dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors like to thank the patients participating in this study. Christina Zeitz  likes to acknowledge her former boss  Professor Wolfgang Berger  for letting her take the  CSNB  project and  Professor José-Alain Sahel  for letting her continue working on this disease. The authors show gratitude to  Elisabeth Dubus ,  Anna Verschueren , and  Marcela Garita  for antibody gifts and  Audrey Schalk  for helping to prepare figures. The study was supported by the  Fondation Voir et Entendre ,  Prix Dalloz  for " La Recherche en Ophtalmologie " (CZ), LABEX  LIFESENSES  [Reference ANR-10-LABX-65] supported by  French State Funds  managed by the  Agence Nationale de la Recherche  within the  Investissements d' Avenir Program  [ ANR-11-IDEX-0004-0 ] and the publication was supported by an award from the  Retina Research Foundation  (MFM).
The expression of Mlp124478 in plant cells alters A. thaliana transcriptome. To better understand how Mlp124478 functions in plant cells, and since it alters plant morphology and susceptibility to pathogen, we investigated whether Mlp124478 alters gene expression in A. thaliana. We performed transcriptome profiling of 4-days-old A. thaliana Mlp124478 stable transgenic line and control plants expressing GFP. We identified 98 up-and 294 down-regulated genes, respectively (Fig. 6A , Supplementary Dataset). To test the robustness of the transcriptome data, we used qRT-PCR to assess the expression of 3 randomly selected up-regulated genes and 7 down-regulated genes. Transcriptome and qRT-PCR data correlated well, although quantitative differences were detected (Supplementary Fig. 3 ). GO term enrichment analysis was applied to the deregulated genes to determine relevant biological processes.
Mlp124478 binds DNA. The localization of Mlp124478 in planta, the presence of a DNA-binding motif and alterations at the transcriptional, morphological, and immune levels prompted us to investigate whether Mlp124478 associates with DNA molecules. In a first time, we screened for Transcriptional Factor Binding Sites (TFBS) in the promoter sequences of all up-and down-regulated genes identified in our transcriptome analysis. We identified four different TFBS, which were very abundant among the up-(43 genes out of 98) and down-regulated (30 genes out of 294) genes (Supplementary Dataset). TFBS in the up-regulated gene set included ABF1 and TGA1a which belongs to the basic region/leucine zipper motif (bZIP) transcription factor (TF) family; and TCP16 belongs to the TCP (TEOSINTE BRANCHED 1, CYCLOIDEA and PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR 1) TF family.
Images were annotated with LAS AF Lite software. Chromatin immunoprecipitation (ChIP)-polymerase chain reaction assay. ChIP assays were conducted, as described previously, with minor modifications  56. Briefly, 300 mg of 2-weeks-old A. thaliana Mlp124478 stable transgenics and Col-0 plants expressing GFP were collected in tubes containing 10 mL of phosphate-buffered saline (PBS), which were replaced by 10 mL of 1% formaldehyde to cross-link tissue under vacuum infiltration. To quench the cross-linker, 0. 125 M glycine was added after removal of formaldehyde, followed by vacuuming, incubation for 5 min, and tissue-rinsing with 10 mL cold PBS. Cross-linked tissues were dried on paper towel for nuclei isolation. Sonicated chromatin was immunoprecipitated with 50 µL/mL anti-GFP microbeads (MACS, Miltenyi Biotec Inc.
Control and transgenic plants were extracted in triplicate. Eluted total RNA was quantified and sent to the Plateforme d' Analyses Génomiques of the Institut de Biologie Intégrative et des Systèmes (Université Laval, Quebec City, Canada) for library construction and sequencing with the Ion Torrent Technology. Differential expression was analyzed with green line workflow of the DNA subway in the iPlant collaborative pipeline (now CYVERSE) (Cold Spring Harbor Laboratory), including A. thaliana-Ensembl TAIR 10 as reference genome. Genes with a Q-value ≤ 0. 05 and a fold-change ≥ 2 were considered as significantly differentially expressed and were further investigated for Gene Ontology (GO) enrichment. The Cytoscape software (version 3. 1. 1)  58 with the plug-in ClueGO and CluePedia  59  was used to visualize functions enriched among deregulated genes.
The qRT-PCR thermal profile was: 95 °C for 2 min, 40 cycles of 95 °C for 5 s, 58 °C for 10 s, and 72 °C for 5 s. To analyze the quality of dissociation curves, the following program was added after 40 PCR cycles: 95 °C for 1 min, followed by constant temperature increases from 55 °C to 95 °C. Actin 1 served to normalize all RT-qPCR results. The expression levels of each gene were calculated according to the ∆∆Ct method  60. Three technical replicates for each treatment were analyzed. Standard deviation was computed by the error propagation rule. Bioinformatics analyses. Clustal Omega (http://www. ebi. ac. uk/Tools/msa/clustalo/) was used to align sequences of the nine gene members of the CPG2811 SSP family, which were later manually annotated. Phylogenetic trees were generated by COBALT (http://www.
ncbi. nlm. nih. gov/tools/cobalt/cobalt. cgi). SignalP 4. 0 (http://www. cbs. dtu. dk/services/SignalP/) predicted signal peptides. NLStradamus (http://www. moseslab. csb. utoronto. ca/NLStradamus/) forecast nuclear-localizing signals. Transcription factor-binding sites (TFBS) were identified and analyzed with the AthaMap (http://www. athamap. de/search_gene. php)  61 , Pscan (http://159. 149. 160. 88/pscan/)  62 and PlantPan (http://plantpan2. itps. ncku. edu. tw/index. html)  63 databases. Consensus TFBS sequences were retrieved from the Pscan database. Promoter sequences were obtained individually with TAIR's SeqViewer (http://tairvm09. tacc. utexas. edu/servlets/sv), and TFBS-specific primers (Supplementary Table  1 , Primer Nos 4-38) were designed with Primer3Plus (http://www. bioinformatics.
Acknowledgements Funding for the project was provided by  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grants  to HG. The project in HG's laboratory was also partially funded by an institutional research chair held by HG. BA was funded by an international PhD scholarship from the  Fonds de Recherche du Québec sur la Nature et les Technologies (FRQNT). Travel grants from the  Conseil Franco-Québécois de Coopération Universitaire (CFQCU)  were also awarded to BA, KCGS and CL. IBS. obtained a mobility grant from  MITACS. BP was supported by an  Institut National de la Recherche Agronomique (INRA)  Contrat  Jeune Scientifique , by the  European Union , as part of the  Marie-Curie   FP7 COFUND People Program , through an  AgreenSkills' fellowship  (under grant agreement No.
267196 ), and by the  Laboratory of Excellence ARBRE , through a mobility grant ( 12RW53 ). CL was supported by  INRA  in the framework of a Contrat Jeune Scientifique and by the  Labex ARBRE  ( ANR-11-Labex-002-01 ,  Lab of Excellence ARBRE ). KCGS was supported by the  MITACS-Globalink scholarship program. SD is supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-12-LABXARBRE-01 ) and the  Young Scientist Grant POPRUST  ( ANR-2010-JCJC-1709-01 ).
On rare occasions, double rearrangements or even oligoclonal patterns may be observed  35. In such cases, gel electrophoresis is not the preferred technique for clonality assessment and more sensitive methods such as fragment analysis should be applied. Once clonality has been confirmed, the final step prior to sequencing relates to the purification of the PCR product to ensure that sequences of high quality are obtained. Finally, the IMGT/V-QUEST tool is the resource recommended for IG sequence analysis by ERIC. As the IMGT databases are constantly updated and report results in a consistant and well annotated manner, this tool ensures standardized analysis and facilitates comparative analysis amongst different clinical or academic facilities. As immunogenetic analysis in CLL has prognostic and, in particular, predictive implications, robust analysis of the IGHV-IGHD-IGHJ gene rearrangement including the assignment to major CLL stereotyped subsets, is no longer only desirable but of key importance.
Acknowledgements We thank present and past members of our research groups, the IgCLL group and ERIC, for their commitment and enthusiasm in studying immunogenetics in CLL. We would also like to acknowledge the trustful collaboration of all the members of the IMGT/CLL-DB initiative and, in particular,  Professor Marie-Paule Lefranc  and  Dr Veronique Giudicelli ,  Laboratoire d'Immunogenetique Moleculaire, LIGM, Universite Montpellier II, Montpellier, France , and IMGT, the international ImMunoGeneTics information system, for their enormous support and help with IG gene sequence analysis. This work was supported in part by grants from  TRANSCAN-179 NOVEL JTC 2016 ;  Associazione Italiana per la Ricerca sul Cancro AIRC  ( Investigator Grant  # 20246  and Special Program Molecular Clinical Oncology-5 per mille # 9965 ), Milano, Italy;  Progetti di Rilevante Interesse Nazionale (PRIN)  # 2015ZMRFEA ,  MIUR ,  Rome, Italy ; The  Swedish Cancer Society , the  Swedish Research Council, Knut and Alice Wallenberg Foundation ,  Karolinska Institutet ,  Uppsala University ,  Uppsala University Hospital  and the  Lion's Cancer Research Foundation , Uppsala; ODYSSEUS  Programme , implemented under the "Action for the Strategic Development on the Research and Technological Sector", funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund).
As many of the models use image registration to determine and represent the motion there are also questions about which registration algorithm(s) should be used. And finally the best ways of using the motion models to correct different types of images and to plan and guide different treatment deliveries need to be established. Although there is lot of remaining work to be done for motion models to reach their full potential, it is worth bearing in mind that it may not be necessary to find perfect solutions to all these issues before motion models become more clinically adopted. It is generally agreed that the motion seen in most 4DCT datasets is not the true motion that the patient undergoes during treatment delivery, but it is the best approximation that is available. If it can be demonstrated that motion models give a better approximation of the true motion, and if their limitations and uncertainties are understood, then they could have a promising future in radiotherapy and other applications.
To meet the clinical requirement, calculation time in minutes, the use of graphics processing unit (GPU) technology have been considered and investigated for fast MC-based TPS  [50]. An example for a fast MC implementations is the gPMC code developed by Fracchiolla et al. [31]. With gPMC it takes only up to 22 s to simulate 10 million protons from the source to achieve ∼ 1% relative statistical uncertainties. During the 4D workshop in 2016, another fast MC dose engine, called MCsquare (many-core Monte Carlo), was presented  [109]. It uses parallel computation architecture and allows to accurately simulating a therapeutic plan in less than one minute. MCsquare has been benchmarked with the GATE/Geant4 MC application (http:// www. opengatecollaboration. org/) and could be used as an efficient tool for dose (re)calculations on the day of the treatment in combination with OpenReggui (https://www.
While it is tempting to use ML for just about everything, care should be taken to understand what it can or cannot do and to use it when appropriate. One of the key points always lies in finding out exactly which part or parts of conventional algorithms can be approached with ML techniques. As an example, the framework to tackle (deformable) registration problems typically consists of a model to deform a moving image into the fixed image, a metric to measure how similar the deformed and fixed image are and an optimization part which loops until the metric shows good agreement. For challenging multi-modal datasets, it is not clear which metric is the best to compare images. An interesting approach is to use a linear combination of conventional metrics. The best combination depends on the context i.
The Kabuli type has been grown traditionally in the Mediterranean basin and Central Asia, while the Desi type has been mainly produced in the Indian subcontinent, East Africa, and Central Asia  (Wani et al. , 2014). In Algeria, chickpea is the second important legume crop after faba bean, with the Kabuli type being the most abundant on the market. At the same time, Algeria is the third largest chickpea importer in the world. Therefore, increasing local production will have a positive economic impact. Mutation breeding is an effective and important approach to legume improvement. Induced mutation technique has proved to be successful for improving traits in a wide variety of crops. To date, more than 3,274 varieties in more than 224 plant species derived from mutagenesis programs have been officially released as listed in the FAO/IAEA Mutant Varieties Database (MVD) 1.
Among these, 493 mutant variety pulses are registered, with 21 improved chickpea mutants released for cultivation  (Gaur et al. , 2007; Saǧel et al. , 2009; Mutant Variety Database [MVD] , 2016). Mutations can be induced by physical or chemical mutagens. Among physical mutagens, gamma rays are the most frequently used, accounting for 64% of the radiation-induced mutant varieties  (Maluszynski et al. , 2000; Ahloowalia et al. , 2004; Jankowicz-Cieslak and Till, 2015). Induced mutations have a great potential of enhancing genetic variability and thus improving yield in chickpea through effective handling of the mutagenized populations  (Wani, 2011). The efficiency of early generation (M 2 ) selection in mutation breeding experiments has been reported in various crops  (Kozgar, 2014). Therefore, the selection for quantitative traits, such as yield, can and should preferably be carried out in early generations such as an M 2.
Statistical Analysis The analysis of variance for ten phenotypic characters was carried out using Genstat software version 12. 1  (VSN International Ltd. , 2009). Genotypic and phenotypic coefficient of variation (GCV, PCV), broad sense heritability (H 2 ) and genetic advance as percentage of mean (GA %) were calculated using the following equations: Genotypic Variance (σ 2 g) σ 2 g = (MSG -MSE)/r, Where MSG is the mean square of genotypes, MSE is the mean square of error, and r is the number of replications. Phenotypic Variance (σ 2 p) σ 2 p = σ2g + σ 2 e, Where σ 2 g is genotypic variance and σ 2 e is the mean squares of error. Error variance (σ 2 e) σ 2 e = MSE.
(1999)  and selection intensity (k) was assumed to be 5%; where k = 2. 06, a constant. σp is the phenotypic standard deviation and H 2 is the broad sense heritability. Genetic advance as percentage of mean was categorized as low (0-10%), moderate (10-20%), and high (>20%)  (Johnson et al. , 1955). Pearson's correlation coefficients (r) were calculated among all the measured traits using IBM SPSS for Windows Version 20 (2011). Path coefficient analysis permits a critical examination of specific direct and indirect effects of characters and measures the relative importance of each of them in determining the ultimate goal yield. In the present study, we used yield as a dependent variable. This analysis was done using the IBM SPSS AMOS Windows Version 24 (2016) software. To determine the genetic affinity of M 2 chickpea families and to perform grouping, clustering analysis was done with R statistical software (eclodist, stats and clv packages) using the Mahalanobis's distance matrix in Ward2 hierarchical agglomerative clustering method  (Murtagh and Legendre, 2014).
Database Search for Candidate Genes Involved in Resistance to Ascochyta Blight, Fusarium Wilt, and Early Flowering in Chickpea Primers were designed with the use of NCBI's database and Primer3 program. The web based programs Coddle http://www. proweb. org/input/ and Primer3 and http://bioinfo. ut. ee/primer3-0. 4. 0/.
Genetic Variation Among Families A mutant population of 4050 M 2 plants representing 135 families was produced from treatment of chickpea seed with 280 Grays gamma irradiation. Visual observations were made on this population to determine the effect of the irradiation treatment. The analysis of data shows that the maximum coefficient of variation (CV%) was observed for total number of seeds/plant and seed yield (58. 2% and 58. 1%, respectively) followed by total number of pods/plant (55. 3%), number of primary branches (31%), hundred seed weight (24. 1%), height of first pod (19. 3%) and number of seeds/pod (18. 4%). Whereas, the coefficient of variation for all other morphological characters was less than 15%. The least coefficient of variation was recorded for days to flowering (2. 7%). Analyses of variance showed a very highly significant difference among the families for all the traits studied.
Selection of Promising M 2 Mutant Results of Dunnett's test for the ten traits studied are listed in Table  6. These data indicated variability among the chickpea  families. For all the traits there were families with mean values significantly different from the control Ghab 4. According to Dunnett's test, 37 M 2 families superior to the control were determined for five agronomical traits [days to flowering, days to maturity, height of first pod (cm), number of primary branches and number of seeds/pod]. Considering the number of days to flowering and the number of days to maturity, 18 and 13 families, respectively, presented mean values higher than that of the control. Only one family showed mean values lower than control, while the others remained in the same class as the control. For the height of first pod, four mutant families showed means scored above the control.
Development of Low-Cost TILLING for Mutation Discovery in Chickpea An important step in the development of low-cost TILLING is the production of genomic DNA that is of high molecular weight and of sufficient quantity for planned assays. We first sought to test molecular weight of genomic DNA produced using a home-made low-cost extraction protocol versus a commercial Qiagen kit. Agarose gel analysis shows that both methods produce a single high molecular weight band (Figure  4A ). The concentration of genomic DNA was then estimated using agarose gel analysis by comparison with standards of known concentration (Figure  4B ). From this we conclude that both extraction methods produce genomic DNA of sufficient quality and quantity for low-cost TILLING. Database Search for Candidate Genes Involved in Resistance to Ascochyta Blight, Fusarium Wilt, and Early Flowering in Chickpea Sequences of candidate genes were obtained from the NCBI database.
The largest data sets on mutation frequency come from chemical mutagenesis based TILLING experiments. Chemical mutagens such as EMS (ethyl methyl sulfonate) are known to induce primarily single point mutations. The expectation is that SNP mutations can accumulate to higher levels that other mutation types because a high percentage of SNP mutations will have no negative effect on gene function. Reported mutation densities in diploids has ranged between 1 mutation per 150 kb to over one mutation per one million base pairs  (Till et al. , 2018). In chickpea, mutation frequency was estimated by TILLING platform through the analysis of 768 M 2 progenies (obtained with 0. 2% EMS mutagen) using 20 targets comprising genomic DNA and cDNA sequences. The frequency of mutations determined was one per 165 kb.
ACKNOWLEDGMENTS We are thankful to the  National Liaison Officer NLO (IAEA)   Mr. Messaoud Baaliouamer  of the  Algerian Commission of Atomic Energy (COMENA)  for providing the necessary facilities. We thank  Mr. Rabah Yefsah  of the  COMINA  for timely help of the irradiation treatment of the seeds. We thank  Dr. Pierre Sourdille  ( UMR 1095 GDEC, INRA Clermont-Ferrand, France )  IAEA  expert of ALG5026 for his guidance and advice. We also thank  Mr. Owen A. Huynh  of  Plant Breeding and Genetics Laboratory, IAEA Austria , for his help during the realization of the molecular part. Special thanks are given to  Leila Bouzid  of the  National Institute for Agricultural Research of Algeria , to  Adel Kadri  Assistant lecturer at the  University of M'sila (Algeria ), to  Dahbia Tabti  a Ph. D.
H. fraxineus version 2 genome approaches chromosome-level contiguity. The H. fraxineus version 2 genome assembly (Hf-v2. 0) is 62. 28 Mbp distributed across 145 scaffolds. Here, we analyse scaffolds greater than 10 kbp, a total of 23 scaffolds or 62. 08 Mbp (Fig. 2 ; Supplementary Analysis 2). Both Hf-v1. 1  16  and Hf-v2. 0 have been released (open resources) online and are hosted on the Open Ash Dieback github repository (https://github. com/ash-diebackcrowdsource). The Hf-v2. 0 gene annotation pipeline built on that of Hf-v1. 1  16  and identified 10,945 genes (11,097 transcripts, 1,516. 50 mean CDS length, 3. 49 mean exons per gene; Supplementary Analysis 3). Telomeric repeats are identified at both ends of 14 scaffolds and at one end of all but one of the remaining scaffolds (Supplementary Table  2 ).
Annotations (9,737) from Hf-v1. 1 were transferred to Hf-v2. Augustus v2. 7  45  was used to predict additional protein-coding genes using protein and RNA-seq alignments (16 libraries, Supplementary Table  16 ) with the previously trained H. fraxineus model. We ran a transcript extension protocol on all genes to identify whether those genes within 100 bp of an upstream gene had evidence for extension. We also checked this extended gene dataset for potential secreted proteins. This protocol allowed the identification of 36 genes that had not previously been recognized as having a methionine start codon which were included into our effector-mining pipeline (Supplementary analysis 3). We used a pipeline described previously  32  to identify putative effectors. Briefly, transcripts with a signal peptide were identified and from these we remove transcripts with transmembrane or mitochondrial localization signals and then transcripts with repeats, the disulphide bonds and nuclear localization signals are identified.
Phasing fruiting body samples. We used Shapeit2  48  , to phase fruiting body data, this first applies a phase informative read step (extractPIRs) to group SNPs present on the same read pair and then phases remaining SNPs using population level polymorphism (assemble --states 1,000 --burn 60 --prune 60 --main 300 --effective-size 564,000 --window 0. 5). All Japanese samples including the haploid strain (as control) were phased. As required indels were removed from the dataset before phasing. Effective population size (ϑ = 2N e μ) was calculated assuming a mutation rate of 5 × 10 -9 per generation per site  49  using the mean nucleotide diversity present in 100-kbp windows in the Japanese population. SNP diversity and divergence analysis. Polymorphism statistics and sliding windows, π and F ST were conducted using VCFtools v0.
1. 13  47. DNAsp v5. 10. 01 50 was used to calculate population genetic statistics per gene and PAML v4. 9  51  was used (YN00) in pairwise mode to calculate the average P N :P S ratio nonsynonymous (P N ) and synonymous (P S ) values for polymorphism in each gene with at least one synonymous mutation. The sm package 52 in R v3. 2. 1 compared the density distributions of P N :P S. Output data for all genes are combined in Supplementary Table  9  (see also Supplementary Analysis 6). Data are represented using SplitsTree v4. 13. 1  53  using the neighbour-net algorithm and CIRCOS v0. 67-5  54. Parental cross and linkage analysis. Two H. fraxineus isolates of opposite mating types, LWD054 and LWD067, were collected from stem lesions on trees in Norfolk, UK in 2014. Crosses were made according to a previously reported method  55  except that isolates were kept at 25 °C in 16 hours light.