dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrieving expression plots, count tables and sequences: The NvERTx database can be accessed using multiple points of entry: by searching the annotations (Fig. 2Ai ), using the BLAST tool (Fig. 2Bi ) or exploring the co-expression clusters (Fig. 2Ci )(see section: Exploring Gene Expression Clusters. ) To demonstrate this, we use the Nematostella Brachyury protein (Nvbra, genebank ID: AAO27886. 2) as an example. To retrieve transcripts corresponding to Nvbra we can use the search function to query the transcript annotations (Fig. 2Ai ). To search for Nvbra, we can use the Nemve1 gene model ID from the current genome assembly (jgi|Nemve1|770), the NCBI GenPept accession number (gi|122058623), the NCBI GenBank accession number (AAO27886. 2), or the gene name ('Brachyury') (Fig. 2Ai ). Using any of these queries identifies several transcripts that correspond to brachyury (Fig.
2B ). Multiple matching transcripts reflect the different isoforms predicted by the transcriptome assembler. We can confirm which transcripts correspond to Nvbra by examining the annotations that are reported with the search results. It is normal for different isoforms to have slightly different annotations as each transcript was individually annotated. Clicking the NvERTx. 4 numbers fills in the field on the left of the screen, enabling the user to directly compare their expression profiles (Fig. 3A ). Similarly the BLAST tool can be used to retrieve NvERTx. 4 transcripts using either a nucleotide (BLASTn) or protein (tBLASTn) query. The reported alignments can be then used to identify the correct transcripts. For example using tBLASTn against the transcriptome with Mus Musculus Brachyury (NCBI GenBank accession number AAI20808.
1) we find several homologous transcripts, the first of which is Nvbra (Fig. 2C ). Again clicking the NvERTx. 4 number fills in the field on the left of the screen enabling the user to obtain the expression plots and annotations. Once the NvERTx. 4 IDs for Nvbra, NvERTx. 4. 100808 and NvERTx. 4. 100809, are selected we can query the database by clicking 'Plot!' on the left (Fig. 2Aiii ). The first page that appears displays the transcripts expression during regeneration and embryogenesis (Fig. 3A ). We can see that the expression of Nvbra exhibits two peaks during regeneration beginning at 8hpa and 60hpa while during embryogenesis Nvbra is expressed early, rapidly peaks at 20hpf, then decreases throughout development. Note that the expression profiles for the two transcripts are perfectly super-imposed and appear as one.
This is because transcripts corresponding to the same Nemve1 best hit are quantified equivalently as they are from the same 'gene'. To distinguish transcript isoforms from separate genes we can compare the individual transcripts in the 'alignment' tab where a MUSCLE sequence alignment is reported  (Edgar, 2004 )(Fig. 3F ). In this case we observe that NvERTx. 4. 100809 is a longer assembled isoform of Nvbra. The results tabset also includes the normalized count tables (Fig. 3B ), transcript annotations (Fig. 3C ), the sequences in FASTA format (Fig. 3D ), and links to bibliographical resources including PubMed articles citing the protein and a PaperBlast query (Price and Arkin, 2017)(Fig. 3E ). In the annotations tab, we can also see which co-expression cluster the transcript belongs to for embryogenesis and regeneration.
As this web application is intended to complement and expand upon existing resources, we provided transcript models that have been annotated using a variety of gene/protein databases (nr, tremble, Nemve1). Sequencing technologies are evolving to achieve longer reads, and assemblers will soon provide more robust transcriptomes and in the future we plan to take advantage of these technologies to improve our transcript models. We also plan to grow the database as future datasets examining embryogenesis and regeneration emerge. Finally, we foresee merging this resource with an existing spatial gene expression database found at http://www. kahikai. org/index. php?content=genes  (Ormestad et al. , 2011). This will enable identification syn-expression groups, genes which are co-expressed both spatially and temporally  (Niehrs and Pollet, 1999) , and further facilitate studies comparing differential gene usage during embryogenesis and regeneration.
Transcriptomic datasets: The sequences that served as input into our de novo assembly is comprised of two datasets: One spanning the first 24 hours of embryogenesis originally reported by  Tulin et al. 2013 (Tulin et al. , 2013)  and another spanning the first 144 hours of regeneration generated in house (see library preparation and sequencing). The embryonic dataset was downloaded from the Woods Hole Open Access Server (http://darchive. mblwhoilibrary. org/handle/1912/5613, last accessed June 1, 2017) and includes Illumina HiSeq 100bp paired end sequencing prepared from Nematostella embryos at 0, 6, 12, 18, and 24 hours post fertilization. The regeneration dataset includes Illumina NextSeq 75bp paired end sequencing from regenerating Nematostella at -1,  0, 2, 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 96, 120  and 144 hours post amputation (see library preparation and sequencing section for details).
The sequence data that was used for transcript quantification is comprised of four datasets. Three datasets spanning embryogenesis: originally reported in Fischer et al. (Fischer et al. , 2014) ( http://darchive. mblwhoilibrary. org/handle/1912/5981, last accessed June 1, 2017)(Illumina HiSeq 100bp paired end replicates sampled hourly from 0-19 hours post fertilization), the second embryonic dataset originally reported in  Helm et al. 2013 (Helm et al. , 2013)  (NCBI short read archive Project: PRJNA189768)( Illumina HiSeq 50bp single end replicates sampled from 2, 7, 12, 24, 120 and 240), and the third embryonic dataset was generated in house sampled at  24, 48, 72, 96, 120, 144, 168, and 192  hours post fertilization (Illumina MiSeq 75Bp single end replicates). The regenerative data used for quantification is the same used for the transcriptome assembly (Illumina NextSeq 75bp single end triplicates).
Library preparation and sequencing: Two novel RNAseq datasets, one spanning embryonic, larval and postmetamorphic development and the other regeneration were generated for this study: For the embryonic dataset, approximately 250 embryos per time point were cultured in 1/3 strength artificial seawater at 18°C. At each time point,  24, 48, 72, 96, 120, 144, 168, and 192  hours post fertilization, the embryos were transferred to 500ml of Tri-Reagent and homogenized for 15 seconds with a pistle. The resulting lysate was snap frozen in liquid nitrogen and stored at -80°C. This was repeated to obtain duplicates for each time point. After all the samples were collected, the RNA lysate was extracted using two phenol-chloroform extractions and precipitated with isopropanol. For the full extraction protocol, see:  (Layden et al.
, 2013). The resulting nucleic acids were treated with the TURBO DNA-free kit from Invitrogen (AM1907) for 10 minutes at 37°C. The resulting RNA was quantified with a Qubit spectrometer and RNA integrity was checked on an Agilent Bioanalyzer 2100. 100ng of RNA was used as input to the Illumina Truseq stranded mRNA library Prep for Neoprep kit and the libraries were prepared using the automate Illumina Neoprep system. 75Bp single end sequencing was carried out on the NextSeq500 sequencer For the regenerative dataset, approximately 350 six-week-old Nematostella juveniles per time point were amputated below the pharynx. At each time point, -1,  0, 2, 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 96, 120, and 144  hours post amputation, the juveniles were transferred to 500ml of Tri-Reagent and homogenized for 15 seconds with a pistle.
Trinity de novo assembly For the de novo assembly, paired end reads from regeneration  (-1, 2, 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 96, 120 and 144 hpa)  and embryonic dataset  (0, 6, 12, 18, and 24 hpf, (Tulin et al. , 2013) ) were filtered of ribosomal sequences by aligning to Nematostella mitochondrial and ribosomal sequences using Bowtie2  (Langmead et al. , 2009)  and retaining the unmapped reads. These surviving reads were input into Trinity (v2. 4. 0) for assembly  (Haas et al. , 2013). To annotate the assembly the ORFs of the open reading frame were found using OrfPredictor  (Min et al. , 2005)  and the resulting peptide sequences were compared to the NCBI nr database using PLAST  (Nguyen and Lavenier, 2009)  with an evalue cutoff of 5e-5. The transcriptome was also compared to the Uniprot databases, swissport and trembl, using BLASTx and PLASTp respectively with an evalue cutoff of 5e-5.
Quantification To quantify the RNAseq data, single end reads for each dataset, regeneration and the three embryonic datasets Fischer, Helm and this study, were aligned to the Trinity assembly using Bowtie2  (Langmead et al. , 2009). Read counts were quantified using RSEM  (Li and Dewey, 2011). To account for the many isoforms per gene reported by Trinity, transcripts were compared to the Nemve1 filtered gene models using BLASTn and counts for transcripts with the same Nemve1 hit were combined. Transcripts with low read counts, those that did not have >5 counts in at least 25% of the samples, were excluded. Each dataset was then normalized separately using the R package edgeR and the counts per million (cpm) mapped reads were calculated. To correct for batch effects of the embryonic datasets after normalization, the R function ComBat from the SVA packages was used on log2(cpm+1) transformed data using timepoint as a categorical covariate  (Leek et al.
Fuzzy c-means clustering and GO term enrichment. For each Nemve1 gene model, transcriptional expression profiles were clustered using the R package mFuzz  (Kumar and E Futschik, 2007). The cluster number was set to 9 for the regeneration data and 8 for the embryonic datasets as these numbers represented the point at which the centroid distance between clusters did not significantly decrease when new clusters were added (inflection point). For each cluster, GO term enrichment was calculated using a Fisher's exact test and the R package topGO on the GO terms identified from comparing the transcriptome to the Uniprot database (all identified GO terms were used as a background model). The resulting GO term list was reduced and plotted using a modified R script based on  REVIGO (Supek et al. , 2011).
NvERTx website: The website was constructed using the Django python framework (https://www. djangoproject. com/). Plots are generated using the Plotly javascript library (https://plot. ly/javascript/). The source code for the website can be found at https://github. com/IRCAN/NvER_plotter_django. Datasets from the database can be found at http://ircan. unice. fr/ER/ER_plotter/about. , 2, 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 96, 120 , and 144 hpa. Aiii: Multiple transcripts can be queried simultaneously. Aiv: Users can also directly explore co-expression clusters from embryogenesis and regeneration to identify groups of co-expressed genes. B) Screen shot of results from searching the annotations using the term "Brachyury". C) tBLASTn results using Mus.
Ethnic Diversity and Chance to Find a Donor As of January 2018, more than 32 million potential AUDs and CBUs are listed in the global Search & Match Service of WMDA. Almost 95%   1997 1998 1999 2000 2001 2002 2003 2004 2005   The chance of finding a well-matched donor varies for patients belonging to different ethnic groups. In 2014, the National Marrow Donor Program (NMDP) study demonstrated that whereas approximately 75% of Caucasian patients are likely to identify an 8/8 HLAmatched AUD, the rate is much lower for ethnic minority and mixed-race patients. This is due to the higher genetic diversity of HLA haplotypes in African and Asian populations compared to Europeans and the lower representation and poorer availability of ethnic minority donors in the worldwide pool  (Gragert et al. 2014).
Donor Profile WMDA defines an unrelated donor as "a person who is the source of cells or tissue for cellular therapy product. Donors are unrelated to the patient seeking a transplant". Donor centres recruit volunteer donors from 16 to 55 years of age with variations in individual policies. Although donors can remain on the database until they are 60, donor centres try to recruit more young volunteers, as donor age has been proven to be linked to better HSCT outcomes  (Kollman et al. 2016). According to the World Marrow Donor Association (WMDA) data, approximately 50% of donors listed globally are younger than 35 (see Fig. 3. 3). Medical suitability for donation, gender diversity, behaviour and psychological risks are constantly changing factors in donor recruitment and management. Donor centres align their policies with national and international standards and recommendations, including donor suitability guidelines produced by the WMDA on https://share.
Recruitment, Retention and Data Confidentiality volunteer is challenging. Registries and donor centres must ensure they are recruiting the preferred donors (usually younger donors) who are appropriately counselled to fully understand their commitment. Registries and donor centres use a combination of methods to recruit potential donors including patient-related drives, targeting special groups, e. g. universities, uniformed services, engaging blood donors or online recruitment. The approach depends on the laws of the country and takes traditions, religion and habits into account. The same factors influence donor retention. Considering several options and alternative donors in urgent cases is a recommended practice. By signing to a donor centre or registry, a potential donor agrees that his/her data are registered in the global database.
Donor Attrition Time to transplant is reported to be a factor of overall survival  (Craddock et al. 2011). Formal search for an unrelated donor on average takes about 2 months. However, more and urgent search requests are to registries, where transplant centres are hoping to get a donor workup in weeks rather than months. Not all potential donors listed on the database will be available for donation due to different reasons, including medical or personal circumstances or loss of contact with the registry. It varies in different countries. According to WMDA annual questionnaire, in 2017, the recommended target for donor availability at verification typing stage was 80% and at work-up stage 95%. Registries and donor centres are working hard to keep in contact with their donors to have updated information to help reach the donor without delays.
Spectroscopic Membership Determination We identified a total of 31 members in the AAT and IMACS combined dataset, 9 of which are members in the Tuc III core and were previously confirmed in  Simon et al. (2017). The other 22 members are in the Tuc III tails and are identified for the first time in this work. Adding the 26 members from the Tuc III core from  Simon et al. (2017) , the total sample in the Tuc III stream increases to 48 stars. As shown in Figure  1 , the member stars form a clear peak around v hel ∼ -100 km s -1 and [Fe/H] ∼ -2. 5. Though a few candidate stars are at a similar velocity or metallicity if we only consider one property or the other, combining the two quantities separates the Tuc III members from non-members, as shown in the lower right panel of Figure  1. We note that the membership identification of Tuc III stream members is a subjective selection process using the following parameters: velocity, metallicity, color, magnitude, and the spectrum itself.
The reanalysis from  Shipp et al. (2018)  with improved datasets indicate the stream is slightly wider at σ w = 0. 18 •. Surprisingly, we found three apparent member stars at least 3σ away from B = 0 (see Figure  2 ), including one BHB member (B = -0. 69 • ), one RGB member (B = -0. 692 • ) and one RHB member (B = -0. 54 • ), respectively. Finding 3 out of 22 members at B > 3σ w may indicate that the stream profile is non-Gaussian. This can naturally arise if Tuc III is a globular cluster since while ∼ 5% of stars will escape per relaxation time due to tidal stripping  (Hénon 1961) , ∼ 1% will escape from the core (e. g. Spitzer 1987; Baumgardt et al. 2002; Alexander & Gieles 2012 ) with a much larger velocity dispersion due to multi-body interactions. The ∼ 17% stars ejected from the core would thus produce a much broader stream than the stars which were tidally stripped.
3. 2. Velocity Gradient As shown in Figure  1 , a clear velocity gradient, with increasing velocity towards larger right ascension (α 2000 , or equatorial east), is present in the Tuc III stream. In this section, we calculate the systemic velocity and velocity gradient using the tail stars measured in this paper, and compare the systemic velocity with what was derived in  Simon et al. (2017). We also perform the fit with different setup to check if the results changes with different fitting parameters and different datasets. The results are summarized in Table  2. We first fit the RVs (v) and RV uncertainties (δ v ) of 21 tail members (after excluding one probable binary member) via a maximum likelihood approach and a 3parameter likelihood function similar to  Li et al. (2017)  to derive the systemic velocity v hel of the stream, i.
A comparison of the posterior distributions of the velocity gradient and velocity dispersion of the Tuc III tails using 22 tail members from this work and those of the Tuc III core using 26 core members are shown in the left and middle panel of Figure  5. For both datasets, the 4-parameter fit described above is used. In  Simon et al. (2017) , the velocity dispersion of the Tuc III core was not resolved (i. e. , σ < 1. 5 km s -1 at 95% confidence level). As shown in the middle panel of Figure  5 , the velocity dispersion of the tails is likely slightly higher than the core, though the posterior distributions of the two largely overlap. The larger velocity dispersion in the tails may be a natural consequence of the ongoing tidal disruption.
Of all the thin streams known so far, the tidal tails of the globular cluster Palomar 5 (Pal 5) are in many ways similar to the Tuc III tails -both have an unambiguous progenitor identified and a similar velocity dispersion (see below). First detected in SDSS  (Odenkirchen et al. 2001) , the stellar stream of the Palomar 5 globular cluster extends over at least 22 •  (Ibata et al. 2016). A velocity gradient (in heliocentric frame) between 0. 4 -1. 0 km s -1 deg -1 was first detected by  Odenkirchen et al. (2009)  and later confirmed with larger data sets  (Kuzma et al. 2015; Ibata et al. 2017 ). While at a similar heliocentric and Galactocentric distance, it is noteworthy that Tuc III possesses a velocity gradient 10× larger than that of Pal 5. Ibata et al. (2017)  find that the stellar mass of the tidal tails is 3× the mass of the core, which is very similar to the Tuc III stream, though Tuc III appears shorter on the sky (5 • vs.
2010; Blaña et al. 2012). Regarding the ultra-faint dwarf Segue 2,  Kirby et al. (2013)  argued that it was tidally stripped because it does not lie on the stellar mass-metallicity relationship. A similar argument was made for the Tuc III core  (Simon et al. 2017). Including the stellar mass in the tidal tails will move Tuc III into agreement with other dwarf galaxies with respect to the stellar mass-metallicity relationship. In contrast with Tuc III, there are not clear tidal features seen in the Segue 2 stellar distribution, even with data that reaches the main-sequence of Segue 2 (Belokurov et al. 2009).
Additional metal-poor members farther from the center of the stream may be found in future observations if such a metallicity gradient is genuine. We found that in a color-color diagram of g -r vs. r -i, all the member stars in the Tuc III stream are systematically redder in r -i color (or bluer in g -r) than most non-member stars. The high precision of DES photometry allows us to identify metal-poor stars photometrically. This metallicity-dependent color offers a more efficient method for selecting metal-poor targets and will increase the efficiency of selecting stream members for future spectroscopic follow-up programs. Furthermore, the color-color selection can eliminate foreground metal-rich disk stars and improve the detection significance in finding dwarf galaxies and stellar streams using DES data (or other imaging surveys with a similar or better photometric precision).
Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. 92-594724. 9 355. 54135 -59. 79024 19. 806 19. 398 DR1 AAT 2. 4 -129. 83 DES J234258. 94-591925. 4 355. 74557 -59. 32372 19. 784 19. 363 DR1 AAT 6. 0 -112. 15 DES J234310. 16-595556. 6 355. 79232 -59. 93240 20. 956 20. 758 DR1 AAT 3. 1 -82. 66 DES J234311. 08-590221. 5 355. 79615 -59. 03932 19. 330 18. 851 DR1 AAT 2. 2 -73. 82 DES J234325. 17-594435. 1 355. 85489 -59. 74308 18. 763 18. 320 DR1 AAT 4. 5 -82.
TSL thanks  Jo Bovy  and  Sergey Koposov  for helpful conversations. JDS acknowledges support from the  National Science Foundation  under grant  AST-1714873. ABP acknowledges generous support from the  George P. and  Cynthia Woods Institute for Fundamental Physics and Astronomy at Texas A&M University. DE thanks  Mark Gieles  for helpful discussions. DE acknowledges financial support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement no. 308024. EB acknowledges financial support from the  European Research Council. The authors thank the anonymous referee for careful reading of the manuscript and providing useful comments. Based on data obtained at Siding Spring Observatory via program A/2016A/26. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present.
(Bechtol et al. 2015)011)otlib(Hunter2007), numpy(Van Der Walt et al. 2011), PyGaia 12 , scipy(Jones et al. 2001), ugali(Bechtol et al. 2015)13. Velocity and metallicity measurements of the observed stars. Comment binary BHB BHB subgiant MSTO RHB Prob e 0. 00 1. 00 1. 00 0. 99 0. 00 0. 99 0. 98 0. 98 0. 97 0. 91 0. 99 0. 92 1. 00 0. 74 0. 00 0. 90 0. 98. 0. 99 1. 00 0. 99 0. 83 0. 81 0. 89 0. 88 1. 00 0. 88 0. 00 0. 00. 0. 49 0. 78 0. 00 0. 86 0. 87 0. 85 1. 00 0. 99 0. 82 Mem d 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 The DES partic-ipants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union.
IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has re-ceived funding from the European Research Council under the European Union's Seventh Framework Pro-gram (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Ex-cellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Insti-tuto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U. S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Gov-ernment retains and the publisher, by accepting the arti-cle for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevoca-ble, world-wide license to publish or reproduce the pub-lished form of this manuscript, or allow others to do so, for United States Government purposes.