dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental data Annual rainfall recorded between 1985 and 2013 in the meteorological station closest to Upper Volaja (Lat: 46. 25°N, Long: 13. 58333°E, Kobarid, Slovenia) ranged between 1600 and 3400 mm. The maximum daily rainfall over the same time period was recorded on 7 November 1997 (252 mm) (electronic supplementary material, figure  S1 ). An ONSET temperature logger recorded mean daily water temperature in Upper Volaja. Missing water temperature data in 2004 (all year) and 2005 (from 1 January to 10 June) were estimated using water temperature recorded in the stream Lipovscek (Pearson's r = 0. 98 for daily water temperature data between 2004 and 2014). Annual growing degreedays (GDDs,  [20] ) and mean annual T showed very little variation from 2004 to 2014 (mean ± s. d. GDDs = 1204.
Density and movement We estimated density of 0+ fish only in September, because fish emerged a few days before the June sampling. We estimated density of fish older than 0+ for age, size-class or cohort using a two-pass removal protocol  [21]  as implemented in the R [22] package FSA  [23]. Three passes provided the same density estimates as two passes  [24]. Total stream surface area (746. 27 m 2 ) was used in the estimation of fish density (in fish ha -1 ). We assessed the contribution of individuals from AW (source) to Upper Volaja (sink) by estimating for each year of sampling the proportion of fish that were not sampled in Upper Volaja either at 0+ in September or 1+ in June. Fish with adipose fin cut were assumed to be born in Upper Volaja or be early incomers (i. e. fish migrating into Upper Volaja when younger than 1+ in September), while fish with intact adipose fin were assumed to be born in AW and be 'late incomers', that is fish dispersing into Upper Volaja when 1+ in September or older.
In the estimation procedure, we used a log-link function for k and L ∞ , because both parameters must be non-negative. We set log(k (ij) ) = α 0 + α 1 (j) + α 2 x ij + σ u u ij log(L (ij) ∞ ) = β 0 + β 1 (j) + β 2 x ij + σ v v ij and t 0 = γ 0 , ⎫ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎭ (2. 2) where u ∼ N(0, 1) and v ∼ N(0, 1) are the standardized individual random effects, σ u and σ v are the standard deviations of the statistical distributions of the random effects, i is the individual, j is the index for groups (e. g. cohort), α   [27, 28]. One of the features of ADMB is the ability to fit generic random-effects models using an empirical Bayes approach (i. e. priors are estimated from data) using the Laplace approximation  [29]. Since the growth model operates on an annual time scale and more data on tagged fish were generally available in September of each year, we used September data for modelling lifetime growth.
Growth in size between sampling intervals We used generalized additive mixed models  [30]  to model variation in mean daily growth G d (in mm d -1 ) between sampling occasions using length L, Age, GDDs over sampling intervals by Season (i. e. the relationship between GDDs and G d is modelled differently for the two Seasons), and D >0+ as predictors, plus fish ID as a random effect. Since we expected potential nonlinear relationships between the two predictors and G d , we used candidate smooth functions for L and GDDs. We carried out model fitting using the R package mgcv  [31]  and model selection as in the section 'Variation in size-at-age 0+'.
Recruitment Brown trout living in Upper Volaja spawn in December-January and offspring emerge in June-July. Local recruitment in Upper Volaja has been observed. Females achieve sexual maturity when bigger than 150 mm, usually at age 2+ or older, and can be iteroparous  [19, 32]. We used density of fish with L > 150 mm as density of potential spawners at year t (D s,t ) and density of 0+ in September of year t as a measure of recruitment (R t ). The most popular stock-recruitment models (e. g. Ricker's, Beverton-Holt's, Cushing's) rarely provide a good fit to recruitment data of small freshwater fish populations. Following Vincenzi et al. [24] , we thus used generalized additive models to model variation in R t using density of potential spawners in September of year t -1 (D s,t-1 ) and GDDs for year t up to emergence time (we assumed from 1 January to 31 May for standardization purposes) as predictors.
For each sample measurement, daily parameters were calculated based on the individual effective morning and evening milk production. Kinetics of rumen pH. Rumen pH was monitored continuously throughout the experiment using a commercial sensor (eBolus; eCow, Exeter, UK). One sensor per cow was immersed in the reticulum through the oesophagus with the use of dedicated balling gun. The sensor was calibrated according to the manufacturer's recommendations before use. Each sensor was set up to record mean pH over 15 min (96 data points/day) with an accuracy of ±0. 1. Data were downloaded every 15 days using eCow handset (smartphone + antenna) with eCow android application. Rumen samples. Rumen samples were collected from the six fistulated cows for 2 weeks in P1 (weeks 3 and 4), 4 weeks in P2 (weeks 1 to 4) and 2 weeks in P3 (weeks 1 and 3).
Calculations and statistical analysis Rumen sensor pH data. Collected data were summarized with Excel Software and a Visual Basic for Application program was developed to synchronize and process the raw reticulo-rumen pH kinetics from all sensors. Each raw sequence of pH measurements is a time series (Xt) which may be modelled by adding a trend and cyclical components of different time constants: Xt = Dt + Ct + Nt where Dt is the offset and long-term drift due, respectively, to initial calibration (±0. 05 pH unit), measurement accuracy over time (±0. 1 pH unit/30 days) and cow initial pH value; Ct the daily variation impacted by meal delivery frequency and diet composition; and Nt the high-frequency noise due to a matrix effect (rumen) and environmental conditions (temperature, pressure, electrical artefacts).
Statistical analysis All data were analysed using R3. 2. 3 software. For each variable, individual data collected daily (or several days a week) throughout the experiment were averaged per week. When necessary, data were log-transformed before analysis in order to create a normal distribution and to reach assumptions of the statistical models (LPS, total protozoa, pH indicators: time pH < 5. 6, 5. 8, 6, time NpH <-0. 3, <-0. 5 and AUC NpH <-0. 3, <-0. 5). Three different data analysis methods were implemented successively: (1) A linear mixed model to identify the variables significantly modified between the 8 weeks of the experiment (2 weeks in P1, 4 weeks in P2 and 2 weeks in P3): Y ijk = µ + A i + B j + C k + ɛ ijk , where Y ijk is the dependant variable, µ the mean, A i the fixed effect of period, B j the fixed effect of week nested in period, C k the random animal effect and ɛ ijk the residual error term (denominator degrees of freedom (DDFM), Kenward-Roger).
These results confirm that absolute pH indicators have several limitations for SARA detection. In a harsh environment like reticulum where rumen fluid could slowly diffuse into the sensor and poison the reference electrode, drift (Dt) and noise (Nt) are frequently observed (Kaur et al. , 2010). In our experiment, both Dt and Nt were detected from the data of 10 sensors. This long-term drift started at day 29 and lasted until the end of the study. In addition, we observed large initial between-animal variability in daily average rumen pH, starting at 6. 1 to 6. 7, with a standard deviation of 0. 25 pH units in P1. Consequently, absolute rumen pH changes in SARA syndrome also appear to vary greatly, as reported in several studies (Schwartzkopf-Genswein et al. , 2003; Penner et al. , 2007; Mohammed  et al.
Acknowledgements The present research (PhD) of  Clothilde Villot  was financially supported by  Valorex, Terrena and Lallemand. The authors thank the personnel of the  INRA experimental unit (Herbipôle)  for cow welfare, feeding and sampling. The authors particularly thank  Lucette Genestoux ,  Clothilde Dos Santos  and  Aurélie Ameilbonne (Lallemand)  for skilled technical assistance and  Diego Morgavi  for reviewing this paper thoroughly.
Determination of the Fatty Acid Profiles of Milk Thistle Oils and Nigella Oil by Gas Chromatography The lipids were extracted from the various oils (milk thistle seed oils from different areas of Tunisia (Zaghouan, Bizerte, Sousse) and nigella oil) according to the method of Moilanen and Nikkari  [44]. C19:0 was used as an internal standard. The lipids were transmethylated using boron trifluoride in methanol, in accordance with Morrison & Smith  [45]. The fatty acid methyl esters were then analyzed under the same conditions as those described by Debbabi et al. [46]  and Badreddine et al. [47]. The data were processed with EZChrom Elite software (Agilent Technologies, Massy, France) and are reported in mg/g total lipids. Before the high performance liquid chromatography (HPLC) analysis, 0. 5 g of the seed oil was diluted with 5 mL of hexane, and 5 µL of the sample was then injected.
KRL (Kit Radicaux Libres) Test The antioxidant potential of milk thistle oils, nigella oil, α-tocopherol, silymarin, resveratrol, and ferrulic acid was evaluated with the KRL (Kit Radicaux Libres) test, as previously described  [46, 50]. The data are presented in Trolox equivalents. Hemolysis was recorded using a 96-well microplate reader by measuring the decay of the optical density at 450 nm on a spectrophotometer. For each well, absorbance measurements were made 75 times, once every 150 s.
Profiles of Fatty Acids, Phytosterols, Tocopherols, and Polyphenols Cold-Extracted from Milk Thistle Seed Oils from Different Area of Tunisia Milk thistle seed oil (MTSO) is known for its high level of secondary metabolites, such as phenolic acids, tocopherols, fatty acids, and phytosterols, which depend on many intrinsic (genetic) and extrinsic (environmental) parameters  [60] [61] [62]. In the present study, we focused on the fatty acids, phytosterols, polyphenols and tocopherols present in MTSO. Data obtained were compared with nigella seed oil which is mainly empirically known for its toxic activity. Milk thistle seed oil (MTSO; from Tunisia-Bizerte, Sousse and Zaghouan) and nigella seed oil (NSO; from Tunisia) were analyzed. The fatty acid compositions of these oils are given in Table  1.
The cytoprotective effects of MTSO and nigella seed oil on 7KC and 24S-induced plasma membrane permeability were evaluated by flow cytometry after staining with propidium iodide (PI). Plasma membrane permeability was determined by the percentage of PI positive cells. Data are means ± SDs of two independent experiments carried out in triplicate. The significance of the differences between the vehicle and cells treated with 7KC, MTSO, nigella seed oil, or α-tocopherol was calculated by ANOVA tests (Sidak's multiple comparisons); * p ≤ 0. 05. The significance of the differences between the cells treated with 7KC or 24S, 7KC or 24S with Figure  3. Effect of milk thistle seed oil (MTSO) on 7-ketocholesterol (7KC)-and 24S-hydroxycholesterol (24S)-induced plasma membrane permeability in 158N murine oligodendrocytes.
After 24 h of culture, 158N murine oligodendrocytes were cultured for 24 h without or with 7KC or 24S (25 µM) in the absence or presence of MTSO (Area: Zaghouane, Bizerte, and Sousse; 0. 1% v/v) or α-tocopherol (400 µM), which was used as a positive control. MTSO, nigella seed oil, and α-tocopherol were added to the culture medium 2 h before 7KC. The cytoprotective effects of MTSO and nigella seed oil on 7KC and 24S-induced plasma membrane permeability were evaluated by flow cytometry after staining with propidium iodide (PI). Plasma membrane permeability was determined by the percentage of PI positive cells. Data are means ± SDs of two independent experiments carried out in triplicate. The significance of the differences between the vehicle and cells treated with 7KC, MTSO, nigella seed oil, or α-tocopherol was calculated by ANOVA tests (Sidak's multiple comparisons); * p ≤ 0.
Acknowledgments: The authors acknowledge  Stéphane Grégoire  and  Lucy Martine  ( Inra, Dijon, France ) for fatty acids and tocopherols analyses,  Emmanuelle Camus-Prost  ( Laboratoire Spiral, Couternon, France ) for polyphenols analysis, and  Philippe Durand  ( Laboratoire Spiral, Couternon, France ) for the KRL test.
Chemical Exchange Saturation Transfer. CEST measurements were based on 1 H-  15  N HSQC experiments by applying constant wave saturation of 500 Hz in the  15  N channel. As the exchange is probed between unbound MccJ25 (having sharp resonances) and the slow tumbling 250 kDa complex with McjD embedded in bicelles (having significantly broad resonances), a series of large offsets was employed (-10, -8, -6, -4, -3, -2, -1. 5, 0, 1. 5, 2, 3, 4, 6, 8, and 10 kHz), resulting in CEST profiles of symmetric shape (Supporting Information Figure  1 ). An additional spectrum, saturated at -100 kHz, was recorded as a reference. The 1 H-15 N-HSQC spectra recorded using different offsets were made with a data matrix consisting of 2048 (t 2 , 1 H) × 200 (t 1 ,  15   where j is the offset employed in each CEST measurement and I j and I 0 are the intensities of the peak corresponding to the amide of the residue in the spectra recorded with the offset j and with an offset at -100 kHz (reference spectrum).
ACS Chemical Biology Articles employed in restrained molecular dynamics simulations using the CamShift method,  45  following established protocols that have been highly successful in characterizing the structures of small peptides using the information contained in NMR chemical shifts. 20, 46 he restrained molecular dynamics simulations were performed by averaging chemical shift restraints over four replicas, as previously described,  20, 46  using the structure of MccJ25 with PDB code 1Q71. 7 he calculations were made using an implementation of the GROMACS package  47  that allows the simulations to be restrained using the CamShift program. 45 Each of the four replicas was equilibrated separately by starting from independent equilibrations of the starting structure of MccJ25 accommodated in a dodecahedron box of 121 nm 3 in volume.
MccJ25 was mixed with McjD protein solution at different concentrations (Supporting Information Figure  7 ). MccJ25-ΔFV was added at a final concertation of 8 μM. The data for the apo-McjD and in complex with MccJ25 in Figure  3C  have previously been published,  16  and they have been reproduced in this manuscript to show the difference with the MccJ25-ΔFV variant. ATPase Activity of Proteoliposome Reconstituted McjD. McjD was reconstituted in proteoliposomes using the rapid dilution protocol as in ref 15. The ATPase activity of McjD in the presence of different peptides and antibiotics was measured as previously described. 14, 15 The final concentration of the peptides in the assay was 1 mM made in 100% dimethyl sulfoxide (DMSO). Antibiotics and anticancer drugs were also added at 1 mM final concentration made either in water or in 100% DMSO depending on the compound solubility.
■ ACKNOWLEDGMENTS We would like to thank  H. van Ween  for providing us with the drug hypersensitive strain. J. D. H. is supported by a  Deutsche Forschungsgemeinschaft (DFG) Research Fellowship  (grant number  HE 7699/1-1 ). This work was supported by the  Leverhulme Trust  ( RPG-2015-350  to A. D. S. ) and the  Medical Research Council  ( MR/N020103/1  to K. B. and A. D. S. ).
Systematic literature review 7 The GMO Panel assessed the applicant's literature searches on maize MZHG0JG, which included a scoping review, according to the guidelines given in  EFSA (2010 EFSA ( , 2017a)). A systematic review as referred to in Regulation (EU) No 503/2013 has not been provided in support to the risk assessment of application EFSA-GMO-DE-2016-133. Based on the outcome of the scoping review, the GMO Panel agrees that there is limited value of undertaking a systematic review for maize MZHG0JG at present. Although the overall quality of the performed literature searches is acceptable, the GMO Panel considers that future searches on maize MZHG0JG should be improved. The GMO Panel therefore recommends the applicant to: • Ensure that enough search term variation is used (covering possible synonyms, related terms, acronyms, spelling variants, old and new terminology, brand and generic names, lay and scientific terminology, common typos, translation issues); • Use controlled vocabulary (subject indexing) in the electronic bibliographic databases where it is available, and, where subject headings are available, use both free-text terms and controlled vocabulary in the searches; • Include intended trait-specific search terms; • Assess the relevance and risk assessment implications of publications retrieved via searches beyond electronic bibliographic databases.
3. 3. Comparative analysis 9 3. 3. 1. Overview of studies conducted for the comparative analysis Application EFSA-GMO-DE-2016-133 presents data on agronomic/phenotypic characteristics, as well as forage and grain composition, of maize MZHG0JG derived from field trials performed at eight sites in the USA in 2013. In addition, the application contains data on characteristics of seed from maize MZHG0JG (Table  2 ). 10.
Organs and tissues from all animals were collected and those from animals that consumed the 41. 5% incorporation level test and control diets were subjected to a detailed histopathological examination. Upon completion of the histopathologic assessment of all tissues, histopathology was reviewed by a peer review pathologist. In the first analysis provided by the applicant, test and control diets were compared by pooling data from diets at 10% inclusion rate (10% diets) and at 41. 5% inclusion rate (41. 5% diets). However, the GMO Panel noted that the 10% and 41. 5% diets had a different composition in terms of ingredients (for both test and control), and therefore considers it inappropriate to pool data from two qualitatively different diets groups in the analysis. For this reason, the applicant was requested to analyse the data for the 10% and 41.
025 lg/g fresh weight) is used as worst-case scenario. Since the concentrations of the mEPSPS and PAT proteins were not analysed in processed foods, their concentration in these commodities is estimated using the ratio between the total protein contents of processed foods and maize grains. The assumption is that no losses of the newly expressed proteins occur during processing. Regarding consumption data, a conservative scenario with 100% replacement of conventional maize is considered, as no specific data are available on the consumption of commodities containing maize MZHG0JG. Maize oil is excluded from the assessment because proteins are not expected to be present in oil. Consumption data of the relevant commodities (e. g. corn bread, corn flakes, corn milling products, cornmeal porridge, corn grain, corn snacks, sweet corn, popcorn, etc.
) were retrieved by the applicant from the available summary statistics of the EFSA Comprehensive European Food Consumption Database (accessed in April 2018). 26 EFSA's consumption database contains information on food consumption data at individual level from the most recent national dietary surveys in different EU Member States  (EFSA, 2011; Huybrechts et al. , 2011; Merten et al. , 2011) , and is regularly updated with new consumption data collected in the context of the EU Menu project. 27 cute dietary exposure in high consumers within each dietary survey and age class is estimated by summing the exposure derived from the 95th percentile consumption for the dominant food commodity 28  among consumers only and those exposures derived from the mean consumption of the remaining food categories in the total population  (EFSA, 2015).
Animal dietary exposure Daily dietary exposure (DDE) to the mEPSPS and PAT proteins newly expressed in maize MZHG0JG was provided by the applicant across different livestock animal species (e. g. poultry, swine, cattle and sheep) based on EU estimates issued by OECD (2013) for animal body weight, daily feed intake and the inclusion rates (percentage) of maize grains and maize grain by-products (i. e. milled by products, hominy meal, gluten feed and gluten meal) in animal diets. A conservative scenario with 100% replacement of the conventional maize (maize grains, milled by products, hominy meal, gluten feed and gluten meal) is considered. The levels of mEPSPS (42. 79 lg/g fresh weight) and PAT (LOD = 0. 025 lg/g fresh weight) proteins in grains from maize MZHG0JG 25 are used as occurrence data and to estimate levels (as fed) of these proteins in maize grain by-products, 29  based on the protein content in feed fractions relative to maize grain and assuming that no protein is lost during the processing.
The choice ultracentrifugation on a 15-50% sucrose gradient. RNA from polysomal fractions were isolated and subjected to Ligase-Mediated Poly(A) Test (LM-PAT). After amplification with gene specific forward primers of the resulting cDNA, the distribution of poly(A) tail lengths was visualized by agarose gel electrophoresis. The size of the DNA ladder fragments is indicated in base pairs on the left, the number of ribosomes in each fraction is indicated below the gel. 3D surface plot analysis of the agarose gel image was obtained using ImageJ software with minimum set to 0%, maximum to 100% and z-scale to 1. 0. of this set of genes was dictated by some technical constraints such as the abundance of the mRNAs in the cultured HCT116 cells  [22]  and the absence of multiple polyadenylation sites that gave complex poly(A) tail profiles.
The poly(A) peak areas of the density profiles were measured using ImageJ software and the ratio of long poly(A) tail area on total profile area was calculated for the four fractions (4-5, 6-7, 7-9 and 10+ ribosomes) of the 3 polysome fractionation experiments with either control (sh-Ctrl) cells or eRF3a-depleted cells (sh-3a1). For MYC mRNA (B) and ACTB mRNA (D), bars represent the mean values of the three experiments. Error bars indicate the standard deviation. Statistical significance of differences between sh-Ctrl and sh-3a1 were calculated with paired Student's t-test. For statistically significant differences, p-values are indicated above the bars; p-values ≥ 0. 05 were considered as not statistically significant (n. s. ). to the fraction containing mRNAs carrying 6-7 ribosomes, the long poly(A) tails are relatively homogeneous in length, clustered in a peak at ~230 A residues.
On each density profile, the dash line indicates the boundary of the long poly(A) peak area used for the peak area measurements. For each experiment, the dash line position was determined on the fraction presenting a clear separation of the two peaks (usually fraction 4-5) and set at the same position for the other fractions. The poly(A) peak areas of the density profiles were measured using ImageJ software and the ratio of long poly(A) tail area on total profile area was calculated for the four fractions (4-5, 6-7, 7-9 and 10+ ribosomes) of the 3 polysome fractionation experiments with either control (sh-Ctrl) cells or eRF3a-depleted cells (sh-3a1). For MYC mRNA (B) and ACTB mRNA (D), bars represent the mean values of the three experiments. Error bars indicate the standard deviation. Statistical significance of differences between sh-Ctrl and sh-3a1 were calculated with paired Student's t-test.
1). Then, clustering of similar sequences (97% identity threshold for an affiliation at the genus level on the V3-V4 regions of the 16S rRNA gene) was performed with Uclust v1. 2. 22q (  35 ) through an open-reference OTU picking process and complete-linkage method, finally creating groups of sequences or "Operationnal Taxonomic Units" (OTUs). An OTU cleaning step corresponding to the elimination of singletons was performed. For each OTU, the most abundant sequence was considered as the reference sequence and taxonomically compared to the Greengenes database, release 13_8 (www. greengenes. gov) by the RDP classifier method v2. 2  (36). Various diversity indices were computed using QIIME  (34). α-diversity metrics included the Shannon diversity index, the number of observed OTUs and the Chao1 index.