dataesr/scientific-paragraphs-categorization huggingface dataset:
07-0. 11 Mkm 2 , 0. 06-0. 11 Mkm 2 , and 0. 08-0. 15 Mkm 2 for S GIEMS-2 , S RFW , and S WAD2M respectively. Regionally, the simulated IAV of global wetland area is mainly contributed by regions south of 30 ° N (54-84%, 51-90%, 61-88% for S GIEMS-2 , S RFW , and S WAD2M ) and boreal regions (29-38%, 36-48%, 31-47% for S GIEMS-2 , S RFW , and S WAD2M ). Even though the parameters were calibrated with observed wetland extent without temporal variations (MAX), the IAV in global wetland area from most simulations shows significantly positive correlations with Fig. 6  Evaluation of the simulated seasonal cycle of wetland extent against wetland calibration data. Mean seasonal cycle of wetland area from wetland calibration data and simulations based on SM from NCEP-DOE, MERRA-2, ERA5, and GLDAS-Noah v2.
Usage Notes We provide 28 sets of monthly global/regional wetland extent products but users can choose the group of simulations they want based on the wetland definitions of different wetland calibration data. Among seven SM inputs, the optimal simulation is suggested to be the one using SM which reproduces the interannual variability of observation-based wetland data best. Due to the omitted unfrozen soil layers from our algorithm for water table depth, we note the potential uncertainties in wetland simulation over high latitudes. Moreover, to satisfy the need for the more accurate absolute value of monthly wetland extent for S GIEMS-2 and S WAD2M , we provided additional simulations calibrated with yearly maximum wetland area. To provide more choice for users, we also  provided additional simulations calibrated with the union of long-term maximum wetland extent (MAX_all) in our resulting products.
Acknowledgements This study was supported by the  National Natural Science Foundation of China  (grant numbers  41722101 ,  41830643 , and  41671079 ). The GIEMS-2 developments partly benefited from funds from the  VERIFY  project of the  European Community. Ben Poulter  and  Zhen Zhang  acknowledge funding from the  NASA   Terrestrial Ecology Program  and the  Gordon and Betty Moore Foundation  through grant  GBMF5439  " Advancing Understanding of the Global Methane Cycle " to  Stanford University.
Dataset Period (month) Spatial resolution Temporal resolution Soil thickness Data Access GIEMS-2 (1992-2015, monthly) WAD2M (2000-2018, monthly) G2017 (long-term maximum) (0. 25°×0. 25°) Global Record of Daily Landscape Freeze/Thaw Status from Kim et al. (ref. 42 ) mask Koppen climate classification system Soil temperature at 70 cm Remove rice paddies Parameter f ma x Calibration Simulation Global wetland dynamic products (monthly, 0. 25°×0. 25 °) Fig. 1 Diagram of workflow for parameter calibration and the simulation of global wetland dynamics. NCEP-DOE 198001-202012 ~210 km Daily 0-0. 1 m, 0. 1-2 m https://www. esrl. noaa. gov/psd/data/gridded/data. ncep. reanalysis2. gaussian. html MERRA-Land 198001-201512 0. 5° × 0. 667°Monthly 1. 0-8. 0 m https://goldsmr2. gesdisc. eosdis. nasa. gov/data/MERRA_ MONTHLY/MSTMNXMLD.
5. 2. 0/ MERRA-2 198001-202012 0. 5° × 0. 625°Monthly 1. 3-8. 5 m https://goldsmr4. gesdisc. eosdis. nasa. gov/data/MERRA2_ MONTHLY/M2TMNXLND. 5. 12. 4/ ERA5 198001-202012 0. 25° × 0. 25°Monthly 0-0. 07 m, 0. 07-0. 28 m, 0. 28-1 m, 1-2. 89 m https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-single-levels-monthly-means?tab = form ERA5-Land 198101-202011 0. 1° × 0. 1°Monthly 0-0. 07 m, 0. 07-0. 28 m, 0. 28-1 m, 1-2. 89 m https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-land-monthly-means?tab = form GLDAS-Noah v2. 0 198001-201412 0. 25° × 0. 25°Monthly 0-0. 1 m, 0. 1-0. 4 m, 0. 4-1 m, 1-2 m https://disc. sci. gsfc. nasa. gov/datasets/GLDAS_ NOAH025_M_V2. 0/summary?keywords = GLDAS GLDAS-Noah v2. 1 200001-202009 0. 25° × 0. 25°Monthly 0-0. 1 m, 0. 1-0.
CASE STUDY Dynamic model (  1 ) is simulated in Matlab according to the particular characteristics of each vehicle. Series of data are generated by simulation and used to determine the Koopman operator. Data is sampled each T s = 0. 1s with a simulation horizon of T h = 10000s generating 100000 samples. The initial conditions vary randomly from [0,100] each 200s; the control input varies from [500,1500] each 87s. A set of 990 samples with a unique initial condition is used for verification purposes. The simulation scenario consists of seven vehicles with different mass, tire rolling coefficients, and tire radius. These values are shown in Table  1. The general parameters of the system are shown in Table  2. The set of observables or basis functions is given by Φ = [1 v v 2 exp(0. 1v) sin(v) cos(v)].
Data Analysis Average race speed was calculated for each participant and average checkpoint speed for each of the 10 race checkpoints (Table  1 ). The average race speed was calculated for each runner by dividing the race's total distance by their final net time. Furthermore, average checkpoint speed was calculated for each runner by dividing checkpoint distances in km by the time each runner needed to cover the checkpoint distance  [42, 43]. Additionally, the percentage of change in checkpoint speed (CCS) was calculated in relation to the average running speed. CCS was calculated for each of the 10 checkpoints to evaluate the pacing strategy for each runner  [42]. Finally, the average change in checkpoint speed (ACCS) was calculated for Fig. 1  The course profile of 'Spartathlon' each participant as a mean of the 10 CCSs  [44].
e. , men and women) and their interaction (age group × sex). Finally, another two-way ANOVA was also performed on ACCS to assess differences between the four performance groups (i. e. , HL, MHL, MLL, and LL), sex (i. e. , men and women) and their interaction (performance group × sex). For all ANO-VAs, the posthoc Bonferroni test was performed. Effects size was presented via eta squared (ŋ 2 ), where the values of 0. 01, 0. 06, and above 0. 14 were considered small, medium, and large, respectively (Cohen). Alpha level was set at p ≤ 0. 05. All statistical tests were performed using Microsoft Office Excel 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS 20 (IBM, Armonk, NY, USA).
To avoid any confusion due to the PGKs nomenclature inconsistency in previous reports, we will name here the Arabidopsis isoforms targeted to chloroplast as cPGK (hence cPGK1 and cPGK2) and the cytosolic version as a plain PGK (PGK3). The Arabidopsis chloroplastic and cytosolic PGKs are highly similar (Figure  1 ). cPGK1 shares 91% and 84% amino acid identity with cPGK2 and PGK3, respectively. Without the TP, the amino acid identity between both chloroplastic forms (cPGK1 and cPGK2) increases to 94%. Interestingly, the plant PGKs isoforms have been shown to be phylogenetically closer to the bacterial PGKs than to other eukaryotic PGKs  [29]. The Arabidopsis PGKs isoforms share around 60% of protein sequence identity with the Geobacillus stearothermophilus PGK, a PGK whose 3D-structure has been solved (PDB ID: 1PHP).
Such atypical PGKs have previously been described  [30]. A PGK of Klebsormidium nitens, a streptophyte algae, was retrieved in GenBank, as the most similar non-plant sequence to this plant MT-containing PGKs (with 90% of coverage), and added to the set of sequences (Figure  S1 ). Finally, 55 sequences were retained for phylogenetic analysis, including the 43 plant sequences (Table  S1 ). To look at the evolutionary relationship between plant PGKs, we built a phylogenetic tree using the maximum likelihood approach. Plant PGKs are clustered in two groups, group I and group II, supported by strong bootstraps of 99 and 100, respectively (Figure  2A ). All three Arabidopsis PGKs are located in group I, which can be further divided into two subclades (with branches represented in green and blue) corresponding to chloroplastic and cytosolic proteins, respectively.
Their predicted nuclear localization could make them candidates for involvement as part of DNA-polymerase-α complexes  [33]. For the rest of the study, we will focus on the group I PGKs, to review their functions in the chloroplast compartment, and in the cytosol of higher plants. ), maize (https://www. maizegdb. org/, accessed on 24 October 2021), and cucurbits database (http://cucurbitgenomics. org/organism/3) for a conservation analysis. The retrieved sequences are aligned with MUSCLE (https://www. ebi. ac. uk/Tools/msa/muscle/, accessed on 26 October 2021). Subsequently, using protTest software V3. 4. 2 (https://github. com/ddarriba/prottest3/releases, accessed on 25 October 2021), the WAG matrix is predicted as the best model of amino acids substitution. The obtained tree shows 2 groups (group I and group II) of PGKs that likely have a distinct origin.
Group I contains plant cytosolic and chloroplastic PGKs coloured in blue and green, respectively. This group is closely related to Chlamydomonas reinhardtii chloroplastic PGK, which might be its ancestor  [29]. A second class of PGK, in purple colour, forms group II, showing a striking divergence from group I. The members of this group all have a predicted methyltransferase (MT) domain (IPR029063)  [30]. Klebsormidium nitens has a chloroplastic PGK (GAQ85336. 1) displaying a homologous structure domain, as opposed to the plant group II PGKs. PGKs from others organisms are black-coloured. Organism names are preceded by the protein accession numbers for each protein sequence. The PGKs group II contains six atypical PGKs proteins from Poaceae, Cucurbitaceae, and Solanaceae (Figure  2A ). Members of this group, which includes the aforementioned retrieved K.
The protein sequences are retrieved from GenBank (https:// www. ncbi. nlm. nih. gov/genbank/, accessed on 24 October 2021), maize (https://www. maizegdb. org/, accessed on 24 October 2021), and cucurbits database (http://cucurbitgenomics. org/organism/3, accessed on 24 October 2021) for a conservation analysis. The retrieved sequences are aligned with MUSCLE (https://www. ebi. ac. uk/Tools/msa/muscle/, accessed on 26 October 2021). Subsequently, using protTest software V3. 4. 2 (https://github. com/ddarriba/prottest3/releases, accessed on 25 October 2021), the WAG matrix is predicted as the best model of amino acids substitution. The obtained tree shows 2 groups (group I and group II) of PGKs that likely have a distinct origin. Group I contains plant cytosolic and chloroplastic PGKs coloured in blue and green, respectively.
This group is closely related to Chlamydomonas reinhardtii chloroplastic PGK, which might be its ancestor  [29]. A second class of PGK, in purple colour, forms group II, showing a striking divergence from group I. The members of this group all have a predicted methyltransferase (MT) domain (IPR029063)  [30]. Klebsormidium nitens has a chloroplastic PGK (GAQ85336.
3D Protein Models of Arabidopsis PGKs Confirm the High Similarity between Cytosolic and Chloroplastic Forms The obtention of the PGKs 3D protein structures by X-ray crystallography were instrumental to understand the PGKs organisation and enzymatic domains. The first protein 3D structures were characterized for the yeast (PDB ID: 1FW8) and the horse PGKs  [34, 35]. Both structures are similar in terms of described domains: the enzyme N-terminal and C-terminal domains contain two separate substrate sites that bind 3-PGA and ATP, respectively  [35, 36]. Here, we selected the G. stearothermophilus PGK, a close homolog to Arabidopsis PGKs-around 60% of sequence homology-and for which a 3D structure is available (PDB ID: 1PHP) as a reference model for prediction. Using its structure as a template, protein 3D models were built for all three Arabidopsis PGKs using the Swiss-Model work space (https://swissmodel.
The obtained tree shows 2 groups (group I and group II) of PGKs that likely have a distinct origin. Group I contains plant cytosolic and chloroplastic PGKs coloured in blue and green, respectively. This group is closely related to Chlamydomonas reinhardtii chloroplastic PGK, which might be its ancestor [29]. A second class of PGK, in purple colour, forms group II, showing a striking divergence from group I. The members of this group all have a predicted methyltransferase (MT) domain (IPR029063) [30]. Klebsormidium nitens has a chloroplastic PGK (GAQ85336. 1) displaying a homologous structure domain, as opposed to the plant group II PGKs. PGKs from others organisms are black-coloured. Organism names are preceded by the protein accession numbers for each protein sequence. (B) Illustration diagrams of domains found in group I and group II plant PGKs.
Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2. 1 cm). 97. 7% and 85. 3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants.
Therefore, understanding the genetic control and the response to environmental cues of these traits at individual plant level is a major challenge in the context of climate change  [9, 10, 11] , and the engineering of agro-ecological practices based on complex plant mixtures. High-throughput phenotyping (HTP) platforms enable the collection of plant images of a large number of genotypes growing under different environmental conditions on a regular basis  [12, 13]. Such image datasets can then be processed automatically  [14]  to capture the 3D morphology of plant shoots and extract phenotypic traits  [15]. Among the existing methods, the Phenomenal pipeline  [16]  proved relevant to process large datasets of multiple species of agronomic interest, providing an endto-end solution to extract a 3D plant reconstruction from a set of 2D images taken at regular viewpoints around the plant.
To overcome this limitation, time-series analysis could be used to group several occurrences of a same leaf in a temporal series of images, in order to get a 3D + t representation of the plant. While this task relates to multiple object tracking  [19] , this framework cannot be directly applied to leaf tracking, since plants undergo major topological and morphological changes over time, with new leaves appearing and growing due to organogenesis, and others collapsing then disappearing during senescence  [20]. Instead, specific leaf tracking methods have been proposed for 2D images of rosette plants from a top view  [21, 22, 23, 24]. While a few studies have developed leaf tracking methods on 3D reconstructions (e. g. cotton  [25] , cucumber  [26] , tomato  [27]  and maize  [28, 27] , most of them have only been validated on limited datasets that may not reflect actual HTP conditions (thousands of plants, large genetic diversity and growing scenarios).
Moreover, these datasets were often limited to young plants which are less challenging to analyse than in later growth stages (emergence of the reproductive organs, more occlusion due to leaf crossings, more frequent disappearance of leaves due to senescence). While these methods offer various solutions for associating leaves with similar geometry (e. g. length, azimuth), they rarely use topology (i. e. the spatial organisation of leaves on the plant  [29] ). Plant topology offers valuable information for leaf tracking since (i) it defines the identity of leaves, as leaves appear from bottom to top along the stem axis on plants such as maize, and (ii) it is redundant over time, thus helping to maintain leaves identity. Plant topology was used in  [28] , but without considering leaf morphology.
We therefore seek a new leaf tracking method exploiting both leaves morphology and topology, with the objective of handling complex HTP datasets, covering all stages of maize development. Using first the topological order of leaf ranks along the maize stem, a reconstructed maize plant can be represented at any date by a spatial sequence of leaves, ordered from the bottom to the top of the stem. This sequence is obtained through a segmentation step and may therefore contain artefacts resulting from segmentation errors. For instance, the maize ear can be misidentified as a leaf, or some leaves can be unidentified due to occlusions, leading to extra or missing leaves in the sequence. Some leaves may also emerge or fall from the plant between two successive sequences. From this point of view, comparing two successive leaf sequences is analogous to comparing two homologous genetic sequences, which are similar except for a few extra or missing elements.
Sequence alignment algorithms are commonly used to insert gaps in such genetic sequences in order to match their common elements  [32]. This framework was redesigned here to match leaves with a similar morphology in successive topological sequences of maize leaves. In this paper, we propose a novel robust method to address the current issues of maize HTP. First, organs are segmented on 3D reconstructed volumes at all time steps using a former described method  [16] , publicly available on GitHub (Phenomenal: https:// github. com/ opena lea/ pheno menal) and Zenodo (https:// doi. org/ 10. 5281/ zenodo. 14366 33). This method is complemented by a new stem detection algorithm based on deep-learning to locate more precisely the stem tip, which separates the mature part of the maize plant from the developing one.
After that, leaves of the basal mature parts are aligned using a sequence alignment algorithm, and then tracked backwards to the upper developing parts of the plant. We apply this method on a challenging image dataset acquired at the PhenoArch platform consisting of a diverse panel of maize genotypes developing from plant emergence to late flowering stage, under various levels of water stress. The accuracy of tracking is globally assessed by comparing leaf rank predictions and key dynamical traits to manual annotations. Such organ-level phenotypic traits allow to fully describe the global plant development and architecture, as well as individual leaf growth dynamics.
Plant material and dataset composition The pipeline was tested on a dataset from an experiment conducted in 2017 involving a set of 60 commercial maize hybrids representative of breeding history in Europe during the last 60 years. This material covers a wide range of plant architecture, growth and development, leading to an appreciable variability of performances in the field  [33]. The experiment was conducted in the Pheno-Arch phenotyping platform (Fig. 1A ) hosted at the M3P (Montpellier Plant Phenotyping Platforms)  [6]. Briefly, plants were sown in 9L pots filled with a 30:70 (v/v) mixture of a clay and organic compost. Two levels of soil water content were imposed: (i) retention capacity (WW, soil water potential of -0. 05 MPa) and (ii) water deficit (WD, soil water potential of -0. 3 MPa).
These features are extracted from the segmented leaves  [16]  and concatenated into a feature vector v ∈ R 4 summarizing the leaf morphology: (1 Weights w h = 0. 03 and w l = 0. 004 were fine-tuned to scale features and adjust their relative importance. Each pair of matched leaves is associated to a cost c vv equal to the euclidean distance between the feature vectors v 1 and v 2 of those two leaves: ) v = v i 1≤i≤4 = [cos (α), sin (α), w h • h, w l • l] A gap penalty parameter g is used to penalise the addi- tion of each of the n gaps placed in the alignment of sequences S t1 and S t2. We define g from c adj = 4. 23 , the average value of c vv (v i , v j ) for all couples of topologically adjacent leaves i and j in the sequences S t of our dataset, and w gap = 3 a weight: Using a different parameter value for the terminal gaps has proven effective in aligning sequences of different lengths  [41].
Validation with manual measurements Ground-truth data was manually collected on a randomly selected subset of plants with late harvesting. This validation data was then used to evaluate tracking performance, and the accuracy of the phenotypic traits obtained with the pipeline. Leaf ranks were annotated on 30 plants at each time point using the images (10980 annotations). Segmented leaves corresponding to artefacts (e. g. the ear of maize) were not annotated. A subset of 10 plants were randomly selected for manual measurements on images. Leaf lengths and leaf insertion heights were measured at the ligulation stage for all ranks (113 and 173 annotations respectively). Additional leaf lengths measurements were also performed for leaf ranks 6 and 9 during their whole growth phase (234 annotations).
Stem height was annotated for all time points (369 annotations). Ligulated and visible leaf stages were measured in the greenhouse on the 355 plants with late harvesting, with an average of 7 time points per plant (2289 and 1891 annotations respectively). Ligulated leaf stage is given by an integer, while visible leaf stage is given by a real number: for example, a value of 7. 4 means that the last visible leaf is of rank 7, and has reached 40% of its growth. The phenotypic traits were compared with groundtruth observation using the following metrics: bias, rootmean-square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R 2 ). The pipeline conception and the data analysis were performed with Python. (7)  t vis (r) = t med (r -1) + t med (r) 2 (r ∈ N +) (8) t lig (r) = h -1 s h pf (r -1) (r ∈ N +).
A robust and consistent alignment of segmented plants over time The full pipeline was run on the whole late harvesting dataset, (355 plants). Each plant was observed on an average of 43 time points with an average frequency of 24 h, from plant emergence until 3 days after panicle deployment, making a total of 237,600 images analysed. Leaf tracking output is illustrated in video in Additional file 2. 1. 7% of time points were automatically discarded due to abnormally shaped stems (Additional file 3). The global quality of the pipeline was assessed by leaf rank assignment accuracy, which is defined as the percentage of exact matches between the predicted rank of segmented leaves and manually annotated ground-truth ranks (observation). This metric was evaluated separately depending on whether leaves were identified as ligulated or growing, since their tracking relies on different algorithms (Table  1 ).
In plant phenotyping, tracking is often done step by step, by successive pairwise comparisons of reconstructed plant models, which could lead to the propagation of errors from the first alignments computed to the end of the time-series. Sequence alignment methods allow the formulation of a global resolution algorithm for this optimization problem. In this study, we used an optimization method called progressive alignment  [32] , which progressively integrates the models of successive time steps by comparing them to a "profile", representing all the Sequence alignment algorithms are known to be highly dependent on the choice of their parameters, especially the gap penalty  [52] , thus further parameters fine tuning should be considered to optimise the method on more challenging datasets in the future.
Temporal tracking enhances the robustness of 3D reconstruction This work focused on the temporal processing of 3D segmented plant reconstructions. Each plant segmentation is performed at a given date independently of other dates, and can contain inaccurate leaf reconstructions, and segmentation artefacts. With our tracking method, potential error reconstructions can be compensated over time, by grouping several observations of a same leaf in the timeseries. The sequence alignment framework also helps to overcome segmentation artefacts (e. g. missing leaves, ear misidentified as a leaf ) by setting them apart. However, such segmentation errors were still often responsible for subsequent tracking errors in our dataset. This is particularly visible in the advanced stages of growth where the leaves emerge more frequently, are longer, and therefore intersect more, making the segmentation task more error-prone.