dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the untreated Jurkat NNP dataset • Remove all proteins with less than nine SILAC ratios (only full coverage data). • Annotate proteins with some of the organellar subcellular local- ization markers from  (Itzhak et al, 2016) , and with EV markers (see Appendix Table  S1 ). • Visualize the data structure by performing PCA using SIMCA 15 (Umetrics), with log SILAC ratios scaled to unit variance. o Since PCA is sensitive to leverage by extreme values, which can render the scores plot less informative, 11 proteins were excluded (Dataset EV1). These proteins were however included in the nearest neighbour and network analysis described below. All following analysis steps are calculated on the fly for one or several query proteins and can be reproduced either by following the analysis steps in the interactive Excel sheet or using the python code provided on github (https://www.
github. com/JuliaS92/EVProfiler). Nearest neighbour profile analysis Nearest neighbour profile analysis across the EV fractions was performed largely as described in  (Borner et al, 2014) , who originally reported the approach for the analysis of intracellular vesicle populations. Briefly, proteins with similar fractionation profiles as a given query protein are retrieved. Profile similarity between proteins is evaluated by calculating the pairwise Manhattan distance (absolute summed difference at every point of the two profiles). The query itself is retrieved as top hit (with a distance of 0), and other proteins are listed in order of increasing distance to the query. Different quality filters can be applied (eg minimum number of SILAC quantification events, minimum cosine correlation of profiles across repeats, enrichment level in the F3 extracellular vesicle fraction), to adjust the stringency and sensitivity of the analysis.
These insights cannot be judged from a PCA plot alone, as the dimensional flattening invariably loses information contained in the original nine-dimensional dataset. The network analysis was carried out in python using the networkx library  (Hagberg et al, 2008). Network nodes are selected based on the distance ranking and pseudo z-scoring as described above. Per default, nodes were selected from the overall nearest 50 proteins that showed a positive profile correlation across replicates, were found in the nearest 75 neighbours in at least two replicates and were at least fairly close (pZ > 1. 64) to the query. To achieve an unidirectional assessment of all connections within this network, all pairwise Manhattan distances between the query's 250 closest neighbours were calculated. The distances between the network nodes were then categorized by the quantile they occupy in this distribution; all distances beyond the 50 th or 25 th percentile (as indicated) were discarded from the network.
Thus, if two queries from areas of dataspace with different density are selected, this will become apparent in the number and colour of the edges. This network based analysis, as well as the underlying scores and linear neighbourhoods, as provided in the supplemented Excel files, was compiled into an interactive dashboard, which is available online (http://evprofiler. institut-curie. org). This was implemented in python using the holoviz framework and its bindings to several other python libraries (https://holoviz. org). All genes in the NNP profiling dataset can be queried, and all parameters described above can be adjusted as needed. Analysis of the comparative dataset (untreated vs HIV-infected) For global PCA maps and nearest neighbour analysis, control and HIV fractionation profiles were analysed separately.
HIV-1 proteins were also annotated. PCA was then performed using SIMCA 15 (Umetrics). Log ratio data were UV scaled. Of note, each protein entered into the analysis with one or two sets of six SILAC ratios (ProteinX_con (profiles replicates A and B), and/or Protein X_HIV (profiles replicates A and B)), to obtain one set of joint loadings for both conditions, and hence directly comparable scores plots (see  (Itzhak et al, 2017) ). Since PCA is sensitive to leverage by extreme values, which can render the scores plot less informative, 62 proteins were excluded from the analysis (Dataset EV2). These proteins were however included in the nearest neighbour prediction and MR plot analysis described below. Nearest neighbour profile analysis across the extracellular vesicle fractions was performed largely as described above for the NNP set, but separately for untreated and HIV-infected datasets.
Since most proteins are not expected to change profiles between conditions, the delta profiles largely reflect experimental noise. Using the robust Mahalanobis distance calculation implemented in Perseus 1. 6. software  (Itzhak et al, 2016; Tyanova et al, 2016) , for each delta profile a P-value of likelihood of deviation from the population mean was obtained. A very small P-value indicates a potential outlier, i. e. a protein undergoing a genuine profile shift. For each protein, two independent P-values were thus obtained, from the two replicates. For a stringent analysis, the higher (ie less significant) of the two Pvalues was chosen and squared (as the second replicate had an equal or lower P-value). This was then corrected for multiple hypothesis testing using the Benjamini-Hochberg approach.
Statistical analyses Bioinformatic analysis of proteomic data, generation of the network neighbour predictor and analysis of the ctrl/HIV comparative dataset: see details in section "Bioinformatics analysis of proteomic data". Other statistical analyses: Statistical analyses of particle concentration and multiplex bead-based flow cytometry data were performed with GraphPad Prism version 8. 0. 2 (GraphPad software, California USA), by one-way ANOVA followed by Dunnett's posttest  (Fig 6B and G)   • Raw data: PRIDE PXD019074 (http://www. ebi. ac. uk/pride/arc hive/projects/PXD019074) for Untreated Jurkat (NNP dataset); PXD016626 (http://www. ebi. ac. uk/pride/archive/projects/PXD01 6626) for Control and HIV-infected Jurkat (comparative dataset). • code: github: https://github. com/JuliaS92/EVProfiler.
Acknowledgements Clotilde Th ery and her team acknowledge the following INSERM U932 members for providing tools and helpful discussions:  Dr Nicolas Manel ,  Dr Philippe Benaroch ,  Dr Claire Hivroz ,  Dr Sebastian Amigorena ,  Dr Nicolas Ruffin ,  Dr Andr es Zucchetti ,  St ephanie Dogniaux. The authors acknowledge the  Cytometry platform at Institut Curie's Research Centre , and the assistance of its team to perform Cytometry. The work was funded by  INSERM and Institut Curie  and grants from  NIDA  ( DA040385 , to KWW, CT and MO, and  DA047807 , to KWW); ANRS (2015-1 CT, LMJ); SIDACTION (17-1-AAE-1138, CT);  INCa  ( INCA-11548 , CT),  Fondation ARC  ( PGA1 RF20180206962 , CT);  French National Research Agency  ( ANR-10-IDEX-0001-02   PSL *,  ANR-11-LABX-0043  and  ANR-18-CE13-0017-03  to CT); and by the  NIH Common Fund  through the  Office of Strategic Coordination/Office of the NIH Director  ( UG3CA241694 , to KWW).
The Proteomic platform of  Institut Curie  is supported by  "R egion Ile-de-France " and  Fondation pour la Recherche M edicale grants  (to D. L. ). Electron microscopy imaging was enabled by the  French National Research Agency  through the  "Investments for the Future" program  ( France-BioImaging ,  ANR-10-INSB-04 ), and  PICT-IBiSA , member of the  France-BioImaging national research infrastructure , supported by the  CelTisPhyBio Labex  ( N°ANR-10-LBX-0038 ) part of the IDEX PSL (No. ANR-10-IDEX-0001-02 PSL ). Georg H. H. Borner  and  Julia Schessner  were funded by the  Max Planck Society for the Advancement of Science , and by the  German Research Foundation  ( DFG/Gottfried Wilhelm Leibniz Prize MA 1764/2-1 ); they wish to thank  Matthias Mann  for his support.
To give equal weight to enrichment and depletion, ratios are either log10-transformed or transformed as follows: Ratios equal or greater than one are not transformed. Ratios smaller than one are inverted and multiplied by À1. Hence, a ratio of 0. 1 is converted to À10, to indicate 10-fold depletion (Dataset EV1). • Calculate enrichment of EV fractions F1-F3 versus full proteome. o Calculate median normalized protein intensities across replicates. o For each protein, divide the median intensities of fractions F1-F3 by the normalized full proteome intensity, yielding an enrichment factor Fraction X/Full proteome. o.
Amplification specificity for each real-time PCR reaction was confirmed by analysis of the dissociation curves. Each sample measurement was made in duplicate and four independent RNA biological samples were prepared for each condition. Determined cycle threshold (Ct) values were then exploited for further analysis. Cycle threshold is defined as the calculated cycle number at which the PCR product crosses the threshold of detection. This value tells how many cycles it took to detect the signal from your samples. Seven most stable reference genes were selected by GenEx software (MultiD) and the geometric mean of the five most stable genes (Glk/SCO2126, AspS/SCO3795, GyrA/SCO3873, GyrB/SCO3874, and RpoB/SCO4654) was used to normalize the data (HrdB/SCO5820 and RecG/SCO5566 were excluded). The determination of the relative gene expression ratio was achieved using the Ct method using three biological replicates  (Pfaffl, 2001).
The values of Ct of SC and of the phoP mutant of SL were normalized and standardized by log transforming, mean centering and autoscaling  (Willems et al. , 2008). All data were subjected to the Student test and the results were presented as the mean of delta-delta-Ct with error bars representing 95% confidence interval. Deletion and Over-Expression of the sco0921-20 Operon or of sco0920 or sco0921 Alone in Wild Type and sco0921-20 Deletion Mutants of S. lividans TK24 and S. coelicolor M145 All strains and plasmids used for these constructs are listed in Supplementary Table  S2. DNA fragments were amplified by PCR from genomic DNA of SL and SC prepared as described previously  (Sambrook and Russell, 2001) , using primers listed in Supplementary Table  S3. Primers used for PCR amplification and gene sequence analysis (Sci-Ed Software) were designed with Clone Manager Professional 9 software and purchased from IDT (Integrated DNA Technologies, Leuven, France).
ACKNOWLEDGMENTS We acknowledge the contribution of  Eric Jacquet  and  Naima Nhiri  from the  QPCR/TSA  platform of ICSN (https:// icsn. cnrs. fr/plateformes/qpcr). CNRS,  Gif-sur-Yvette  for the analysis of relative expression of the studied genes using realtime quantitative PCR (RT-qPCR) and the 2 (- Delta Delta C(T) ) method.
FUNDING This work was funded by several  Institutions (CNRS and University Paris-Saclay ) and grants including the ANRs PROBIO3 (https://www. inrae-transfert. fr/fr/102-8-projets/307-probio3)and  Innovantibio  ( ANR-17-ASTR-0018 ) that covered the salaries of CL and TD as well as the cost of the analysis of the lipidome of the strains performed on the Lip(Sys)2 platform (http://www. lipsys2. universite-paris-saclay. fr/) and analysis of relative expression of genes using real-time quantitative PCR (RT-qPCR) and the 2 (- Delta Delta C(T) ) method performed on the QPCR/TSA platform of ICSN (https://icsn. cnrs. fr/plateformes/qpcr). CNRS, Gif-sur-Yvette by  Eric Jacquet  and  Naima Nhiri.
The objective of this study was to test two candidate biomarkers of feed efficiency in growing cattle. A database was built using performance data from 13 trials conducted with growing heifers, steers and young bulls and testing 34 dietary treatments. Different breeds were used with Charolais (37%), Simmental (15%), and cross-bred (40%) cattle being the most numerous. The database included 759 individual records for animal performance and laboratory data for N isotopic discrimination measured in plasma or muscle (∆ 15 N animal-diet ; n = 749) and plasma urea concentration (n = 659). Feed conversion efficiency (FCE) and residual feed intake (RFI) criteria were calculated for a duration ranging between 56 and 259 d, depending on the trial. For FCE prediction, mixed models included the random effects of study, treatment within-study and pen within-study (i.
Experimental data Individual observations for animal performance data and Δ 15 Nanimal-diet and plasma urea analysis were recovered from published (ID#1to ID#7) or unpublished (ID#8 to ID#13) feed efficiency studies in growing-fattening cattle (Table  1 ) conducted across 4 EU countries (France, United Kingdom, Ireland and Switzerland). Overall, the database comprised of 13 experimental studies, 34 dietary treatments and individual animal values (n_indiv = 759) for feed conversion efficiency (FCE) and RFI, Δ 15 Nanimal-diet (n_plasma = 659 and n_muscle= 90) and plasma urea (n_indiv = 659) measured in young growing bulls (9 studies), steers (3 studies) and heifers (1 study). Feed efficiency tests lasted from 56 d (steers; ID#5 and ID#13) to 259 d (young bulls; ID#8) with an average of 136±75 d.
For each study, the ADG of each animal was calculated as the slope of the regression of body weight on time, with body weight recording conducted every week, fortnightly or twice per month depending on the study. Residual feed intake (RFI) was calculated from the whole database as the difference between observed and predicted dry matter intake within-contemporary group (CG), the latter defined as the group of animals from the same study, fed the same diet and sharing the same pen (n_contemporary group = 58). The number of animals per CG averaged 13 ranging from 5 (ID#2) to 34 (ID#3). For the RFI model, observed DMI was predicted from the CG effect, average daily gain (ADG) and mean metabolic body weight (mBW 0. 75 ), the latter calculated at the middle of the feed efficiency test. The RFI model was as follows: Yij = β0 + CG + β1(mBW 0.
ethz. ch/infrastructure/isolab. html). Individual isotopic data from published trials (ID#1 to ID#7) were recovered and included in the database. In-house standards (glutamic acid or tyrosine) were included in each run every 10 samples to correct for possible time-variations in the analysis. Results were expressed using the delta notation according to the following equation: δ 15 N = [(Rsample / Rstandard) -1] x 1 000, where Rsample is the N isotope ratio between the heavier isotope and the lighter isotope ( 15 N / 14 N) for the sample being analyzed, Rstandard the N isotope ratio of the internationally defined standard (atmospheric N2, Rstandard = 0. 0036765), and δ is the delta notation in parts per 1000 (‰) relative to the standard. Samples were analyzed in duplicate and measurement errors for δ 15 N analyzed in the in-house standards were always <1.
Analysis of the relationship between feed conversion efficiency and either N isotopic fractionation or plasma urea Simple and mixed effect models As a first approach, both simple and mixed-effect models were tested to assess how well Δ 15 Nanimal-diet and plasma urea correlated with FCE at the dietary treatment and individual levels. Thus, two different datasets were used in this analysis, the first containing dietary mean values for both variables and allowing us to strictly test treatment effects (i. e. diets and production context) and the second with all individual observations allowing us to assess between-animal variability (i. e within-CG relationship). For simple linear regression analysis, the lm function of R software was used while the nlme package of R  (Pinheiro and Bates, 2000)  was chosen for modeling the mixed-effect regression equations used.
The random effects were tested on the intercept, slope or both. When information criterion statistics (AIC, BIC) were similar for two models, the log-likelihood ratio test criteria performed with the command ANOVA (model 0, model 1) in R was used to determine the best model. Random-effect structures were always compared (AIC, BIC) using the restricted-maximum likelihood method. Because the model accuracy depends on both the prediction model error (root mean square error of prediction; RMSEP) and the magnitude of the variability to be predicted, we extended the evaluation to other statistical criteria. Indeed, we calculated the ratio of RMSEP to the standard deviation of observations from the whole database (RSR global) or within-condition (RSR condition) to evaluate and compare models. The models were judged to be satisfactory if RSR ≤ 0.
70  (Moriasi et al. , 2007). The RMSEP from simple and mixed-effect model were obtained by using the chillR package in R while the RSR used the MuMIn package in R. To test the effect of the breed on the explored relationship, a sub-dataset was created from those studies including at the same time early or intermediate vs late maturing breeds (ID#4, ID#5, ID#9, ID#11 and ID#13; n = 312). Animals were coded either as "Late" if the breed was a late-maturing pure or cross-bred, or alternatively "Early" (including breeds and cross-bred animals considered as early or intermediate maturing breeds). The breed (Early vs Late) was included in the model as a fixed effect on the intercept and slope (biomarker × breed interaction) while the study and diet were considered as random.
Although the response of FCE to plasma urea concentration was significant across studies for dietary treatment means or individual observations (Eq. 6-7), there was no significant relationships within-study (Eq. 8). Surprisingly, very weak but still significant relationships were found between FCE and plasma urea concentration within-treatment (Eq. 9) and within-CG (Eq. 10). Likewise, for Δ 15 Nanimal-diet, no gain in model performance was found when adding the pen in addition to treatment effects. When breeds were categorized as early vs late maturing breeds from a sub-dataset, it was observed that the slope of the best model to predict FCE from Δ 15 Nanimal-diet (Eq. 4) tended to be higher (+0. 005; P = 0. 06) in Late vs Early maturing breeds while similar (P = 0. 53) in the best prediction model from plasma urea.
003, Figure  4D ). Although the two tested biomarkers were significantly correlated to feed efficiency traits at the individual level when using the whole dataset (Figure  4 ), their ability to discriminate the top 20% highest and lowest individuals within-CG in terms of feed efficiency varied (Figure  5 ). Indeed, both Δ 15 Nanimal-diet and plasma urea concentration succeeded to discriminate the most extreme animals within-CG in terms of FCE (Figure  5A  and 5B; P<0. 001 and P=0. 002), but only Δ 15 Nanimal-diet was significantly different (P<0. 001) for extreme RFI animals (Figure  5C ).
10 kg/kg and 0. 13 kg/kg, respectively (±1. 96 × RMSEP at the 95% CI). This means for instance that the mean FCE obtained in the only trial conducted with growing heifers in our dataset (ID#1; mean FCE = 0. 09; mean Δ 15 Nanimal-diet = 4. 24‰) can be significantly discriminated at 95%CI from that observed in several other studies (ID#2, ID#3, ID#7, ID#8, ID#9 and ID#12; mean FCE = 0. 21 kg/kg; mean Δ 15 Nanimal-diet = 2. 76‰) when using Δ 15 Nanimal-diet (Table  1 ). Likewise, the production context promoting the highest FCE in young bulls in our dataset (#ID9; mean FCE = 0. 26 kg/kg; mean Δ 15 Nanimal-diet = 1. 95‰; mean ADG = 1. 78kg/d) can be significantly discriminated from other trials using young bulls or steers (ID#4, ID#5, ID#11 and ID#13; mean FCE = 0. 14 kg/kg; mean Δ 15 Nanimal-diet = 3.
033 kg/kg [eq. 4; Table  2 ] and -0. 034 kg/kg [Figure  4A ]) and very close to the response of NUE to Δ 15 Nanimal-diet variations previously found by meta-analysis (-0. 035 kg/kg; Cantalapiedra-  Hijar et al. , 2018a). Although caution is needed comparing different databases and studies, this could mean that on average both traits, FCE and NUE, are highly correlated at the individual level and so that one unit of change in Δ 15 Nanimal-diet is on average associated to similar changes in FCE or NUE (around 0. 035 kg/kg). Given that Charolais, a late-maturing and lean breed, was the most representative breed in our database it can be expected that a change in body protein retention had a strong and proportional increase in body weight gain, explaining in part the proportionality between FCE and NUE changes at the individual level.
Ion mobility spectrometrymass spectrometry (IMS-MS) The IMS-MS experiments were performed using a Waters SYNAPT G2 hybrid quadrupole/time of flight instrument equipped with an ESI LockSpray™ source, (Waters, Manchester, UK). The SYNAPT HDMS system was calibrated using sodium formate cluster ions (2 mg. mL -1 ) and operated in 'V' resolution mode (resolution 20,000 FWHM for full width at half maximum). The ESI parameters were in positive ion mode: capillary voltage 3. 1 kV; sample cone voltage 70 V; source temperature, 90 • C; desolvation temperature, 300 • C; desolvation gas flow (N 2 ), L. h -1. The data were acquired using a 50-2,000 m/z range with 1 s scan time and 0. 02 s interscan delay. Sample solutions were infused into the source at 400 μL. h -1 with a syringe pump (Cole-Palmer, Vernon Hills, Illinois, USA).
The IMS conditions were: gas flow (N 2 ), 90 mL. min -1 ; IMS cell pressure, 2. 98 mbar; IMS traveling wave height voltage, 40 V and T-wave velocity, 550 m. s -1 for 2AB glycans and 700 ms -1 for permethylated 2AB derivatives. Helium gas flow was 180 mL. min -1. IMS wave delay was 450 μs. Data were recorded from three independent preparations and processed using the Mass Lynx 4. 1 and the Drift Scope 2. 2 softwares (Waters, Manchester, UK). Collision Cross Section (CCS, Ω) of oligomannosides were deter-mined according to the literature data  (Smith et al. , 2009). Depending on their charge, shape and size (related to Ω), the ion migrates at different velocities and pass through the IMS cell at different drift times (t d ). The ion velocity (v) is related to the ion mobility (K) by Eq. (  1 ): K = v E = L t d E (1) where E is the electric field and L the length of the IM cell.
In our case, two sets of TWIMS cell calibration were performed, the first to determine TW CCS of non-permethylated 2AB labelled N-glycans for which [M + Na] + adducts of dextran were used, the second to determine TW CCS of permethylated 2AB derivatives for which [M+2Na] 2+ ions of dextran were applied. The experimental values for TW CCS were measured with a relative error below 2% as reported in the literature  (Hines, May, McLean, & Xu, 2016). The drift times of both sample and reference ions were accurately determined by fitting the extracted ion mobility spectra with a Gaussian regression (OriginPro 2016 b9. 3. 226 software, OriginLab).
Multistage tandem mass spectrometry analysis Permethylated 2AB N-glycans were analyzed by ESI-MS n (n = 2-4) using a Bruker HCT Ultra ETD II quadrupole ion trap (QIT) mass spectrometer and the data were processed using the Esquire Control 6. 2 and Data Analysis 4. 0 softwares (Bruker Daltonics, Bremen, Germany). The ESI parameters, in positive ion mode, were as followed: capillary voltage set at -3. 5 kV, end plate offset at -500 V, skimmer and capillary exit voltages set at 40 V and 200 V, respectively, nebulizer gas (N 2 ), pressure, drying gas (N 2 ) flow rate and drying gas temperature were 10 psi, 7. 0 L. min -1 and 300 • C, respectively. Helium pressure in the ion trap was 1. 3 × 10 -5 mbar. The data were acquired using a 200-2200 m/z range, using a scan speed of 8100 m/z per second.
h -1 by means of a syringe pump (Cole-Palmer, Vernon Hills, IL, USA). External calibration was performed using the 'tuning mix' from Agilent Technologies (Santa Rosa, CA, USA). All mass spectra were manually interpreted according to the literature  (Domon & Costello, 1988; Prien et al. , 2009). The Glycoworkbench software v2. 1 was used to confirm the assignment of the fragment ions.