dataesr/scientific-paragraphs-categorization huggingface dataset:
Particular attention should be paid to surface soil properties in areas prone to soil crusting  (Valentin et al. , 2008; Gal et al. , 2017) , which mainly include loamy soils  (Rawls et al. , 1990)  and also arid and semi-arid soils  (Valentin and Bresson, 1992) , producing high total runoff  (Yair, 1990; Casenave and Valentin, 1992; Karambiri et al. , 2003; Bouvier et al. , 2018). Thus, using other sources of information than soil texture to derive the geographic distribution of soil properties may lead to clearer and broader improvements of the simulated water budget than the ones analysed here owing to mineral soil texture maps alone. Code availability. The version of the ORCHIDEE model used for this study is based on tag 2. 0, freely available from http://forge. ipsl. jussieu. fr/orchidee/browser/tags/ORCHIDEE_2_0/ORCHIDEE/ (Peylin et al.
, 2020)  Small modifications were coded to read new maps of soil texture or soil parameters, and the corresponding code can be obtained upon request to the first author. Data availability. The GLEAM dataset used in this study can be freely accessed from http://www. GLEAM. eu (GLEAM, 2020). Primary data used in the analysis and other supplementary information that may be useful in reproducing the author's work can be obtained by contacting the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-3753-2020-supplement. Author contributions. ST, AD and CV designed the research. ST performed the simulations, analysed the data and prepared a draft of the manuscript. All the authors contributed to interpreting results, discussing findings and improving the manuscript.
Acknowledgements. The ORCHIDEE simulations were performed using the IDRIS computational facilities ( Institut du Développement et des Ressources en Informatique Scientifique ,  CNRS, France). Some of them were designed by  Lukas Gudmundsson  and  Matthias Cuntz  for the  SP-MIP  project. Review statement. This paper was edited by  Albrecht Weerts  and reviewed by three anonymous referees.
Bound antibodies were detected with the appropriate Alexa-labelled secondary antibodies (Supplemental Table  S2 ) prior to nuclear staining with Dapi (Sigma,catalog number D9542) and embedding into ProLong Gold antifade reagent (Invitrogen, catalog number P36930). Fluorescently-stained sections were digitalized (40X) using a PerkinElmer Vectra Polaris imaging system and Phenochart software (Akoya Biosciences). on July 17, 2020. © 2020 American Association for Cancer Research. cancerimmunolres. aacrjournals. org Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on July 14, 2020; DOI: 10. 1158/2326-6066. CIR-20-0074.
Quantification of human staining Stained slides were scanned on the Hamamatsu NanoZoomer 2. 0-HT Digital slide scanner (40X mode). Scans were viewed and images acquired using the NDP. view2 software. For quantification, we developed a script (based on ImageJ) optimized to be used with interactive surfaces (https://figshare. com/articles/Custom_toolbars_and_mini_applications_with_Action_Bar/3397 603/3). The program and the manual are freely available at https://mycore. corecloud. net/index. php/s/0K61LqHBrnNKShX. Randomly chosen images of non-invasive tumor areas (3 per tumor, 5X magnification) were projected on an interactive digital whiteboard. A pathologist determined the regions of interest (ROIs) corresponding to tumor cell nest or stroma. These ROIs where extracted after color deconvolution and thresholding to quantify CD45 staining.
Patient survival and correlation matrix data Public patient data (GSE27020) were analyzed by the Kaplan-Meier plotter tool (ProggeneV2 prognostic database) as described  (22). The cohort was separated by the median of corresponding gene expression as "High" and "Low", respectively. Gene expression was correlated to relapse free survival (RFS). The correlation matrix analysis by Corrplot package (R software) (https///github. com/taiyun/corrplot, Taiyn and Simko) was performed on gene expression data derived from RNA chip analysis from HNSCC tumors of 68 patients  (23). The graphical representation was generated using the R package corrplot. The multiple testing corrections were performed using the pound method  (24).
Read counts were found by the HTseq-count tool of the Python package HTSeq  (29)  with default parameters to generate an abundance matrix. Differential analyses were performed using the DESEQ2 (30) package of the Bioconductor framework. Up-regulated and down-regulated genes were selected based on the adjusted p-value (<0. 10) and the fold-change (> +/-0. 8) (Supplemental Table  S3 ). Deregulated gene expression analysis was performed by using the PANTHER version 11  (31)  and REACTOME software  (32). Tongue tumor pieces from WT and TNCKO mice were resuspended in Laemmli buffer (10 mM Tris pH 6. 8, 1mM EDTA, 5% ß-mercaptoethanol, 5% SDS, 10 glycerol, 1/100 antiproteases). Proteins were extracted for 1 hour upon sonication (4 times for 5 minutes).
The twenty most abundant peptides were selected on each MS spectrum for further isolation and higher energy collision dissociation (normalized collision energy set to 27), excluding unassigned, monocharged and superior to seven times charged ions. The dynamic exclusion time was set to 40 seconds, and "Peptide match selection" parameter of the software. The raw data obtained for each condition were processed with MaxQuant (version 1. 6. 0. 16)  (33). Peaks were assigned with the Andromeda search engine with full trypsin (Trypsin/P) specificity against an in-house generated protein sequence database containing all mouse protein entries extracted from UniProtKB-SwissProt (17 007 sequences, taxonomy identifier: 10 090, release 2019-04-09). Carbamidomethylation of cysteines was set as fixed modification whereas oxidation of methionines and protein N-terminal acetylation were defined as variable modifications.
Proteome profiler array Proteins were extracted from 5 WT,  5   in 1X PBS) following the manufacturer's instructions and protein concentration of tumor samples was determined by optical density measurement (NanoDrop 2000). The expression of immunomodulatory molecules in tumor samples was measured using the Mouse XL Cytokine Array Kit (Biotechne, catalog number ARY028) according to the manufacturer's instruction. After membrane blocking, equal protein amounts from 4 or 5 pooled tumors per group were applied to the membrane overnight. Two membranes were used for each group to have an experimental duplicate. The revelation of each membrane was done by using the Cemi Reagent Mix provided in the kit and a Chemidoc Imager XRS (BioRad). Quantification was done by measuring pixel density with the Image J software.
Dapi (Sigma) was used to visualize nuclei. After embedding in FluorSave Reagent (Calbiochem, catalog number 345789), sections were examined using a Zeiss Axio Imager Z2 microscope. Pictures were taken with an AxioCam MRm (Zeiss,) camera and Axiovision software. Control sections were processed as mentioned above with omission of the primary antibodies. The image acquisition setting (microscope, magnification, light intensity, exposure time) was kept constant per experiment and in between experimental conditions. For quantification of immune cells and positive staining area, the ImageJ software was used. CCL21, CCR7 and gp38 scoring is based on the criteria described in Supplemental Table  S6. At least 2 sections of 5 different tumors/mice were quantified per condition. The number of immune cells was reported in correlation to the total number of Dapi positive cells.
Raman microspectroscopy Raman images were acquired using a WITec alpha300R Raman microscope (WITec, Ulm Germany). In the upright set-up a 532 nm laser was focused through a 60x dipping objective (NA 1. 0) to excite Raman scattering on tissues sections. Tissue areas within the TMTs of on July 17, 2020. © 2020 American Association for Cancer Research. cancerimmunolres. aacrjournals. org Downloaded from 150 x 200 μm were scanned using a pixel size of 1 μm and an acquisition time of 0. 08 seconds per pixel. For each tumor 2-3 Raman images were generated. The spectral images were then further processed and analyzed using WITec Project Five software (WITec, Ulm, Germany). After cosmic ray removal and background correction, spectra of each pixel were area-normalized. The tumor stroma was identified based on a specific spectral pattern, predominantly resembling collagen fibers.
For each image all pixels resembling this matrix pattern were averaged. Spectral patterns of stromal matrix in WT and TNCKO were compared using univariate statistics and (Principal Component Analysis) PCA. To identify CCL21 in the tumor stroma, a reference spectrum of purified mouse CCL21 (457-6C; R&D systems) was acquired. Peaks at 757,1030, 1210, 1319 and 1615 cm -1 are specific for CCL21. This spectrum was used to decompose the ECM spectra in a True Component Analysis (WITec). Here the CCL21 reference was first employed on a CCL21-positive lymph node to identify CCL21 in a physiological condition. The CCL21 spectrum was extracted from the lymph node Raman scans and employed on the Raman data from tumor stroma. For quantification relative intensities of CCL21 in WT and TNCKO stroma was normalized to the collagenous stroma area.
Boyden chamber chemoretention assay The DC2. 4 chemoretention assays were done with the same set up as described in the migration protocol. After 5 hours (CCL21 conditions) or 8 hours (CM conditions) of migration, the DC2. 4 cells attached to the lower surface of the transwells were fixed in 4% PFA and stained with Dapi. Pictures were taken and analyzed by the ImageJ software.
Statistical analysis For all data, Gaussian distribution was tested by the d'Agostino-Pearson normality test. When data followed a Gaussian distribution, statistical differences were analyzed by unpaired t-test (with Welch's correction in case of unequal variance) or ANOVA one-way with Tukey post-test. Otherwise, the Mann Whitney test or a non-parametric ANOVA followed by Dunns post-test were used to verify significance of the observed differences. All statistical analyses were performed using the GraphPad Prism software. Mean ± SEM. p values < 0. 05 were considered as statistically significant, *p < 0. 05; **p < 0. 01; ***p < 0. 001.
An immune suppressive TME in human OSCC correlated with poor prognosis To address whether immune suppression through TNC/CCL21/CCR7 is potentially relevant in human OSCC, we investigated human tumors for TNC and the LEC marker podoplanin. As the murine model mimics the early phases of the human disease, we focused on noninvasive OSCC tissue areas. Similar to the murine tumors, LEC were embedded in TNC-rich stroma (Fig. 7A ). We co-stained the tumor tissue for CCL21 and TNC and observed CCL21 expressed in TNC-rich stroma by cells with flat nuclei forming tubes, likely representing LEC (Fig. 7B ). By investigating publicly available gene expression data (GSE27020), we determined expression of TNC and immune suppressive markers. High TNC expression (above the median) (as well as TGFβi) correlated with shorter time of survival until relapse (RFS), yet not with overall survival (OS) or metastasis-free survival (MFS) (Supplemental Fig.
Acknowledgements We thank  O. Lefebvre ,  F. Steinbach ,  A. Klein ,  C. Arnold , and the animal facility for technical support,  A. Molitor  and  A. Pichot  for RNA sequencing,  K. Schlattmann ,  C. Alampi ,  M. Chami , and  K. Qvortrup  for EM imaging assistence,  N. Toussan  for CD45 immunostaining, and  A. Jung  for access to gene expression data. Supported by grants from  INCa ,  Ligue National Contre le Cancer and the Foundation ARC  ( PAIR-VADS11-023 : EVO and GO) and AAP2017. LNCC/EVO (EVO, FA, and GO) and the  Ligue Régionale contre le Cancer, INSERM and University Strasbourg  (to GO), and  Cancéropôle PACA  and  LABEX SIGNALIFE program  ( ANR-11-LABEX-0028-01 : EVO and FA);  Deutsche Forschungsgemeinschaft  ( EXC 2180 ,  INST 2388/64-1 ),  Ministry of Science, Research and Arts of Baden Württemberg (Az.
While stimulation with 1-6% ethyl tiglate (ET) during 5 s activated several glomeruli in the close vicinity of the M72 glomerulus, lowering the odor concentration in the range of 0. 1% enabled us to isolate the most sensitive glomerulus, which was then selected for the rest of the study. Figure  1b  illustrates a typical neurovascular response to the stimulation protocol (1% ET, 5 s) used to build a database from 15 mice, and compute a microscopic transfer function (µTF) between neuronal Ca 2+ and capillary vascular responses. Deconvolution approaches based on Fourier transformation or Toeplitz matrix were too sensitive to the noise of Ca 2+ and RBC velocity traces to compute a reproducible µTF. We thus chose to optimize a transfer function based on the gamma-distribution function, which is commonly used as a basis for the hemodynamic response function (HRF) in BOLD fMRI.
Right top, axons converging in the two M72 glomeruli are imaged with a stereoscope through a chronically implanted PMP window (photographs were taken for all mice). Right bottom, an image showing a capillary labeled with Texas red. A broken line is used in the linescan acquisition mode to monitor Ca 2+ in the neuropil (dendritic GCaMP6f) and RBC velocity in the capillary. b For each mouse, a microscopic transfer function (μTF) is convolved with Ca 2+ signals, the μTF being optimized to predict RBC velocity changes in response to odor 1% ET (5 s). c Top, μTFs optimized for each mouse (n = 15). The orange curve is the μTF optimized from Ca 2+ and RBC velocity responses illustrated in bottom, which gives the prediction curve overlaid in orange on the RBC response. d Quantification (mean ± SD, n = 15 mice) of prediction robustness for each μTFs optimized using either the TF derived from the same mouse (black symbols, single self prediction) or data from other mice (blue symbols, mean cross-validation).
The μTF from mouse #1 (square symbols) gives the best 'self' vascular prediction (see c) and good predictions across mice. It was selected as the standard μTF to predict vascular responses from Ca 2+ responses. Gray shadow (#12-15) for Pearson coefficients obtained with data acquired in mice from Boido et al, 2019  22. e Examples of vascular response predictions from three mice using the standard microscopic TF and optimized for the amplitude (see Methods). Source data are provided as a Source Data file. displacement of the two imaging systems at the same coregistered location with a micrometric resolution (Fig. 3b ), as well as the collection of micro/mesoscopic functional dataset. We first acquired 2PM neuronal and vascular data from the most responsive glomerulus upon 1% ET (5 s). fUS power Doppler signals were then collected from (1) the single voxel centered on the most responsive glomerulus, and (2) from a small voxel group (the specific voxel plus its five nearest neighbors) to account for the vascular backpropagation (Fig.
3c, d ). Figure  4a  shows that the time course of 2PM Ca 2+ , RBC velocity and mesoscopic fUS ΔPD/PD responses acquired at two odor concentrations (1 and 6% ET) and two stimulation durations (120 ms and 5 s). The responses increased with either odor duration or concentration (n = 5 mice). Vascular responses (microscopic and mesoscopic) were remarkably similar (Pearson coefficients between RBC velocity and fUS data (single voxel): 0. 52 ±. 21 (1%, 120 ms ET), 0. 73 ± 0. 05 (6%, 120 ms ET), and 0. 67 ± 0. 13 (1%, 5 s ET), 0. 70 ± 0. 05 (6%, 5 s ET) mean ± SD). Using our previous TF optimization approach (single gamma-component), we obtained an MTF between neuronal Ca 2+ and the co-registered single fUS voxel signal. In comparison with the µTF, this neuron-derived MTF had a slower decay (Fig.
Whisker stimulations evoked brief changes in the local field potential (LFP) gammaband power and in light reflectance. Even though neuronal (Ca 2+ versus gamma-band power in the LFP) and vascular (RBC velocity versus IOS reflectance) are different proxies signals, the MTF was still robust in predicting mesoscopic CBV changes (Supplementary Fig. 4b, d ). Nonetheless, an ad hoc TF could be further optimized using our computing approach (Supplementary Fig. 4b-e , brown versus orange predictions), yielding a significant improvement to the prediction quality. In total, the MTF computed in the OB remains surprisingly robust in predicting vascular responses in the neocortex, even though it improves with optimization based on the dataset. Statistical analysis of single versus six voxels fUS responses.
The odor delivery was finely time-locked with the inhalation period, detected with the nasal thermocouple. fUS data acquisition and post processing. fUS imaging was performed as follows  22. In brief, a linear ultrasound probe (128 elements, 15 MHz central frequency, Vermon, Tours, France), connected to the ultrasound scanner (AixplorerTM, Supersonic Imagine, Aix-en-Provence, France), was placed 3 mm above the window. Custom transmit/receive ultrasound sequences were written in Matlab (Mathworks, USA). The backscattered echoes of ultrasound plane waves were collected and beam formed to produce OB echographic images, in the coronal plane. To increase the SNR of each echographic image taken at 500 Hz, the echographic images were compounded by transmitting several tilted plane waves and adding their backscattered echoes.
fUS analysis. All analyses were performed with custom made software developed in Matlab 2018a (Mathworks, USA). Beam-formed data resulted in 500 Hz frame rate time series. GLM analysis of fUS data was performed with SPM12. fUS recordings were single voxel under-sampled at 20 Hz using a cubic interpolation ('interp1' function, 'pchip' option, Matlab). Each frame was then converted to a NIfTI file. The absence of movement was previously assessed using a custom made Matlab script. No realignment or spatial smoothing was performed with SPM. Regressors used in GLM analyses were the RBC velocities recorded in the same mice. We applied a statistical threshold of p < 0. 01 + FEW correction, in response to each stimulation condition. 2PM acquisition and data analysis. 2PM imaging was performed using an ultraflexible microscope  44  and data were collected and analyzed using a custom software.
Ca 2+ data were interpolated (10 ms cubic interpolation, 'interp1' function, 'pchip' option, Matlab), turned into ΔCa 2+ traces by subtracting the average value of the last 5 s of baseline. RBC velocities were extracted and analyzed using a custom software  22. RBC velocity data were interpolated (200 ms, cubic interpolation, 'interp1' function, 'pchip' option, Matlab), turned into ΔRBC/RBC traces by subtracting the average value of the last 5 s of baseline and then dividing by it. A median filter of three time points was applied to some traces to attenuate overestimated RBC velocity values. Co-registration of fUS and two-photon imaging. To ensure that we image the same brain volume with fUS and 2PM, we attached the ultrasonic probe to microscope objective with a custom printed ABS holder (Makerbot 2×, USA).
Using Ca 2+ and RBC velocity data collected with 2PM and fUS CBV data within the co-registered brain volume, we built TFs across the different imaging modalities. We used a fitting approach with a single-gamma distribution function, as there was no undershoot in flow in our data. We added a time-shift parameter with the heaviside unit step function, resulting in a total of four parameters for the optimization problem. These parameters capture the slope, the decay, the amplitude and time-shift, respectively, but they are not independent from each other: the optimization problem presents multiple minima configurations, a condition that is not favorable for quasi-Newtonian optimization algorithms. All analyses were performed with a custom-written software in Matlab 2018a. The four-parameters function, TF t ð Þ ¼ Hðt À p 3 Þp 4 ðtÀp 3 Þ p 1 À1 p p 1 2 e Àp 2 tÀp 3 ð Þ Γ p 1 ð Þ , was optimized by minimizing the sum of the square residuals, using simulated annealing, a machine-learning algorithm ('simulannealbnd' function, Matlab).
Note that this is a nondeterministic algorithm: the same set of initial values can bring to different sets of optimized values for the parameters, unless the initial set represent a stable minimum for the optimization function. Note that it was not necessary to modify the Matlab default initial temperature (100) and temperature function ('temperatureexp' function, Matlab). Lower and upper boundaries were set to 10 -3 and 10, respectively, for all parameters. Upper bounds were occasionally increased up to 20 during some optimizations. For Ca 2+ to RBC velocity optimization the presence of the time shift granted a better fit of the onset phase of the responses. For Ca 2+ to fUS optimization, we used a 0-0. 5 s time shift. Transfer functions were computed between two time series (From and To).
Both were cut between 5 and 27 s (for 30 s acquisitions) and between 5 and 59 s (for 60 s acquisitions). From signals were Ca 2+ responses and were interpolated at 50 ms (cubic interpolation, 'interp1' function, 'pchip' option, Matlab), as Ca 2+ data can be noisy. To signals were either RBC velocity responses, or 1 and 6 voxels Power Doppler responses. The residual sum was computed as the difference between the To signal and the convolution of the From signal and the TF, down sampled to match To. Simulated annealing being a nondeterministic algorithm, the initial values of the parameters are crucial for the outcome and the same set of initial values can bring to different outcomes at each optimization run. We used an iterative process for which we started with an initial set of values (the ones of the first gammacomponent of the standard HRF (6; 1; 0.
In detail, each TF, optimized in a given mouse dataset, was then used to predict the vascular responses of the rest of dataset made other mice recordings. The best TFs, finally called µTF or MTF, were chosen being the best performing on its dataset and on dataset from other mice, on the basis of the highest average Pearson coefficient and smallest coefficient of variability, the first preferred when the two conditions were not met by any mouse. Numerical values of the optimized TF parameters are provided in Table  1. Analysis of data from Rungta et al. Vascular responses are those described as juxta-synaptic capillary in Fig. 7  of the original paper  5. TF was computed as before, except for the fact that time series were only 15 s long. Data were collected in four OB glomeruli from three different mice  5.
Analysis of data from Winder et al. Data from the original paper  27  are available on the following link: https://psu. app. box. com/v/Winder2017-Code-Data. We used the data (gamma-band LFP and IOS reflection changes in response to contralateral whisker stimulations) from the 12 mice (*_EVENTDATA_ files). We averaged every trace for both gamma-band LFP and IOS reflection changes. Data were changed into relative values and then averaged for each mouse. TF was computed as before  27. Prediction computation. Micro and mesoscopic vascular predictions were computed by convoluting the Ca 2+ responses or the step functions with the correspondent TFs. An optimization of the amplitude of the responses was performed to best match predictions over experimental data. We optimized a scaling factor with a classical derivative-free method ('fminsearch' function, Matlab) using the residuals between the prediction and vascular responses from 2 to 8 s after the odor application onset as a cost function.