dataesr/scientific-paragraphs-categorization huggingface dataset:
The four major depth parameters in the framework are evaluated from twelve soil factors, identified to evaluate the adequacy of soil to support root growth, for which rules have been developed and parameterised based on sixteen soil properties. These rules are consistent and reliable, whereas a few, particularly the rule to evaluate the abruptness of textural change, deserve additional attention. The abruptness of textural change could be evaluated more consistently from textures given by a continuous depth function rather than by depth intervals which increase with depth. For the majority of rules, parameterisation also appears to be quite reliable. Parameterisation was mild in those cases that literature was too ambiguous. However, many of the, unscaled, thresholds that evaluate the rootability of soil depth intervals are relevant only for quite extreme soil property values, which do occur in the soil profiles datasets but, except for sodium, rarely or not on the maps.
0. Instead of using thresholds, a scalable approach could be used in line with what was suggested by  Driessen and Konijn (1992)  or  Kaufmann et al. (2009). Such would require the soil factors to be independent though. RZD determines RZ-PAWHC to a much larger extent than PAWHC and SFEF. However, data availability did not permit to adequately validate rootability and RZD, and therefore RZ-PAWHC. Field observations on restricted auger depths, collected according to the land degradation surveillance network procedures  (Vågen et al. , 2010 (Vågen et al. , , 2016)) , could serve as proxy for restricted rootable depths but data have not become available. In general, the accuracy of the RZ-PAWHC map is limited by the accuracy of the soil property maps from which the RZD map was derived. Besides, by using another DSM technique, these maps might be improved with additional soil profile data, either from existing data sources or newly collected from the field.
The accuracy of the RZD map could not be validated quantitatively, due to absent data on rootability and RZD, but is limited by the accuracy of the soil property maps from which the map was derived. Most of these soil property maps are smoothed compared to the observations (regression to the mean), especially for properties with skewed data. New, improved maps can be produced, within the operational framework here developed, upon the availability of additional soil data relevant to evaluate RZ-PAWHC over at least 100 cm depth and possibly using different DSM techniques such as stochastic simulation. Key in such update is to map the uncertainties associated with the soil property maps and to assess how the errors propagate into the RZ-PAWHC map. Adequate data on rootability, or rooting, are solicited to better validate the current assessment as a key step towards an increasingly accurate consolidated product, which is critically important for better targeting agronomic R&D interventions in SSA.
Acknowledgements This research was a collaborative effort, coordinated by  ISRIC -World Soil Information  (isric. org), of the  Africa Soil Information Service  project (AfSIS; africasoils. net)and the  Global Yield Gap  and water productivity  Atlas  project (GYGA; www. yieldgap. org). Funding to support this work was provided by the  Bill and Melinda Gates Foundation (BMGF)  (Investment ID:  OPP1082520 ) which made the AfSIS and GYGA projects possible. The  GYGA  project acknowledges.
9 15. 3 5. 0 98 0-570 c pH-H 2 O - 54,867 18,055 6. 2 1. 2 6. 0 3. 3 2. 1-11. 3 c Sand g/100 g 58,322 1408 54 25 55 -1. 8 0-100 c Silt g/100 g 58,318 0 16 13 13 23. 1 0-100 Clay g/100 g 58,321 0 30 20 27. 4 9. 5 0-97 CEC cmolc/kg 52,886 0 14. 4 15. 1 8. 3 73. 5 0-179 Exch. aluminium cmolc/kg 26,791 0 0. 7 2. 3 0 - 0-76. 7 a c Summary statistics from the AfSP database. d Summary statistics from the AfSP database plus virtual profiles derived from legacy soil maps. e Deviation (shift) of mean from median, relative (%) to median, indicative for skewness. a Deviation of mean from median, relative (%) to median.
Mass spectrometry analysis Protein preparation for mass spectrometry-based proteomic analyses was as described  [53]. Briefly, extracted proteins were stacked in the top of a SDS-PAGE gel (NuPAGE 4-12%, Invitrogen) before in-gel digestion using trypsin (Promega, sequencing grade). Resulting peptides were analysed by online nanoLC-MS/MS (UltiMate 3000 coupled to LTQ-Orbitrap Velos Pro and Ultimate 3000 RSLCnano coupled to Q-Exactive Plus, Thermo Scientific, for Spp1 and Mer2 interactomes analyses, respectively) using a 120-min gradient. Peptides and proteins were identified and quantified using MaxQuant (version 1. 5. 8. 3  [54] ) and SwissProt database (June 2017 version, Saccharomyces cerevisiae S288c taxonomy). Only proteins identified with a minimum of two unique + razor peptides were taken into account for further analyses.
Statistical analyses were performed using ProStaR  [55]. Proteins identified in the reverse and contaminant databases, proteins only identified by site and proteins exhibiting less than 3 intensity values in one condition were discarded from the list. After log 2 transformation, intensity values were normalized by median centering before missing value imputation (replacing missing values by the 2. 5 percentile value of each column); statistical testing was conducted using limma t-test. Differentially recovered proteins were sorted out using a log 2 (fold change) cut-off of 7 and a FDR threshold on remaining p-values of 1% using the Benjamini-Hochberg method. hybridized on an Agilent 44K yeast whole-genome oligonucleotide array as described  [6]. Microarray images were read using an Axon 4000B scanner and analyzed using GenePix Pro 6.
0 software (Axon Instruments, Inc. ). Files were converted to text files and analyzed using the R software. The signal was normalized, denoised, smoothed and peak calling was done exactly as described before  [18]. The R code has been deposited to the github depository at https:// github. com/ValerieBorde/ChIPchip-analysis. In all figures, ChIP ratio is the decile-normalized ratios after denoising and smoothing using a 1 kb (Figs 2A, 2C and 3B) or 2 kb (Fig 3C ) window. To each probe of the array, we attributed the mRNA level of the corresponding gene determined from vegetative cells  [57]  or cells at 4 hr in meiosis  [58]. For probes lying in a promoter, the mRNA level of the downstream gene was attributed, and for probes in divergent promoters, the mean value of the two divergent genes was attributed.
For generating the graphs in Figs 2B and 3D, the average signal of the probes at the summit of the top 100 peaks (100 values) was calculated, and compared to the signal for the entire microarray (41479 values). For generating the boxplot graphs in Fig 3C , we considered the ChIP signal at the probes corresponding to the summit of the top 200 Red1 (for axis) or DSB peaks. The definition of Red1 and DSB peaks from previously published datasets  [23, 59]  has been described previously  [56].
Acknowledgments We thank  Scott Keeney  and  Bruce Futcher  for reagents. We thank  Corinne Grey  and  Arnaud De Muyt  for critical reading of the manuscript, and  Daniel Holoch  and  Raphae ¨l Margueron  for discussions. We thank the recombinant proteins platform of  Institut Curie  for the Spp1 antibody, and the support of the discovery platform and informatics group at EDyP.
This work was supported by  Institut Curie ,  CNRS ,  Labex DEEP  ( ANR-11-LBX-0044 ) part of the  IDEX Idex PSL  ( ANR-10-IDEX-0001-02 PSL ), by  ANR-12-BSV6-0009 , and by  Fondation pour la Recherche Medicale  (supported by the  Proteomics French Infrastructure  ( ANR-10-INBS-08-01 grant ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The ChIP-chip data generated in this study have been deposited in the Gene Expression Omnibus database, accession number GSE102790. Processed data for all chromosomes are provided in S3 Table. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository via the url: http://www. ebi. ac. uk/pride/archive/projects/PXD007590.
These results again indicate the formation of a minor population of dimers of trimers, as seen for 1D8  N18. The two oligomeric species can be separated by SEC, and reinjection of the isolated major peak of trimers yields only a very minor peak at the elution volume of the trimer-dimers (Supplementary Figure  8d ). 1D8 N/C EGa1 performed very similarly to 1D8 N18 in CD and cooperative thermal denaturation studies (Supplementary Figure  8e, f ). SAXS showed that 1D8 N/C EGa1 contains the same trimerized TIE XVIII core seen for 1D8  N18. The binding domains, however, are distributed evenly around the plane defined by the TIE XVIII core (Fig. 3a  and Supplementary Figure  8g , h, Supplementary Table  2 ), resembling a playground roundabout. Using the SAXS data and structural information from the protein data bank (PDB), we created a homology model intended to clarify the layout of the six binding domains around the core, as the linkers' length and flexibility permit several configurations, e.
1%) matrix onto a GroundSteel massive 384 target (Bruker Daltonics). An Autoflex III MALDI-TOF/TOF spectrometer (Bruker Daltonics) was used in linear mode with the following settings: 5000-40,000 Th window, linear positive mode, ion source 1: 20 kV, ion source 2: 18. 5 kV, lens: 9 kV, pulsed ion extraction of 120 ns, high gating ion suppression up to 1000 Mr. Mass calibration was performed externally with protein 1 standard calibration mixture (Bruker Daltonics). Data acquisition, peak peaking, and subsequent spectra analysis was performed using the FlexControl 3. 0 and FlexAnalysis 3. 0 software (Bruker Daltonics). Size exclusion chromatography with multi-angle light scattering. Static light scattering experiments were performed at room temperature using a Superdex 200 Increase 10/300 GL column (GE Healthcare) attached in-line to a DAWN-HELEOS light scattering detector and an Optilab rEX differential refractive index detector (Wyatt Technology).
The column has an exclusion volume of 8. 6 ml, and no absorbance (no aggregated protein) was observed in any of the injections. The column was equilibrated with running buffer (PBS+150 Mm NaCl) and the SEC-MALS system was calibrated with a sample of bovine serum albumin (BSA) at 1 g/l in the same buffer. Then 100 μl samples of the two antibodies 1D8 N18 and 1D8 N/ C EGa1 at 1 g/l in the running buffer were injected into the column at a flow rate of 0. 5 ml/min. Data acquisition and analysis were performed using the ASTRA software (Wyatt Technology). The reported molar mass corresponds to the center of the chromatography peaks. After separation of the monomeric species by SEC a second injection in the SEC-MALS system was done at 0. 26 g/l. Based on numerous measurements on BSA samples at 1 g/l under the same or similar conditions, we estimate that the experimental error in the molar mass is around 5%.
Circular dichroism. Circular dichroism measurements were performed with a Jasco J-810 spectropolarimeter (JASCO). The spectra were recorded on protein samples at 0. 2 g/l in PBS using 0. 2 cm path length quartz cuvettes at 25 °C. Thermal denaturation curves from 10 to 95 °C were recorded on the same protein samples and cuvette by increasing temperature at a rate of 1 °C/min and measuring the change in ellipticity at 218 nm. Small-angle X-ray scattering. SAXS experiments were performed at the beamline B21 of the Diamond Light Source (Didcot, UK). The proteins were concentrated and prepared at 4 °C prior data collection. Samples of 40 μl of 1D8 N18 and 1D8 N/ C EGa1 at concentrations of 3 and 6 mg/ml were delivered at 4 °C via an in-line Agilent 1200 HPLC system in a Shodex Kw-403 column, using a running buffer composed by 50 mM Tris pH 7.
5+150 mM NaCl. The continuously eluting samples were exposed for 300 s in 10 s acquisition blocks using an X-ray wavelength of 1 Å and a sample to detect (Pilatus 2M) distance of 3. 9 m. The data covered a momentum transfer range of 0. 032 < q < 3. 695 Å -1. The frames recorded immediately before elution of the sample were subtracted from the protein scattering profiles. The Scatter software package (www. bioisis. net) was used to analyze data, buffer-subtraction, scaling, merging, and checking possible radiation damage of the samples. The data set corresponding to 1D8 N18 at 3 mg/ml could not be further analyzed due to aggregation. The R g values were calculated with the Guinier approximation assuming that at very small angles q < 1. 3/R g. The maximum particle distribution, D max , and the distance distribution were calculated from the scattering pattern with GNOM, and shape estimation was carried out with DAMMIF/DAMMIN, and all these programs are included in the ATSAS package  52.
Interactively generated PDB-based models were made for the two antibodies based in templates obtained with the program RaptorX. Real-space scattering profiles of the models were computed with the program FoXS. Kinetic measurements using BLI. The interactions between immobilized m4-1BB and 1D8 IgG, 1D8 N18 , 1D8 N5 , and 1D8 N0 were investigated on an Octet RED96 system (Fortebio). Mouse 4-1BB was immobilized onto AR2G biosensors (Fortebio) using 10 µg/ml of m4-1BB in 10 mM acetate buffer at pH 6, over 20 min, to a signal of 1. 8 ± 0. 4 nm. The kinetics experiment for each antibody used 4 unregenerated m4-1BB-coated biosensors, 2 of which were associated with 4 nM of antibody in kinetics buffer (PBS+0. 1% BSA+0. 05% Tween 20), and 2 with 2 nM. Association was run for 1 h, followed by 3 h of dissociation in analyte-free kinetics buffer.
The acquired sensorgrams were globally fit to a 1:1 model using the Octet Data analysis software. Kinetics experiments were performed at 37 °C while shaken at 1000 rpm. The avidity of the interaction between immobilized hEGFR and analyte 1D8 N/C EGa1 was investigated similarly. The hEGFR was immobilized onto AR2G biosensors using 3 µg/ml of hEGFR in 10 mM acetate buffer at pH 5. Association and dissociation were both measured for 2 h. To demonstrate the ability of 1D8 N/C EGa1 to bind both of its antigens in tandem, m4-1BB was immobilized onto AR2G biosensors, and 4 nM of 1D8 N/C EGa1 or 1D8 N18 were allowed to associate with 2 biosensors each for 1 h. The biosensors were briefly moved into kinetics buffer for 10 min, after which 1 biosensor loaded with each antibody was moved into 10 nM hEGFR for 1 h while the other biosensor remained in kinetics buffer.
Characterization of the microscope point spread function, i. e. , the focal volume where the fluorescent dyes diffuse in and out, was done employing 2 μg/ml soluble purified EGFP (Biovision)  34. The series of images recorded for RICS were of 256 × 256 pixels, with a pixel size of 80 nm, and employing 2-μs dwell time. RICS analysis and diffusion coefficient quantification was done by employing the SIM FCS 4 software (G-SOFT Inc. ). Every time trace from the time series was carefully observed to avoid possible artifacts due to dramatic photobleaching, which otherwise would affect the diffusion coefficient quantification. By these means, we avoided employing detrending algorithms that sometimes can bias the analysis. RICS analysis was performed using a moving average (background subtraction) of ten to discard possible artifacts arising from cellular motion and slow-moving particles passing through.
The obtained two-dimensional autocorrelation map was fitted to get the diffusion surface map that was represented in three dimension. For the different region of interest (ROI) analyses within the same cell, the corresponding region was drawn employing a square region of 64 × 64 pixels. Selected regions were defined as where there was the presence or absence of receptor clustering. For statistical purposes, each condition was studied on minimum five different cells, and in each cell a minimum of three different ROIs were analyzed. Brightness and contrast of the fluorescence and differential interference contrast were optimized with the ImageJ software. Diffusion values were represented in a whisker and box plot using Ori-ginPro (OriginLab). Pharmacokinetics study. Female CD-1 mice (n = 24/group) received a single i.
Pharmacokinetic parameters were calculated using the Prism software (GraphPad Software). Molecular imaging in tumor-bearing mice. A431 cells (1 × 10 6 ) were implanted s. c. into the dorsal space of 6-week-old female Hsd:athymic Nude-Foxn1nu mice. Tumor growth was monitored two times a week by measuring the diameter of the tumors with a calliper, and tumor volumes were calculated according to the formula: Volume = (D × d2/2), where D is the longest axis of the tumor and d is the shortest of a prolate ellipse. When tumor volume reached about 0. 180 cm 3 , mice were randomly allocated to different treatment groups (n = 3/group) and i. v. injected with PBS or NIR-labeled antibody solution (1 mg/ml) in PBS. Mice were imaged under anesthesia at 24 h under the IVIS Spectrum CT in vivo imaging system (Xenogen) at the indicated wavelengths and were analyzed using the Living Image 3.
Slides were deparaffined in xylene and re-hydrated through a series of graded ethanol washes, ending in a final rinse in pure water. Slides were incubated with a rat monoclonal anti-CD8a (1:200 dilution) (OTO94A; Monoclonal Antibodies Core Unit CNIO) followed by a rabbit anti-rat secondary antibody (cat#ab6703, Abcam) and a visualization system (Novolink Polymer anti-Rabbit, Leica) conjugated with HRP. Nuclei were counterstained with Harris' hematoxylin. Positive control sections known to be primary antibody positive were included for each staining run. Whole digital slides were acquired with a slide scanner (AxioScan Z1, Zeiss), and total versus positive cells were automatically quantified (AxioVision 4. 6 software package, Zeiss). Toxicity studies. Three-month-old female C57BL/6 received a weekly i.
p. dose of 3H3 IgG or 1D8 N/C EGa1 (6 mg/kg) for 3 weeks. Mice were anesthetized and bled on days 0, 7, 14, and 21. To obtain mouse serum, blood was incubated in BD microtainer SST tubes (BD Biosciences), followed by centrifugation. Serum was stored at -20 °C until use. One week after the last dose of antibodies, mice were euthanized and the liver, spleens, lungs, and pancreas were surgically removed, weighted, and fixed in 10% paraformaldehyde for 48 h. Then fixed tissues were washed and embedded in paraffin. Tissue sections (5 µm) were stained with hematoxylin and eosin, Sirius red, or Masson's trichrome (Sigma-Aldrich) for collagen staining. Lymphocyte infiltration in the liver was quantified using the ImageJ software. A similar study was performed in 2-month-old female BALB/c mice, but in this case the 1D8 N/C EGa1 was injected i.
p. every 3/4 days (6 mg/kg) for 3 weeks. The 3H3 IgG was administered with the same regimen: a weekly i. p. injection (6 mg/kg) for 3 weeks. Luminex assay. Blood was collected from the treated mice on the indicated days, and the levels of inflammatory cytokines (IFN-γ, IL-6, TNF-α) in serum samples were determined using a Luminex Milliplex Magnetic Bead Kit (Merck Millipore). Statistical analysis. Statistical analysis was performed using the GraphPad Prism Software version 5. 0. All the in vitro experiments were done in triplicates and values are presented as mean ± SD from one of at least three separate experiments. Significant differences (P value) were discriminated by applying a two-tailed, unpaired Student's t test assuming a normal distribution with *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0.
001. To assess differences in tumor growth, tumor diameter for individual mice in each treatment group are presented. Survival curves for the different treatment groups were created using the Kaplan-Meier method, and two or more survival curves were analyzed using log-rank (Mantel-Cox) test. For the other in vivo studies, data were presented using a scatter plot as mean ± SD, and P values were determined by unpaired Student's t test to assess the differences between treatment groups.
Acknowledgments We thank  M. Jure-Kunkel ,  M. Rescigno  and  A. Villalobo  for reagents, M. G. Gonzalez Bueno  and  B. Acosta  ( IIBm ) for technical support, and the staff of beamline  B21at Diamond Light Source (Didcot, UK)  for their excellent technical assistance. This study was supported by grants from the  European Union  [ IACT  Project ( 602262 ), iNEXT Project ( 1676 ) and a  Marie Curie Career Integration Grant  ( PCIG13-GA-2013-618914 )], the  Ministerio de Economía y Competitividad  ( CTQ2017-83810-R, RTC-2016-5118-1 ,  SAF2017-83267-C2-1-R ,  SAF2017-89437-P , and  PTQ-16-08340 ), the  Fondo de Investigación Sanitaria/Instituto de Salud Carlos III  ( PI16/00357  and  PI16/00895 ), the  UK Research and Innovation  ( 18130023 ), and the  Danish Council for Independent Research  ( DFF-6110-0053 ).
Such systems can be constructed for interactive training of games (including chess) as well as any other area of cognitive activity such as reading, mathematics, programming, chemistry and engineering. We have constructed an instrument for capturing and interpreting multimodal signals of humans engaged in solving challenging problems (see Figure  1 ). Our instrument, shown in Figure  2 , captures eye gaze, fixations, body postures, and facial expressions signals from humans engaged in interactive tasks on a touch screen. As a pilot study, we have observed these signals for players engaged in solving chess problems. The experimentation equipment used for data collection. On top, a Kinect2 device looking down at the player. In the middle, a webcam to capture the face. At bottom, the touch screen equipped with an eye-tracker presenting the chess game.
These views are respectively at left, right and center of Figure  1. The wooden structure is rigid to fix position and orientation of all sensors. The lighting conditions are controlled by 2 USB LED lamps on the sides. Recordings are used to estimate subjects' understanding of the current situation and their ability to respond effectively to challenging tasks. Such recorded data are to be used to detect when participants are challenged beyond their abilities. In this paper we present results with experiments that evaluate the event to which our set up can capture information about cognitive abilities. Section 2 discusses current methods for capture and interpretation of physiological signs of emotion and awareness. This lays the ground for the design of our experimental setup presented in Section 3.
This structure has been made using a laser cutter. Several software systems were used for recording and/or analyzing data. The Lichess Web Platform (https://en. lichess. org/ (last seen September 2017)) serves for playing and recording games. Two commercial software systems provide both online and offline information: Tobii Studio 3. 4. 7 for acquisition and analyze of eye-gaze; Noldus FaceReader 7. 0 for emotion detection. Body posture is provided by two different means: by the Kinect 2. 0 SDK and by using our enhanced version of the Realtime Multi-Person Pose Estimation software  [6]. Considering the state-of-the-art results of the second software, we decided to keep only this one for this experiment. During the study, data were recorded from all sensors (Kinect 2, Webcam, Screen capture, user clicks, Tobii-Bar) using the RGBD Sync SDK (https://github.