dataesr/scientific-paragraphs-categorization huggingface dataset:
Nuclear and mitochondrial pairwise distances The nuDNA-G137 dataset (16966 nt) and mtDNA-GH82 dataset (1742 nt) were used to calculate pairwise distances in PAUP � v4b10  [50]  (see Tables A and B in S4 Appendix). For the nuclear dataset, we performed calculations considering the five taxonomic hypotheses mentioned above. For the mtDNA dataset, we primarily performed calculations based on the three main mitochondrial haplogroups, named N, E and S depicted in Fig  1B , but considered also the five possible hypotheses of species delimitation described in the Multispecies coalescent analyses section. A is supported by the separate analyses of 4 introns, i. e. ACP5 (PP = 0. 75), CTAGE5 (PP = 1), CWF19L1 (PP = 1) and SOS1 (PP = 1), and members of this group share eight molecular signatures detected in five markers; G.
Multispecies coalescent analyses We constructed a MSC species-tree from the nuDNA-G274-O6 dataset using � BEAST. The topology is similar to the supermatrix topology of Fig 1A, with maximal support (PP = 1) for G. camelopardalis s. s. A, G. giraffa and G. tippelskirchi. However, the monophyly of G. camelopardalis s. s. B, G. camelopardalis s. s. C and four subspecies (antiquorum, peralta, reticulata, and thornicrofti) was also highly supported (PP = 1) in the MSC tree (Fig A in S5 Appendix). The subspecies tippelskirchi was found monophyletic, but with low PP support (= 0. 39). The analyses based on STACEY showed highest support for five distinct giraffe species, i. e. , G. camelopardalis s. s. C, G. giraffa, G. peralta, G. reticulata and G. tippelskirchi, a pattern found in 87% of the trees.
Phylogenetic analyses of the mitochondrial fragment The Bayesian tree reconstructed from the mtDNA-GH82O3 dataset (1776 nt) is shown in Fig 1B. It shows the existence of three major geographic haplogroups: northern (N), eastern + southeastern (E), and southwestern (S) giraffes. The N haplogroup is supported by both Bayesian and bootstrap analyses (PP = 1; BP = 93). It includes all haplotypes detected for G. camelopardalis s. s. A, as well as one divergent recognized are distinguished by different colours. Individuals characterized by a rare allele (in the subspecies) are highlighted with a black frame. The numbers of mutations between alleles are indicated on the branches. https://doi. org/10. 1371/journal. pone. 0217956. g003 haplotype of G. tippelskirchi (TIP15, EU088334) sequenced by Brown et al.
In the fourth column are shown the support values provided by the three Multispecies coalescent (MSC) methods, i. e. BPP, STACEY and � BEAST. In the fifth column are listed the results Web of Science (January 2019). Using Bayesian inference, STRUCTURE is a model-based clustering method to detect population structure and assign individuals to K populations  [26]. However, many published results based on STRUCTURE are not reproducible because the genotypes were not available or the parameters used for the analyses were not fully detailed by the authors  [52, 70]. The program STRUCTURE was previously used to infer genetic structure in giraffe populations, using either genotypes from 14 microsatellite loci of 381 individuals  [29]  or phased alleles of seven introns for 105 giraffes  [38]  or rather the extended dataset of 21 introns for 137 individuals  [12].
Brown et al. [29]  suggested the existence of at least six species, but the optimal K was not determined using either the method of Evanno et al. [57]  or that of Pritchard et al. [51] , and their results are not reproducible, because the microsatellite data were not made available. According to Winter et al. [12] , "K = 4 shows four well resolved groups and is supported as best fitting number of clusters by several statistical methods", but they did not provide any details on the model and method used for their STRUCTURE analyses. Using the same dataset, comprising allelic information of 21 nuclear introns for 137 giraffes, we tested 16 different models under STRUCTURE in order to shed more light on giraffe population structure. Considering the method of Evanno et al. [57] , 58% of the analyses provided support for two distinct populations of origin (K = 2), 25% for three distinct clusters (K = 3), and only 17% confirmed the result obtained by Winter et al.
camelopardalis s. s. A, G. giraffa, and G. tippelskirchi (Figs 2, 3, 4 and 5). obtained in the phylogenetic analyses including the markers supporting each taxonomic hypothesis in the separate analyses of 21 introns and mtDNA, as well as the support values obtained from supermatrix (PP = posterior probability; BP = bootstrap percentage) and SuperTRI analyses (SBP = SuperTri Bootstrap Percentage; MPP = Mean Posterior Probability; Rep = Reproducibility Index) ("-": not found). In the sixth column are detailed the mean pairwise distances between individuals of the same taxon calculated using either nuDNA data (concatenation of 21 introns, above) or mtDNA (below) (Since all the mitochondrial sequences of the subspecies giraffa belong to haplogroup E, they were considered as tippelskirchi for distance comparisons; see Discussion for more details on mtDNA introgression).
This problem explains the highly variable root position in our mitochondrial analyses: with MrBayes, the first haplogroup to diverge is either S (Fig 1B, PP = 0. 37; BP = 60) or E (if the two bovid species are excluded as outgroup taxa, data not shown, PP = 0. 55); with BEAST, haplogroups E and S are found to be sister-groups (PP = 0. 74), as in the mitochondrial tree of Fennessy et al. [38]. The nuclear dataset provided more signal for resolving basal relationships within Giraffa. As indicated in Fig  1 , our phylogenetic analyses supported a sister-group relationship between G. giraffa and G. tippelskirchi (PP = 0. 82; BP = 71). This node was found monophyletic with 6 independent markers (C1orf74, DDX1, COL5A2, SAP130, USP33 and USP54). By comparison, SuperTRI analyses clearly showed that the two other hypotheses (either G.
Its grouping with Rothschild's giraffes may be explained by interbreeding between reticulata and rothschildi either in zoos  [77]  or in the wild, as field observations have documented the occurrence of reticulata X rothschildi hybrid phenotypes in Kenya  [78]. Unfortunately, these hybrid individuals or populations were not yet studied for nuclear genes. The mitochondrial haplotype RET9, which was detected by Brown et al. [29]  in a single reticulated giraffe (accession number: EU08821), is intriguing because it is divergent from all other sequences of haplogroup N. We propose two hypotheses to explain its divergence. The first hypothesis assumes the retention of ancestral haplotypes in wild populations of reticulated giraffes; it will be confirmed if identical or similar haplotypes are discovered in other reticulated giraffes.
The 21 nuclear introns were analysed independently and in combination. The phylogenetic trees obtained from the separate analyses of the 21  independent introns are detailed in S2 Appendix and the Bayesian nuclear tree of the concatenated dataset (17276 nt) is depicted in Fig 1A. The results of other analyses (ML bootstrap [BP] and SuperTRI indices [SBP/MPP/ NRep]) are indicated only for the nodes supported by posterior probability (PP) values � 0. 9, as well as for nodes discussed in the text (e. g. , subspecies). The monophyly of Giraffa is supported by all analyses and almost all markers separately (NRep = 20), and the genus is diagnosed by 158 exclusive synapomorphies in the nuclear genes. Within Giraffa, 19 nodes are supported by PP � 0. 9 in the Bayesian tree of the nuDNA supermatrix (Fig 1A; Fig Y in S2 Appendix), but only three of them are associated with BP > 90: (1) the clade here named G.
The PhD thesis of AP was funded by  LabEx BCDiv  (https://labex-bcdiv. mnhn. fr)(project written by AH in 2015). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir " n  ANR-11-IDEX-0004-02.
In China, irrigation is widespread in 40. 7% cropland to sustain crop yields. By its action on water cycle, irrigation affects water resources and local climate. In this study, a new irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE-CROP land surface model which describes crop phenology and growth in order to estimate irrigation demands over China from 1982 to 2014. Three simulations were performed including NI (no irrigation), IR (with irrigation limited by local water resources), and FI (with irrigation demand fulfilled). Observations and census data were used to validate the simulations. Results showed that the estimated irrigation water withdrawal (W) based on IR and FI scenarios bracket statistical W with fair spatial agreements (r = 0.
With the economy growth and technique improvement, China experienced fast irrigated area expansion by 51% since the 1980s  (Zheng et al. , 2004). It is reported that until 2017, 6. 78 × 10 8 m 3 cropland area are irrigated by 3. 77 × 10 11 m 3 •year -1 accounting for 62. 3% of total water usage (http://www. stats. gov. cn/tjsj/ ndsj/2018/indexch. htm). In this study, we develop a new irrigation module in a physical-based land surface model ORCHIDEE-CROP (ORganizing Carbon and Hydrology in Dynamic EcosystEms,  Wang, 2016)  with a crop module for wheat, maize, and rice to simulate the patterns of irrigation demand in China. ORCHIDEE-CROP accounts for most known biophysical responses of vegetation to climate change as well as crop phenological stages  (Wang, 2016). The irrigation model considers the most used irrigation techniques in China, especially paddy irrigation for rice.
New Irrigation Module in ORCHIDEE Irrigation processes were introduced in the standard version of ORCHIDEE by de  Rosnay et al. (2003)  and further developed by  Guimberteau et al. (2012). However, these early developments were made for hydrological modeling without considering the nature of cropland managements. For example, water and energy budgets of croplands are shared with other plant functional types (e. g. , forests and grasslands) in the same grid, which makes the simulated irrigation to be distributed uniformly among different vegetation types. We have now developed a new irrigation scheme in the crop branch of ORCHIDEE model called ORCHIDEE-CROP  (Wang, 2016; Wang et al. , 2017; Wu et al. , 2016). Crops are modeled with specific equations for phenology and carbon allocation, previously calibrated for wheat, maize, and rice at site and regional scale.
One of the key functions of ORCHIDEE-CROP that make our results significantly different from  Guimberteau et al. (2012)  is the inclusion of the crop module. In the standard version of ORCHIDEE, crop was treated as a productive grassland with a long growing season that makes possible irrigation to occur all year around. In the crop model, crops have specific planting dates, and their growing period is generally shorter for major cereal crops. Second, in  Guimberteau et al. (2012) , I req was constrained by an irrigation fraction of each grid cell, defined as the ratio of irrigated area over total cropland area. This assumption was not optimal because the water balance of rainfed and irrigated plants was inconsistently calculated for a single soil tile. This setting was unrealistic for crop modeling as it implies that rainfed crops also received irrigation water.
Observation-Based Data Sets for the Evaluation of Water Budgets 2. 2. 3. 1. Gridded Evapotranspiration There are numerous global gridded evapotranspiration (ET) data products derived from different algorithms  (Mueller et al. , 2013). Most of those ET products are observation-based models that estimate potential ET (ET p ) by the Penman-Monteith, Priestley-Taylor, or other formulas. Then actual ET is scaled from ET p using other factors such as soil moisture. Thus if the soil moisture variable used in an ET model does not contain information on irrigation, this ET product is not suitable for evaluating our model simulations. We selected GLEAM v3. 2a (Global Land Evaporation Amsterdam Model,  Martens et al. , 2017)  and FLUXCOM  (Jung et al. , 2009)  from the ET products listed by  Mueller et al.
(2013) , because they account for irrigation, to some extent. The GLEAM model calculates ET with a two-layer soil model assimilating top-soil moisture from the European Space Agency Climate Change Initiative (ESA CCI) SM product. GLEAM should therefore account indirectly for irrigation. FLUXCOM ET upscales FLUXNET (global network of eddy covariance towers) observations by using LPJmL (Lund-Potsdam-Jena managed Land) simulations through a Model Tree Ensemble (MTE) method  (Jung et al. , 2009). Thus, FLUXCOM ET is a hybrid product using flux towers, climate and satellite data, and the LPJmL simulation results. We selected two ET products that may contain the effect of irrigation: the Peking University (PKU) catchment water balance model from  Zeng et al. (2014)  which used a Multiple Tree Ensemble model and eddy-flux towers for downscaling spatial and temporal patterns of ET within each river catchment and the SEBS ET data set from  Chen, Su, et al.
Irrigation Impacts on Water Cycles and Surface Energy Budget Figure  6  presents the impacts of irrigation on the water and surface energy fluxes. Averaged over China (left panel of Figure  6 ), I has a positive impact on ET, SM, and deep drainage, but a negative one on runoff. Significant ET increase is found in the YLRB from March to July by as much as 33. 6% (Figure  S7 ). However, such difference is less than the uncertainties given by the spread of different ET products, implying difficulty to validate the impact of irrigation on ET by current ET data sets. Irrigation also increases net radiation (R n ), because it promotes LAI by reducing water stress and consequently decreases surface albedo. Last, I increases expectedly latent heat flux and decreases sensible heat flux leading to a slight decrease of surface temperature (T s ) over irrigated crop tiles.
Irrigation Module and Perspectives Compared to the earlier version of ORCHIDEE used for hydrological modeling  (Guimberteau et al. , 2012) , the current irrigation module is now capable of considering the impacts of several techniques on irrigation practices. Three irrigation techniques are considered, and irrigation demand depends on the specific technique (equation (  1 )). Independent energy balance is estimated for each soil tiles, which precisely capture the interactions between crops and local climate. Moreover, three scenarios are developed for different irrigation scenarios. One includes water balance, in which irrigation will be limited by renewable water resources. It is used to investigate the effects of irrigation practices on hydrological cycle. Another scenario fulfills irrigation demand excluding water balance.
Conclusions The irrigation module presented in this study and coupled to ORCHIDEE-CROP has the advantage to compute irrigation demand and application for specific irrigated crop soil tiles in each grid cell, allowing mass closure and avoiding to give irrigation water to other vegetation types. Irrigation demand calculation can be chosen for three different irrigation techniques, including paddy irrigation for rice. The performance of the model was evaluated for the spatial patterns of irrigation withdrawal across different provinces, with both freshwater limited (IR) and full irrigation (FI) simulations showing a spatial correlation of ≈0. 68 with independent withdrawal census data. The correlation is larger when irrigation withdrawal in the model includes a realistic irrigated fraction and efficiency.
4a ). e OCT catheter (Dragon y, Optis Imaging Catheter) was advanced through a 6F guiding catheter and pulled back (automatic triggering by saline without contrast) at a speed of 36 mm/s (5 frames/mm) for 75 mm, covering the entire length of MV and SB from the distal to the proximal xed marker (Fig. 1a ). Lumen segmentation of the OCT frames was carried out semi-automatically using echoPlaque 4. 0 (INDEC Medical Systems, Los Altos, CA, USA; Fig. 4b ). e detailed steps of the bifurcation lumen reconstruction are illustrated in Fig. 4. Brie y, the segmented OCT frames were imported into Grasshopper 3D (visual programming language and environment that runs within the Rhinoceros 3D) and packaged in a straight line along the catheter center (Fig. 4c ). e OCT frame misalignment was corrected with an in-house script (Fig.
OCT and angiography data were acquired according to the protocols mentioned above. Both lumen and wall were 3D reconstructed following the steps of our proposed methodology. We followed a step-wise approach for the delineation of the outer borders in OCT images. Our approach worked successfully in > 95% of images and involved the following steps: (1) In case of ill-de ned outer wall borders, we were limiting the outer wall at the margin of the complete signal loss (Supplementary Information Fig. S3a ), (2) In case the margin of complete signal loss could not be identi ed in < 180 degrees of vessel circumference, we were interpolating the visible outer wall border (Supplementary Information Fig. S3b ), and (3) In case the margin of complete signal loss could not be identi ed in > 180 degrees of vessel circumference, we were discarding that particular OCT frame and segmenting an adjacent frame following the same steps 1-2.
A er segmenting the vessel wall in OCT images, the wall was 3D reconstructed following the same steps as with the lumen (applying the same frame orientation correction and frame rotation as with the lumen), and the bifurcation was built by combining the lumen and wall interface (Fig. 5a ). e reconstructed bifurcations could be meshed with hexahedral elements (Fig. 5b ). To assess the overall time-e ciency of our method, we calculated the processing time for each step in each of the seven cases. Statistical analyses were performed with the statistical package GraphPad Prism 8. 0 (GraphPad Inc. , San Diego, CA, USA). Continuous variables were expressed as median (IQR). e lumen areas of OCT and μCT models were normalized by calculating the z-score as (absolute area-µ)/σ with µ representing the mean area and σ the standard deviation of the mean.
005 and 0. 004 degrees for lumen shape and bifurcation angles, respectively. Likewise, the reproducibility of our methodology was remarkably high (Table  3  and Supplementary Information Tables  S3  and S4 ). Another key feature of our methodology is the excellent feasibility and versatility in a variety of real patient data and diseased bifurcation anatomies with varying degrees of calci cation. Notably, our method was quite e ective in reconstructing the wall along with the lumen, providing the framework for computational studies and a better understanding of plaque burden and complexity. Our method was time-e cient and user-friendly Total time for whole process in part due to the use of a visual programming language tool (Grasshopper 3D). Unlike the traditional text-based code, such as Matlab, Grasshopper 3D allowed modularization, seamless work ow-even for operators without programming background-and semi-automation of the reconstruction process, with only minimal manual intervention for model checking and parameter setting.
tricornutum or F. cylindrus genome sequence. We did not include these incomplete gene models and the 61% of genes without any significant blast hit and their associated protein IDs in our model. However, we opted to keep all cellular reactions (enzymatic reactions, transport, etc. ) from the P. tricornutum model based on the general high similarity between proteins of P. tricornutum and F. cylindrus and included reactions for which no significant homologous genes were detected without any gene sequence association to provide a first robust reconstruction of a metabolic model for F. cylindrus. Additionally, subcellular locations of homologous proteins in F. cylindrus were predicted using web-based software (ChloroP, MitoProt, and DeepLoc-1. 0) and shown to be identical for 96. 6% of proteins in P.
Model Implementation in MATLAB Model development, sensitivity analyses, and flux distribution calculations were performed using MATLAB 2018 (The MathWorks Inc. , Natick, MA, USA). All custom codes are available on GitHub (https://github. com/michellavoie4/Resilience-Fcylindrus). The scripts for the local and global sensitivity analysis to changes in the 69 parameters of the model are detailed in the files localsens_script. m and globalSA_script. m using the functions localsensFBA. m and FBAlight. m. The global sensitivity analysis was done using the Morris method, which is recommended for models with a high number of parameters. When running the Morris method, we used the Sensitivity Analysis For Everyone (SAFE) toolbox in MATLAB, taking into account the latin hypercube resampling strategy and 6500 model evaluations  [38].
Sensitivity of growth to reaction or gene deletion was assessed with the Constraint-Based Reconstruction and Analysis (COBRA) toolbox functions singleRxnDeletion() or singleGeneDeletion() using FBA or MOMA (see the file ReactionDeletionSensitivity. m) with the prior addition of constraints on photon uptake (i. e. , allowing a 1. 5-fold increase or decrease with respect to the control without reaction deletions) to avoid modeling unrealistically high light harvesting in response to deletions. We set the computed growth rate to 0 when reaction or gene deletions lead to infeasible problems. FBA and MOMA were performed using the Gurobi Optimizer Version 7. 5. 2 (Gurobi Optimization Inc. , Houston, TX, USA) solver (http://www. gurobi. com) with the Constraint-Based Reconstruction and Analysis (COBRA) toolbox v3.
Network Theory Analysis We extended the analysis of structural robustness of the network based on single reaction deletions by additionally calculating connectivity and centrality indices of metabolites involved in active reactions. Therefore, the COBRA metabolic network (a directed bipartite network) was exported to Python 3  [39]  for further analyses (see Section SI. 3 for further details and GitHub files: bipartite. m, main_network_analysis. py, and network_metrics. py) using the package Networkx  [40]. The general structure of the metabolic model in Python, which consists of two types of nodes (i. e. , metabolites or reactions), was the same than that of the COBRA model. However, we only considered active reactions (i. e. , around a quarter of all reactions) in the Python bipartite network, since we aimed to study the effect of deletion of active reactions on model properties.
Conclusions We have derived the first genome-scale model for the polar diatom Fragilariopsis cylindrus and developed a MATLAB/Python workflow to analyze the sensitivity and robustness of the model. Our methodology is transferable to GSMs of other species, with some modifications in the code, and will help to improve the analysis of cellular metabolism in future studies using systems biology approaches. Specifically, our results highlight the high robustness of the F. cylindrus growth rate and metabolism to an array of cellular perturbations. Strikingly, our in silico predictions confirm that the highly divergent alleles that are unique to F. cylindrus increase metabolic robustness to cellular perturbations and likely contribute to the ecological success of F. cylindrus in polar environments.
L. , B. S. -B. , J. S. , S. G. , A. A. , S. V. H. , A. F. , and J. L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Sentinel North program of Université Laval supported by funding from the Canada First Research Excellence Fund (Excellence postdoctoral fellowship to Michel Lavoie and research grants to Johann Lavaud and Antoine Allard). It was additionally funded by the International Human Frontier.