dataesr/scientific-paragraphs-categorization huggingface dataset:
High-resolution genetic map construction. We used a two-step strategy to construct the genetic map. First, a screen for markers that were present in at least 140 individuals (missing data ratio <6%) identified a total of 793 markers. JoinMap 4. 0 and Lep-Map 2. 0 softwares were used to build linkage groups after removing 277 markers that were severely distorted (χ 2 > 30, P < 0. 01). Three individuals were excluded during marker analysis and mapping due to excessive missing alleles. Obscure markers that were assigned to different locations of a linkage group were removed after comparing the marker location determined by the two programs, and then the genotype matrix of rest of the markers were re-ordered until the relative order between markers arranged by the two programs did not conflict.
After end repair, purification, and elution, dATP overhangs were added to the DNA fragments. A paired-end P2 adapter containing T-overhangs was ligated to 20 μl of sheared, size-selected, P1-ligated, and pooled DNA templates with a specific barcode. The ligated material was then purified, eluted, and subjected to PCR enrichment and sequencing on a HiSeq2500 next-generation sequencing platform (Illumina, San Diego, CA, USA) with PE150 mode. Sequencing data for each individual were then de-multiplexed according to the specific barcode and index. Length of raw reads was 142-146 bp after removing barcode sequences. The original sequencing datasets have been deposited in the NCBI SRA repository with the accession number PRJNA516159. Read processing and SNP identification. Raw reads were processed with the Stacks pipeline (version 1.
41) to call SNPs 57. Raw reads were firstly filtered to generate clean reads with the process_radtags program (parameters: -r, -c, -q). Only the forward reads were kept for analysis as the reverse reads are at irregular distance from the restriction enzyme cutting site. To avoid artifacts, the following criteria were used to filter raw reads: (1) putative duplication reads generated by PCR amplification in library construction were discarded; (2) reads with adapter contamination were removed; (3) reads with ≥10% unidentified nucleotides (Ns) were discarded; (4) reads with average Pred quality score over a 15 bp sliding window below 10 (90% confidence) were removed; (5) reads without the correct partial 5 bp EcoRI recognition sequence (AATTC) were discarded. Then the ustacks program was used to align clean read sequences into exactly-matching stacks (or alleles) and compared the stacks to form a set of putative loci using a maximum likelihood framework for each individual (parameters: -m 3, -M 2).
Loci that were two standard deviations above the mean depth of coverage were excluded, and then the cstacks program was used to merge loci of each parent into a catalog. The between-individual distance parameter of cstacks for mismatches was set 4 (parameter: -n 4). Then sstacks was run to match every individual in the population (including the two parents) against the catalog to identify locus/haplotype combinations in each individual. Finally, genotypes program was executed to call and export genotypes in a format compatible for JoinMap 4. 0. The minimum number of matching progeny required for a locus was set as 122 to 140. Genetic linkage map construction. We used a two-step strategy to construct two versions of the map with different genotype profiles. The first version (Version 1. 0) was constructed with markers that were genotyped in more than 94% of offspring (140 individuals, missing data rate <6%, strict parameter), while the second version (Version 2.
A 6-kb sequencing library was constructed and sequenced in paired-ends to evaluate the map quality by Blast searching for neighboring markers using ustacks program (Supplemental Fig. S8 ). The coding of linkage groups and their corresponding relationships between Version 1. 0 and Version 2. 0 is shown in Supplementary Table  S3. Genome coverage of the newly-built linkage map was estimated with the equation c = 1-e -2dn/L , where d is the average interval of markers, n is the number of markers, and L is the length of the linkage map estimated by L = l*(m + 1)/(m -1) (m is the number of markers in the linkage group)  64. Anchoring contigs from the rose genome assemblies to the linkage map. The OB genome was recently obtained from doubled-haploid plants using single-molecule real-time sequencing  6, 10.
To verify the collinearity between our genetic map and the reference genomes and to place more contigs to the pseudo-chromosomes, we anchored the contigs to the linkage map using RAD-tag markers. Initially, all markers from the high density genetic map were searched on the genome assembly with BLAST + 2. 6. 0 to integrate contig sequences  65. Full genome sequence was downloaded from Genome Database for Rosaceae (https://www. rosaceae. org). The marker sequences were blast searched against contigs with a cutoff E-value of 10 -15. If a query hit one contig, the contig was assigned to the LG; if a query hit two or more contigs with a less than two-fold difference in the E-value, we did not assign any of these contigs. In cases where a contig was hit by multiple markers from different linkage groups, the contig was assigned to LGs with more than 2/3 markers.
Inconsistency between the LGs and the pseudo-molecules of the assemblies was used to detect chromosome rearrangement events. Finally, total length of contigs anchored to the LGs was calculated. Collinearity information was generated using Circos software  66. To identify centromeric contigs, we aligned the 13 centromeric repeat sequences identified by Raymond et al. 6 using Blast  67. Gene and transposable element (TE) annotations were retrieved for both genome assemblies. We filtered out TEs classified as "putative host genes", and discarded gene annotations having their exons overlapped by TE annotations on more than 30% of their length. We also identified chloroplastic and mitochondrial contigs from Hibrand Saint-Oyant's genome by comparing them to reference sequences CM009590. 1, KF753637.
Sequence comparison with Fragaria vesca. To examine the conservation of synteny between rose and strawberry (F. vesca), the marker sequences of our genetic map were searched against the genome sequences of Fragaria (v1. 1 and v4. 0. a1 assemblies; http://www. rosaceae. org) using the BLAST + 2. 6. 0 program with default parameters  65. Markers with low quality ratio were excluded: (1) The length of the alignment was less than 100 bp; (2) The hit was found at multiple locations on the scaffolds (potentially corresponding to repetitive elements); (3) E-value < 10 -5. Alignments between rose LGs and the strawberry genome were visualized with Circos 66.
Acknowledgements We are grateful to  Profs. De-Zhu Li , and  Zhenhua Guo  for offering access to high-performance computers. We thank  Prof. David H. Byrne  for the  R. wichuraiana 'Basyes' Thornless' (BT)  materials. This work was partially facilitated by the  Germplasm Bank of Wild Species of China  and the  Pôle Scientifique de Modeĺisation Numeŕique of the ENS de Lyon-France. This work was supported by grants from  National Natural Science Foundation of China  ( 31660583 ,  31160402 ), the  National High Technology R&D Program of China  ( 2011AA100208 ), the  CAS Pioneer Hundred Talents Program  ( 292015312D11035 ), and the  Yunnan Recruitment Program of Experts in Science.
2014 ). Numerous names have been suggested as synonyms of R. vinifera (e. g. R. mannii, R. wendlandii, R. diasticha Burret), but these are not related to the species we describe here. Fred Stauffer indicates that the specimen conserved at the M herbarium (M0208480, information on specimen) was made by extracting a few rachillea from the holotype in G. comments on a previous version of the manuscript. This work was also part of the PALM-A-GOV project to TLPC, publicly funded through the ANR (the French National Research Agency) under the "Investissements d'avenir" programme with the reference ANR-10-LABX001-01 Labex Agro and coordinated by Agropolis Fondation within the I-SITE MUSE (ANR-16-IDEX-0006).
Acknowledgements This study was supported by  Agropolis Fondation  ( RAPHIA  project) under the reference ID  1403-026  through the  «Investissements d'avenir» program  ( ANR-10-LABX-0001-01 ;  I-SITE MUSE :  ANR-16-IDEX-0006 ) and  Agence Nationale de la Recherche  ( ANR-15-CE02-0002-01 ), both to TLPC. We are grateful to  Fred Stauffer  for pointing us towards the type specimen of R. vinifera in G, for general discussions about Raphia and for constructive comments on a previous version of the manuscript. We thank  Valère Salako  and  Fred Stauffer  for sharing their field observations, and  Marc Appelmans  for sending us scanned photos of G. Mann collections from GOET. Finally, we thank  John Dransfield  for.
Methods Data were recorded on two mouse lines that were divergently selected for environmental variability of BW. Details of the selection process are in Formoso-Rafferty et al. [14]. In the current paper, "low" and "high" will be used for the line with, respectively, a low and high environmental variability of BW. Briefly, this selection experiment demonstrated a divergent response for the residual variance of BW and that it was positively correlated with traits related to welfare and robustness. Data for animals from three consecutive generations of the selection experiment  (12, 13 and 14)  were available. Hence, 40 females (four full-sib females from 10 random litters) per generation and line were chosen at weaning, divided into four treatment groups (1 full-sib per group) that were assigned different combinations of feed restriction during the growth and reproduction stages.
Statistical model for female growth and fertility The data used in this study included weekly weights for each female from weaning to 10 weeks of age, with an initial 240 records and a final count of 225 at the end of the experiment. The weights for each week were analyzed with a model that included the effects of R-GP (ad libitum or feed-restricted), line (low or high), generation (12, 13 or 14), the litter size in which the female was born (with 3 levels, i. e. from 4 to 9, 10 to 11 and 12 to 15 pups), and the first order interactions between line, generation, and R_GP. It would have been better to include also a genetic effect to separate the influence of selection from the environmental effect of the generation, but this was not possible because of the small number of animals in the dataset.
Statistical model for birth weight of offspring The dataset contained 1275 records for BW of the progeny of 116 females with at least one parturition, 158 litters, and 4900 pedigree records. The heteroscedastic model of SanCristobal-Gaudy et al. [23]  was fitted to the BW data. This model assumes that the BW mean and its residual variance are both affected by genetic and environmental factors and can be written as: where µ and η represent the systematic effects that affect the mean and environmental variability of BW, respectively, m and ν are random effects that account for the y = µ + m + l + e (η+ν/2) ε, maternal genetic effects that affect the mean and variability of BW, respectively, and l , is a random litter effect with variance σ 2 l. Vectors m and v of the genetic values were assumed to follow a joint distribution: where ⊗ denotes the Kronecker product and A is the additive genetic relationship matrix between animals.
The pedigree information went up to the founding population of both lines and, thus, we could assess the genetic influence of line on BW by considering differences in the means of breeding value predictions for the animals involved in the experiment, which means that the systematic effect of line explains exclusively the environmental effects associated with line. The heteroscedastic model was solved through a double hierarchical generalized linear model (DHGLM) developed by Felleki et al. [24]  within the frame of ASReml (ASReml Release 4. 1 software)  [21]. Since this model assumes a residual variance for each level of combination of systematic effects  [7] , when required, the estimate of the residual variance for a given level of a given effect was obtained by averaging over the solutions of all other effects as: m v ∼ N 0, σ 2 m ρσ m σ v ρσ m σ v σ 2 v ⊗ A , where bij is the solution for a particular level l of a sys- tematic effect s , and n s is the number of levels of the sys- tematic effect.
[7]. The genetic component of the environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can affect their welfare  [41]. There is some evidence that environmental variability is under genetic control  [7]. In addition to the parameters estimated in several datasets  [7] , two selection experiments that used variability of BW as a selection criterion were performed with success, one in mice  [14]  and one in rabbits  [13]. Feed costs represent the largest financial input in livestock production systems but can be reduced by reducing feed intake  [42, 43]. However, limiting feed intake is also expected to reduce production and, therefore, livestock must be selected based on feed efficiency rather than only on feed intake  [44]. Based on our findings, animals that show less environmental variability of BW are more robust, which means that they cope better with the environmental challenge of feed restriction.
Embryos were allowed to develop through meiosis II and then mounted on 2% agarose pads to film under slight compression. Live imaging of PCM recruitment was performed on a Yokogawa CSU X1 spinning disk confocal mounted on a Zeiss Axio Observer Z1 inverted microscope equipped with a 63x 1. 4NA Plan Apochromat lens, 120 mW 405 nm and 100 mW 488 nm and 561 nm solidstate lasers, 2D-VisiFRAP Galvo FRAP module and Photometrics CoolSNAP-HQ2 cooled CCD camera and controlled by VisiView software (Visitron Systems). For quantitative analysis of PCM recruitment during the normal embryonic cell cycle and during conditions of metaphase arrest, 11x1 mm GFP/mCherry z-series (binned 2x2) as well as single plane DIC images were acquired every 30 s from meiosis II until the end of the first mitosis or 10 min into mitotic arrest, using low laser illumination to minimize photobleaching.
Temperature Shift Experiments Temperature shift experiments were performed on a Yokogawa CSU-X1 spinning disk confocal mounted on a Nikon Ti-E inverted microscope equipped with a 603 1. 4NA Plan Apochromat lens, 100 mW 491 nm and 561 nm lasers and Photometrics Cool-SNAP-HQ2 cooled CCD camera and controlled by MetaMorph software (Molecular Devices). 9-13x1mm GFP/mCherry z-series (binned 232) as well as single plane DIC images were acquired at irregular intervals from meiosis II until the end of the first mitosis or 10 min into mitotic arrest, using low laser illumination to minimize photobleaching. CherryTemp (CherryBiotech), a microfluidicsbased temperature controller was used to control and adjust temperature during imaging. Embryos were imaged in meiosis medium sandwiched between a 24x60mm coverslip and the Cherry chip as described in  (Davies et al.
Laser Ablation of Centrioles Laser ablation experiments were done on a Yokogawa CSU X1 spinning disk confocal mounted on a Zeiss Axio Observer Z1 inverted microscope equipped with a 100x 1. 3NA EC Plan-Neofluar lens, 100 mW 488 nm and 561 nm solid-state lasers, 355 nm passively Q-switched pulsed laser (16-mW average power, 0. 8 mJ/pulse, repetition rate 21 kHz, pulse width 400 ps) and Evolve EM512 EMCCD camera (Photometrics) and controlled by VisiView software (Visitron Systems). 6x1 mm GFP/mCherry z-series as well as single plane DIC images were acquired every 15 s. Worms were cut in meiosis medium and embryos mounted on 2% agarose pads. Embryos were allowed to develop until a certain stage (prophase or prometaphase), at which point centrioles were targeted with a 355-nm laser using the iLas Pulse system (Roper Scientific).
ACKNOWLEDGMENTS We thank  Kareem Elsayad  and members of the  Dammermann lab  for discussions,  Karen Oegema  for critical reading of the manuscript, the  Caenorhabditis Genetics Center ,  Dhanya Cheerambathur ,  Anthony Hyman ,  Verena Jantsch , and  Karen Oegema  for strains and reagents,  Christoph Sommer  for help with data analysis, and the  MFPL BioOptics facility  for technical assistance. This work was supported by grant  Y597-B20  from the  Austrian Science Fund (FWF)  to A. D. as well as by an  STSM grant  to T. L. from  COST Action   BM1408. T. L. is a student of the  DK Chromosome Dynamics , funded by the  FWF  ( W1238-B20 ).
, 2008) , with cells fixed in 4% formaldehyde in PBS for 12 min. Finally, immunofluorescence on HeLa cells with directly-labeled affinity-purified antibodies to CEP192 (amino acids 1-211, this study) and SASS6 (amino acids 501-657, this study) and unlabeled antibodies to PLK-1 (pT210) was performed as in  (Shukla et al. , 2015) , with cells fixed with 2% PFA in PBS for 10 min and post-fixed with ice-cold methanol for 5 min at -20 C. 3D widefield datasets were acquired using a 100x 1. 4NA Super Plan Apochromat lens on a DeltaVision microscope equipped with a 7-Color SSI QUANTIFICATION AND STATISTICAL ANALYSIS Image Analysis PCM recruitment analysis was performed as previously described  (Laos et al. , 2015). Individual measurements were normalized by dividing by the mean intensity of measurements made over the interval of 30 to 90 s after metaphase onset in control embryos.
Genotyping Tsetse flies were genotyped at 10 microsatellite loci (Supplementary Tables  S1  and S2 ). Locus X55-3 is from  (Solano et al. , 1997). Loci XpGp13 and pGp24 come from  (Luna et al. , 2001)  and GPCAG from  (Baker and Krafsur, 2001). Loci, B3, XB104, XB110 and C102 were kindly supplied by A. Robinson, Insect Pest Control Laboratory (formerly Entomology Unit), Food and Agricultural Organization of the United Nations/International Atomic Energy Agency [FAO/IAEA], Agriculture and Biotechnology Laboratories, Seibersdorf, Austria. Finally, loci pGp20 and pGp27 came from a microsatellite bank of G. palpalis gambiensis (S. Ravel, personal communication). Those the name of which begins with the letter X are X-linked. We thus analyzed two data sets: one without these loci but with all individuals and the other with females only and all loci.
More characteristics for describing the loci used can be found in the Supplementary Table  S2. In the laboratory, legs were dried and then subjected to chelex treatment as previously described  (Ravel et al. , 2007). The 10 PCR reactions were then carried out in a thermocycler (MJ Research, Cambridge, UK) in 20 µl final volume, using 10µl of the diluted supernatant from the extraction step as template. After PCR amplification at the microsatellite loci, allele bands were routinely resolved on ABI 3500 XL sequencer (Applied Biosystems, USA). This method allows multiplexing and the use of four dyes (blue, red, green and yellow). Allele calling was done using GeneMapper V 4. 1software (Applied Biosystems) and the size standard GS600LIZ short run. In order to confirm subspecies determination, we amplified partial sequences of ITS1 following the protocol described by Dyer et al  (Dyer et al.
For this, all missing genotypes were converted into homozygous individuals for allele 999 as recommended  (Chapuis and Estoup, 2007). Isolation by distance was undertaken with Rousset's approach  (Rousset, 1997)  where, in a two dimension framework Rousset's Index FST_R=FST/(1-FST) follows Effective population sizes (Ne) were estimated through several methods and softwares. The heterozygote excess method  (Balloux, 2004)  could not be used due to the excessive presence of null alleles in almost all loci (see below). The Linkage disequilibrium method  (Waples and Do, 2010; Peel et al. , 2013)  and the coancestry method  (Nomura, 2008)  were undertaken with NeEstimator v 2. 1  (Do et al. , 2014). For LD method, several threshold values are proposed for minimal allele frequencies to be used (above 0, 0.
01, 0. 02 and 0. 05). We used the average across usable values obtained with all methods. We also used the inter and intra correlation method  (Vitalis and Couvet, 2001b)  with the software Estim  (Vitalis and Couvet, 2001a ) (available at http://www. t-demeeus. fr/ProgMeeusGB. html). We then computed the average, minimal and maximal (MiniMax range) effective population sizes across methods weighted by the number of usable values obtained in each case. Effective population size reflects the demography and other phenomena as reproductive strategy and/or historical perturbations and its estimation also varies across methods  (Krafsur and Maudlin, 2018). We nevertheless expected a good correlation between Ne and the census size (Nc) of the corresponding subpopulations  (De Meeûs et al. , 2019).
Acknowledgments This study was funded by the  Education and Research Ministry of Côte d'Ivoire , as part of " Contrat Désendettement Développement " ( C2D ) managed by  IRD (Institut de Recherche pour le Développement). The work presented is also part of "Targeting tsetse: a demonstrating project" and " Trypa-NO! ",which are supported by  Bill & Melinda Gates Foundation. Djakaridja BERTE is supported by a grant from  IRD , " Allocation de Recherche pour une Thèse au Sud ", Contrat No. 862286D. This research is part of the  International Joint Laboratory on Vector Borne Disease (LAMIVECT)  financed by the  IRD.
Growth and Feed Conversion Assessment During the experimental period, the amounts of supplied feed were recorded, and unconsumed feed was weighted after siphoning, filtering, and drying. These data were used to calculate the feed consumed. Shrimp were sampled biweekly until the end of the 8 weeks for growth assessment. Mortality was monitored daily. Growth performances were estimated through the assessment of weight gain, average daily weight gain, specific growth rate (SGR), and mortality rate. Feed use performances were estimated by feed conversion (FCR) and protein efficiency (PER) ratios. WG = W t -W 0 (1) ADG (g/d) = W t -W 0 rearing days (2) SGR (%/d) = 100 × ln(W t ) -ln(W 0 ) t (rearing days) (3) FCR = Total dry feed intake W t -W 0 (4) PER = W t -W 0 Total feed intake x Protein content of the diet  (5)  where W t is the weight per shrimp per tank obtained at time t; WG: weight gain; ADG: average daily gain; PER: protein efficiency ratio.
Hematoxylin and Eosin (H&E) Staining Histological examinations were performed in INFO-PAT laboratory, Poznań, Poland. Following fixation described in 2. 3 section, brains were embedded in paraffin, and sliced into 4 μm coronal sections and stained with hematoxylin and eosin at 22-24 °C. The slides were evaluated by light microscopy (BX61VS, Olympus, Tokyo, Japan), and scanned using a digital camera (HVF22CL 3CCD, Hitachi, Tokyo, Japan) and the Panoramic Viewer software (3DHISTECH, Budapest, Hungary).
Hematoxylin and Eosin (H&E) Staining Histological examinations were performed in INFO-PAT laboratory, Pozna ń, Poland. Following fixation described in Section 2. 3, brains were embedded in paraffin, and sliced into 4 µm coronal sections and stained with hematoxylin and eosin at 22-24 • C. The slides were evaluated by light microscopy (BX61VS, Olympus, Tokyo, Japan), and scanned using a digital camera (HVF22CL 3CCD, Hitachi, Tokyo, Japan) and the Panoramic Viewer software (3DHISTECH, Budapest, Hungary).
ESI conditions were as follows: Gas temperature and flow were 300 • C and 9 L/min, respectively; sheath gas temperature and flow were 350 • C and 11 L/min, respectively; capillary voltage was 3500 V. The fragmentor was always set at 150 V. The data obtained were analyzed by MassHunter Qualitative Analysis software. A calibration curve was established using commercially available UA in the range of 1 ng/mL to 100 ng/mL.