dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging data This section describes the selected input imaging data and its associated metadata. For the purpose of this work, only the highlevel metadata details describing each data product are required for the cross-match algorithms. These metadata are publicly available through each individual ESA archive, either via direct web access, or via their table access protocol (TAP) 12  services. This protocol was developed within the scope of the International Virtual Observatory Alliance (IVOA) and is aimed at providing basic access to all public metadata tables set on each archive or scientific data center. The standard metadata columns involved in this work are: the start time of the observation (usually referred to as t_min in the IVOA Observation Data Model Core Components standard, or ObsCore standard 13  ); the end time of the observation (or t_max); the exposure duration (t_exptime); and the instrument footprint or field of view (FoV) of the observation in sky coordinates (s_region).
From the above time metadata columns, we found that the t_exptime is a more reliable tracker on the total amount of real exposure time than the time difference between the start and the end time of the observation. The difference in time is considered to be the total time required to execute the observation, including time on source, internal calibrations, slewing, settling, etc. In other words, this time difference is usually the effective exposure time on source, plus all the overheads that are required to complete the observation. Hence, the t_exptime metadata was used for the pipeline cross-match software. However, as shown in Sect. 4, there are some deviations to this definition (i. e. , exposure duration values longer than time differences) that can lead to caveats in our catalog. This is true because, as described in Sect.
3, this work is particularly sensitive to the time information, so any discrepancies between the published metadata available in the archives with respect to the real timestamp of the observation will introduce false detections in our final catalog. Finally, the high-level metadata information available in each individual archive is also integrated and available through the ESASky server, either from the client interface or through the TAP protocol from any Virtual Observatory application (e. g. , TOPCAT 14  ,  Taylor 2005). These metadata views are the curated high-level metadata representation for a given observation, that is, it provides a single set of metadata (i. e. , row entry) per observation. Thus, these are the final metadata tables used as input in this work. ESASky integrates all ESA astronomy high-level metadata and associated data products and also provides access to other external facilities (ESO, MAST, and CADC) high-level products via TAP.
Ephemerides sampling For this sampling, an even time sampling of the apparent position of the SSO from each satellite point of view is performed first and stored locally for a fixed time interval of ten days. This computation is performed with the Eproc v3. 2 suite provided by IMCCE  (Berthier 1998). The time span of this sampling is linked with the life-time of each mission. To take into account the satellite reference frame for the ephemerides computation, Eproc software was provided via the SPICE spk kernels 16   (Acton et al. 2018)  with the orbital information for each mission. In the case of the HST, this file was publicly available at NASA's Navigation and Ancillary Information Facility (NAIF 17  ), whereas the XMM-Newton kernel was provided by the Science Operations Centre (SOC) at ESAC.
Results The full list of all potential detection candidates from the SSOSS is publicly available via the ESASky TAP server 18  , which can be accessed in a variety of ways, for instance, from the VO tool TOPCAT and from the astroquery TAP module  (Ginsburg et al. 2019). These tables provide both the start and end time positions and uncertainties for each detection based on the time information in each observation, including the theoretical apparent magnitude in the V band, the theoretical proper motion components, and the distance to the SSO from the satellite's PoV. In this section, we analyze these results for the asteroid population and describe the different estimations applied to compute the limiting magnitude or flux sensitivity for each of the instruments included in this work to produce a final catalog of asteroid serendipitous detections above the instrument's sensitivity.
This final catalog included in this work is a subset from the tables available via the SSOSS, thus containing the same metadata columns plus an additional column per instrument to ease the identification of potential detections above instrumental noise, either with the theoretical flux value for IR detections (Herschel), the limiting magnitude per observation (XMM-Newton) or the expected signal-to-noise ratio (S/N) per detection (HST). These results are available as a subset of the above-mentioned TAP server, under the sso_20190705 schema. To illustrate the output from this service, we provide for each mission a small sample of the brightest serendipitous detections to illustrate the information provided in this catalog (Tables  B. 1-B. 3 ). Taking into account the fact that the SSO cross-match pipeline had to be run separately for each mission (different satellite point-of-view), we provide a total of 3 tables of potential detections of asteroids.
These tables contain: (1) object name or preliminary designation as provided by the Lowell Observatory (astorb), or by the IMCCE (cometpro); (2) object identifier if provided; (3) observation identifier; (4) start position (J2000 equatorial coordinates RA 1 ,Dec 1 in degrees) of the object from the satellite PoV. This is the predicted position of the object at the start time of the observation, provided by the observation metadata stored in the ESDC astronomy archives; (5) end position (idem, RA 2 ,Dec 2 in degrees) of the source from the satellite point-of-view at the end time of the observation, The total number of potential geometrical cross-match detections, targeted versus the serendipitous detections per satellite is presented in Table  2. The current implementation of this service provides the entire list of geometrical cross-matches between SSOs trajectories and instrument FoV.
Many objects may however be too faint to be detected in each observation, depending on the specific limiting magnitude or flux sensitivity integration time and energy band of each observation. Thus, in the following subsections, we analyze each instrument individually to present a selection of candidates above instrumental threshold. Future releases of the SSOSS will include this computation of the apparent magnitude of the SSOs at the wavelength of the observation automatically. Finally, it is important to note that the SSOSS was developed with the aim at providing a mission-agnostic tool to allow A38, page 6 of 20 SSO detection discoveries within the existing ESA astronomy archives. The standardization of this service does not allow full flexibility to accommodate the individual particularities of each mission data set.
HST For HST, the total number of detections returned by the crossmatch is 165 607. After removing planets and natural satellites from the sample, 134 974 observations remain of 14 367 distinct asteroids. Using the user-set TARGET and MOVING_TARGET metadata in the eHST archive, we identified about 58% of the computed observations to be targeted at these SSOs. For the remaining approximately 55 000 serendipitous observations, we modeled the source spectra in a first-order approximation using the STIS solar spectrum  (Bohlin et al. 2014 ). The solar spectrum was scaled to match the predicted source magnitude in the respective HST observation band. This magnitude was computed using the predicted V-band magnitudes provided by the crossmatch and color-correction terms derived from the solar spectrum for all pairs of the V-band and the HST filters.
We then computed the expected HST count rates using the pysynphot python package by the STScI, as well as an estimated background rate, yielding an approximate signal-to-noise ratio for each asteroid depending on the exposure time, observation wavelength, and predicted source magnitude. Simulating the observations revealed that about 59% (∼32 000) of the serendipitous detections should exhibit signalto-noise ratios above 3 and could thus be identified in the images. However, the actual number of serendipitous observations will be lower due to the ephemerides uncertainty of the SSOs: only 6% of the potential serendipitous observations belong to known SSOs where the orbit uncertainty is below 202 , about the size of one edge of the FoV of the Advanced Camera for Surveys (ACS) aboard HST. It is important to note that due to the inconsistencies in the reported start and ending times of the observations in the published metadata, a fraction of the cross-matches will be false detections.
The Herschel measurements alone cannot constrain Psyche's thermal inertia or surface roughness due to the unfavorable close-to pole-on viewing geometry. In our second analysis, we combined our new measurements with the above-mentioned CSO data point and published thermal measurements from IRAS, AKARI, and WISE as provided by the asteroid thermal infrared database 20  (Szakáts et al. 2020, and references therein). We also added the two rebinned Spitzer-IRS spectra (SL1 from 7. 5-14 µm from 2006 and SL1 from 5. 2-8 µm from 2004), as presented by  Landsman et al. (2018). We repeated our radiometric analysis using  Viikinkoski et al. (2018)  spin-shape solution for the combined thermal IR data set, now applying the Vesta-like emissivity model (see  Müller & Lagerros 1998, Fig. 4, lower panel)  to explain the PACS and SPIRE measurements.
Conclusions In this work, we present the ESASky Solar System Object Search Service, developed in the ESAC Science Data Centre and included in the ESASky application, with the intent of facilitating the scientific exploitation of the ESA archival astronomy data holdings by the Solar System community. A set of all the positional cross-matches and uncertainties, apparent magnitudes and distances, of all asteroids included in the astorb database by July 2019 with respect to all the high-level imaging observations stored at the ESDC Astronomy Archives for the Hubble Space Telescope, Herschel, and XMM-Newton Observatory is provided. Together with the results currently provided by this service, we included three catalogs with the selected potential detections with magnitudes or thermal fluxes above instrumental sensitivity.
Acknowledgements. MM acknowledges funding by the  European Space Agency  under the research contract  C4000122918. TM has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme , under Grant Agreement no 687378, as part of the project " Small Bodies Near and Far " (SBNAF). BC and JB acknowledge support from the  ESAC Faculty Visitor Programme. This research has made use of the  SVO Filter Profile Service  (http://svo2. cab. inta-csic. es/theory/fps/)supported from the  Spanish MINECO  through grant  AYA2017-84089.
Background: Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes. Result: Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity.
Background Due to decreasing cost of sequencing, RNA-seq has become a mainstay of gene expression studies on a wide variety of organisms spanning the tree of life. A vast majority of these organisms do not have well-annotated reference genomes or transcriptomes, which are required by standard RNA-seq data analysis pipelines. A conventional work-around for such "non-model" organisms has been to assemble a transcriptome sequence from the RNA-seq reads, which is then used as reference. Functional inference of differentially expressed genes are done by aligning the assembled contigs to reference protein databases to find orthologs. In some applications of RNA-seq in non-model organisms, transcriptome assembly, which requires massive computational resources, tends to be an overkill. This is especially the case when the main aim is to identify over or under-expressed genes, and not, say, to discover novel transcripts.
In such cases there is no direct interest nor enough sequencing depth to reconstruct good quality transcript sequences. Apart from being resource hungry, transcriptome assembly is also known to introduce errors such as over-extension, mis-assembly, etc  [1, 2]. In our prior work, we proposed the novel pipeline SAMAR  [3] , which directly aligns RNA-seq reads to a protein database -the one that would have been after-all used by the assembly-based approach for functional analysis -to measure expression and for subsequent differential expression analysis. Another recently proposed software tool Seq2Fun  [4]  also uses nucleotide-to-protein alignment for gene abundance quantification and functional profiling of RNA-seq data. An obvious outcome of this direct alignment approach is the drastic reduction of computational costs compared to the assembly-based approach.
More interestingly, we showed that this approach has significantly higher precision and recall than assembly-based approach in identifying differentially expressed genes. Here we sought to further speed up SAMAR, by exploiting the fact that gene expression levels can be estimated based on just the knowledge of which reference sequence(s) each read maps to, without the need to compute alignments. For the problem of mapping RNA-seq reads to a reference transcriptome, this idea of replacing traditional alignment by faster mapping techniques is known to provide significant speed-ups  [5, 6, 7]. We adapted to our case of mapping RNA-seq reads to a reference protein database, one such mapping technique, called quasi-mapping, the main idea of which is to determine the mapping by rapid look-ups of sub-strings of a query sequence.
Implementation details We implemented the quasi-mapping method in the Rust programming language  [10]  using the RustBio library  [11]  for the suffix array and succinct representation of the bit vector for efficient rank queries. We incorporated the mapping step, the counting step, and DESeq2  [12]  for differential analysis into a Snakemake  [13]  pipeline. The software is available at https://bitbucket. org/project_samar/samar_lite.
Read simulation For benchmarking, we used an RNA-seq read dataset that simulates a typical smallsample-size and low-coverage whole-genome gene expression experiment from our prior work  [3]. The dataset was generated using Polyester  [14]  from the proteincoding transcriptome (BDGP6. 28) of D. melanogaster obtained from Ensembl Genes 101 and containing 28,692 transcripts of 13,320 genes. We simulated two groups of read sets, with three replicates in each group. For the first group, we set the mean expression levels to be proportional to the FPKM values from an arbitrary poly-A+ enriched real RNA-seq data (ArrayExpress E-MTAB-6584). In the second group, around 30% of the transcripts were simulated to be differentially expressed with different levels of up-regulation and down-regulation. The transcripts were chosen by randomly selecting genes and setting only the highest expressing isoform to be differentially expressed.
Tools compared We compared our mapping performance against several existing tools for DNAprotein alignment/mapping of large data: LAST  [18]  (version 1060), DIAMOND  [19]  (version 2. 0. 12), and Kaiju  [20]  (version 1. 9. 0). We selected LAST as it was the alignment tool used in SAMAR  [3]. We selected DIAMOND because it is a widely used DNA-protein alignment tool for big sequence data, although mainly focusing on metagenomic data. Kaiju, which employs maximum exact matches (MEM) to compute mappings, was originally proposed for metagenomic data, but it has recently been used by Seq2Fun  [4]  for RNA-seq dataset profiling. The commands used to run each tool is provided in the Supplementary Material.
Evaluation metric We evaluated mapping correctness as follows. Consider a read r from a transcript of D. melanogaster gene g. Let M g be the set of protein products of g. When using D. melanogaster proteome as reference, r was defined to be correctly mapped if at least one of the proteins it was mapped to, is in M g. When using the other references, we determined correctness using pre-computed orthology maps between D. melanogaster-D. ananassae and D. melanogaster-D. grimshawi and D. melanogaster -A. gambiae pairs, which were obtained from InParanoid  [21]. We defined r as being correctly mapped if at least one of the proteins it was mapped to is in an ortholog group containing a protein from M g. A read with no mappings reported is not counted as correct or incorrect. Mapping performance and running times are plotted in Figure  2  and Figure  3 , respectively.
Pipelines compared Of the possible parameters for each tool, we used the normal preset for DIAMOND, greedy for Kaiju, maximum mismap probability of 0. 95 for LAST, and k-mer 7 with coverage threshold 40 for our method. The mappings were counted using the method described in the Methods section. For LAST, we used a slightly different variation offered in SAMAR, in which the counts are normalized by the length of the regions that have reads mapping to them rather than the entire length of the protein. The counts are then passed to DESeq2  [12]  for differential expression analysis. Additionally, to mimic the case of a non-model organism, we pretended that D. melanogaster reference transcriptome doesn't exist; and we ran a typical assemblybased pipeline consisting of: Trinity  [22]  for de-novo transcriptome assembly, followed by Bowtie2  [23]  for mapping the reads to the assembly, RSEM  [24]  for counting, tximport  [25]  for gene-level aggregation using the gene-to-transcript mapping provided by Trinity, and finally DESeq2  [12]  for differential analysis.
Discussion Our method is > 1000× faster than assembly-based approach while being more accurate We set out to build a quick-and-dirty differential gene expression analysis pipeline by avoiding transcriptome assembly and replacing a slower alignment step with quasimapping. As can be seen from the D. grimshawi and D. ananassae plots in Figure  4 , even when replacing sensitive full alignment step with the quasi-mapping step, our pipeline for identifying differentially expressed genes outperforms assembly-based approach. This gain comes with dramatic speed-up. Transcriptome assembly of a dataset containing a total of roughly 120 million pairs of reads, with 4 cores and 8 threads engaged took more than 7 days to complete. Our mapping takes less than 10 minutes for handling the same data, making it more than 100 times faster.
Effect of reference proteome The availability of an accurate reference protein database is key to the performance of all methods evaluated in this paper, but more so for our method which relies on exact k-mer matches. This is seen, perhaps unsurprisingly, in our evaluations, where our method seems to be most sensitive to evolutionary divergence. Another factor that could affect performance is the presence of large number of highly similar sequences in the reference. This could be as a result of high level of recent gene/genome duplications, high amount of repetitive sequences in transcripts due to transposable elements, or because the reference spans multiple species and contains orthologs (e. g. UniProtKB). For all methods, it would be interesting to investigate the extent of performance degradation due to these factors at the level of mapping and in downstream functional analysis.
LC-MS/MS and data analysis Rice seedlings were grinded by liquid nitrogen; core histones were extracted by H2SO4 and digested by trypsin. Lysine crotonylation (Kcr) and butyrylation (Kbu) peptides were enriched by pre-washed antibody beads (PTM Biolabs, Hangzhou). The eluted peptides were cleaned with C18 ZipTips (Millipore) according to the manufacturer's instructions, followed by analysis with LC-MS/MS. The resulting MS/MS data was processed using MaxQuant with integrated Andromeda search engine (v. 1. 5. 2. 8). Tandem mass spectra were searched against UniProt_Oryza sativa database (https://www. uniprot. org/) concatenated with reverse decoy database. False discovery rate thresholds for protein, peptide, and modification site were specified as 1%; minimum peptide length was set at 7; and the site localization probability was set as > 0.
ChIP-seq and data analysis DNA from chromatin immunoprecipitation was used to construct sequencing libraries following the protocol provided by the Illumina TruSeqChIP Sample Prep Set A and sequenced on Illumina HiSeq 2000 with PE 150 method. Trimmomatic (version 0. 32) was used to filter out low-quality reads. Clean reads were mapped to the rice genome (MSU7. 0) by Bowtie2 (version 2. 2. 5), allowing up to two mismatches. Samtools (version 0. 1. 19 ) was used to remove potential PCR duplicates, and MACS software (version 1. 4. 2)  [32]  was used to call histone modification peaks by default parameters (bandwidth, 300 bp; model fold, 10, 30; p value, 1. 00e-5). Wig files produced by MACS software were used for data visualization by IGV (version 2. 3. 88). Genes that have modification peaks in gene body or 2 kb upstream in both replicates were defined as modified genes.
RNA-seq and data analysis RNA samples were isolated using TRIzol reagent (Invitrogen). The RNA-seq libraries were prepared using the Illumina TruSeq RNA Sample Preparation Kit and sequenced on Illumina HiSeq 2000 with PE150 method. RNA-seq data were filtered by Trimmomatic (version 0. 33) to remove contaminations and low-quality reads. Tophat (version 2. 0. 14) and Cufflinks (version 2. 2. 1)  [33]  were used to map clean reads to rice genome (MSU 7. 0) and calculate differentially expressed genes. Genes with adjusted p value < 0. 05 and fold change > 4 in starvation or submergence treatments were considered differentially expressed.
Gene ontology analysis Gene ontology classification provided in Rice Genome Annotation Project (http://rice. plantbiology. msu. edu/) was used to assign genes to a hierarchical biological process following the criteria of the Gene Ontology Consortium databases (http://www. geneontology. org/external2go/tigr2go). The p value of a particular GO item was calculated with Pearson's chi-squared test and corrected by FDR, with an FDR cutoff of 0. 05 as the significance threshold.
Availability of data and materials The complete mass spectrometry data produced by this research can be found in ProteomeXchange (http://www. proteomexchange. org/) with the accession number PXD009091  [34]. The high throughput sequencing data generated in this study are deposited into GEO databases with the accession number GSE111308  [35]. Publicly available datasets used are as follows: H3K4me3 and H3K27me3 ChIP-seq data were from GSE19602  [36] , H3K4me2, and H4K12ac ChIP-seq data from GSE26734  [37] , H3K9me2 ChIP-seq data from GSE81436  [38] , drought-treated RNA-seq data from GSE81462  [39] , salt-treated RNA-seq data from GSE101734  [40] , and cold-treated RNA-seq data from GSE67373  [41].