dataesr/scientific-paragraphs-categorization huggingface dataset:
In terms of performances, as being less sophisticated (using a simplified 3D rendering, while Morse has all 3D capabilities) and being agnostic in terms of programming languages (i. e. , allowing fast C/C++ implementation of user modules, whereas Morse is limited to Python scripts) the VirtualEnaction platform is a priori expected to be more efficient in terms of CPU usage. However, with a larger humanpower all what has been developed within VirtualEnaction could have been developed in Morse. The main application regarding neuroscience is to test cognitive computational models in realistic conditions. Very simply, a behavioral experiment is performed on an animal model or on humans, usually with a training phase, the measurement phase and the data analysis. In order to formalize the obtained result a computational model is proposed that explains the data, and may also have prediction regarding other falsifiable future experiments.
Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid-to high-latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different data sets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded data sets of soil organic carbon content.
, 2003; Krinner et al. , 2005). Bioclimatic limits consist of direct limiting factors (e. g. , minimum temperature for survival) and indirect limitations that control primary productivity and in turn the competitive ability of a PFT (e. g. , optimal temperature for photosynthesis, various temperature and moisture phenological controls of leaf-out and senescence). PFTs with a better tolerance to extreme climate conditions and higher growth efficiency during the growing season are more competitive than others and their distribution will therefore expand. The competence of any PFT is dependent on the underlying plant traits that define this PFT. The traits for a given PFT are fixed in most DGVMs but can also be variable within PFTs based on trait-climate relationships derived from a trait database.
ORCHIDEE is the terrestrial surface component of the Institut Pierre Simon Laplace (IPSL) Earth system model. Since the first model description by  Krinner et al. (2005) , the representation of existing processes has been improved and new processes have been implemented, such as a physically based multi-layer soil hydrology scheme  (de Rosnay et al. , 2002)  and a scheme describing soil freezing and its effects on root-zone soil moisture and soil thermodynamics  (Gouttevin et al. , 2012). These new parameterizations have been evaluated for static runs in which the geographical distribution of PFTs is specified based on observed satellite land-cover information. Yet, their influence on the simulated PFT distribution when the vegetation dynamics module is activated has not been addressed. The original vegetation dynamics module in ORCHIDEE (hereafter ORC-VD) described by  Krinner et al.
The evaluation methodology developed here could be used for other DGVMs as well, and is thus of general interest for the DGVM modeling community. We present a new parameterization of vegetation dynamics in the ORCHIDEE high-latitude version (ORC-HL) described by  Gouttevin et al. (2012) , with modifications to the equations and parameters describing tree mortality, thermal constraints and a calibration of photosynthesis parameters (v cmax /j max ) (Sect. 2. 2). The results of the original module (ORC-HL-OVD) and of the new parameterization (ORC-HL-NVD) are evaluated  (Sects. 4, 5). Because the biogeochemical and physical processes that characterize high latitudes interact in a complex way with the processes that control vegetation structure, in Sect. 6 we performed and analyzed factorial model simulations changing one process or driver at a time to isolate their impacts on vegetation distribution.
ORCHIDEE high latitude ORCHIDEE consists of two main modules: SECHIBA (the surface-vegetation-atmosphere transfer scheme) which simulates energy and water exchanges between the atmosphere and land surface at a half-hourly time step, as well as photosynthesis based on enzyme kinetics  (Ducoudré et al. ,   T min,crit : minimum temperature limitation ( • C), below which the mortality rate will increase as Eq. (  3 ). k BG : maximum background mortality rate (yr -1 ) for tree PFTs. v cmax,opt : optimal maximum rubisco-limited potential photosynthetic capacity (µmol m -2 s -1 ). j max,opt : maximum rate of photosynthetic electron transport (µmol m -2 s -1 ). a crit : critical leaf age for leaf senescence (days); the dependence of v cmax and j max on leaf age for PFTs 4 and 7 was eliminated as described in Sect.
2. 2. 3. 1993; de Rosnay and Polcher, 1998), and STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems;  Viovy, 1997)  which simulates carbon dynamics at a daily time step, including carbon allocation, biomass accumulation, litter and soil carbon decomposition, and phenology. STOMATE includes a dynamic vegetation module with equations adapted from the LPJ model  (Sitch et al. , 2003)  as described by  Krinner et al. (2005). ORC-HL is an evolution of ORCHIDEE including additional high-latitude processes, described by  Gouttevin et al. (2012). In particular, the simple two layer soil hydrology  (Ducoudré et al. , 1993)  was replaced by an 11-layer diffusion scheme  (de Rosnay et al. , 2002) , which describes water infiltration and diffusion through soil in a physically based way.
Simulation protocol Six different runs with ORC-HL (Table  2 ) were performed to test the impact of the new dynamic vegetation parameterizations and parameter calibrations. Since the modifications in the vegetation dynamics module were mainly for temperate and boreal PFTs, the simulation domain is the Northern Hemisphere from 20 to 90 • N. All runs were conducted at 2 • resolution. The climate forcing files were from the 6-hourly CRU-NCEP data set (http://dods. extra. cea. fr/store/p529viov/ cruncep/V4_1901_2012/readme. htm), resampled from their  Each simulation was preceded by a spin-up from bare ground (i. e. , fractional cover of PFT 1 equals to 1 everywhere). For the standard run with the new vegetation dynamics parameterizations (NEW), in spin-up, ORC-HL-NVD was forced by repeatedly using CRU-NCEP 1901-1920 climate data and constant preindustrial CO 2 concentration (285 ppm) for 250 years.
Then the soil carbon submodel was driven by the previous outputs for 1000 years for the soil carbon pools to reach equilibrium; this was followed by another 50 years of ORC-HL-NVD to complete the spin-up. Each transient simulation from 1850 to 2010 was started from the last year of the spin-up, forced by historical CRU-NCEP climate and rising CO 2 concentration. No climate data were available before 1901, so for that period randomly selected years between 1901 and 1920 were used. The OLD run used the original vegetation dynamics equations from  Krinner et al. (2005)  in the ORC-HL version so that comparing NEW and OLD allows us to evaluate the improvements listed. The other four runs (EXP1-3, STAT) were similar to NEW except for one different setting for each run (Table  2 ). In EXP1, we deactivated soil freezing to test its impact on vegetation distribution.
(2011). For Siberia, two additional regional land-cover maps were used, the PFT map of Siberia at 1 km scale from  Ottlé et al. (2013)  based on the GlobCover2005 product  (Bicheron et al. , 2006) , hereafter OSIB, and the Russian land-cover data set produced by the International Institute for Applied Systems Analysis  (Schepaschenko et al. , 2011) , hereafter IIASA, which was converted into a PFT map using the cross-walking method of  Poulter et al. (2011). Along with ESA, GLC and VCF, the five land-cover products were used to evaluate the model skill at simulating the vegetation distribution across Siberia. The PFT maps were aggregated at 2 • × 2 • , matching the resolution run by ORCHIDEE in this study. Figure  2  displays an RGB composite-color map of the vegetation fractional cover partitioned between broadleaf (including evergreen and deciduous, red), needleleaf evergreen (green), and needleleaf deciduous (blue) trees, from the five PFT maps.
Simulated GPP was evaluated using the data-derived field obtained from FLUXNET data, satellite fAPAR and gridded climate and land-cover data using a model tree ensemble  (Jung et al. , 2011) , hereafter MTE. A recent forest carbon density map  (Thurner et al. , 2014)  for Northern Hemisphere boreal and temperate forests (30-80 • N), derived from radar remote sensing of growing-stock volume (GSV), was used to evaluate modeled forest biomass. For soil carbon stocks, the simulated soil carbon density was compared with the Harmonized World Soil Database (HWSD; 0-1 m depth, FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and the Northern Circumpolar Soil Carbon Database (NCSCD;  Hugelius et al. , 2013). Since the model results for soil carbon are not fully comparable to NCSCD due to lack of peatland carbon accumulation and cryoturbation processes in ORC-HL, metrics were not applied to soil carbon for establishing a model score.
Metrics for GPP and forest biomass evaluation GPP and forest biomass were evaluated using gridded observational data containing uncertainty estimates. The metric for model performances was defined as S G,c or S B,c = σ O |X c,M -X c,O | , ( 12 ) where S G,c /S B,c is model skill at simulating GPP or forest biomass for grid cell c; X c,M is GPP or forest biomass for grid cell c, simulated by model; X c,O is GPP or forest biomass for grid cell c, from observation; and σ O is the standard deviation of the observation. In grid cells where |X c,M -X c,O | < σ O , indicating a model-data difference within the uncertainty of the observational data, S G,c or S B,c is set to 1. The S G,c or S B,c of each grid cell were averaged over the Northern Hemisphere to get an overall score (S G or S B ).
S4  in Supplement). These showed robust results for S V. The forest areas simulated by the dynamic simulations and estimated from the land-cover products were aggregated to country level and compared with independent forest area from national forest inventories  (Pan et al. , 2011)  (Table  4 ). In OLD, forest areas are systematically overestimated, especially for Asian Russia and Canada. The bias is decreased in NEW, for which most of the differences are less than 30 % except for an overestimation in Canada (50 %). This overestimation is, however, within the differences between the three land-cover products and the forest inventory data at country scale (Table  4 ). Forest areas estimated by VCF are systematically lower than inventory data, due to the difference in forest definition mentioned previously.
Case study for Siberia, using regional land-cover data sets For Siberia, the same metrics were calculated based on five observational data sets (ESA, GLC, VCF, OSIB and IIASA). As shown in Fig. 9a , the average β for NEW versus all data sets is significantly reduced compared to OLD along all longitudes, with a larger reduction (improvement) in central Siberia and the most eastern part of Russia. The average values of β over Siberia for NEW versus ESA, GLC, VCF, OSIB and IIASA are 0. 59, 0. 46, 0. 38, 0. 35 and 0. 41, respectively, equivalent to 0, 10, 51, 45 and 26 % reduction compared with OLD, respectively. The average β between different data sets is 0. 37, with larger β between ESA and VCF (0. 50) and between GLC and VCF (0. 47), and smaller β for GLC and IIASA (0. 23), VCF and OSIB (0.
Code availability The ORCHIDEE model used as a starting point in this study is ORCHIDEE rev1322. The source code can be obtained at http://forge. ipsl. jussieu. fr/orchidee/browser/ branches/ORCHIDEE-MICT/ORCHIDEE?rev=1322. A detailed documentation and the forcing data needed to drive ORCHIDEE can be found at http://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation and http://forge. ipsl. jussieu. fr/orchidee/wiki/Forcings. ORC-HL-NVD is derived from rev1322 with the modifications presented in Sect. 2. 2, the source code of which can be obtained upon request (http: //labex. ipsl. fr/orchidee/index. php/contact). The modifications of ORC-HL-NVD from rev1322 are also implemented in ORCHIDEE standard version (trunk), recorded as the difference between rev2672 (source code: http://forge. ipsl.
Two approaches were conducted to assemble the produced 1. 71 ˆ10 8 paired-end reads (Genbank accession number SRS533250). On the one hand, de novo assembly using the Newbler 2. 8 program  [38]  was carried out with the following parameters: 90% minimal overlap identity and 20 to 25 bases of minimal overlap length. On the other hand, all the reads were mapped on the AcMNPV-C6 (C6) genome (accession number NC_001623) using the bwa software  [39]. The second approach allowed bridging of the nine homologous repeat regions (hrs), which could not be resolved with the de novo assembly strategy. We manually compared with Geneious 8 the large contigs from the de novo assembly to the sequence from the mapping, to order the contigs, complete the misassembled sequence and finally generate the WP10 consensus genome (accession number KM609482).
Mutation Detection and Analyses To detect polymorphism within the WP10 genome population, 1. 50 ˆ10 8 paired-end reads, with at least 100 consecutive bases of Phred quality score above 30 (99. 9% base call accuracy, bases below this threshold were trimmed), were re-mapped on the AcMNPV-WP10 consensus sequence using the bowtie 2 software  [42]. Single nucleotide polymorphisms (SNPs) and short insertions and deletions (indels) were detected by using SAMtools mpileup  [43]. We previously showed that a genome represented by a single read could be PCR amplified and Sanger sequenced  [34]. However, to take into account possible experimental errors linked to Illumina sequencing, we set a mutation frequency above the error rate (>10 ´3 for all the possible changes, or >2. 5 ˆ10 ´4 per nucleotide) as threshold to the genuine detection of variation.
Geneious (versions 6. 1. 7 and 8. 0. 5) was used to visually validate variations. The repartitions of the SNPs in different frequency groups was assessed by k-means clustering  [44]  with the R function "kmeans"  [45]. K-means computations, whereby each SNP was addressed to a cluster, were iterated 100 times with and without shuffling of the reads between iterations. The final assignment of a given SNP to a particular cluster corresponds to the consensus of the individual k-means classifications. To represent the k-means clusters, violin plots were drawn with the "vioplot" R package  [46] , combining the basic summary statistics inherent to box plots with the information available from local density estimates. To discriminate between positions evolving under non-random versus neutral processes, Tajima's D statistics  [47]  have been calculated at each position of the genome using a homemade script (available upon request).
Nucleotide Variation in the AcMNPV-WP10 Genome Population To study in depth the genetic variation present within the WP10 genome population, we mapped 1. 50 ˆ10 8 paired-end reads with quality scores >30 on the WP10 consensus sequence. The mean sequence coverage for these analyses was 124,221X allowing at each position the significant detection of mutations present at a frequency higher than 2. 5 ˆ10 ´4, above the sequencing error rate for any nucleotide. The accuracy and quality of the dataset was previously attested by PCR validation of the extremely rare insertion of transposable elements detected in single reads  [34]. We detected 3243 SNPs with a frequency higher than 2. 5 ˆ10 ´4 on our 133,926 bp long sequence, found in reads positioned in both orientations of genome and supported by both reads of a pair.
When considering the SNPs from cluster 1, which represent the vast majority (78%) of all SNPs, 52% are non-synonymous but are in extremely low frequency in the population (0. 12%), and thus appear functionally negligible. To further assess under which process our baculovirus population evolved, we ran a homemade script estimating Tajima's D for every positions of the genome. The estimated mean Tajima's D was ´0. 55, which represents a population evolving close to the mutation drift equilibrium  [47]. Deleterious mutations are usually eliminated and thus less likely to be sampled in a given population than neutral mutations. In the case of baculoviruses, cells are commonly multiply infected  [19]  and the virions transmitted in groups within the occlusion bodies (Figure  1 ). This reduces the purifying selection on single genomes within the population because deleterious mutations can be maintained through complementation  [23, 24].
In contrast, if they are more distant, they come from a genome where they are closer, so there is a deletion between them (Figure  2 ). Unfortunately, we could not use this strategy to find large insertions because the 260 bp insert produced using Illumina strategy is shorter than the 2 ˆ151 bp reads. Few large insertions have however been detected in this dataset by another strategy based on the analysis of chimeric reads  [34]. In contrast, the strategy was quite efficient in identifying large deletions, outlined by pairs of paired-end reads more distantly mapped than expected. The 5% reads farthest to the mean distance were mapped to determine the location and repartition of the biggest deletions in the genome (Figure  5 ). We found big deletions across the entire genome. However some regions appeared more prone to deletions as we obtained coverage spikes in homologous regions, i.
e. , the hrs 1, 2, 3, 4b and 5. distant, they come from a genome where they are closer, so there is a deletion between them (Figure  2 ). Unfortunately, we could not use this strategy to find large insertions because the 260 bp insert produced using Illumina strategy is shorter than the 2 × 151 bp reads. Few large insertions have however been detected in this dataset by another strategy based on the analysis of chimeric reads  [34]. In contrast, the strategy was quite efficient in identifying large deletions, outlined by pairs of paired-end reads more distantly mapped than expected. The 5% reads farthest to the mean distance were mapped to determine the location and repartition of the biggest deletions in the genome (Figure  5 ). We found big deletions across the entire genome. However some regions appeared more prone to deletions as we obtained coverage spikes in homologous regions, i.
Acknowledgments This work has been supported by the  European Research Council  (starting grant:  Genovir 206205 ) and the  CEA-Genoscope  (project  AP08/09 # 19 ). This work has also been carried out with the technical support of the Genomic and  Microscopy Departments at François Rabelais's University. We thank  Julien Gaillard  for the electronic microscopy help and  Julie Poulain  for the sequencing details.
All genes are taken into account and all possible "gene combinations" are compared. Then, the standard deviation values of each ΔCt set of each pair of genes is determined. Reference genes are ranked according to the arithmetic mean of standard deviation values, which must be as low as possible to be the most stable. Box-and-whiskers charts can also be generated to show the distribution of ΔCt values in each pair of genes in the samples and allow us to compare ΔCt variability of each reference gene against all others. The bar is a line which represents the median (middle of dataset). The 75 and 25 percentiles represent the upper and lower limits of the boxes, and the whiskers refer to the highest and lowest ΔCt values among the samples excluding outsiders. The HKG with the lowest variability is the most stable  [21].
BestKeeper Analysis. Unlike ΔCt comparative method and GeNorm software, BestKeeper considers not only "intergene" relation, but takes also into account the "intragene" variation, which assesses samples variation in the same gene. In this approach, the ideal HKGs are expected to have a stable expression in the same tissue or sample  [12]. For this, standard deviation (SD: ±Ct) and covariance (CV: %Ct) are the two important values to assess HKG stability. For a studied reference gene, SD of Ct values must be as low as possible and must not be >1; otherwise it will be excluded. A second step consists of studying the correlation with BestKeeper index and selecting the most correlated reference gene to this index. The tool simulates an ideal HKG, or the "best" HKG, for which it calculates, for each sample, a BestKeeper index defined as the geometric mean of Ct values of all HKGs for this sample, as shown in the following formula: 𝐵𝑒𝑠𝑡𝐾𝑒𝑒𝑝𝑒𝑟 𝐼𝑛𝑑𝑒𝑥 = 𝑧 √CP1 × CP2 × CP3 × ⋅ ⋅ ⋅ × CP𝑧; (4) see  [23].
Then it compares this index to each reference gene by a pairwise correlation and regression analyses and calculates a coefficient of correlation () and the probability. "" should be as close to 1 as possible and "" as low as possible. The BestKeeper index can also be compared to a further ten genes, for the same samples, to decide whether they are differentially expressed. Moreover, this algorithm calculates invariances, InVar (±Ct), of each sample to validate its stability; these values must be <3-fold the least one. A freely Excel based spreadsheet software exists and allows us to calculate automatically these values to make the choice of the most stable HKGs easier. It has been established that sample size has a minimal effect on this tool  [23] , http://www. gene-quantification. de/bestkeeper.
165 for both of them, which is around 0. 15 as suggested by the software) (Figures 4(b1) and 4(b2)) and two reference genes for ASCs ( 2/3 = 0. 134 below 0. 15) (Figure  4 (b3)). Finally, the intergene analysis by GeNorm recommended SDHA/ACTB and UBC to study GSCs, TBP/ALAS1 and SDHA for dGSCs, and GAPDH/B2M or SDHA/HPRT1 to study ASCs (Table  4 ).
BestKeeper Analysis. Raw Ct values of each group (GSCs ( = 8), dGSCs ( = 8), and ASCs ( = 6)) were uploaded to BestKeeper software in the form of an Excel spreadsheet. Firstly, BestKeeper excluded candidate HKGs with highest standard deviation (SD) and the covariance (CV). For GSCs, RPII and ALAS1 had the highest SD (1. 66, 1. 31) and CV (6. 99, 5. 18), respectively, and were therefore excluded from analysis. Using pairwise correlation analysis and regression analysis, BestKeeper then compared the intergene relations of remaining candidate genes based on its index. UBC, HPRT1, TBP, and RPS18 had a low correlation with BestKeeper index ( = 0. 685, -0. 050, 0. 622, and 0. 684, resp. ) and thus were also excluded from analysis, even though they may have low SD and CV like UBC, HPRT1, and TBP (Figure  5 (a)).
NormFinder Analysis. Normalized values of the three groups were introduced into NormFinder software as recommended by the authors  [11]. The algorithm is presented as a free complement in Excel program. The analysis calculates the value of stability  of each reference gene and ranks them from the most stable with the lowest  to the least stable. For intragroup analysis, NormFinder ranked SDHA, B2M, and ACTB as the most stable ( = 0. 248, 0. 300, and 0. 317, resp. ) for GSCs. TBP, SDHA, and ALAS1 were the most stable for dGSC ( = 0. 222, 0. 234, and 0. 335, resp. ), and SDHA, ΔCt variability with pairwise comparisons of the complete set of candidate housekeeping genes shown as boxes and whiskers: medians (lines), 25th and 75th percentiles (Boxes). Genes with lowest variability were the most stable.