dataesr/scientific-paragraphs-categorization huggingface dataset:
From a Euclid perspective, the combination with multiband, deep optical photometry is essential for cluster mass estimates at all redshifts in order to identify background galaxies. From an LSST perspective, the addition of NIR photometry enables precise photoz's beyond z ∼ 1. 4, and thus an increased density of background sources with secure redshifts for clusters at z 1. The combination of LSST high signal-to-noise ratio (S/N) optical photometry and Euclid space-based shape measurements is well suited to push weak-lensing studies to higher cluster redshifts z cluster 1-1. 3. The required background source galaxies at z 1. 5 are still well resolved in the Euclid VIS data for shape measurements. Even if individual galaxy photo-z's may be too noisy, the faint high-redshift tail of the expected galaxy distribution can still be selected from deep LSST colors (and calibrated on the deep fields) with low to moderate contamination from the foreground and cluster galaxies (see Figure  1  and  Schrabback et al.
Transients and Variable Sources The transient survey of LSST is one key observational scientific difference with Euclid. The Euclid wide survey will be covered in only a single visit, making it inadequate for transient searches (the Euclid deep fields will have multiple visits over six years). However, transient and variable source searches could still be enhanced through coordination. For example, cosmological analyses based on Type Ia supernovae (SNe Ia) from LSST would benefit from NISP data on the host galaxies, e. g. , Euclid could provide spectroscopic redshifts, via NISP spectroscopy (NISP-S), for a fraction of LSST SNe Ia host galaxies, helping to remove possible systematic biases when using photometric redshifts alone  (Olmstead et al. 2014). Beyond improving redshifts, the Euclid data would help improve the classification and photometry of LSST transients.
This will be timely in that Euclid will have been operating for long enough that systematics should be under control and the coordination of fields, and/or including a trigger for follow-up, will be of great use in improving classifications. Moreover, for SNe Ia, even a few serendipitous NIR data points overlapping with the LSST light curve (NISP-P and NISP-S) could improve the model fitting of these joint data, thus producing better distance estimates for a subsample of LSST SNe. That is, SNe Ia are better "standard candles" in the NIR  (Wood-Vasey et al. 2008). It is well-known that SNe Ia have a second light curve maximum in the NIR, thus extending their lifetime (in the observer frame) and increasing the opportunity for serendipitous detections by Euclid. Second, the high resolution Euclid VIS imaging will provide important extra information about the host galaxies.
For the color-redshift method, Euclid and LSST have similar requirements on sample purity, quality, and knowledge of the selection function. At present, the Complete Calibration of the Color-Redshift Relation (C3R2;  Masters et al. 2017 ) project aims to be complete to RIZ < 25 using spectroscopic data from Keck and VLT (located in several LSST deep fields including CDFS, SXDS, and COSMOS). This should be sufficient for the Euclid wide survey, which will reach to RIZ < 24. 5 AB magnitudes. Although not as deep as the LSST lensing sample of i < 25. 3, this Euclid training set will cover a significant fraction of the LSST color-color space for galaxies, so that a smaller number of additional spectra will be needed to calibrate LSST. Based on previous studies,  Newman et al. (2015)  estimate that ∼ 20, 000 spectra in total would be needed to calibrate the LSST color-redshift relation down to the LSST weak lensing imaging depth.
Numerical Simulations and Supercomputing LSST and Euclid will each require extensive numerical simulations for a range of tasks including forecasting, developing analysis techniques, and the eventual analysis of the observational data. Carrying out the simulations, transforming them into synthetic sky maps, validating the results, and serving the data in an easily accessible way are all major efforts; they require large computing and storage resources in addition to a sufficient workforce to develop the modeling and analysis pipelines. Quantifying the supercomputing resources required for numerical simulations for each survey is a challenging process. LSST has around 30 expert members that have been active in defining cosmological software and hardware infrastructure requirements. Two reports have been delivered that define the computational requirements for LSST cosmology for the years 2016-2022.
Morphological Classification of Galaxies and Machine Learning Galaxies display a wide range of morphologies, which encode information about their potential energy and angular momentum, as well as their cold gas content, the influence of cosmic environment on galactic structure, and the history of mass accretion and mergers from which the galaxies were built. As morphology is considered a fundamental property of the galaxy population and provides a window into the origin and fate of individual galaxies, surveys of galaxies like LSST and Euclid are significantly enhanced through the morphological classification of each object they discover. LSST and Euclid will provide images for billions of galaxies. Although each galaxy will have many parametric measurements (luminosity, shape, size), the sheer size of this database will be prohibitive for manually (visually) classifying these galaxies using the traditional Hubble sequence.
Photo-z codes having a long history of development and have matured rapidly in recent years. The requirements on scatter and out-lier rates for Euclid have been achieved in real-world datasets with existing codes  (Laigle et al. 2016; Hildebrandt et al. 2010) , although the demonstrations to date at Euclid 's depth have utilized substantially more bands of photometry than Euclid +LSST would provide. These codes also meet requirements for LSST on simulations  (Graham et al. 2017) , albeit with the assumption of perfect knowledge of templates or very large representative training sets. Calibration requirements are also similar for both surveys ( §3. 1). Apart from algorithms, a key requirement for photometric redshifts for both projects will be enormous training and calibration samples that may only be obtained with highly multiplexed optical and infrared spectrographs on large telescopes.
Methodological There are several ways in which methodological coordination can lead to improved reproducibility and new science. In developing methodologies for cosmological probes and algorithms to measure these observables, inter-experiment collaboration is already helping in many ways. As it is difficult to develop common algorithms to work on the raw-data analysis, catalog-level manipulation to create summary statistics is more commonly done. There are many case studies from current surveys in which common inter-experiment algorithm development leads to more robust and validated code, for example CAMB  (Lewis et al. 2000) , GalSim  (Rowe et al. 2015) , and SNANA  (Kessler et al. 2010). In addition to individual-experiment cosmological probes, the multi-experiment combination enables additional statistics to be applied to the joint dataset.
This must be defined early on as astrometric corrections are applied before cataloging takes place. Coordination at the methodological level should also occur to clarify the particular approaches used for object detection and thus significance. Beyond increasing the reliability of sources inside the catalogs, the benefit of coordinating at the catalog level can be to allow the creation of common source lists between the two datasets, for instance, to build samples on which shear measurement calibration can be inspected/compared. A limit of coordination at the catalog level is that it is not sufficient to guarantee the compatibility of photometric measurements, unless explicit coordination has also happened for the definition of a photometric reference system and for the measurement approach itself.
Coordination in photometric measurements is also a possibility. As § 2 mentions, both collaborations have an interest in accessing each others' photometric capacities: Euclid to cover its wide visible band with a set of narrower filters and LSST to increase its wavelength coverage with the Euclid NIR bands. In that context, one can envision a coordination at the photometric measurement level, each collaboration providing, as a service, forced photometry on its imaging data. This is probably the minimum level of coordination (above no coordination at all). It is probably also the level where data independence is maximum. To achieve this, however, once again a number of steps have to be taken so that the imaging datasets are compatible with one another. Quite obvi-ously, these will include defining common astrometric and photometric references, but also sharing information on data processing steps up to the construction of calibrated images (individual and co-added) so that uncertainties in the photometry are properly taken into account.
Survey planning There are a number of opportunities for coordination within the planning of the LSST and Euclid surveys. These include common survey footprints, depth as a function of passband, time of observations (including contemporaneous observations), software for processing and calibrating the pixel data, common calibration catalogs, and interfaces for sharing catalogs and resources. Many of these coordination activities can be accomplished prior to the launch of Euclid or the engineering first light for LSST. As described in §4. 4, for the main LSST survey area, comprising 7,000 square degrees of overlap, the LSST will reach the equivalent depth of Euclid within the first ∼10 months of observations for all except the u-band observations. Prioritizing a uniform sampling of the sky by the LSST (as opposed to a rolling cadence where less of the sky is observed but more frequently) and optimizing the scanning strategy for Euclid would maximize the speed at which this joint dataset could be derived (although the impact of such a change on LSST transient science would need to be studied).
Tier 1 or Cross-survey Associations would connect individual entries in the catalogs across the two missions, establishing one-to-one as well as one-to-many mappings. This would require coordination on the databases and their designs. In establishing the mappings, positional and photometric comparisons would be used in the first round, and other parameters would be added if needed in later rounds. These associations would take into account the effects of varying spatial resolution in the different surveys and the astronomical properties of galaxies, such as their extent on the sky. They would characterize the severity of confusion for objects or parts of the sky. The products from Tier 1 would include association tables listing for each object in survey A all of the related objects in survey B, with annotation about the details of the relations, e.
The LSST image processing algorithms already need to handle a factor of 2. 5 in wavelength (350nm -1µm), and extending this to NIR data may be merely a matter of degree, depending on the difficulties imposed by the undersampling in NISP data. Additionally, the LSST data are deep enough that adding Euclid data is not expected to add significantly to the number of objects being characterized. It is likely that both datasets will need to be ingested into both pipelines, given that the processing methodologies, driven by the different scientific use of the data, could well be very different. This approach would also address some of the concerns about loss of independence of the two pipelines and cross-checking of results and cosmological constraints. A deciding factor here will involve the trade-offs between additional costs of code adaption and integration, processing, and storage and the savings from sharing code, storage and processing.
Acknowledgments: J. R. and A. K. were supported by  JPL , which is run under a contract for NASA by  Caltech. J. R. , A. K. , P. C. , R. B. , R. L. and  R. Mandelbaum  were supported in part by  NASA  ROSES grant  12-EUCLID12-0004. A. N. T. is supported by a  UK Space Agency Euclid  grant and a  Royal Society Wolfson Research Merit Award. Y. M. was supported by the  French CNES Space Agency and Institut National des Sciences de l'Univers of CNRS. T. S. acknowledges travel support from the  German Federal Ministry for Economic Affairs and Energy (BMWi)  provided via  DLR  under project No. 50QE1103. T. K. is supported by a  Royal Society   University Research Fel-lowship. V. F. C. is funded by the  Italian Space Agency (ASI)  through contract  Euclid -IC (I/031/10/0 ) and acknowledges financial contribution from the agreement  ASI/INAF/I/023/12/0.
Zoantharians are a group of cnidarians that are often found in association with marine invertebrates, including corals, in shallow and deep-sea environments. However, little is known about deep-sea zoantharian taxonomy, specificity and nature of their associations with their coral hosts. In this study, analyses of molecular data (mtDNA COI, 16S, and 12S rDNA) coupled with ecological and morphological characteristics were used to examine zoantharian specimens associated with cold-water corals (CWC) at depths between 110 and 800 m from seamounts and island slopes in the Azores region. The zoantharians examined were found living in association with stylasterids, antipatharians and octocorals. From the collected specimens, four new species were identified: (1) Epizoanthus martinsae sp. n. associated with the antipatharian Leiopathes sp.
External Morphology The external morphology of specimens was examined using preserved material and images of live specimens (only for zoantharians associated with the octocorals Callogorgia verticillata and the antipatharian Leiopathes sp. ) maintained in aquaria at the DeepSeaLab facilities of DOP-UAz. Live specimens of zoantharians associated with C. verticillata and Leiopathes sp. were collected from the southern slope of the Faial-Pico Channel (38 • 30'N 28 • 37'W) at depths of 320-360 m with the manned submersible "Lula" (Rebikoff-Niggeler Foundation) on September 2009. Observations of zoantharian colonies were made using a dissecting microscope Leica MZ 16FA, and measurements taken using Image-J 1. 45 software  (Rasband, 2012). The following data were obtained for the characterization of zoantharians: for preserved specimens, polyp dimensions (oral disk diameter, polyp height), color of polyps, presence of particle encrustation on zoantharian's ectoderm (e.
g. , sand, foramiferan tests, sponge spicules, and coral sclerites) and coral host species. For live specimens (zoantharians associated with the octocoral C. verticillata and the antipatharian Leiopathes sp. ), polyp dimensions of live polyps and tentacle number were also noted. Data on polyp dimensions are presented as the minimummaximum (n = 7-38 polyps/colony, 1-3 colonies per species). In the case of zoantharians associated with gorgonians, the observed sclerites within the zoantharian specimens were compared with gorgonian sclerites after tissue removal using a sodium hypochlorite solution. In the case of the zoantharian associated with C. verticillata, morphological information presented in this paper, including observations and measurements on live specimens, is based on newly collected material in the Azores and Mediterranean, and complements previous information already included in the original description of the zoantharian in  Carreiro-Silva et al.
Non-template controls were included to all PCR reactions to detect any genomic DNA contamination. Electrophoresis of PCR products on a 1% agarose gel was performed to evaluate the integrity of the products. Finally, all amplified products were purified using ExoSAP-IT (USB Corporation) and sent for sequencing to the BMR Genomics (Padua, Italy) facility using the same set of primers. Newly obtained sequences were deposited in the NCBI database. GenBank accession numbers for all molecular markers and for each species used to reconstruct the phylogeny are reported in Supplementary Table  1.
Phylogenetic Analyses Additional sequences of closely related species (Supplementary Table  1 ) were retrieved from the NCBI database to reconstruct the phylogeny of zoantharians from the Azores. All sequences were aligned using MAFFT v. 7  (Katoh and Standley, 2013)  and sections with large indels were rearranged by eye. For the 16S, the V5 region was aligned following  Sinniger et al's (2013)  suggestions. In addition, indel events were coded as binary characters with SeqState  (Müller, 2005)  using simpleindel coding approach (SIC;  Simmons and Ochoterena, 2000)  and added to the existing alignment. Moreover, 16S and 12S rDNA alignments were also automatically edited by GBlock v. 0. 91b  (Castresana, 2000)  to eliminate potentially poorly aligned positions; the configuration was set in agreement with the suggestions from  Montenegro et al.
3. 3  (Miller et al. , 2010). BI was done implementing the HKY+G model  (Hasegawa et al. , 1985; Yang, 1993)  for DNA sequences and a binary model for the coded indel events, assuming unlinked parameters and rates for each gene and a site-specific rate model for the protein-coding COI gene. BI calculations were conducted with the help of the BEAGLE library  (Ayres et al. , 2012)  and run for 20,000,000 generations until the standard deviation of split frequencies value was less than 0. 05. For the ML approach, DNA sequences were analyzed in a partitioned dataset (COI, 16S, and 12S) following a RAxML Workflow interface and using the Maximum Likelihood/Thorough Bootstrap approach and GTR+gamma model, while an autoMRE criterion was used for bootstrapping (660 bootstrap iterations). In both approaches, representatives of the family Epizoanthidae  Delage and Hérouard, 1901  were used  as an outgroup and inferred trees were viewed with Figtree v.
Nevertheless, specimen (DOP-4090) from the Voador Seamount clearly falls within the Parazoanthus s. str. clade and represents a FIGURE 7 | Bayesian phylogenetic tree obtained using concatenated sequences (COI, 16S, and 12S) and indel events coded as binary characters. Values at branches represent Bayesian posterior probabilities and bootstrap values obtained for the Maximum Likelihood tree. Nodes where Bayesian posterior probability was under 0. 50 are collapsed, while nodes with only Bayesian support and no bootstrap values were not inferred with the Maximum Likelihood approach. Samples collected in this study are listed in brackets next to the species names, while (*) marks the sequences collected from the NCBI database; list of species with associated Genbank accession numbers is available in Supplementary Table  1.
ACKNOWLEDGMENTS This study was supported by the  European Union 's  Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) through a  Marie-Curie IRG Fellowship  to  MCS (CoralChange , project no  231109 ), project  CoralFISH  ( FP7 ENV/2007/1/21314 ), the  Portuguese  funded project  CORAZON  ( PTDC/MAR/72169/2006 ) and  CONDOR  project [supported by a grant from  Iceland, Liechtenstein, Norway  through the  EEA Financial Mechanism  ( PT0040 /2008 )]. This work is a contribution to the  ATLAS  project and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 678760. The paper reflects the authors' views and the  European Union  is not responsible for any use that may be made of the information it contains. MCS was supported by an invited scientist grant by  Fundação para a Ciência e a Tecnologia (FCT)  through the strategic project ( FCT/UID/MAR/04292/2013 ) granted to MARE.
ÍS was supported by an  FCT   PhD grant  ( SFRH/BD/101113/2014 ). The authors wish to thank the monitoring programme ARQDAÇO funded by the  Regional Government of the Azores  and  DEECON  project (ESF proposal No  06-EuroDEEP-FP-008  and  SFRH-EuroDEEP/0002/2007 ), for providing specimens. The authors are also grateful to  Ricardo Medeiros  for map elaboration,  Rodrigo Sá da Bandeira  and  Maria Rakka  for help with figures elaboration and  Domitilia Rosa ,  Valentina Matos , and  Andreia Braga-Henriques  for their help with the histological techniques. The  Rebikoff-Niggeler Foundation  is gratefully acknowledged for their in situ photographs and collection of zoantharian live specimens taken within the  CORAZON  project. Special thanks go to  Lea-Anne Henry  for her precious help proofreading English language and grammar, and  Bert Hoeksema ,  Kei Matsuyama , and  Max Wisshak  for helping with nomenclature questions.
An ABA responsive element-like motif was enriched in the promoters of DEGs Motif analysis was conducted on the upstream promoter sequences of DEGs for discovery of cis-regulatory elements that might give insight into the observed gene expression patterns. A motif was identified that resembled the ABA responsive element (ABRE), a G-box family motif recognized by bZIP family transcription factors that is found in the promoters of many ABA responsive genes  [31]. This ABRE-like element was enriched in the promoters of genes up-regulated in the susceptible interaction at high temperature at 3 hpi, genes down-regulated in the susceptible interaction at 24 hpi, and genes down-regulated in the resistant interaction at all time points (Fig 7 ; S8 Table ). Several other motifs identified from the Plant cis-acting Regulatory DNA Elements database  [32]  were also enriched in the DEGs, including motifs resembling the TATA box, the light-responsive IBOXCORENT, the anaerobic-responsive GCBP2ZMGAPC4, the root growth-related TELOBOXATEEF1AA1, and the axillary growthrelated UP2ATMSD (Fig 7 ; S8 Table ).
RNA from pathogen-treated leaves was submitted to Michigan State University Genomics Core for TruSeq mRNA library preparation with multiplex barcodes and sequencing via Illumina HiSeq 50 bp single read sequencing. For qRT-PCR, cDNA was generated from the previously collected RNA using Quantabio qScript cDNA SuperMix kit. Primers and thermal cycler conditions for qRT-PCR follow  [50]  for bZIP23, bZIP72 and  [51]  for VSP2. Data was analyzed using the ΔΔCT method  [52].
Gene expression analyses Sequence reads were processed with FASTX Toolkit 0. 0. 13  [53]  to remove low quality reads. The high-quality reads were aligned to the MSU RGAP 7. 0 rice reference genome  [54]  using TopHat 2. 1. 1  [55]  and counted using HTSeq 0. 6. 1  [56]. Sequence reads and gene counts are available in the Gene Expression Omnibus repository under accession number GSE95668. Differential gene expression analyses were conducted using the Bioconductor package edgeR  [57, 58]. Genes were considered differentially expressed in a condition if FDR-corrected p-value was less than or equal to 0. 01. Rice wound response genes were identified from publicly available microarray data (NCBI Gene Expression Omnibus Accession GSE77097). The top 100 genes were chosen from this data by fold change.
Fisher's exact test was used for GO term enrichment analysis. A GO term was considered statistically significant if FDR-corrected pvalue was < = 0. 05. Heatmaps were prepared using the heatmap. 2 function from the R package gplots  [59]. Hierarchical clustering of hormone biosynthesis genes was performed with the hclust R function using the WPGMA method  [49]. Hormone-responsive genes used in analysis were identified from a microarray study  [30]. Kernel density estimates were prepared with the density function from the R core package  [49]. DREME  [60]  was used for motif discovery with the 1000 bp sequences upstream of putative transcription start sites from the reference genome. STAMP  [61]  was used for DNA motif matching.