dataesr/scientific-paragraphs-categorization huggingface dataset:
, EGFP1F Primer 20 GAC CAC TAC CAG CAG AAC S 637-656 b 2006 [24] AC EGFP2R Primer 19 GAA CTC CAG CAG GAC CAT G AS 768-750 b EGFP Probe (VIC/ 22 AGC ACC CAG TCC GCC CTG S 703-724 b TAMRA) AGC A a According to the Pasteur virus (PV) RABV genome sequence (GenBank accession number M13215). b According to the cloning vector pEGFP-1 sequence (GenBank accession number U55761). doi:10. 1371/journal. pntd. 0004812. t001.
The aim of this study was to assess and characterize Mycobacterium tuberculosis complex (MTBC) genotypic diversity in Tanzania, as well as in neighbouring East and other several African countries. We used spoligotyping to identify a total of 293 M. tuberculosis clinical isolates (one isolate per patient) collected in the Bunda, Dar es Salaam, Ngorongoro and Serengeti areas in Tanzania. The results were compared with results in the SITVIT2 international database of the Pasteur Institute of Guadeloupe. Genotyping and phylogeographical analyses highlighted the predominance of the CAS, T, EAI, and LAM MTBC lineages in Tanzania. The three most frequent Spoligotype International Types (SITs) were: SIT21/ CAS1-Kili (n = 76; 25. 94%), SIT59/LAM11-ZWE (n = 22; 7. 51%), and SIT126/EAI5 tentatively reclassified as EAI3-TZA (n = 18; 6.
This study aimed to specifically describe the genotypic diversity of Mycobacterium tuberculosis complex (MTBC) in Tanzania, as well as neighbouring and other countries in Africa (having spoligotyping data deposited in SITVIT database), with reference to their global distribution. The study also assessed the association between HIV serological status and M. tuberculosis lineages for people who apart from TB had HIV. To better highlight region-specificity or similarity and get a larger view on MTBC distribution in Tanzania, Mycobacterium tuberculosis complex (MTBC) genotypic diversity was characterized and compared to diverse genotypes available in other African countries. Basing on genotyping information, this paper also tentatively proposes a sublineage which seems to be phylogeographically specific for Tanzania.
Neighbouring countries The main neighboring countries of Tanzania are Kenya and Uganda in the north; Rwanda, Burundi, and the Democratic Republic of the Congo in the west; and Zambia, Malawi, and Mozambique in the south (http://en. wikipedia. org/wiki/Tanzania). The description of M. tuberculosis strain profile used the findings from the current study in Tanzania by Mbugi et al  [15]  and already published data of existing patterns from other countries in the international genotyping database SITVIT2.
Spoligotypes and new names assignment Spoligotyping was performed using a commercially available spoligotyping kit (Isogen, Bioscience BV, Maarssen, The Netherlands) as previously described by Kamerbeek et al  [25]. The resulting spoligotypes were reported in octal and binary formats (Table  1 ) and compared to existing patterns in an international genotyping database SITVIT2, which is an updated version of previously released SITVITWEB database  [26]  available at: http://www. pasteurguadeloupe. fr:8081/SITVIT_ONLINE/. Spoligotype patterns were grouped as spoligotype international types (SITs) if they shared identical spoligotype patterns with patterns present in the existing database. Spoligotypes that had no match with previously reported patterns, represented by unique exemplars, were considered as orphans.
Data analysis Data were entered using Microsoft Excel and later transferred to relevant software for analysis. STATA version 12 and MegaStat softwares were used for this statistical analysis and a p-value <0. 05 was considered significant. Standard deviation (SD) and means were calculated for age of patients. Odds ratio (OR) and 95% confidence interval (CI) were calculated for comparison between HIV information versus PGG1 and PGG2/3 lineage groups. The spoligotype patterns from Dar es Salaam and the Serengeti ecosystem study were pooled together and reanalysed for comparison with already known patterns from neighbouring countries (Kenya, Uganda, Zambia, Malawi, and Mozambique), other African countries (Sudan, Ethiopia, Nigeria, Cameroon, Namibia, South Africa, Zimbabwe, and Madagascar) and those available globally in SITVIT2 database.
Phylogenetic analysis and mapping MLVA Compare V1. 03 software (Genoscreen; Lille, France) was used to draw the minimum spanning trees (MSTs). The SIT or orphan spoligotype number appeared inside each node, and the distance (number of spacers of difference) between two nodes was shown on the edge linking these nodes. These phylogenetic trees were coloured in function of various characteristics such as the MTBC lineages described in SITVIT, the cities of isolation, and the HIV serological data of patients. The MST is a graph which is undirected and connected. The MST links all isolates together with the fewest possible linkages between nearest neighbours. Furthermore, a spoligoforest was drawn as a "hierarchical layout" using the SpolTools software (available through http://www. emi. unsw. edu.
Distribution of Spoligotype International Types (SITs) in this study A total of 54/57 SITs containing 264 isolates matched a pre-existing shared-type in the SIT-VIT2 database, whereas 3/57 SITs (n = 4 isolates) were newly created. A total of 27/57 SITs containing 238 isolates were clustered within this study (2 to 76 isolates per cluster) while 30/ 57 SITs containing 30 isolates were unique (in addition 25 orphan strains were identified, which brings the number of unclustered isolates in this study to 55/293 or 18. 8%, and clustered isolates to 238/293 or 81. 2%). The distribution of Spoligotype International Types (SITs) in this study is shown in Table  3.
Clusters containing 3 or more isolates and their worldwide distribution in the SITVIT2 database The results from Dar es Salaam and the Serengeti ecosystem studies were collectively analyzed and proportions of various lineages estimated to represent TB lineages potentially found in Tanzania. There were no major differences from what was reported in a separate Serengeti ecosystem and the combined results, which included samples from Dar es Salaam study  [14]. A very high proportion of SIT21/CAS1-Kili was recorded in this pooled study (n = 76 or 25. 9% of isolates) (Table  3 ). The description of SIT/lineages and worldwide distribution, as compared to the SITVIT2 database, of clusters containing 3 or more isolates is presented in Table  4. Apart from SIT21/CAS1-Kili, other SITs/lineages were observed in the order from high to low proportion as SIT59/LAM11-ZWE, SIT126/EAI5, SIT53/T1, SIT26/CAS1-Delhi, SIT1/Beijing, SIT8/EAI5, SIT345/T3-ETH, SIT42/LAM9, SIT420/T2-Uganda, SIT2196/LAM11-ZWE, SIT244/T1, SIT37/T3, SIT135/T2-Uganda, SIT149/T3-ETH, SIT25/CAS1-Delhi, SIT50/H3 and SIT128/T2-Uganda.
The CAS strain is said to emerge over 9,000 years ago  [43]  spreading globally. Using available genetic tools, spoligotyping and MIRU-VNTR, classification, surveillance on TB transmission trends and epidemiology have been made possible  [30]. The CAS strain for example has been classified as belonging to the East-African Indian TB strain, lineage 3 according to other researchers  [44]. In the SITVIT database, this lineage (labeled CAS) includes the CAS1-Delhi, CAS1-Kili, CAS2 and CAS (or CAS-like)  [45]. Originally, while the EAI lineage is more ancestral, predominating in the southern part of India, the CAS lineage is predominant in the north of India and more modern  [49]. The Manu lineage is a newly-described ancient clade, closely related to an Indo-Oceanic lineage 1 of an ancestral EAI strain of TB  [44].
(Continued) A total of 54/57 SITs containing 264 isolates matched a preexisting shared-type in the database, whereas 3/57 SITs (n = 4 isolates) were newly created. A total of 27/57 SITs containing 238 isolates were clustered within this study (2 to 76 isolates per cluster) while 30/57 SITs containing 30 isolates were unique (for total unique strains, one should add to this number the 25 orphan strains, which brings the number of unclustered isolates in this study to 55/ 293 or 18. 77%, and clustered isolates to 238/293 or 81. 23%). Note that SITs followed by an asterisk indicates "newly created" SITs due to 2 or more strains belonging to an identical new pattern within this study or after a match with an orphan in the database; SIT designations followed by number of.
Description of clusters containing 3 or more isolates in this study, and their worldwide distribution in the SITVIT2 database. Distribution in Regions with > = 3% of a given SITs * Distribution in countries with > = 3% of a given SITs ** E 65. 97, EURO-N 10. 08, EURO-W 6. 3, ASIA-W 5. 25, AMER-N 5. 25, AFRI-S 4. 83 TZA 34. 66, ETH 9. 87, MDG 9. 66, USA 5. 25, ZAF 4. 83, ZMB 4. 62, SWE 3. 99, KEN 3. 57 AFRI-S 15. 26, EURO-W 14. 64, AMER-N 3. 3 ZMB 21. 86, ZWE 15. 88, ZAF 15. 26, BEL 13. 4, TZA 11. 13, USA 3. 3, MWI 3. 3.
Acknowledgments The  Southern African Centre for Infectious Disease Surveillance (SACIDS)  is acknowledged for providing a  Postdoctoral Research Fellowship  to EVM and PhD candidacy for  BZK. Financial support was received from the  Wellcome Trust  Grant [WT087546MA];  MUHAS Sida Sarec Small Grant  [ 000/3177 ]. Prof Mark Rweyemamu , The Executive Director of SACIDS is acknowledged for stimulating the idea and advice. The  London International Development Centre in the UK  and staff are acknowledged for collaboration. We thank the participants' authorities in Tanzania for allowing us to conduct our study in Dar es Salaam and The Srengeti ecosystem. The  WHO Supranational TB Reference Laboratory,Tuberculosis & Mycobacteria Unit, Institut Pasteur de la Guadeloupe  is acknowledged for stimulation of ideas to mine up data from TB database for evaluation of TB strains in Tanzania as related to other African Countries.
Database Lineage** Unique or Clustered SIT*** 2267 701377400001771 1 (0. 34) 25 CAS Unique 2269 703377400000771 1 (0. 34) 20 CAS Unique 2364 703601740003771 1 (0. 34) 25 CAS1-Delhi Unique 2391 703377400003771 1 (0. 34) 11. 11 CAS1-Kili Unique 2484 737377677760771 1 (0. 34) 25 T1 Unique 2736 777777777760741 1 (0. 34) 25 T1 Unique 3880 437774777760730 2 (0. 68) 50 T2-uganda Clustered 4056* 477777777413411 2 (0. 68) 100 EAI5 Clustered 4057* 777017777760771 1 (0. 34) 50 T1 Unique 4058* 477777777413371 1 (0. 34) 50 EAI5 Unique *strains: 4056* this study n = 2; 4057* this study n = 1, SDN n = 1; 4058* this study n = 1, NLD n = 1. ** Lineage designations according to SITVIT2; "Unknown" designates patterns with signatures that do not belong to any of the major lineages described in the database. *** Clustered strains correspond to a similar spoligotype pattern shared by 2 or more strains "within this study"; as opposed to unique strains harboring a spoligotype pattern that does not match with another strain from this study.
2a  and Supplementary Dataset 1). Southern blot analyses, using the P-loop region present in the four NB-LRR genes (NB-1, 2, 3, 4) as a probe, confirmed that the CcRpp1 locus in G119-99 contains four NB-LRR genes (Supplementary Fig. 2a ). Using the markers generated to fine-map the CcRpp1 locus, we analyzed the other resistant lines (G59-95, G146-97) with P. pachyrhizi isolate PPUFV02 (Supplementary Table  3 ). Resistance mapped to the same region in the C. cajan genome, suggesting that resistance in G59-95 and G146-97 is conferred by the CcRpp1 locus. To ascertain the level of variation at the CcRpp1 locus we cloned NB-1, 2, 3 and 4 from all three accessions by PCR followed by Sanger sequencing (Supplementary Table  3 ). Interestingly, variation was observed both in the number of NB-LRR paralogs and their sequences (Fig.
3c , Supplementary Datasets 5-7 and Supplementary Table  5 ). Increased transgene expression was observed in the homozygous plants compared with the hemizygotes, suggesting that expression level influences efficacy of CcRpp1. To test whether P. pachyrhizi disease resistance is caused by autoactivity of CcRpp1, we tested plants homozygous for the transgene against Fusarium virguliforme, a filamentous fungal plant pathogen that causes sudden death syndrome (SDS) in soybean. We did not observe increased resistance against this pathogen when we compared plants homozygous for the transgene with null segregants, indicating that the response observed against P. pachyrhizi is specific (Supplementary Fig. 4  and Supplementary Datasets 8-10). In addition, preliminary agronomic data indicate there is no adverse affect of CcRpp1 expression on plant development, a phenotype often associated with auto activity of plant immune receptors  28  (Supplementary Fig.
5a, b ). Finally, we did not observe segregation distortion in germination rate when multiplying hemizygous plants originating from the CcRpp1. 7. 1 event (Supplementary Fig. 5c , Supplementary Datasets 11-16 and Supplementary Table  6 ). In conclusion, we have identified and cloned a gene from C. cajan that confers resistance to P. pachyrhizi when expressed in soybean. Previous work did not identify novel soybean germplasm that displayed immunity to ASR and identified only 33 accessions with moderate RB type resistance, and thus revealed that the number of ASR resistance genes in soybean germplasm is limited  16. Resistance genes that provide immunity to ASR are a valuable resource. Thus, the significance of this work is the demonstration that it is possible to effectively transfer a dominant resistance gene from a related legume into soybean.
Methods and any associated references are available in the online version of the paper. Accession codes. European Nucleotide Archive (ENA): LN879382, UCDavis BAC Cc13A15 (referred to in text as Cc13A15); LN879378, NB-1; LN879379: NB-2; LN879380: NB-3; LN879381: NB-4; and study accession PRJEB9933: raw sequencing data.
Resistant and susceptible bulk cDNAs were subsequently sheared using a Covaris S20 to obtain average fragment sizes of 200 bp. These sheared cDNAs were used to generate Illumina libraries using the sequencing generation kit (Cat no. FC-103-1002) and the sequencing kit (v3) (Cat no. FC-104-1003), and sequenced with the Illumina GAII sequencer. We generated data by sequencing on two lanes, one for each F 2 bulk: (i) the resistant bulk containing 3. 07 Gb in 20 million paired reads and (ii) the susceptible bulk containing 2. 96 Gb in 19. 5 million paired reads. The raw sequencing data are deposited at the European Nucleotide Archive (ENA), study accession PRJEB9933. We pooled raw data from the susceptible and resistant bulks and generated a filter to select only NB-LRR-associated reads before assembly.
We performed a reciprocal BLAST search between the Arabidopsis 31 and Medicago 32 NB-LRR-associated genes (205 and 369 sequences, respectively) against the 75,778 soybean predicted genes (JGI; http://phytozome. jgi. doe. gov/pz/portal. html#!info?alias=Org_ Gmax) as a database with TBLASTN  33. This database was queried with the soybean predicted genes using BLASTX  33. The resulting NB-LRR-related reads were then de novo assembled with ABySS 34 to obtain a common reference comprising 49 NB-LRR genes. Based on mapping the resistant and the susceptible bulk reads to this common reference respectively, using MAQ  35  (version 0. 7. 1), we mined our data for informative single-nucleotide polymorphisms (SNPs). Using a custom-made Perl script (find_proportional_snps. pl; https://github. com/danmaclean/2bl), we identified several SNPs with a minimum coverage of ten nucleotides that had an allele frequency profile consistent with that expected at the CcRpp1 locus in the F 2 population (a ~66% (0.
55 to 0. 75 nucleotide proportion) and ~0. 33% (0. 25 to 0. 45 nucleotide proportion) reference allele frequency in the bulk resistant and susceptible data, respectively). These polymorphisms were used to generate markers for fine mapping. All the dCAPS markers in this study were generated using the program dCAPS Finder 2. 0 (ref. 36; http://helix. wustl. edu/dcaps/dcaps. html. Marker dCAPS140555 was completely linked to the CcRpp1 locus after analyzing 2,282 gametes (1,141 F 2 susceptible plants). Seven marker sequences genetically linked to CcRpp1 were aligned to the G. max genome (Phytozome v5. 0, http://phytozome. jgi. doe. gov/) using BLASTN  33  (default parameters). The seven linked markers identified in C. cajan displayed strong synteny with both chromosomes 9 and 12, with chromosome 12 being the most syntenic.
G. max is an ancient polyploid and chromosomes 9 and 12 are homoeologous  37. To identify additional polymorphisms and indels for marker development, a 2-Mb region starting from the telomere region on chromosome 12 that contains the identified homologous region was used to align C. cajan variety Asha scaffolds (Version 1; http://www. ncbi. nlm. nih. gov/nuccore/450342093?report=genbank) from the publicly available C. cajan assembly. Based on this sequence we designed additional primers surrounding polymorphic sequences between the resistant G119-99 and the susceptible G48-95 parents that resulted in the identification of five additional markers for fine mapping of CcRpp1 (dCAPS3978, SSR10581, dCAPS52491, SSR2152 and dCAPS239615). The majority of these marker sequences were located within a 1.
We used an NB-LRR-guided BAC library from the reference C. cajan variety "Asha ICPL 87119" to confirm if NB-LRR genes linked to marker dCAPS140555 are also present in C. cajan  38, 39. We identified a BAC clone of 78. 7 kb that contained dCAPS140555 (Cc13A15, ENA:LN879382). DNA was prepared from this clone using the Qiagen Large Construct Kit and sheared to obtain average fragment sizes of 5 kb and sequenced using Sanger sequencing. The reads were subsequently assembled using Phred, Phrap and Conseddata analyses using standard parameters  [40] [41] [42] [43]. Sequence annotation of this BAC revealed the presence of four highly homologous NB-LRR gene paralogs: three full-length genes and one truncated gene. We therefore initiated high-resolution mapping of the 2,282 gametes and identified on gain-and four loss-of-function recombinants delineated by dCAPS52491 and one resistant recombinant delineated by SSR2152.
BAC sequencing and annotation. A BAC library of C. cajan accession G119-99 was ordered from BioS&T (Montreal, Canada) and screened with PCR amplicons comprising sequences surrounding markers dCAPS140555 and SSR2152. Two positive BAC clones (3F and 6G) were identified that together span the entire interval between dCAPS52491 and SSR2152 (Fig. 2a  and Supplementary Fig. 2 ; raw sequencing data is deposited at ENA, study accession PRJEB9933). Several colonies containing BAC 3F (145 kb) and BAC 6G (130 kb) were tested for integrity by DNA fingerprinting using the restriction enzyme HindIII (New England BioLabs). In addition, the BAC sizes of these clones were verified by pulsed-field gel electrophoresis of NotI-digested DNA. Single-colony clones from 3F and 6G were sequenced with PacBio and Illumina MiSeq (The Genome Analysis Centre, Norwich, UK).
Expression analysis of the four NB-LRR paralogs was investigated by aligning the nonchallenged resistant parent G119-99 RNA-Seq reads against the CcRpp1 locus sequence of 205,344 bp in Geneious 47 (version 8. 1. 2; http://www. geneious. com/) using the Align/assemble -> Map to reference default parameters (Supplementary Fig. 3 ). The transcriptome raw data are deposited at ENA, study accession PRJEB9933. Pathogen assays. Pathogen assays were performed as described previously  48. Briefly, spores were heat-shocked at 40 °C for 5 min and subsequently suspended in an aqueous solution of 0. 01% Tween 20, and mixed thoroughly; the spore concentration was then adjusted to 2 × 10 4 sp/ml with a hemocytometer. Plants were spray-inoculated with the urediniospore suspension, incubated at 100% relative humidity in the dark for 24-36 h and then transferred to the greenhouse (C.
To assess the effect of CcRpp1, the plants were scored qualitatively as immune (I; no lesions), partial resistance (PR; red-brown (RB), low sporulating lesions) and susceptible (S; tan, highly sporulating lesions) and quantitatively, by excising and scanning leaves followed by determination of lesion counts using ImagePro7 software (Supplementary Table  4 ). Most null samples were scored 15 d after inoculation, whereas the heterozygous and homozygous plants were scored 29 d after inoculation. To determine the effect of the gene, we compared the transgenic plants to the null plants from the same event; a separate analysis was performed for each zygosity level.
7. 1 events using an RNeasy Plant Mini Kit (Qiagen). Isolated total RNA (1 µg) was DNase-treated and cDNA synthesized using the QuantiTect Reverse Transcription Kit (Qiagen). Absolute quantification of CcRpp1 transcript levels was determined by TaqMan qPCR (TaqMan Gene Expression Master Mix; Applied Biosystems) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) using the primers and probe indicated in Supplementary Table  3. Three technical replicates of each sample were run. QuantStudio 6 and 7 Flex Software was employed for analysis. CcRpp1 transcript levels in transgenic plants are expressed relative to those of a G. max zinc-metallopeptidase gene (XM_003521127) that was used as an internal control for expression studies.
ACKNOWLEDGMENTS This research was supported by grants from the  2Blades Foundation to The Sainsbury Laboratory  and the  Universidade Federal de Viçosa. 2Blades support was funded in part by  DuPont-Pioneer. We wish to thank  R. Godoy  for providing the seeds of the C. cajan accessions used in this work. Work performed by D. R. C. and J. B. was supported by  NSF  award  DBI 0605251. Next-generation and BAC sequencing was financed by the  2Blades Foundation. We thank  G. Etherington  and  C. Schudoma  for bioinformatics support,  J. Pike  for assisting in the preparation of the Solexa sequencing libraries,  S. Perkins  and the  JIC horticultural team  for excellent plant care, and the TSL support team for media preparation. We thank the  DuPont Pioneer Soybean Transformation group  for generating CcRpp1 transgenic events and the Controlled Environment group for growth, maintenance and seed advancement of transgenic material.
We thank  N. Penido ,  E. Feitosa Araújo  and  T. Castro Silva  for excellent technical support during the high-resolution genotyping screenings and  E. S. Mizubuti  for helping with drawing Figure 1b. We thank  D. Da Silva Toledo  and  L. Carlos Costa  for plant care at  UFV. We thank  J. Pacheco Badel  for implanting and curating a searchable seed database to support our genetic studies. We thank  Y. Yamaoka  for kindly providing the Japanese isolate T1-4. We acknowledge  D. Horvath ,  M. Moscou  and  B. Staskawicz  for many helpful discussions.
Introduction Currently, there are several bio-optical in situ datasets worldwide suitable for validation of ocean-colour satellite data. While some are managed by the data producers, others are in international repositories with contributions from multiple scientists. Many have rigid quality controls and are built specifically for ocean-colour validation. The use of only one of these datasets would limit the number of data in validation exercises. It would therefore be useful to acquire and merge all these datasets into a single unified dataset to maximise the number of matchups available for validation and their distribution in time and space and consequently reduce the uncertainties in the validation exercise. However, merging several datasets together can be a complicated task. First, it is necessary to acquire and harmonise all datasets into a single standard format.