dataesr/scientific-paragraphs-categorization huggingface dataset:
| Phenotype analysis The physical findings in 19 affected individuals with PHGDH and PSAT1 variations are summarized in Table  1 , and are provided in more detail in Table  S4. Abbreviations: ACMG, American College of Medical Genetics and Genomics; CADD, Combined Annotation Dependent Depletion; LOVD, Leiden Open Variation Database; NA, not applicable; LPATH, likely pathogenic; PATH, pathogenic; PHGDH, phosphoglycerate dehydrogenase; PLP, pyridoxal phosphate; PSAT1, phosphoserine aminotransferase; VUS, variant of uncertain significance. and appeared to correlate with the degree of microcephaly and skin involvement (Tables  1  and S4 ). Structural CNS abnormalities were evaluated by ultrasound, MRI, or at autopsy. Abnormal gyration was found in 9 of 11 cases and variable in expression ranging from lissencephaly to pachygyria and polymicrogyria.
were novel (previously not reported in NLS). Two PSAT1 variants were recurrent in this cohort: one of the previously reported variants (c. 296C>T, p. (Ala99Val)); observed in four unrelated families) and one novel splice acceptor change (c. 870-1G>T; observed in three unrelated families). The novel PHGDH and PSAT1 variants were either absent from the gnomAD database (https://gnomad. broadinstitute. org/) or present at a very low frequency, compatible with the expected carrier frequency for a very rare recessive disease.
ACKNOWLEDGMENTS We are indebted to all families for participating in this study. We would like to acknowledge  Dr. Natasha Laidlew , who initially suggested the diagnosis in one of the cases and provided important phenotypic information, and  Dr. María-Luisa Martínez-Fernández  for the critical management of biosamples in ECEMC Program of Spain. Financial assistance was received in support of the study by grants from the  German Federal Ministry of Education and Research (BMBF)  (GeNeRARe, FKZ:  01GM1519D ) to M. Z. and from the  Institute of Health Carlos III: Convenio ISCIII-ASEREMAC , and  Fundación 1000 sobre Defectos Congénitos, of Spain  to E. B. -S. and I. R. G.
were novel (previously not reported in NLS). Two PSAT1 variants were recurrent in this cohort: one of the previously reported variants (c. 296C>T, p. (Ala99Val)); observed in four unrelated families) and one novel splice acceptor change (c. 870-1G>T; observed in three unrelated families). The novel PHGDH and PSAT1 variants were either absent from the gnomAD database (https://gnomad. broadinstitute. org/) or present at a very low frequency, compatible with the expected carrier frequency for a very rare recessive disease. Abbreviations: AVSD, atrioventricular septal defect; CAL, callosal hypoplasia or agenesis; CH, cerebellar hypoplasia; CLP, cleft lip and cleft palate; CON, joint contractures; CP, cleft palate; EDE, edema/ swelling of hands and/or feet; GEN, genital hypoplasia/anomalies; het, heterozygous; hom, homozygous; HYD, hydrocephalus/enlarged ventricles; ICH, ichthyosis; KID, kidney anomalies, LIS, lissencephaly spectrum; mat, maternal; OFC, occipital frontal circumference; pat, paternal; RDERM, restrictive dermopathy; SD, standard deviation; TOP, termination of pregnancy; TS, targeted sequencing; VSD, ventricular septal defect; WES, whole exome sequencing.
Molecular organisation and phylogenetic analysis of the mitochondrial genomes From our whole genome sequencing project (DDB/ ENA/GenBank accession number QFZF00000000), the sequence of the complete mitochondrial genome (Gen-Bank accession number CM009895) of F. tricinctum strain INRA104 has been retrieved and annotated (FtriMtDNA). In parallel, the mitochondrial genome of F. avenaceum strain FaLH27 (GenBank accession number JQGE01000002. 1), a phylogenetically closely related species, was retrieved from third party sequencing data produced by Lysøe et al. [17]  and also annotated for the purpose of comparison. The mitochondrial genomes of F. avenaceum strain FaLH27 (FaveMtDNA) and F. tricinctum strain INRA 104 have a size of 49,396 bp and 48,506 bp, respectively and the same average GCcontent of 33%.
A second F. avenaceum annotated mitochondrial genome sequence was also found available in the GenBank for strain FaLH03 (49,402 bp, accession number JQGD01000004. 1). It differs from FaveMtDNA strain FaLH27 by only 6 bp in length, and possesses more than 99. 9% of nucleotide identity. To avoid redundancy, the sequence of the MtDNA of the FaLH03 strain was not included in this study. F. tricinctum strain INRA104 and F. avenaceum strain FaLH27 MtDNAs are close in size, FaLH27 MtDNA being 890 bp longer (representing 1. 8% of variation in length scattered along the whole mitochondrial genome), and show high sequence conservation with 98. 9% nucleotide identity. Annotation results of MtDNAs from both F. tricinctum strain INRA104 and F. avenaceum strain FaLH27 are displayed in Fig. 1 , Additional file 1 (GenBank formatted annotated sequence of the F.
tricinctum strain INRA104 MtDNA) and Additional file 2 (GenBank formatted annotated sequence of the F. avenaceum strain FaLH27 MtDNA). For easier comparison of the molecular organization of the F. tricinctum and F. avenaceum MtDNAs with others previously annotated MtDNAs in the fungal kingdom  [20]  and especially in the Fusarium genus, including those reported by Al-Reedy et al. [18] , both sequences were annotated by arbitrarily fixing the first nucleotide as the 5′ end of the rnl gene encoding the large ribosomal subunit RNA. This first nucleotide corresponds to positions 40,818 bp in F. tricinctum INRA 104 MtDNA and 21,516 bp in F. avenaceum FaLH27 one (GenBank accessions CM009895 and JQGE01000002, respectively). F. tricinctum and F. avenaceum MtDNAs possess the same set of mitochondrial structural genes than all the Fusarium mitochondrial genomes reported to date  [18].
Posterior probabilities (Bayesian inference; 1,000,000 generations) are indicated in red. Labels show species names followed by the name and/or number of the strain and the GenBank accession number corresponding to the sequence used. Basidio = Basidiomycota; Asco = Ascomycota; FSSC = Fusarium solani Species Complex; FSAMSC = Fusarium sambucinum Species Complex; FTSC = Fusarium tricinctum Species Complex; FOSC = Fusarium oxysporum Species Complex; FFSC = Fusarium fujikuroi Species Complex (cob i1), and three in cox1 (cox1 i2, cox1 i3 and cox1 i4). Each of these five introns was assumed to represent orthologous sequences on the basis of a shared insertion site in the CDS of the gene as well as a high nucleotide sequence identity (> 99%). In addition to these five conserved introns, F. tricinctum possesses one intron in nad5 (nad5 iFtri1) and one intron in the 5′ part of cox1 (cox1 iFtri1) that are not present in F.
tricinctum strain INRA104 et F. avenaceum strain FaLH27 containing polymorphic microsatellite sequences arranged in a palindromic organisation. The (A) n microsatellite type repetitions are in green, the (T) n microsatellite type repetitions are in blue, separated by spacer sequences (from two to generally four nucleotides) in yellow. SNP and Indels leading to polymorphic regions and located near or within microsatellite sequences are highlighted in red and a part of the mitochondrial Lv-uORF (818 bp) (Fig. 5a,  b  and c , respectively). For each marker, all orthologous sequences of strains and species belonging to the FTSC and available in the GenBank (see Additional file 4 Table  S6  for accession numbers) have been added to the analyses (i. e. , 40 rpb1 sequences and 60 rpb2 sequences in total; see Fig.
tricinctum vs. F. avenaceum at the beginning of the alignment, and an insertion of 39 to 45 nucleotides. These events are responsible for the erosion of the uORF in the F. tricinctum strains. These Indels are accompanied by several flanking interspecific SNPs. Strikingly, the large F. tricinctum inserted sequence is bordered by a copy of a polymorphic To explore the origin and evolution history of each of these mitochondrial introns, orthologous sequences available in the GenBank have been compiled (see Additional file 4 Table  S6  for accession numbers). Intron sequences were considered orthologous when they were members of the same Position class (PCl), as previously defined  [22] : each PCl contains intron sequences possessing both the highest percentage of nucleotide identity and the same location in the exonic sequence (CDS).
Results are summarized in Table  2. Among the nine introns reported in F. tricinctum and F. avenaceum mitochondrial genes, only nad5 iFtri1 did not possess any orthologous sequences in other Fusarium Fig. 5  Unrooted phylogenetic trees of strains belonging to the Fusarium tricinctum species complex (FTSC), including strains of the species F. tricinctum, F. avenaceum, F. acuminatum, F. arthrosporioides, F. torulosun, F. flocciferum, and F. petersiae. Trees are based on the nuclear markers rpb1 partial sequences (798 bp) (a), rpb2 partial sequence (762 bp) (b) or a part of the mitochondrial Lv-uORF (818 bp) (c). Posterior probabilities (Bayesian inference; 1,000,000 generations) are indicated in red. Species name is followed by the name of the strain and accession numbers when sequences were retrieved from Genbank species.
Discussion The mitochondrial genomes of F. tricinctum strain INRA104 (48. 5 kb) and F. avenaceum strain FaLH27 (49. 4 kb) were found highly similar in size and sequence Fig. 6  Unrooted phylogenetic trees of Fusarium spp. strains based on the sequences of orthologous mitochondrial introns of F. tricinctum and/or F. avenaceum cox2 i1 (a), nad4L iFave1 (b), cob i1 (c), cox1 iFave1 (d), cox1 iFtri1 (e), cox1 i2 (f), cox1 i3 (g), cox1 i4 (h). Posterior probabilities (Bayesian inference; 1,000,000 generations) are indicated in red. Labels show species names followed by the name and/or number of the strain and the GenBank accession number corresponding to the sequence used. Basidio = Basidiomycota; Asco = Ascomycota; FSSC = Fusarium solani Species Complex; FSAMSC = Fusarium sambucinum Species Complex; FTSC = Fusarium tricinctum Species Complex; FOSC = Fusarium oxysporum Species Complex; FFSC = Fusarium fujikuroi Species Complex.
When compared with other mitochondrial genomes available in the whole monophyletic genus Fusarium, the sizes of F. tricinctum and F. avenaceum MtDNAs are close to the sizes reported for 61 strains of the FOSC, ranging between 38. 7 kb in F. oxysporum f. sp. melonis and 53. 4 kb in F. oxysporum f. sp. lycopersici  [19] , as well as for F. verticillioides (53. 7 kb) of the FFSC  [18]. Other species have larger MtDNAs, such as F. solani (63 kb,  [18] ) a member of the FSSC located at the base of the phylogenetic tree of the Fusarium genus  [12]. Species of the FSAMSC have considerably larger MtDNAs  [31, 32] , in the range of 93. 4 kp for Fusarium gerlachii (GenBank accession number KM486533) to 103. 8 kb for F. culmorum (GenBank Accession number KP827647). As previously reported in numerous fungal genera  [22] , the mitochondrial genome size variations observed in the Fusarium genus does not involve the highly conserved set of mitochondrial genes and is not related to the phylogenetic distances separating these Fusarium species.
Mitochondrial genome annotation The complete mitochondrial genome sequences of F. tricinctum strain INRA104 and F. avenaceum strain FaLH27 were previously obtained  [16, 17]. Their full annotation was performed and manually curated after comparison with sequences in GenBank and EMBL databases using BLASTn and BLASTp  [43]. To identify protein coding genes, MtDNA sequences were searched for open reading frames (ORF) using the ORFfinder software (https://www. ncbi. nlm. nih. gov/orffinder/). GeSeq  [44]  was further used for the annotation of F. tricinctum INRA104 and F. avenaceum FaLH27 guided by the mitochondrial genomes of F. verticillioides, F. solani, and F. graminearum as annotated in MiTo-Fun (http://mitofun. biol. uoa. gr/index. html). Predictions were then crossed and confirmed by multi-alignments of nucleic acids and predicted protein sequences performed using Clustal Omega  [45]  and MUSCLE  [46].
Mitochondrial transfer RNA genes (tRNAs) were located using the tRNA Scan-SE  [47]. Mitochondrial maps and corresponding Genbank files were generated using ApE (http://jorgensen. biology. utah. edu/wayned/ape/) and SnapGene software (from GSL Biotech; available at snapgene. com). Introns were first identified by Blast analysis. For each intron, its borders (5′ and 3′ ends) were established after deletion of the highly conserved exonic sequences of each mitochondrial CDS then confirmed by alignment with orthologous sequences. In the same way, the assignment to group I and subgroup were deduced from those given to their orthologous sequences, mainly in Al-Reedy et al. [18]  or Ferandon et al. [22]  for cox1 introns.
, Madison, Wis, USA) in a final volume of 50 μL buffer. PCR conditions were: 95 °C for 5 min; 35 x (95 °C for 30 s; primer hybridization at the indicated temperature (Additional file 3: Table  S4 ) for 30 s; elongation at 72 °C for 90 to 120 s); 72 °C for 5 min. DNA sequencing and sequence analysis PCR products were sequenced, according to the Sanger method, on both strands by Genewiz (UK), with both forward and reverse primers used to obtain each PCR product. GenBank Accession numbers of the sequenced PCR products are indicated in Additional file 3: Table  S3. Sequence comparisons with GenBank and EMBL databases were performed with BLAST  [43]. Multi-alignments of nucleic acids were performed with MUSCLE 3. 8. 31 [46]  and subsequently cleaned with Gblocks 0. 91b  [49]. Phylogenetic trees were computed using MrBayes 3.
Funding This work was supported by grants from the  National key research and development program of China  (Program number  2016YFD0501207 ) and the  Department of Microbiology of the Food Chain (MICA)  of the  French National Institute for Agricultural Research (INRA). CG PhD fellowship is funded by  Region Nouvelle-Aquitaine  and the  INRAE Department SPE. These funding had no role in the design of the study, collection, analysis and interpretation of data or in writing the manuscript.
The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monitoring of SOC dynamics due to land use change and climate change. However, current estimates of SOC stock and its spatial distribution have large uncertainties. In this study, we test whether we can improve the accuracy of the three existing SOC maps of France obtained at national (IGCS), continental (LUCAS), and global (SoilGrids) scales using statistical model averaging approaches. Soil data from the French Soil Monitoring Network (RMQS) were used to calibrate and evaluate five model averaging approaches, i.
e. , Granger-Ramanathan, Bias-corrected Variance Weighted (BC-VW), Bayesian Modelling Averaging, Cubist and Residual-based Cubist. Cross-validation showed that with a calibration size larger than 100 observations, the five model averaging approaches performed better than individual SOC maps. The BC-VW approach performed best and is recommended for model averaging. Our results show that 200 calibration observations were an acceptable calibration strategy for model averaging in France, showing that a fairly small number of spatially stratified observations (sampling density of 1 sample per 2,500 km 2 ) provides sufficient calibration data. We also tested the use of model averaging in data-poor situations by reproducing national SOC maps using various sized subsets of the IGCS dataset for model calibration.
, 2013)  for Europe and AfSIS  (Hengl et al. , 2015)  for Africa. As a result, there are often multiple maps available for a given soil property in a given area produced using various soil databases, environmental covariates, and DSM methods. Users may have multiple maps of the same property with different predictions and different map accuracy which may lead to confusion regarding which map should be used or whether the maps could or should be combined. It is possible to select the most suitable soil property map for a specific region, when the map accuracy can be evaluated using an independent validation dataset. When deciding to combine maps, the hypothesis is that the information provided by the maps is complementary and that a more accurate map may be obtained by merging the input maps using model averaging approaches  (Caubet et al.
, 2019). The model averaging option needs an independent validation dataset and independent calibration data to train the model averaging algorithm. Previous studies showed the potential of model averaging in improving the accuracy of soil property maps of pH, soil texture, and available water capacity  (Malone et al. , 2014; Padarian et al. , 2014; Clifford and Guo, 2015; Román Dobarco et al. , 2017; Caubet et al. , 2019). The choice between selecting a single map and combining multiple maps is not trivial, and many countries need to make this choice because of the increasing number of different prediction maps of the same soil property. It is particularly relevant to data-poor countries that may have very few or even no data to derive reliable country-based maps, and that could benefit from collecting a limited number of calibration samples to merge the national map with other existing products using model averaging.
French national soil organic carbon maps Numerous maps have been generated for France following the GlobalSoilMap specifications. The most recent product  (Mulder et al. , 2016a ) used all available point data for France, both from the French Soil Mapping and Inventory Program (Inventaire, Gestion et Conservation des Sols, IGCS) and an systematic grid aiming at monitoring French soil properties (RMQS). More details about these two datasets can be found in the study of  Mulder et al. (2016a). For this study, we used the same GlobalSoilMap approach as  Mulder et al. (2016a) , but we set aside the RMQS grid to be used as an independent dataset for calibrating the model averaging algorithms and evaluating map accuracy (see Sections 2. 3 and 2. 4). A total of 30,381 soil profiles from the IGCS dataset were used to generate SOC maps at the first three GlobalSoilMap depth intervals  (0-5, 5-15, 15-30 cm).
The IGCS dataset is a compilation of soil profiles from many programs that mostly focused on agricultural soils. As a result, the soil profile density is high in some regions (Fig. 1 ), whereas it is low in other regions; some land uses are over-or under-represented in the calibration dataset. SOC contents at the GlobalSoilMap depth intervals were obtained by applying equal area quadratic splines  (Bishop et al. , 1999; Malone et al. , 2009)  to soil profile data, as outlined in  Mulder et al. (2016b). Spatially exhaustive covariates, including climate zones and meteorological data, vegetation, topography, geology, soils, and land management, were resampled to 90 m resolution. Details about these environmental covariates are given in  Mulder et al. (2016a). In this study, the national SOC map (named IGCS SOC map hereafter) for the topsoil (0-20 cm) was calculated from SOC maps of 0-5, 5-15, and 15-30 cm by a weighted averaging approach, where the weights are proportional to the layer thickness (Fig.
Continental and global scale soil organic carbon maps In addition to the aforementioned national SOC map, we also obtained SOC maps for France from continental (LUCAS) and global (SoilGrids) soil map products. The LUCAS SOC map (Fig. 2b ) contains SOC predictions for the topsoil (0-20 cm) at 1 km resolution for Europe  (Aksoy et al. , 2016). A total of 23,835 soil samples were used for model calibration. These soil samples were collected from LUCAS (19,860 samples), BioSoil (3,379 plots from forest soil), and SoilTrEC (387 samples from local soil data from six different critical zone observatories in Europe). From these datasets, about 3,500 sites were located in France. A regression kriging model was fitted to generate a SOC map using observed SOC content and 15 environmental covariates. The SoilGrids SOC map (https://soilgrids.
org, v0. 5. 3, Fig. 2c ) was extracted from the study of  Hengl et al. (2017) , in which SOC was mapped at seven standard depths  (0, 5, 15, 30, 60, 100, and 200 cm)  at a resolution of 250 m for the globe. These SOC maps were based on about 150,000 soil profiles along with 158 remote sensing-based soil covariates. Maps were produced by fitting an ensemble prediction from random forest and gradient boosting trees. From the 150,000 soil profiles, nearly 3,000 were located in mainland France, mainly originating from the LUCAS database. For this work, the topsoil SOC map was calculated from SoilGrids SOC maps at 0, 5, 15, and 30 cm depth using trapezoidal numerical integration  (Hengl et al. , 2017). The LUCAS and SoilGrids SOC maps were resampled to 90 m using bilinear interpolation and reprojected to the Lambert 93 coordinate system to match these with the national SOC map.
Independent soil data for model averaging calibration and SOC map validation To evaluate the accuracy of the input and merged maps, an independent validation dataset and an independent dataset for calibration of the model averaging algorithm were needed. These datasets were derived from the RMQS French systematic grid, which covers different soil, climate, relief, and land cover conditions (Fig. 1 ). The RMQS dataset is a 16 km × 16 km square grid where sampling sites are at the centre of each grid cell, covering mainland France  (Jolivet et al. , 2006). For each site, 25 individual core samples were collected by a hand auger and mixed into a composite sample, both for 0-30 cm and 30-50 cm depth intervals. For more detailed information about the soil sampling design and laboratory analyses, refer to  Martin et al.
(2009). Because there were no SOC measurements for a depth of 0-20 cm for the RMQS sites, we calculated these values depending on land use: 1) for most agricultural soils, SOC concentration decreases at a small rate with depth in the topsoil because of ploughing; thus, SOC content at 0-20 cm is close to that of 0-30 cm  (Arrouays et al. , 2001). We therefore used SOC at 0-30 cm to represent the SOC at 0-20 cm for RMQS sites under agricultural soils; 2) for natural soils (grassland and forest), SOC usually decreases with depth in the topsoil. Therefore, we first calculated SOC at 0-20 cm and at 0-30 cm by equal area quadratic splines using 5785 grassland and forest soil profiles from the IGCS dataset. We then fitted a linear model between SOC at 0-20 cm and SOC at 0-30 cm (SOC0-20 cm =1. 04×SOC0-30 cm +0.
Generic framework for model averaging Fig. 3  shows the generic framework for model averaging, which includes four steps. We first explain the procedure used for selecting the calibration and validation subsets from the RMQS dataset. To obtain spatially representative calibration and validation datasets, equal-size clustering (iterative nearest neighbour approach,  Monlong, 2018)  was applied to the RMQS sites (Step 1), which resulted in spatially compact clusters. This was done for five cluster sample sizes  (4, 10, 20, 50, and 100). Note that the cluster sample size is only approximately the same for all clusters because the total number of observations  (i. e. , 1996)  is not always a multiple of the cluster sample size. Fig. 4  shows the spatial distribution of the clusters. In Step 2, a k-fold cross-validation framework  (k = 4, 10, 20, 50, 100)  was used to separate a calibration set by randomly allocating one observation per cluster to each fold.