dataesr/scientific-paragraphs-categorization huggingface dataset:
One of the mutations was synonymous, two of them produced amino acid changes predicted as neutral by PROVEAN software and three were predicted as deleterious. The TILLING mutants were subjected to three rounds of self-pollination selecting for the five non-synonymous mutations in CmVPS41 to get homozygous plants and to prevent the presence of contaminating mutations in other parts of the genome related to the phenotype. After challenging plants of the five families carrying non-synonymous substitutions with CMV-LS, all of them showed infected plants, indicating that none of the mutations conferred total resistance to the virus. However, mutant plants carrying the mutation D360N showed milder symptoms than CharMono, the susceptible control parental line of the TILLING population. Moreover, after 21 dpi, the plant upper leaves of the mutant D360N seemed to recover, unlike PS and CharMono susceptible control plants.
Identification of a mutation associated with the resistant phenotype by the analysis of a diverse collection of melon accessions. Out of the three non-synonymous changes between PS and SC found in exons 4, 5 and 13 of CmVPS41 (Fig. 2 ), only the substitution L348R at exon 5 is predicted as deleterious for the protein by PROVEAN and MutPred software (Supplementary Table  S5 ), whereas the substitutions P262A and S620P should be neutral. This suggests that the change L348R could be the causal polymorphism of the resistant phenotype given by the SC allele of CmVPS41. To confirm this hypothesis, we genotyped CmVPS41 in a collection of 52 diverse melon accessions  35, 36  with molecular markers developed to detect the three SNPs (Supplementary Table  S1 ) and studied the correlation of each mutation with the susceptibility or resistance to CMV-LS.
A search in the PLAZA 3. 0 database (http://bioinformatics. psb. ugent. be/plaza/), which contains data of plant genomes that have been fully sequenced, shows that CmVPS41 belongs to the orthologue family ORTHO03D004047. This gene is present in 29 species out of 31 included in PLAZA 3. 0 and in 22 of these species the gene is present as a single copy (Supplementary Table  S7 ), as it is the case in C. melo. In these species, VPS41 might also have a role in CMV transport in the susceptible species. The coded protein, contains a Clathrine domain, characteristic of many proteins involved in vacuolar maintenance and protein sorting  38  , and a WD-40 domain, presumably involved in protein-protein interaction. The causal polymorphism L348R is outside both of them and is located in a highly conserved region of the gene (Fig.
After PCR amplification, fragments were visualized with LICOR IR2 sequencer (Li-corInc, Lincon, New England, USA) as described previously  60. For SNPs genotyping, three methods were used: Sanger sequencing, Taqman probes and CAPS assay (SNPs/sanger, SNPs/Taqman and SNPs/CAPS). For SNPs/Sanger and Indel/Sanger sequencing markers, primers flanking the polymorphism were designed also with Primer3. PCR amplified fragments were sequenced by capillary electrophoresis at Macrogen (Macrogen Europe, Amsterdam, The Netherlands). Sequences were analyzed with Sequencher ® version 5. 0 sequence analysis software (Gene Codes Corporation, Ann Arbor, MI USA http:// www. genecodes. com) and aligned with PS and SC sequences in order to determine the SNPs allele. Probes for SNPs/Taqman markers were ordered as Custom TaqMan ® SNP Genotyping Assays (Applied Biosystems, Foster City, CA, USA) by submitting the SNP and flanking sequence.
Genotyping was performed using a LightCycler 480 instrument (Roche Applied Science, IN, USA), mixing Taqman probes with LightCycler 480 Probe Master (Roche Applied Science, IN, USA) following manufacturer's instructions. Genotype calling was done with the endpoint genotyping analysis procedure available from the LightCycler 480 software version 1. 5. (Roche Applied Science, IN, USA). For SNPs/CAPS markers, primers flanking the polymorphism were designed also with Primer3. PCR amplified fragments were digested with the restriction enzyme, either BsiEI or TseI (New England BioLabs Inc. , MA, USA) following manufacturer's protocol. Restriction fragments were resolved on a 2% agarose gel. cmv1 fine mapping. Search for recombinants was conducted for successive small intervals on an F2 population from the cross between PS and the NIL SC12-1.
If no recombinants were available, new F 2 plants were screened until less than 0. 1% of F 2 plants screened were recombinant. Functional analysis of the cmv1 region. The smallest cmv1 interval was analyzed using the annotation of the C. melo genome version 3. 5. 1  62  which is available from the melon genome website (http://melonomics. net/ files/Genome/Melon_genome_v3. 5. 1/). The available gff3 file was used to identify genes and genetic features present in the cmv1 region. Manual curation of the predicted genes was performed by Blastp of the predicted proteins against the non-redundant protein sequences database (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Polymorphisms between PS and SC lines had been previously detected from re-sequencing data  28, 58. The Plaza database, version 3.
Determination of the expression levels of CmVPS41mRNA by Real Time-PCR. Quantitative Real Time-PCR (qRT-PCR) amplification was performed on three experiments, with three biological replicates each, from SC and PS. 100 mg from three different parts of the leaves (limbo, vein and petiole) were collected from each plant. RNA extraction was carried out using the RNeasy PLANT MINI KIT (Qiagen, Germany) and samples were treated with RNAse free TURBO-DNase I (Applied Biosystem, Ambion, CA, USA) following manufacturer's instructions. Total RNA was reverse transcribed with SuperScript III Reverse Transcriptase Kit (Invitrogen, CA, USA) and oligo (dT)20 as reverse primer. Quantitative PCR was performed on a LightCycler 480 Real-Time PCR System (Roche Applied Science, IN, USA), using SYBR Green I Mix (Roche Applied Science, IN, USA).
Acknowledgements We thank  Fuensanta Garcia  for technical support and  Miguel Aranda  for critical reading. The TILLING platform is supported by the  Program Saclay Plant Sciences (SPS ,  ANR-10-LABX-40 ) and the  European Research Council  ( ERC-SEXYPARTH ). This work was supported by grants  AGL2009-12698-C02-01  and  AGL2012-40130-C02-01  from the  Spanish Ministry of Science and Innovation , the  Spanish Ministry of Econom and Competitiveness , through the " Severo Ochoa Programme for Centres of Excellence in R&D "  2016-2019  ( SEV-2015-0533 )" and the  CERCA Programme/Generalitat de Catalunya.
The relative abundance of bacteria increases with depth, and the composition of their communities differs in litter and soil as well  [5]. The turnover of C compounds can be tracked by the analysis of enzymes that mediate them-the carbohydrate-active enzymes (CAZymes). Specifically, glycoside hydrolases (GH) and selected auxiliary activity enzymes (AA) are associated with the decomposition of polysaccharides and lignin, respectively  [12]. The classification of GH and AA proteins or genes into families that contain structurally similar proteins makes it possible to assign catalytic functions to sequences using CAZy pipeline  [12]. CAZymes have previously been studied in forest soil transcriptomes, though only by approaches targeting individual genes by PCR  [5, 13]  or exclusively eukaryotic transcripts at low throughput exploiting Sanger sequencing  [14].
2. 10, -exp_cov auto -cov_cutoff auto -scaffolding on  [48] ) using odd k-mer lengths ranging from 33 to 63. Resulting assembled contigs were merged using CD-HIT v4. 6  [49, 50]  and minimus2 Amos v3. 1. 0 [51]. Assembly of metagenomic reads was performed in the same way as described for the metatranscriptome and sequence data of all contig sequences have been deposited in the MG RAST public database  [52]  under the dataset number 4627252. 3; metatranscriptome contigs are available as 4544233. 3. Metagenome sequencing yielded 567 × 10 6 reads (24 × 10 6 ± 2 × 10 6 reads per sample) that were assembled into 9,380,309 contigs over 200 bases, including 1,882,204 contigs over 500 bases, 569,720 over 1000 bases and 6665 over 10,000 bases (mean length was 454 bases). The longest contig had a length of 179,090 bases.
Annotation of the metagenome and metatranscriptome Contig annotation was first performed in MG RAST with an E value threshold of 10 -5 while also considering the representative hit option (i. e. , single best annotation for each feature) and taxonomic information was retrieved for each identified contig. Because MG RAST is not suitable for annotation of fungal proteins, predicted proteins were subsequently annotated by finding the best protein match in an in-house database containing protein predictions from all publicly available fungal genomes available at the time of analysis (155 genomes). For all hits that received closer hit in terms of E value to the fungal-predicted protein database (FPPD) then using MG RAST, taxonomic information was retrieved from the FPPD. Glycoside hydrolases (GH) and auxiliary enzymes (AA) were identified among the metagenome and metatranscriptome contigs using the CAZy pipeline  [12] , which combines Blast and HMM tools with the manual curation of CAZy database (http://www.
CAZy. org). Protein models are compared with the sequence and profile libraries created from catalytic and non-catalytic modules of the CAZy database. GH and AA families were grouped based on which substrate they act upon, with the guidance of Dr. Bernard Henrissat, the founder of CAZy database (Table  1 ). We considered AA families to be involved in degradation of lignin because lignin is always found in union with polysaccharides in the plant cell wall and thus AA fill a variety of enzyme mechanisms to act on substrates related to lignocellulose conversion  [53]. To assess the abundance in the metagenome and the relative rate of transcription in the metatranscriptome, individual sequence reads from each sample were mapped onto contigs identified as GH or AA using bowtie 2. 2. 1  [54]  with the default settings of end to end alignment-sensitive.
Seasonality of transcription The transcript pools differed significantly between summer and winter in both horizons but seasonality was more pronounced in soil, where it affected 60 CAZy gene families compared to 18 families in litter (Fig. 2a , Additional file 1: Table  S1 ). At the level of functional groups of CAZymes, differences in their metagenome content among seasons was negligible as well as the variation in the taxonomic groups that produced them (Additional file 2: Figure  S1 ). Most of seasonal differences in transcription among the functional groups of CAZymes as classified in Table  1  and the shift in the contribution of taxa to their production (Fig. 3 ) was found especially in soil. Winter samples were marked by an increase in the use of reserve compounds (glycogen/starch and trehalose) while the share of CAZymes targeting recalcitrant plant biomass (cellulose and lignin) decreased.
Gene pool of CAZymes and its transcriptions Our results confirmed that all CAZy families were transcribed by many taxa at the same time and more isoforms of CAZymes were found in the genomes of the most abundant species of bacteria binned from metagenome (data not shown), which suggest high functional redundancy in soil environment, confirming the observations previously made for another functional gene cellobiohydrolase (cbhI), which was also produced simultaneously by hundreds of taxa  [5]. The share of fungi in our metatranscriptome is two times higher than bacteria, while in the metatranscriptomic studies from a maple forest or in peatlands, bacterial CAZy transcripts were 2. 6 to 5-fold more abundant then eukaryotic ones  [57, 58]. This may indicate high importance of fungi in coniferous forests, although the comparison may be biased by the fact that previous studies annotated short reads and used a limited reference database for fungi, so that relevant transcripts might have been overlooked.
Also, eukaryotic transcription is unrelated to the amount of gene copies in genomes because of its complex regulation  [61]. Another cause of low eukaryotic abundance in metagenomes can be the underrepresentation of fungal and other eukaryotic genomes in genomic databases  [62]  and thus the inability to properly identify genes of these organisms. CAZymes in both the metagenome and metatranscriptome, differed significantly in their composition between litter and soil. This is consistent with the fact that the composition of bacterial and fungal communities differs among horizons in the studied ecosystem  [5]  as well as elsewhere  [10, 63] , reflecting the properties of these habitats, such as the availability of C sources  [64]. Litter was enriched in transcripts associated with cellulases and lignin-targeting enzymes, indicating the importance of decomposition of recalcitrant plant biopolymers.
Concretely, bacterial GH13 genes, which are putative α-glucosidases/α-amylases, were the most abundant in metagenomes and in metatranscriptomes as well. GH13 genes, were among the most abundant CAZymes genes present in bacterial genomes  [66]  and were also found to be abundant in another forest soil  [59]  and highly transcribed in peatlands  [58]. Bacterial transcripts of CAZymes with chitinolytic activity confirmed important role of bacteria in the turnover of dead fungal mycelia  [20]. Acidobacteria, Proteobacteria, Bacteroidetes, and Actinobacteria were the most abundant bacterial producers of CAZymes. These groups are known to be abundant in acidic forest topsoil  [44]  and have also been found to be dominant in CAZy production in another acidic environment-the boreal peatland  [58].
As recommended by the kit's producer, samples with % OD > 40% were considered positive. A herd was considered seropositive when at least one of the 11-15 screened females showed a positive result. Individual results (positive/negative/inconclusive) as well as herd status (seropositive/seronegative) were recorded in the French national animal health information database (SIGAL, Information System of the Directorate General for Food).
In this study, we use 9 years of breeding data from a wild population of sociable weavers (Philetairus socius) to investigate the effects of a suite of measures of rainfall and temperature on breeding decisions and output. Sociable weavers are facultative cooperative breeders that are endemic to the semiarid savannah regions of southern Africa  [32, 33] , which are characterized by unpredictable rainfall and extreme temperatures  [34]. Sociable weavers form large colonies of up to 500 individuals who share a massive communal nest with multiple separate chambers, occupied year-round by pairs or groups of up to seven individuals  [32, 33]. Chambers provide thermoregulatory benefits  [35] [36] [37]  and function as individual nests during breeding periods. Breeding occurs both year-round and seasonally, but its timing is likely to be determined by food availability, with experimental food supplementation leading to earlier laying dates and more individuals breeding  [33, 38, 39].
All encounters with snakes (the primary nest predators, principally Cape cobras, Naja nivea, and boomslangs, Dispholidus typus  [22] ) during visits to colonies were recorded to estimate colony-specific predation rates (for inclusion in the analyses of reproductive output, see details below). We excluded from our study all colonies in which food had been experimentally supplemented  [33, 39]. Daily rainfall and temperature data were obtained from the South African Weather Service. We used data from the Kimberley Airport weather station (28°48 S, 24°45 E; 11 km northwest of the study site) rather than locally collected data, as the latter were not available for every day of the study period. Where daily rainfall data were available for both the study site and Kimberley Airport (n = 479 days), they showed similar values, with a mean absolute difference and standard deviation of 1.
Statistical methods The aim of our study was to determine the best climatic predictors of breeding decisions and output (namely, onset and length of the breeding season, probability of breeding, number of clutches laid and number of fledglings produced per season), among a suite of measures of rainfall and temperature (details below). Model selection was based on an information-theoretic approach, and for each analysis we constructed a candidate set of models (see the electronic supplementary material). We used Akaike's information criterion values  [41]  corrected for small sample sizes (AIC c )  [42]  and estimated AIC c differences ( AIC , the model's AIC c minus the minimum AIC c across all candidate models). Models with AIC less than two were considered to be the 'best', opting for simpler models (those with fewer estimated parameters) when more than one model had AIC < 2 and similar fits to the data in terms of log-likelihood  [42].
setting the predictor variables not plotted to their mean values) and correspond to temperatures and rainfall within the ranges recorded across the study years (light grey, dark grey and black lines for minimum, mean and maximum values, respectively). Day 0 corresponds to 28 August (earliest laying date in our dataset was 29 August). Length of the breeding season in relation to mean maximum breeding season temperature. Predicted values (solid line) and 95% CI (dashed lines) were estimated from the LMM in table 2, setting the predictor variables not plotted to their mean values. Points represent observed values for each colony, coloured light to dark according to the number of overlapping points.
Acknowledgements. This study was only possible with the help of numerous ringers and field assistants that helped capture and monitor the colonies used in the study. We thank  Rowan Martin  for stimulating discussion on the effects of temperature on reproduction in our population, and  Res Altwegg ,  Susan Cunningham  and  Sinead English  for further discussion and comments on the analyses and the manuscript. We also thank the two anonymous reviewers whose comments improved the original manuscript. De Beers Consolidated Mines gave us permission to work at  Benfontein Reserve.
Funding. This study was supported by funding from the  FitzPatrick Institute of African Ornithology (DST-NRF Centre of Excellence)  at the  University of Cape Town (South Africa ) to R. C. and C. N. S. ; the  Portuguese Science and Technology Foundation  ( FCT PTDC/BIA-BEC/103818/2008  and  PTDC/BIA-EVF/5249/2014 ) to R. C. ;  St John's College, University of Cambridge (UK)  to C. N. S. ; the region  Languedoc Roussillon  and the  French ANR  ( 09-JCJC-0050-01JCJC  and  15-CE-32-0012 ) to C. D. and the  European programme Marie Curie-IRSES  ( FP7-PEOPLE-2012-IRSES; 'Cooperation' 318994 ) to all. Research was conducted in the scope of the  LIA 'Biodiversity and Evolution '.
Data accessibility. The datasets supporting this manuscript are available as the electronic supplementary material. Authors' contributions. R. M. conceived and designed the study, carried out the statistical analysis and wrote the manuscript. C. D. collected field data and helped writing the manuscript. M. P. and C. N. S. collected field data and critically reviewed the manuscript. R. C. collected field data, participated in the design of the study, and helped interpreting the data and writing the manuscript. All authors gave final approval for publication.
West SSA: Burkina Faso, Ghana, Mali, Nigeria. East SSA: Ethiopia, Kenya, Tanzania, Uganda, Zambia. Sources: US Corn Belt  [14]  and Global Yield Gap Atlas (www. yieldgap. org) for SSA. For SSA, reported values refer to all locations selected following the Global Yield Gap Atlas protocols in the nine countries. A depth of 150 cm is specified for the US Corn Belt, which corresponds to the depth to which maize roots can grow in the absence of any soil physical or chemical constraint limiting root growth  [15]. The range in values for water holding capacity reflects variation in soil texture to that depth. In contrast, with less reliable data on rootable soil depth for SSA, the range in values reflects differences in both rootable soil depth from 50 to 150 cm and the plant-available soil water holding capacity to that depth  [16].
Details about methods and sources of data are described in the supplementary materials methods section available at stacks. iop. org/ERL/12/114036/mmedia. Separate sets of simulations were performed with soil depth specified at 50, 75, 100 and 150 cm across all locations, and with a recently completed map of root zone plant available water holding capacity for SSA, which represents the best available data on rootable soil depth in SSA  [16]. This recent soil depth database has a number of weaknesses, however, due to lack of adequate underpinning data for key soil properties at sufficient spatial resolution, which makes the estimated rooting depths highly uncertain. Thus, the analysis presented here is crucial for assessing the importance of soil depth in estimating rainfed yield potential and its stability at national to regional spatial scales.
With concerns about climate change and increasing variability in rainfall amount and timing  [42] , irrigated crop production is likely to play an increasingly important role, which would contribute to higher and more stable crop yields  [38, 43]. To this end, a key question is how to design a soil sampling plan for SSA to collect data in areas most relevant to current and future crop production, and where data are lacking or most uncertain. Global databases and spatial frameworks such as those developed in the Global Yield Gap Atlas  [25, 26]  and AfSIS  [16]  provide a point of departure to design such a cost-efficient sampling plan by identifying the smallest number of locations required to achieve adequate spatial resolution to cover current SSA crop production area. As shown by previous studies, one cannot assume that other underpinning data used in our analysis, in addition to soil depth, are free of uncertainty (e.
Acknowledgments Support for this research was provided by the  Bill and Melinda Gates Foundation  and the  Daugherty Water for Food Global Institute at University of Nebraska-Lincoln (UNL). We thank other members of the  Global Yield Gap Atlas team  who have been involved in the project:  H de Groot  and  H Boogaard  ( Wageningen Environmental Research ). We thank the country agronomists that contributed to the  Global Yield Gap Atlas  for their respective countries including:  Dr Korodjouma Ouattara  ( Institut de l'Environnement et de Recherches agricoles, Burkina Faso ),  Dr Mamoutou Kouressy  ( Institute of Rural Economy, Mali ),  Dr Abdullahi Bala  ( Federal University of Technology, Minna, Nigeria ),  Dr Samuel Adjei-Nsiah  ( University of Ghana, Ghana ),  Dr Kindie Tesfaye  ( CIMMYT, Ethiopia ),  Dr Ochieng Adimo  ( Jomo Kenyatta University of Agriculture and Technology, Kenya ),  Dr Joachim Makoi  ( Ministry of Agriculture Food Security and Cooperatives, Tanzania ),  Dr Kayuki Kaizzi  ( National Agriculture Research Laboratories, Uganda ), and  Dr Regis Chikowo  ( University of Zimbabwe, Zimbabwe ).
ACKNOWLEDGEMENTS This work was supported by les  Amis de l'Institut Bordet , the  Fonds de la Recherche Scientifique (FRS) , the  International Brachet Stiftung (IBS) , l 'Institut National du Cancer (INCA) , the  Cancéropole d'île de France  and a  Télévie Grant  to VH. MA holds a Postdoctoral Researcher fellowship of the FRS; NR and KD are Scientific Research Worker of Télévie. We thank  Wouter Coppieters ,  Latifa Karim ,  Manon Deckers  and the  GIGA Genomics Platform  for sequencing services and excellent technical support. We thank  Dominique Bron  ( Institut Jules Bordet, ULB ) for comments on the manuscript.