dataesr/scientific-paragraphs-categorization huggingface dataset:
4 mL/min. The gradient was initiated at 37. 5% of phase A: 0 to 4 min: 37. 5 to 50% B, 4 to 8 min: 50% B, 8 to 8. 5 min: 50 to 75% B, 8. 5 to 9. 5 min: 75% B, 9. 5 to 10 min: 75 to 37. 5% B, 10 to 13 min: 37. 5% B. Fifty microliters were injected and the total run time was 13 minutes. Quantification was performed with the Analyst software version 1. 6. 2, using IS and external calibration curve in 9 points (linear regression, see Figs 1 and 2 for concentrations) except for LysoSM509 and LysoGM2 for which the LysoSM and LysoGM1 calibration curve were used respectively.
Results at the detector level The data sample described in Section 4 is used to measure the TEEC and ATEEC functions. In order to study the kinematical dependence of such observables, and thus the running of the strong coupling with the energy scale involved in the hard process, the binning introduced in Table  1  is used. Figure  1  compares the TEEC and ATEEC distributions, measured in two of these bins, with the MC predictions from Pythia8, Herwig++ and Sherpa. The TEEC distributions show two peaks in the regions close to the kinematical endpoints cos φ = ±1. The first one, at cos φ = -1 is due to the back-to-back configuration in two-jet events, which dominate the sample, while the second peak at cos φ = +1 is due to the self-correlations of one jet with itself. These self-correlations are included in Equation (1) and are necessary for the correct normalisation of the TEEC functions.
Correction to particle level In order to allow comparison with particle-level MC predictions, as well as NLO theoretical predictions, the detector-level distributions presented in Section 5 need to be corrected for detector effects. Particlelevel jets are reconstructed in the MC samples using the anti-k t algorithm with R = 0. 4, applied to final-state particles with an average lifetime τ > 10 -11 s, including muons and neutrinos. The kinematical requirements for particle-level jets are the same as for the definition of TEEC / ATEEC at the detector level. In the data, an unfolding procedure is used which relies on an iterative Bayesian unfolding method  [67]  as implemented in the RooUnfold program  [68]. The method makes use of a transfer matrix for each distribution, which takes into account any inefficiencies in the detector, as well as its finite resolution.
Theoretical predictions The theoretical predictions for the TEEC and ATEEC functions are calculated using perturbative QCD at NLO as implemented in NLOJET++  [38, 39]. Typically O(10 10 ) events are generated for the calculation. The partonic cross-sections, σ, are convolved with the NNLO PDF sets from MMHT 2014  [70] , CT14  [71] , NNPDF 3. 0  [72]  and HERAPDF 2. 0  [73]  using the LHAPDF6 package  [74]. The value of α s (m Z ) used in the partonic matrix-element calculation is chosen to be the same as that of the PDF. At leading order in α s , the TEEC function defined in Equation (  1 ) can be expressed as 1 σ dΣ dφ = Σ a i ,b i f a 1 /p (x 1 ) f a 2 /p (x 2 ) ⊗ Σa 1 a 2 →b 1 b 2 b 3 Σ a i ,b i f a 1 /p (x 1 ) f a 2 /p (x 2 ) ⊗ σa 1 a 2 →b 1 b 2 , (2) where Σa 1 a 2 →b 1 b 2 b 3 is the partonic cross-section weighted by the fractions of transverse energy of the outgoing partons, x Ti x T j as in Equation (1); x i (i = 1, 2) are the fractional longitudinal momenta carried by the initial-state partons, f a 1 /p (x 1 ) and f a 2 /p (x 2 ) are the PDFs and ⊗ denotes a convolution over x 1 ,x 2.
Global ATEEC fit As before, the global value of α s (m Z ) is obtained from the combined fit of the ATEEC data in the six bins of H T2. Again, the NNPDF 3. 0 PDF set is used for the final result as it provides the most conservative choice. Also, as in the TEEC case, two additional correlation scenarios have been considered for the JES uncertainty. An additional uncertainty of 0. 0003 is assigned in order to cover the differences. The results are summarised in  The values shown in Table  7  are in good agreement with the values in Table  4 , obtained from fits to the TEEC functions. Also, it is important to note that the scale uncertainty is smaller in ATEEC fits than in TEEC fits. The values of the χ 2 function at the minima show excellent agreement between the data and the pQCD predictions.
First-line therapeutic combinations where in accordance with 2013 WHO guidelines  [4]. Fixed dose combinations were preferred. Those available were: AZT+3TC, TDF+3TC, TDF+FTC, TDF+FTC+EFV. This limited number of ARVs imposed constraints on the initiation of ART and especially on the management of ART changes in PLHIV. The objective of this study was to assess the overall incidence of the first ART change in this population and their causes; as well as the predictive factors for the first ART change. M6 and then every three months. Routine clinical and biological data (age, sex, occupation, marital status, weight, height, clinical stage, HIV serotype, cell blood count, transaminases, serum creatinine, CD4 cell count, plasma viral load) were collected from the medical records of the PLHIV. Data on changes in ART and their causes were collected from medical records and prescriptions classified at the pharmacy.
The data were entered on an Excel database and analyzed using the Stata software version 14. The event of interest was the first change in ART regardless its cause and the random variable studied was the exposure time to the first ART that was defined as the period between the initiation of the first line ART and the date of the first ART change (for any reason) for patients who had a change in treatment prior to  March 16, 2012. For patients who didn't experience an ART change, the data were censored at the date of death or date of last visit. The incidence was estimated using the Kaplan-Meier method. The causes of the first ART changes were grouped into different categories: intolerance, therapeutic failure, ARV stock out, intercurrent disease, desire for pregnancy or ongoing pregnancy. For each category, the overall incidence was measured; as well as the incidence during the first semester of ART and beyond.
The data of our study were collected through two sources: medical records were confronted with prescriptions in order to have comprehensive data on treatment changes in our cohort. Our study was retrospective and, as a result, it has limitations on accessibility and completeness of medical records: therefore, important co-variables such as adherence, preparation of patients for ART were not included in our analysis. Neither was the viral load included, as this variable was only available for a very small proportion of patients at inclusion. The duration of follow-up was relatively short and the number of events low after the first semester. The characteristics of our cohort (advanced age, low BMI, advanced clinical stage and low CD4 cell count) suggest a deep immunosuppression. Similar characteristics were observed in most of the cohorts studied in the RLC  [6, 7].
Acknowledgement The authors acknowledge all the people living with HIV who consent to participate to this study, the social workers of  SMIT/CRCF  ( Mariane Ndiaye Berthé ,  Catherine Fall Sané ,  Absa Ba and Khady Gassama) , the pharmacists of  CRCF (Pr Bara Ndiaye ,  Dr. Mama Basty Koita-Fall ,  Dr. Mamadou Ndoye ) and the medical secretary of the  SMIT/CRCF (Mrs Coumba Guèye Cissé ).
Input and validation data The French meteorological service (Météo-France) provided hourly meteorological data: air temperature and relative humidity at 2 m height, wind speed at 10 m, solar radiation, nebulosity, atmospheric pressure and pluviometry. Two meteorological stations exist at a similar distance (~10 km) from the reservoir: Aix-en-Provence (at an elevation of 173 m asl, 43°32'N 5°25'E) and Vauvenargues (at an elevation of 565 m asl, 43°33'N 5°41'E). We used the data from the station of Aix-en-Provence, since it had less missing data and it measured solar radiation. To account for the altitudinal difference between the meteorological station and Bimont reservoir, we applied an adiabatic gradient correction of -6x10 -3 °C m -1 to air temperature measurements. We used atmospheric pressure measured at Marignane meteorological station (43°26'N 5°13'E) and nebulosity measured at Marseille meteorological station (43°19'N 5°29'E), since they were not available at Aix-en-Provence station.
We applied an altimetric correction to the atmospheric pressure to account for the elevation effect. The SCP provided daily inflow and outflow data for 2010-2014 and monthly average flows for 1993-2013. Since outflow measured at the outlets were not precise enough for our study, outflow was calculated as the sum of downstream flow (8 in Fig. 2 ) and Marseille channel flow (7 in Fig. 2 ). The inflow of La Cause stream was measured at the gauging station of Gaudinettes (43°33'N 5°34'E), about 1 km upstream from the reservoir. The SCP estimated inflow for the rest of the drainage basin from discharge at La Cause basin using Myer formula. We considered infiltration flow as constant and adjusted it around the SCP estimated value to close the hydrologic budget. Irstea Hydrobiology Unit (HYAX) measured water temperature in the La Cause stream in 2009-2010.
So, we used instead the modification proposed by  Koch and Grünewald (2010) : (eq. 2) where f(T a ) is a moving average (eq. 3) and n is the number of days over which the average was calculated. The fitted coefficients in this case were α = 4. 70, β = 15. 28, γ = -0. 22, µ = 21. 34 and n = 68. The fitted regression model had a correlation coefficient of 0. 988 and a RMSE of 0. 7°C. We derived the lake bathymetry from the aggregation of two data sources: raster data measured by HYAX using a depth recorder up to the 329 m asl level; and a 5-m resolution MNT raster obtained from Spot-5 satellite images and provided by the Regional Centre of Geographic Information (CRIGE). Water quality profiles (temperature, dissolved oxygen, conductivity) and Secchi depth were periodically measured in Bimont reservoir since June 2009.
Limitations of the input data In this section, we analyse uncertainties in the input data that can affect the simulation results. Meteorological conditions above a freshwater body can differ from those measured at the nearest meteorological station  (Benyahya et al. , 2012) , often located at several kilometres. To assess meteorological variability in the study area, we compared meteorological measurements at the stations of Aix-en-Provence and Vauvenargues, finding significant differences between them. Average wind speed at Vauvenargues was 0. 60 m/s higher than at Aix-en-Provence. The average difference of 2. 3°C in air temperature could be attributed to the difference in altitude between both stations considering a usual altitudinal gradient of -6°C km -1. Additionally, the data of the meteorological reanalysis SAFRAN  (Quintana-Seguí et al.
N o n -c o m m e r c i a l u s e o n l y Hydrological input data showed several problems related to missing data, uncertainty of flow data and infiltration losses. The flow data contained some short periods with missing data, sometimes for more than one variable at a time. We estimated missing data through a hydrologic budget when possible and by interpolation otherwise. When more than one flow variable had missing data the distribution of flow between the different variables was highly uncertain. In addition, the volume of the infiltration losses and the discharge of the natural catchment area discharge were more uncertain than the other inflows and outflows because of the difficulty in estimating them. We estimated infiltration losses as 0. 29 m 3 s -1 , about 20% of mean throughflow. This estimation is coherent with the estimation of the SCP of 0.
, 2009)  issued by the downscaling model RCA4  (Kupiainen et al. , 2011; Samuelsson et al. , 2011)  applied to the projections of the climate model CNRM-CERFACS-CNRM-CM5  (Voldoire et al. , 2013). We downloaded the projections for the two climate scenarios RCP4. 5 and RCP8. 5, which represent an intermediate emissions scenario and a high emissions scenario respectively  (van Vuuren et al. , 2011) , from the Earth System Grid Federation (ESGF) Portal (http://esgf. llnl. gov/). Regionalized output had a spatial resolution of 0. 11°, approximately 12. 5 km, and daily periodicity. We extracted the regionalized climate model (RCM) projection data for two different 30-year periods: 2036-2065 for a mediumterm horizon, and 2066-2095 for a long term horizon. To limit the effect of initial conditions we initialized scenario simulations two years before the study period for 329.
5 m water level simulations and three years before the study period for 340 m water level simulations. We used a longer initialization time for higher levels because of the longer residence time. We corrected the projections data using the quantile-quantile method  (Boé et al. , 2007). We estimated inflow temperatures using equations 1-3. Although water-air temperature regressions have often been used to predict climate change effects on freshwater temperatures  (Mohseni et al. , 1999; Rübbelke and Vögele, 2011) ,  Arismendi et al. (2014)  cautioned against their use to predict temperatures outside the calibration period. To test the validity of using regressions, we compared the regression predictions to those of the processbased model CALNAT  (Gosse et al. , 2008). Both CALNAT and the regression models had a similar good performance when applied on calibration data, and predicted similar temperatures when applied on climate projections data.
ACKNOWLEDGMENTS We acknowledge the  World Climate Research Programme's Working Group on Regional Climate , and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We thank the climate modelling groups SMHI and  CNRM-CERFACS  for producing and making available their model output. We acknowledge the  Earth System Grid Federation infrastructure. We thank  Météo-France  and the  Société du Canal de Provence  for providing essential data for this study, and  Brigitte Delmas  and  Yann Viala  for their support. We thank our colleagues  Nathalie Reynaud  and  Yann Le Coarer  for their help in preparing the reservoir bathymetry, and  Nathalie Reynaud  and  Thierry Tormos  for the optimization of the algorithm of the Landsat images treatment. We thank members of the  Onema-Irstea consortium Martin Daufresne ,  Tiphaine Péroux ,  Julien Dublon  and  Delphine Rebière  for the acquisition of water temperature and  Secchi depth data.
Altogether these results are in favour of a direct role of the virus particles in the observed phenotype. Consistent with the above results, was the intense labelling of the major capsid protein, MDAORF4, inside the biomass covering the monolayer of cells (Fig  2A ). This suggested that phage production occurred during bacterial colonization of the epithelial cells. To confirm this point, phage DNA and phage proteins were monitored during bacterial colonization of the epithelial monolayer. The quantity of circular MDAF DNA per chromosome increases as a function of time (Table  1)  on endothelial cells (Table  1 ). On the other hand onto fixed endothelial cells, the quantity of circular MDAF DNA per chromosome increased significantly (Table  1 ). Altogether, these results are consistent with the above reported data showing that the prophage does not provide a selective advantage to bacteria colonizing endothelial cells.
The Ibidi μ-Slide flow chambers allow direct observation with inverse microscopy through its transparent plastic bottom. All microscopic observations and image acquisitions were performed on a Leica SP5 confocal microscope. Images were obtained using a x40/1. 3 Plan Apo oil objective lens. At the time of confocal acquisition, the cells were examined using red channel to assess the integrity of the monolayer. Three-dimensional biofilm structures reconstructions were generated using the IMARIS software package (Bitplane AG). Biofilm development was quantified with the COMSTAT computer program using biomass and average thickness parameters  [39]. The results are expressed as a percentage of the biofilm produced by the WT strain, which is set to 100%. Values represent the means of three independent experiments, with the acquisition of at least six image stacks per each channel.
Quantitative PCR Oligonucleotides were designed using the Primer Express software (PE Applied biosystems) to obtain amplicons of the same size (S2 Table ). Real-time PCR was run on an ABI Prism 7700 sequence detection system (Perkin-Elmer Biosystems) using SYBR Green PCR Master Mix (PE Biosystems), according to the manufacturer's instructions. Data analyses for a relative quantification of gene DNA were performed by the comparative Ct (threshold cycle) method according to the manufacturer's instructions (user bulletin 2 for the ABI PRISM sequence detection system) and published data  [41]. The parameter Ct is defined as the cycle number at which fluorescence (which is proportional to the quantity of DNA in the tube during the exponential phase of the PCR) passes the fixed threshold. The relative amount of target after normalization to a chromosomal gene pgm, is obtained by 2 (Ct orf5 -Ct pgm ).
Acknowledgments The authors greatly acknowledge  Meriem Garfa-Traore  and  Nicolas Goudin  of the  Necker Institute Imaging Facility. We also thank  Alain Schmitt  ( Plateforme Cochin Imagerie , pole electronique,  Cochin Institute ) for help with electron microscopy experiments and analysis. We thank  Dr Mathieu Coureuil  for technical advices and helpful suggestions.
Cell-cycle analysis was performed 3 d after transduction and 30 min after BrdU addition, with DAPI staining to estimate the cell DNA content and anti-BrdU antibodies to identify cells undergoing replication. The distribution within the different phases of the cell cycle was estimated by FlowJo software. The FACS profiles are representative of two different transduction experiments with the four constructs. (B) Cell-cycle analysis of naive splenic B cells from restored animals, 3 d after LPS + IL-4 stimulation. Activation was performed on sorted EGFP + B220 + GL7 -PNA low B cells for AID-mKO2-hCDT1 and AID-mCherry-geminin, mutated or not, and on total naive B cells for mutated AID-mKO2-hCDT1. ference between AID G1-Mut and AID S/G2/M-Mut restored cells, likely reflecting the different impact of the degronfluorochrome fusion on AID activity (Fig.
Flow cytometry analysis and cell sorting The following antibodies were used: B220 APC-e780 (clone RA3-6B2; eBioscience), GL7 Alexa Fluor 647 (clone GL7; eBioscience), GL7 e-450 (clone GL7; eBioscience), PNA biotin (Vector Laboratories), streptavidin PE-Cy7 (eBioscience), and CD95 PE-Cy7 (clone Jo2; BD). Dead cells were excluded by staining with SytoxBlue (Invitrogen). Twodimensional cell-cycle analysis of Lin -cells involved S-phase labeling with the APC BrdU Flow kit (BD), followed by DNA staining with 3 µM DAPI staining solution (DAPI hydrochloride; Invitrogen) for 15 min at 25°C. For one-dimensional cell cycle analysis of in vitro-activated B cells, 10 6 cells/ml were incubated for 30 min at 37°C in a 2-µM Hoechst 33342 solution (Invitrogen) in DMEM. Cytometry profiles were acquired with an LSR Fortessa cytometer (BD) equipped with 355-nm and 561-nm lasers, and data were analyzed with Diva (BD) and FlowJo (Tree Star) softwares.
We also thank Barbara Bertocci for suggesting the use of the Fucci restriction system. The team Développement du système immunitaire is supported by the Ligue Contre le Cancer (équipe labelisée) and the Fondation Princesse Grace. This project was initiated with an Agence Nationale de la Recherche grant Mutig (ANR-06-BLAN-082). M. Zivojnovic was supported by a 3-yr grant from Ecole Normale Supérieure and from the Ligue Contre le Cancer for her fourth year of PhD studies, and G. Girelli Zubani was supported by a doctoral contract from Paris-Descartes University. The authors declare no competing financial interests.
Plant material Cherry tomato plants (Solanum lycopersicum), line West Virginia 106 (Wva106), were cultivated in a greenhouse as previously described  (Cheniclet et al. , 2005). Flowers and fruit were collected at various stages at positions two to five in the first to seventh trusses for cytology and ploidy analyses. Two time-course experiments were conducted to study cellular patterns in pericarp according to time. Experiment 1, performed in 2004 and already partly reported  (Cheniclet et al. , 2005) , was devoted to analysing fruit development from anthesis to ripening. We report here new data about the development of each of the pericarp cell layers during fruit growth. In experiment 2, performed in 2015, ovaries from young flowers and fruit up to 4 DPA were analysed in order to characterize cellular events at the time of anthesis and fruit set.
Sections 1-2-µm thick were stained with toluidine blue and imaged on a Zeiss Axiophot microscope with a Spot RTKE camera (Diagnostic Instruments, Sterling Heights, MI, USA). Images were analysed with ImagePro-Plus software (Media Cybernetics, Silver Spring, MD, USA). The pericarp thickness and the number of cell layers across it were estimated in three pericarp portions devoid of vascular bundles. A group of cells from the outer epidermis (E1), the outer sub-epidermal (E2) and inner sub-epidermal (I2) cell layers, and the inner epidermis (I1) (Fig. 1 ) was manually delimited (see Supplementary Fig. S1A  at JXB online) and its cell number, periclinal length, and area measured. For each fruit, these measurements were made in three pericarp portions, each representing 107 ± 48 cells per fruit according to the cell layer and to the developmental stage.
Images were acquired with a Nikon Eclipse 800 epifluorescence microscope equipped with a CoolSNAP HQ2 camera (Photometrics, Tucson, AZ, USA) and fluorescence was recovered with a band pass filter from 435 to 485 nm under an excitation wavelength of 340-380 nm with a ×20 objective. Because tomato chromosomes are small, prophases could not be unambiguously identified and only mitotic figures beyond prophase were detected  (Cong et al. , 2002)  and counted using the ImageJ software (imagej. nih. gov/ij/) and the Cell Counter plugin. Mitotic indices were calculated as the ratio of mitotic nuclei to the total number of nuclei counted, at the level of the whole-pericarp level and of each cell layer. At each stage a minimum number of seven fruits were analysed, with ~1320 nuclei counted per fruit.
Other measurements Flow cytometry was used to determine nuclei ploidy profiles from whole ovaries and from pericarp of developing fruits as reported by  Cheniclet et al. (2005). Ploidy histograms were quantitatively analysed with Flomax software (Partec GmbH, Görlitz, Germany), after manual treatment to exclude noise. When the ovaries of various species were analysed for their ploidy patterns at anthesis, 2C values were calibrated from literature data about DNA content and from ploidy patterns in young leaves. Daily data from experiment 1 were used to calculate the relative rates of fruit and pericarp volume increase, of cell number variation, and of cell expansion in whole pericarp and in given cell layers. By referring to X for any of these growth parameters (Table  1 ), they vary over time according to an exponential function: X = X 0 × e kt.
As a whole, the 2600-fold increase in pericarp volume up to 36 DPA relies on a 15-fold increase in cell number and a 170-fold increase in cell volume. Both processes contribute significantly to pericarp and fruit growth, and their combination supports the dramatic increase in pericarp and fruit volume. The final growth of each cell layer was then calculated as the product of cell number and cell volume rates of change between anthesis and 36 DPA. The three outer cell layers grew by 230-to 390-fold each, whereas the more inner layers grew by 1200-to 1900-fold each. It is remarkable that these two sets of data fall in the range of the increase in fruit outer area (×230) and in pericarp and fruit volumes (approximately ×3000, with 50% of growth due to new M' cell layers, Fig. 6D ), respectively.