dataesr/scientific-paragraphs-categorization huggingface dataset:
yr - 1 (F+VI) to 170 GtC. yr -1 (VI+CO2) at the global scale. The greatest differences with the prior are obtained for the experiments involving flux and satellite data (alone or the two combined). This is directly linked to large corrections in photosynthesis parameters for these experiments (see §3. 3). In comparison, the assimilations involving atmospheric CO2 concentrations data are more conservative with respect to GPP. Assimilating atmospheric CO2 data alone lessens the GPP reduction by a factor of about three compared to assimilations with F and VI data, and the corrections for the joint assimilations using CO2 data is even lower (cf Supplementary Text S6 for details). By propagating the error on the parameters (see § 3. 3) in the observation space (see Eq. 9), we calculated the uncertainty in NEE and GPP fluxes caused by parameter uncertainty for the prior and optimized models.
This is due to the fact that i) F and CO2 data provide a stronger constraint on the annual mean photosynthesis than VI data and that ii) F and CO2 data provide cross constraints on photosynthesis. Experiment VI, in which about ten times fewer parameters are optimized and targeting primarily the timing of phenology, results in the highest posterior GPP errors (although still a reduction from the prior). Finally, one can observe that the posterior errors are higher in the tropics for both NEE and GPP (and the reduction compared to the prior error is lower), which is even more prominent in the experiments using in situ flux data alone or with satellite data, a direct consequence of the lower data availability (eddy-covariance measurements) to constrain the model parameters for tropical PFTs. While the stepwise study showed only few changes in the parameter estimates between the sequential steps (and hence as a function of the data stream from which the parameters were constrained)  (Peylin et al.
, 2016) , our results show a large variability between the assimilation experiments (either between experiments considering a single data stream or between single-vs joint-assimilations). For most parameters, the highest departures from the prior values are obtained for single-data stream assimilations. Higher changes are obtained for flux or satellite data as compared to the estimates retrieved with atmospheric CO2 data alone which remain closer to the prior values. This reflects the lower constraint brought by the CO2 assimilation experiment on photosynthesis and phenology related processes. This is largely due to the correction of the trend bias via a few respiration related parameters, which prevails over the improvement of the other photosynthesis and phenology parameters.
However, given that the estimated parameter errors strongly depend on the setup of B and R matrices and that we did not use error correlations in these matrices, we should only focus on the relative error reduction between experiments. The uncertainty reduction achieved through the assimilation of atmospheric CO2 data is usually lower than when flux and satellite data are assimilated alone, and typically vary between 10% and 60% for most photosynthetic and phenological parameters. Most often, the joint assimilations involving two data streams result in an uncertainty reduction higher or of the same order than that achieved in the single-data assimilations. The joint assimilation combining the three data streams generally results in the highest uncertainty reduction, with values typically between 60% and 90%.
Relative constraints brought by the different datasets We now quantify the impact of each of the three data streams on the analysis using the global average observation influence (quantified by OI) and information content (DFS) metrics defined in § 2. 4. 4. We recall that OI (i. e. trace of S normalized by the number of observations) gauges the average influence that each single observation has on the analysis, while the relative DFS measures the overall weight of one data stream in the optimization (the difference between OI and DFS is due to the number of observations assimilated,  Cardinali et al. (2014) ). OI and DFS are determined for the joint assimilation experiments combining the three data streams. Because of the very large number of observations (above 300,000) involved in the assimilation, only the diagonal elements of the influence matrix (Eq.
10) can be calculated. The trace of S measures the equivalent number of parameters and is equal to 132. Such a value, lower than the number of parameters (  182 ), indicates that the optimized parameters may not be fully independent (although parameter error correlations have been ignored in our B matrix) as already reported in  Kuppel et al. (2012) , or that some are not constrained during the optimisation process (as for instance LAIMAX which estimates remains at its a priori value for some PFTs, Figure  S1  ). The values of OI are provided in Table  4 for   However, the smaller number of atmospheric CO2 data assimilated, compared to flux and NDVI datasets, reduces the overall constraint on the analysis provided by atmospheric CO2 data, as gauged by its relative DFS. Hence, our optimization is mainly controlled by flux data which have an overall contribution of about 75%, that is about 5 times larger than the constraint brought by atmospheric CO2 data and 7 times larger than that of satellite NDVI.
Differences between F+VI+CO2 and F+VI+CO2-2steps are relatively small for both OI and DFS but show a slightly lower weight of atmospheric CO2 data for the 2 steps experiment. The same analysis was performed by discriminating the influence of each PFT for flux and satellite data, and each station for atmospheric CO2 concentrations (Figure  7  -experiment F+VI+CO2). For the flux data, the results are mainly proportional to the number of observations available (hence, the lower results are obtained for BorDBF, TeDBF and TrEBF, for which the number of assimilated data is about one order of magnitude lower than for the other PFTs; see § 2. 2. 1). For satellite NDVI data however, the number of data is the same for each PFT. The discrepancies between PFTs is thus less pronounced than for flux data and related to the ability of the selected parameters to correct the phenology of each PFTs (constrained by the NDVI data).
For TrDBF and C3GRA, the inability to correct the start of the growing season (Kpheno,crit, remains close to the prior values, as seen in Figure  3 ) may explain the lower contribution of these PFTs. For atmospheric CO2 data, the DFS is relatively well distributed across stations, with a mean value of 1. 9 (range 0. 19 -14. 5), in particular in the northern hemisphere. The higher values are found for a few southern hemisphere stations: Halley Station -HBA (6), Syowa -SYO (8. 4), South Pole -SPO (11. 9) and Cap Grim Observatory -CGO (14. 5). Possible reasons for their larger impact may combine: a strong a priori model-data mismatch that is substantially corrected, ocean-driven concentration variations not well captured by the prescribed ocean flux but incidentally well corrected by remote land fluxes, etc.
Caveats and perspectives concerning the initialisation of the soil carbon pools We showed that reaching this global terrestrial carbon sink was mostly achieved by correcting the initial soil carbon reservoirs in the ORCHIDEE model. Their tuning enables the correction of the trend bias between atmospheric CO2 time series measurements at stations and the prior ORCHIDEE-LMDz model. The impact of this trend bias on the optimization performance was highlighted by the quantification of the influence for the three data streams on the optimization, with atmospheric CO2 data having the greatest average observation influence on the solution. A consequence of correcting the trend bias is that the model improvement with respect to other processes (photosynthesis, phenology) is hindered. We attempted to overcome this by setting up a 2-step assimilation process where the trend correction is mostly achieved in the first step by tuning the regional parameters controlling the soil carbon pools.
In addition, we acknowledge that the coverage of the datasets used here is limited (no atmospheric CO2 data nor satellite data after 2010, no in situ flux data beyond 2007) and that we did not assess the potential of other data that can bring relevant additional information on the dynamics of terrestrial carbon fluxes stocks, such as aboveground biomass  (Thum et al. , 2017)  or Solar Induced-Fluorescence  (Bacour et al. , 2019)  which have already been investigated with ORCHIDAS, and with an updated version of the ORCHIDEE model.
Conclusion Terrestrial ecosystem modelers are anticipating the many novel types of observations that are being made available for model evaluation and assimilation. As a result, and in parallel to the growing complexity of TBMs incorporating new biogeo-physical processes related to the carbon and water cycles, new observation operators are being developed to be able to make use of this new wealth of data. We are thus entering an exciting new era, one in which we should both take advantage of a multitude of new datasets and capitalize on past gains in terrestrial model data assimilation understanding to achieve significant reduction in land surface model projection uncertainties. We hope that the metrics explored in this study can benefit a broader set of data assimilation applications, supporting guidance for setting up the framework and for better use of the data to be assimilated.
Acknowledgements This work has been supported by the  CARBONES  project, within the  EU's 7th Framework Program for Research and Development. The authors are very grateful to  LSCE 's IT staff for their support and for the computing resources, as well as to the ORCHIDEE Project Team for developing and maintaining the ORCHIDEE code. https://doi. org/10. 5194/bg-2022-109 Preprint. Discussion started: 31 May 2022 c Author(s) 2022. CC BY 4. 0 License.
Code availability The ORCHIDEE model code is open source (http://forge. ipsl. jussieu. fr/orchidee) and the associated documentation can be found at https://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation. The ORCHIDAS data assimilation scheme (in Python) is available through a dedicated web site (https ://orchi das. lsce. ipsl. fr/). Information about the LMDz model, source code and contact is provided at https://lmdz. lmd. jussieu. fr/le-projet-lmdz-en-bref-en.
Data availability This work used eddy covariance data acquired by the FLUXNET community (https://fluxnet. org/data/la-thuile-dataset/). The NDVI data are derived from the MODIS MOD09CMG collection 5 daily global reflectance products ('https://ladsweb. modaps. eosdis. nasa. gov/missions-and-measurements/products/MOD09CMG). The surface atmospheric CO2 concentration data uses measurements from The NOAA Earth System Laboratory (ESRL) archive (ftp://ftp. cmdl. noaa. gov/ccg/co2/), the CarboEurope IP project (http://ceatmosphere. lsce. ipsl. fr/database/index_database. html), and the World Data Centre for Greenhouse Gases of the World Meteorological Organization Global Atmospheric Watch Programme (http://gaw. kishou. go. jp).
stocks (as the International Soil Carbon Network, Nave et al. (2016), or the global soil respiration database, Jian et al. (2021)) by TBMs is not straightforward because of inconsistencies between the estimated quantities and the model state variables and underlying processes. Still, it is of primary https://doi. org/10. 5194/bg-2022-109 Preprint. Discussion started: 31 May 2022 c Author(s) 2022. CC BY 4. 0 License. importance for the science community to endeavor bridging the gap between state-of-the art estimates of soil carbon stocks and what TBMs simulate over the historical period.
In 7/8 nalC mutants, the ORF was disrupted by a frameshift (+C 450 or -A 486 ). The remaining mutant CNA-A2 exhibited an amino acid substitution (T 24 P) in the gene product. This amino acid residue is not conserved in the TetR family of transcriptional regulators, to which NalC belongs (Figure  S2 ), but it is situated in a helix (Hα1) of the DNA-binding domain of TetR repressors  [21]. To better evaluate the impact of the T24P substitution on NalC oligomerization or DNA-binding, we mapped this mutation on a three-dimensional dimeric model bound to a cognate DNA. As the structure of NalC from P. aeruginosa has not been determined yet, we used the dimeric crystal structure of a homologous TetR protein from Mesorhizobium japonicum (PDB code 3BHQ, 24. 4% of sequence identity), that also has a threonine at position 24 (Figure  S2 ).
aeruginosa mutants CNA-A1 to CNA-A3, and CNA-B1 to CNA-B7 mutants have been deposited in the NCBI data base under the Bio-Project accession number PRJNA663565. Mutations found in genes of mutants CNA-B1 to -B7 are listed in Tables S4-S10. SIFT (https://sift. bii. a-star. edu. sg/, accessed on 1 September 2022) and PROVEAN (cutoff -2. 5) (http://provean. jcvi. org/index. php, accessed on 1 September 2022) algorithms were used to predict the effects of identified substitutions. Three-dimensional structural model of NalC (UniProt reference Q9HXS0) was obtained by homology modeling using MODELLER software  [43] , with 3BHQ as a template structure. Sequence alignment was obtained with ENDscript  [44] , and figures were generated with PyMOL Molecular Graphics System, Version 2. 0 Schrödinger, LLC, New York, NY, USA.
Standards were diluted (1:100) in methanol. A fraction of 25 µL for sample and 2 µL for standards was sprayed as 8 mm bands on a TLC plate (Alugram ® Xtra SIL G UV254, Macherey-Nagel, Hoerdt, France) using an automatic sampler (ATS4, Camag, Moirans, France) connected to visionCATS Camag TLC software V2. 4. The TLC plate was developed in an automatic developing chamber (ADC 2, Camag) with a mobile phase containing cyclohexane:ethyl acetate (7:2) over a 70 mm developing distance. Spots were observed using UV-light at 254 nm (CV-415. LS, Uvitech, Cambridge, UK).
Deep learning analysis for classification from virtual slides. Deep learning for computer vision was used to train models for the binary classification ABMR/Other diagnosis for each antibody. The overall analytical strategy is illustrated in Fig. 2. All analyzed slides were anonymized and digitized into the ndpi format using a Hamamatsu NANOZOOMER 2. 0HT at the objective × 20 (resolution 0. 46 μm/pixel). Using the QuPath 0. 2. 3 software  24  , the renal parenchyma was manually annotated for each slide, defining the regions of interest. Each region of interest was then segmented into square tiles of 512 × 512 pixels. Tiles were numbered for each case and exported into the jpeg format, according to the Aachen protocol for Deep Learning Histopathology  25.
The Resnet50V2 model was loaded with the trained weights based on the ImageNet dataset, excluding the final classification layer. Instead, a GlobalAveragePooling and a Dense layer of 1 neuron were added, the latter with a sigmoid activation function. To make the model more generalizable, i. e. more robust against image variations, we applied image data augmentation during training. As such random image alterations were performed such as horizontal and vertical flips, rotation and channel shifts. To account for the imbalance of the dataset between ABMR and Other diagnosis cases, we used a weighted binary cross-entropy loss function. Thus, a misclassification in the underrepresented ABMR class gave a higher error than the majority "Other diagnosis" class. The model 1 was firstly trained for one epoch with all convolutional layers kept frozen (weights non-trainable), with an Adam optimizer, a learning rate of 1e-04 and a batch size of 64.
Finally, performance from the 5 repeated crossvalidation procedures were averaged. To allow for a visual explanation of the CNN classification, we used the Gradient-weighted Class Activation Mapping (Grad-CAM) approach  28. Briefly, this method uses the final convolutional layer of a trained model to produce a localization map (heatmap), highlighting important regions in the image for class consideration. The heatmap is then superimposed onto the original image. Because Resnet50V2 is not directly suited for this implementation, we used the Xception architecture instead, which we trained in a similar manner as Resnet50V2. The last convolutional layer was set to the "block14_sepconv2_act" layer. Software and statistical analysis. All deep learning models were trained using the keras library with the TensorFlow backend  26, 27  , using either a Tesla T4 or a P100 as graphics processing units.
Area under the curves were calculated using the scikit-learn library. All other statistical analyses were performed using the R software, version 4. 1. 1  29. Cohen's kappa was computed for the evaluation of inter-observer reproducibility between two pathologists, and Light's kappa for the overall inter-observer reproducibility between all four pathologists. Plots were performed using the ggplot2 package, version 3. 3. 5, and correlation analyzes with the cor. test function. In order to easily compare pathologists and deep learning interpretations of immunomarkers, a majority rule was applied to pathological interpretations, where each case was classified according to the report of most pathologists. In case of ties, the interpretation of the pathologist B. C. was retained.
Clinical gait analysis is a promising approach for quantifying gait deviations and assessing the impairments altering gait in patients with osteoarthritis. There is a lack of consensus on the identification of kinematic outcomes that could be used for the diagnosis and follow up in patients. The proposed dataset has been established on 80 asymptomatic participants and 106 patients with unilateral hip osteoarthritis before and 6 months after arthroplasty. All volunteers walked along a 6 meters straight line at their self-selected speed. Three dimensional trajectories of 35 reflective markers were simultaneously recorded and Plugin Gait Bones, angles, Center of Mass trajectories and ground reaction forces were computed. Gait video recordings, when available, anthropometric and demographic descriptions are also available.
However, there is a lack of consensus on the identification of kinematic-related outcomes that could be used as judgement criteria for the diagnosis and/or follow up in patients with hip OA. Either kinematic or kinetic relevant outcomes or their combination remain difficult to identify by classical statistical methods due to the multitude of information resulting from CGA  13, 14. Moreover, these information could be continuous or discrete, time related, space related. Thus, information of the CGA could be extracted in the multiple ways prior described. The data processing is often made with linear statistics, that force to choose a priori one (univariate) or more (multivariate) discrete variables of interest, which naturally leads to a significant loss of information. Notably, temporal information as well as existing interdependency of these variables are not considered.
Conversely, machine learning models could be used to allow more accurate recognition thanks to the correlations identified using data interdependency. Recent studies have shown the utility of machine learning to identify kinematic outcomes, in particular those for patients with hip and knee OA  [15] [16] [17]  , but their clinical relevance requires further exploration. Most of these outcomes allow to link OA severity (WOMAC scores) and kinematic outcomes (Knee flexion or Hip movement during gait) and will facilitate either the rehabilitation or adapt the follow up of patients with significant alteration of the gait pattern. Several datasets of healthy participants have been made available in the literature and can ease the establishment of a broad normative database allowing to match patient characteristics  [15] [16] [17]  , (e.
g. age, sex, height and weight). However, to our knowledge, no dataset has been provided merging data of patients before and after THA and data of healthy participants recorded using the same protocol. Nonetheless, such a dataset is required before using machine learning models for kinematic outcomes identification of OA disease severity. The present dataset has been established on 80 asymptomatic healthy participants (aged between 25 and 82 years) and 106 participants with end-stage unilateral hip OA (aged between 45 and 85 years), before and 6 months after THA, without other comorbidities that could affect the gait. The main objective of this dataset is to allow machine learning to identify the specific kinematic outcomes (spatiotemporal and kinematic parameters) in coxarthrosis in order to allow their automatic recognition by the machine.
Procedure. For each healthy participant, the entire data collection was acquired in a single session with the Nexus software (Vicon, UK). For participants with hip OA the entire data collection was acquired in two sessions (M0 -from 30 to 1 days before surgery and M6 6/7 months after surgery) with the same software. Each session lasted approximately 2 hours. All the sessions were managed by the same experienced operators (DL and PO). The following procedure was adopted: 1. Consent information to the participant: An investigator of the study introduced the laboratory, outlined the hypothesis of the study to establish the database, and explained the procedure of the study and how to conduct the session, including the material used. 2. Medical interview: an interview allowed collecting information at this stage about participant's health status.
This interview aims to gather demographics (age, sex, height, weight, Body Mass Index) and imaging outcomes (including OA side and Kellgren and Lawrence grade imaging score) and to screen the patients for other potential diseases which could effect gait in accordance with the inclusion/exclusion criteria. These data are available in the metadata file on figshare  19. 3. Calibration of the systems: this calibration was performed following the instructions available in the manufacturer's documentation, including the definition of the inertial coordinate system, the dynamic calibration of the cameras, and the zeroing of forceplates. 4. Preparation of the participant: the participant was asked to change clothes to tight-fitting clothes or underwear, including removing shoes and socks as the acquisition was barefoot, and to tie up their hair if necessary.
The operator also collected participants' anthropometric information  19. All participants were equipped with reflective cutaneous markers positioned following the Plug-In-Gait model  20, 21  detailed in Fig. 1  and Table  4. Before walking, each markerset was calibrated for each patient with a static recording described below. 5. Calibration file (Static record): The participant was standing upright in anatomical position, palms facing forward, the gaze close on a picture 3 m in front of them. Three seconds without any movement were recorded. The record was checked by the operator. A new standing trial was performed if any marker was missing or misplaced regarding the PlugIn Gait guidelines. This file is named Calibration in the dataset and included in each volunteer folder. 6. Walking trials: Eight optoelectronic cameras (Vicon MXT40, Vicon, UK) sampled at 100 Hz were used.
Volunteers' metadata. A complete list of volunteers' metadata is available  19  : • ID of volunteers • Demographic parameters (age, sex, height, weight, Body Mass Index) • Anthropometric parameters related to the Plugin Gait markerset • Clinical parameters for OA patient: OA side, Kellgren and Lawrence grade During each gait analysis, a video-recording (Bastler camera, 300 Mpixels, 50 Hz) was made on the frontal and sagital plane of the patients. Two experienced physicians visually classify the disturbances type on video recording of patients during the gait analysis. They had to classify disturbances in 5 categories: • Duchenne, lateral bending of the trunk and the pelvis in the stance side (D3) • Trendelenburg, lateral inclination of the shoulder on the stance side with an opposite inclination of the pelvis (D1).