4. 61 log CFU/g in average). However, Enterobacteriaceae counts presented the highest variability range in comparison with the other groups, as their values ranged from 0. 8 to 9. 8 log CFU/g. Mesophilic aerobic counts were observed as the most abundant group, but the least variable, as its distribution varied between 2.
8 and 9. 8 log CFU/g with the lowest standard deviation. The remaining bacterial indicators, including LAB, Brochothrix spp. and Pseudomonas spp. , had overall average and standard deviation values of approximately the same level. Globally, the results from the whole dataset (84 initial experimental experiments) showed a significant difference in the bacterial counts of the five bacterial indicators observed at the spoilage occurrence time, regardless of the spoilage stage (p < 0. 05, ANOVA).
Conceptualization, S. G. , J. -M. M. , L. C. and N. -D. M. L. ; methodology, N. -D. M. L. ; investigation, N. -D. M. L. ; data curation, S. G. and N. -D. M. L. ; writing-original draft preparation, N. -D. M. L. and S. G. ; writing-review and editing, N. -D. M. L. , S. G. , J. -M. M. , L. C. and M. Z. ; supervision, S. G. ; project administration, M. Z. ; and funding acquisition, M. Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the French Research National Agency (ANR) REDLOSSES project (ANR-16-CE21-0006).
Author Contributions: Conceptualization, S. G. , J. -M. M. , L. C. and N. -D. M. L. ; methodology, N. -D. M. L. ; investigation, N. -D. M. L. ; data curation, S. G. and N. -D. M. L. ; writing-original draft preparation, N. -D. M. L. and S. G. ; writing-review and editing, N. -D. M. L. , S. G. , J. -M. M. , L. C. and M. Z. ; supervision, S. G. ; project administration, M. Z. ; and funding acquisition, M. Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the French Research National Agency (ANR) REDLOSSES project (ANR-16-CE21-0006).
The magic-angle-spinning (MAS) rate was fixed at 9 kHz and each acquisition was recorded at room temperature (293 • K). Chemical shifts were calibrated with external glycin, assigning the carbonyl at 176. 03 ppm. The chemical shift, half-width and area of peaks were determined using a least-squares fitting method using the Peakfit ® software (Systat Software Inc. , San Jose, CA, USA). The DS of acetylated starches were determined with solid-state 13 C-NMR based on the methods developed in liquid-state 1 H-NMR  [18].
[21]. The cross flow (F c ) was set at 0. 84 mL min -1 for the sample introduction (injection at 0. 20 mL min -1 for 300 s) and the focusing/relaxation period (300 s). F out was then set at 0. 84 mL min -1 and F c decreased in 480 s from 0. 4 to 0. 05 mL min -1 , maintained 600 s at 0. 05 mL min -1 , and finally 300 s at 0 mL min -1. Two on-line detectors were used: a MALLS instrument (Dawn ® HELEOS™, WTC, Santa Barbara, CA, USA) fitted with a K5 flow cell and a GaAs laser (λ = 658 nm), and an refractometer (Optilab, WTC, Santa Barbara, CA, USA) operating at the same wavelength (WTC). Solubilization and elution recovery rates, and acquisition and data processing using ASTRA ® software from WTC were established as previously described  [21, 24].
Topology and Surface Chemical Composition Raman spectroscopy analyzes were carried out on powders and films by a LabRAM Visible Raman micro-spectrometer (Horiba Jobin-Yvon, Kyoto, Japan), with a He-Ne laser (λ = 632. 8 nm) and a network of 600 t mm -1. The device was equipped with a confocal microscope (Olympus, Tokyo, Japan) with a ×100 magnification lens. Spectral resolution was 1. 1 cm -1 per pixel. The data were acquired and processed with NGSLabSpec (version 5. 45. 09). Time of flight secondary ion mass spectroscopy (TOF-SIMS) spectra were obtained on films using a TOF SIMS5 (IONTOF GmbH, Münster, Germany) comprising a pulsed ion source (Bi 3+ ) with a current of 0. 35 pA and with charge compensation. Both positive and negative secondary ion spectra were collected for each sample with a range of m/z = 0-200 Da and accumulated from 50 scans.
Calibration: Observations and Protocol We use the following datasets to calibrate our model with observations: a field trial and two hydroponic studies. The field trial contains information on maize (Zea mays) shoot biomass and organ P uptake during a whole growing season in 1996 (with an interval of 7-20 days) and comes from a long-term experiment in Tartas, France  (Plénet et al. , 2000)  where maize response to different P input levels was recorded (three levels, four replicates). The hydroponic studies describe early vegetative response (within 3-4 weeks) of shoot biomass, root biomass and shoot P uptake across five different P levels at experiment end. They contain seven temperate annual pasture species  (Asher and Loneragan, 1967)  and five cereal/legume ones  (Fageria and Baligar, 1989).
This is because aboveground biomass is grouped into shoots in both hydroponic studies  (Asher and Loneragan, 1967; Fageria and Baligar, 1989) , and no root P concentration is given in  Fageria and Baligar (1989). All of the parameters were kept constant during integration and calibrated in order to minimize the prediction error until the incremental parameter change falls below 1%. To start the calibration process we provide initial guess values based on maize, and assume these are the same when calibrating other species. To calculate the assimilation rate k CL we assume leaf specific leaf area (SLA) to be around 150 cm 2 g DW -1 based on our observation dataset, and refer to  Kim et al. (2006)  for the net Root P uptake rate kgP tDW -1 root day -1 C L,max LUE half-saturation point tDW leaf ha -1 r L,max PUE half-saturation point kgP tDW -1 leaf f C,i=S,G C allocation fraction tDW tDW -1 leaf l C,i=L, R, S, G C respiration rate day -1 f P,i=R,S P allocation fraction kgP kgP -1 leaf k mP,i =R, L, S Grain P remobilisation fraction tDW -1 grain day -1 For the hydroponic studies, the units change according to the scale of the experiment to gDW pot -1 for biomass and mgP pot -1 for P.
Since P concentration and final mass are a product of growth and respiration and are not easily described by C:P ratios, we manually adjust the parameters related to allocation of C and P towards stem and root as well as grain P remobilisation, until the modeled plant somewhat resembled maize growth in our observation dataset. Afterwards we rely on the calibration procedure to pinpoint the parameter values. Table  2  provides initial guess values and references for additional clarity. To calibrate the parameters we used Scipy's optimize package  (Jones et al. , 2001). To solve the linear programming problem we linearise Equations 2-4 and use the package CVXOPT  (Andersen et al. , 2018). We integrate the model 160 days for the field trials and 30 days for the hydroponic studies. The calibrated values are compared to our initial guess estimates in Table  3.
Brachypodium Distachyon Plantlets Two days post inoculation (day 9), a first set of ten plants were collected for RNA extraction and defense gene expression quantification. Reverse-transcription, and quantitative real-time PCR were performed as previously described by Gatti et al. [38]. Primers used to quantify expression of two defense genes: PAL and PR9 (peroxidase) and two reference genes from B. distachyon ACT7 and UBC18 are presented in supplementary data 1. Two analytical replicates were considered. At day 17 (10 days post-inoculation), symptoms were observed on a second set of ten plants. Spikelets were considered as symptomatic if 50% or more of floral cavities harbored fusarium symptoms. Also, plants were collected for analysis of fungal biomass. Quantification of fungal genomic DNA in infected spikes was performed as previously described by Pasquet et al.
Cloning and heterologous expression of RGase A. aculeatinus (Genome reference: NW_020291493. 1) endorhamnogalacturonase, RHG (GH28 -EC 3. 2. 1. 171) (GenBank Gene ID: 37148665), endo-β-1,4-galactanase, GAN ( GH53 -EC 3. 2. 1. 89) (GenBank Gene ID: 37150470) and α-Arabinofuranosidase, ABF (GH54 -EC 3. 2. 1. 55) (GenBank Gene ID: 37152657) were amplified from A. aculeatinus cDNAs. Cloning primers used are listed in Table  1. ORFs from RHG and ABF were cloned into pPICZαB, including their signal peptide, with the use of BstBI (to remove α-factor from vector) and NotI restriction enzymes (New England Biolabs, Hitchin, UK). As GAN ORF contain a BstBI restriction site, it was not possible to clone it using the same procedure. GAN ORF was cloned downstream of pPICZαB α-factor with EcoRI and NotI restriction enzymes (New England Biolabs, Hitchin, UK).
Determination of Km, Vmax, and specific activity The kinetic parameters Vmax and Km were calculated by GraFit7 software (Michaelis-Menten/Hill; Erithacus Software, Horley, Surrey, UK), using pectic galactan for GAN, AZCL-RGI for RHG and arabinan for ABF. The reactions were performed at the optimum pH and temperature, using 0. 05 to 4 g. mL -1 substrate concentrations.
NMR Spectra analysis Before NMR analysis, samples were rinsed twice with 99. 9 % D2O (Eurisotop), dried under vacuum, and 1 mg dissolved in 99. 96% D2O (0. 5 ml -1 ). 1 H NMR spectra were recorded, at room temperature on a Bruker Avance 500 spectrometer equipped with a 5 mm BBI probe and Topspin 3. 1 software. 1 H NMR spectra were accumulated using a 30° pulse angle, a recycling time of 1 s and an acquisition time of 2 s for a spectral width of 3 000 Hz for 32 K data points with or without a presaturation of the HOD signal using a presaturation sequence provided by Bruker. The 2D 1 H/ 1 H COSY, 1 H/ 1 H TOCSY, 1 H/ 1 H NOESY, 1 H/ 13 C HSQC and 1 H/ 13 C HMBC spectra were acquired with a standard pulse sequence delivered by Bruker  (Bertin et al. , 2015).
Cloning and expression of recombinant RGIases The full-length genomic DNA sequences of endo-rhamnogalacturonase (RHG) (GenBank Gene ID: 37148665), α-Arabinofuranosidases (ABF) (GenBank Gene ID: 37152657) and endo-β-1,4-galactanase (GAN) (GenBank Gene ID: 37150470) consisted of 1517 (3 introns), 1754 (no introns) and 1597 (1 intron) base pairs, respectively. RHG and GAN were amplified from cDNA from A. aculeatinus culture grown on soy bean RG as the expression of the genes was induced on this substrate (Table  S1 ). As ABF gene does not have intron, its amplification was directly performed on genomic DNA. At the amino-acid level, the sequences were aligned as shown in Fig. 1. The deduced amino acid sequence showed high similarities with enzymes previously annotated from A. aculeatus, 99. 32 % for RHG (Fig.
25 mm, 0. 25 μm) using 5. 5 ultrapure helium (99. 9995% purity) as the gas carrier. The injection was in split mode (1:10), and the temperature was set at 250 °C. The oven temperature started at 100 °C (held for 0. 2 min), increased first until 150 °C (with a ramp temperature of 6 °C/min), and then until 240 °C (ramp temperature 2 °C/min) held for 5 min. A certified reference material (Supelco 37 Component FAME Mix) was used for both the qualitative and quantitative analyses. The retention times and molecular fragment mass data obtained were processed using the instrument software. Each measurement was repeated three times. The fatty acids were identified from the corresponding FAMEs by critical comparison with mass spectral data from NIST/EPA/NIH Mass Spectral Library 2005.
Wild Indian Ocean yellowfin tuna For wild YFT, we considered a length-at-age data set collected throughout the Regional Tuna Tagging Project (RTTP), a large scale mark-recapture program conducted in the Western Indian Ocean between 2005-2007, and the West Sumatra Tuna Tagging Project (WSTTP), a tagging program conducted off the western Indonesian coast in August 2007  [1, 48]. All lengths were measured with a measuring board to the nearest 0. 1 cm in fork length (L F ), i. e. the projected straight distance measured from the tip of the upper jaw to the shortest caudal ray. Fish ages were estimated from readings of saggital otoliths collected from (i) 128 YFT caught through the RTTP and measuring between 43-72 cm L F at tagging and 47. 9-135. 4 cm L F at recapture, (ii) 18 fish caught through the WSTTP measuring between 19-29 cm L F , (iii) 35 fish, including 7 females, collected from 2008-2009 at the Indian Ocean Tuna Ltd.
cannery and measuring between 31-147. 5 cm L F  [47, 64]. Otoliths were prepared for age analysis and read at the "Laboratoire de Sclérochronologie des Animaux Aquatiques" (Brest, France) following the method described by Stéquert  [72]. The number of otolith growth increments was counted repeatedly for different otolith sections  [47, 64]. The ages were estimated from these counts using an ageing error model that explicitly accounts for process and interpretation errors in otolith readings  [10]. We also considered length-weight measurements from defrosted YFT caught in the whole Indian Ocean basin. Fork length was measured with a caliper to the nearest 0. 1 cm and total weight was measured with a scale to the nearest 0. 1 kg  [4]  Finally, we used a data set of batch fecundity, i. e. number of eggs released per batch, derived from gravimetric method applied to 40 YFT females in the size range 79-146.
As juveniles do not store energy for reproduction, the wet weight (W W ) can be expressed from the physical length and the reserve density [E] = E/L 3 V (J. cm -3 ) as follows: W W = δ 3 j L 3 F d V + [E] ρ E (9) This length-weight relationship was fitted to data of wild immature YFT including 87 fish measuring 29-61 cm and weighing 0. 39-4. 2 kg in a Bayesian framework. Without prior knowledge, an uninformative inverse gamma distributions was assigned to [E]. d V was assigned a normal distribution centered on 1. δ j was assumed to vary according to a beta prior distribution between 0 and 1. ρ E was fixed such that the ratio of ρ E to δ j was the same as in Jusup et al. [31]. The parameters estimate was based on sampling approach of Markov Chain Monte Carlo simulations as implemented in OpenBugs version 3.