dataesr/scientific-paragraphs-categorization huggingface dataset:
In the fitting using vpfit we assumed two components for FeII and MgI and six components for the saturated MgII. Velocity zero corresponds to the redshift of the main component from the joint fits of FeII2600 Å, FeII2382 Å, MgI2852 Å. The last panel shows a comparison of the MgII2796 Å absorption line profile with the expected absorption caused by an extended rotating disk of J0423A and J0423B at the position of the quasar sight-line. We find that the absorption cannot be caused by an extended disk of J0423A, but the velocity extrapolated from the disk of J0423B matches the absorption profile. We note, however, that due to the large impact parameter of 133 kpc the absorption is unlikely tracing the disk of J0423B. profiles using VPFIT 4 v. 10. 2. The VPFIT code is developed to fit multiple Voigt profiles to spectroscopic data by minimizing the χ 2.
ACKNOWLEDGEMENTS We thank the anonymous referee for a constructive report. The authors thank  Chris Churchill  for kindly providing us with the reduced HIRES spectrum of the quasar and  Sandhya Rao  for kindly providing us with the broad band images. We also thank  Nicolas Bouché  for his advice on the morpho-kinematic modelling using Galpak 3D. The authors thank  Hadi Rahmani  for useful discussion about intra-group medium. We are grateful to the  ESO  staff at Paranal and in Garching for performing the MUSE observations in service mode. AK acknowledges support from the  STFC  grant  ST/P000541/1  and  Durham University. CP was supported by an  ESO  science visitor programme and the  DFG cluster of excellence 'Origin and Structure of the Universe'. IRS acknowledges support from the  ERC Advanced  Grant  DUSTYGAL (321334 ), a  Royal Society/Wolfson Merit Award  and  STFC  ( ST/P000541/1 ).
Recent studies have shown the potential of DNA metabarcoding for identifying endangered species in TMs and other wildlife forensic samples  [5] [6] [7] [8]. DNA metabarcoding is an approach that combines DNA barcoding with next-generation sequencing (NGS), which enables sensitive high-throughput multispecies identification on the basis of DNA extracted from complex samples  [9]. DNA metabarcoding uses more or less universal polymerase chain reaction (PCR) primers to mass-amplify informative DNA barcode sequences  [10, 11]. Subsequently, the obtained DNA barcodes are sequenced and compared to a DNA sequence reference database from well-characterized species for taxonomic assignment  [9, 11]. The main advantage of DNA metabarcoding over other identification techniques is that it permits the identification of all animal and plant species within samples that are composed of multiple ingredients, which would not be possible through morphological means and would be timeconsuming with traditional DNA barcoding  [5] [6] [7].
For plants, for example, different sets of DNA barcodes have been suggested for different fields of application (i. e. , general taxonomic identification of land plants, identification of medicinal plants, etc. ), and none of them meets the true requirements of universal barcodes  [13]. Also, whilst PCR primers can be designed to accommodate shorter DNA barcode regions for degraded DNA samples, such mini-barcodes contain less information and their primers are more restrictive, often making them unsuitable for universal species barcoding  [5, 14]. The third challenge is the reference sequence database quality and integrity, which is particularly problematic for law enforcement issues, where high quality and reliability are essential. The current underrepresentation of DNA barcodes from species protected under CITES and closely related species critically hampers their identification.
In the process of establishing the standard operating procedure (SOP) for multi-locus DNA metabarcoding, all important aspects of the procedure (i. e. , DNA isolation procedure, DNA barcode marker, barcode primers, NGS strategy, and bioinformatics) were evaluated. The challenges concerning the quality and integrity of the DNA reference database(s) are discussed. The first step was aimed at identifying an ideal DNA isolation method to extract DNA from complex mixtures consisting of both animal and plant tissues. Second, animal and plant DNA barcode markers and corresponding primer sets were identified from literature that allowed good resolution for identifying (endangered) species from a wide taxonomic range. Third, a panel of universal plant and animal DNA barcodes was selected, and a single optimal PCR protocol was identified for efficient amplification of a panel of DNA barcode markers.
Data Description To constitute well-defined complex mixtures, 46 reference specimens were commercially purchased from shops or were provided by the Dutch Custom Laboratory. In addition, 2 TMs that were suspected to comprise endangered species material were also obtained from the Dutch Customs Laboratory. Each reference specimen was identified morphologically. Genomic DNA was extracted from 29 animal and 17 plant reference species for DNA barcoding. Standard cytochrome c oxidase I (COI) barcodes for all animal specimens were generated and individually sequenced using the Sanger method, and they were compared against the Barcode of Life Data Systems and NCBI database for taxonomic confirmation. For plant species, the DNA barcodes rbcL and matK were sequenced to confirm species identity. For a number of plant and animal species, the generated barcode sequence information was deposited in the European Nucleotide Archive (ENA) under accession numbers LT009695-LT009705 and LT718651 (Additional file 1; Table  S1 ).
). The NGS datasets were analysed using the CITESspecies-Detect pipeline. All raw NGS datasets from both analyses were deposited in ENA under accession numbers ERS1545972-ERS1545988, ERS1546502-ERS1546533, ERS1546540-ERS1546619, ERS1546624-ERS1546639, ERS1546742-ERS1546757, ERS1546759-ERS1546774, and study number PRJEB18620 (Additional file 3; Table  S1 ). A web-interface was developed for the CITESspecies-Detect pipeline to allow stakeholders to perform the NGS data analysis of their own samples. The web-interface can be globally accessed via the SURFsara high-performance computing and data infrastructure  [17].
Development and pre-validation of the CITESspeciesDetect bioinformatics pipeline A dedicated bioinformatics pipeline, named CITESspeciesDetect, was developed for the purpose of rapid identification of CITES-listed species using Illumina paired-end sequencing technology. Illumina technology was selected because it produces NGS data with very low error rates compared to other technologies  [2, 22]. Furthermore, the Illumina MiSeq platform enables paired-end read lengths of up to 300 nt, allowing relatively long DNA barcode regions of up to ∼550 nt to be assembled. Also, the multiplexing capabilities of Illumina technology are well developed, allowing for simultaneous sequencing of multiple samples in 1 run, thereby enabling more costefficient NGS. While NGS data analysis pipelines exist that allow processing of Illumina DNA metabarcoding datasets (e.
g. , CLOTU, QIIME, Mothur), the majority have been developed for specifically studying microbial communities using the 16S rRNA gene region. CITESspeciesDetect, developed in this study, extends on the frequently-used software tools developed within the USEARCH  [22]  and BLAST+ packages  [23] , and additionally includes dedicated steps for quality filtering, sorting of reads per barcode, and CITES species identification (Fig. 1 ). The CITESspeciesDetect is composed of 5 linked tools, and data analysis passes through 3 phases: (i) pre-processing of paired-end Illumina data involving quality trimming and filtering of reads, followed by sorting by DNA barcode, (ii) operational taxonomic unit (OTU) clustering by barcode, and (iii) taxonomy prediction and CITES identification. It was found that with the current setup of the pipeline, reads generated for cyt b and mini-cyt b could not be separated based on the forward PCR primer as the forward primers are identical.
It was therefore decided to combine (pool) the overlapping reads of cyt b and mini-cyt b during pre-processing (primer selection) of reads to prevent reads from being double selected. This means that the results of cyt b and mini-cyt b are presented by the CITESspeciesDetect pipeline as cyt b. The same issue was found for COI barcode and mini-barcode markers, for which the results are presented as COI. A parameter scan was performed in order to assess the effect of software settings on the ability to identify species. The evaluation allowed for the identification of important parameters and their effects on the sensitivity, specificity, and robustness of the procedure. Changing the base quality score has a major impact on the number of reads per barcode (Additional file 1; Table  S4 ). Increasing the strictness of the base quality score resulted in decreasing numbers of reads per barcode.
Increasing the minimal DNA barcode length to 250 nt, however, resulted in a failure to identify most plant species with mini-rbcL and rbcL. We implemented a minimum DNA barcode length of 200 nt, except for DNA barcodes with a basic length shorter than 200 nt, in which case the minimum expected DNA barcode length is set to 100 nt for ITS2, 140 nt for mini-rbcL, and 10 nt for the trnL (P6 loop) marker. The parameter scan resulted in specifying recommended parameter values (default setting) for analysing DNA metabarcoding datasets using the CITESspeciesDetect pipeline (see the Bioinformatics analysis section). An online version of the CITESspeciesDetect pipeline with a user-friendly web-interface was developed for skilled analysts with basic, but no expert-level knowledge in bioinformatics  [17].
Pilot study to assess the performance of the DNA metabarcoding procedure using experimental mixtures The DNA metabarcoding procedure was assessed in a pilot study, for which 15 complex mixtures (EM1-EM15) were prepared containing from 2 to 10 taxonomically well-characterized species with DNA barcode reference sequences available in the NCBI reference database (Table  2 ). The experimental mixtures 10 and 11 (EM10 and EM11) were independently analysed twice to verify repeatability of the method (DNA isolation, barcode panel analysis, and pooling). Only mixtures were used with wellcharacterised species (DNA Sanger barcoded and taxonomically verified) ingredients, at known dry weight concentrations, and with high-quality DNA that would allow for an assessment of the performance of the DNA metabarcoding method under optimal conditions.
Next, the (pseudo-)reads were assigned to DNA barcodes based on PCR primer sequences. On average, 96. 44% (min = 88. 78%, max = 98. 21%) of QC pre-processed reads were assigned to DNA barcodes, indicating a high percentage of reads containing the locus-specific DNA barcode primers (Table  3 ). After this, the (pseudo-)reads were clustered by 98% sequence similarity into OTUs. On average, 82. 26% (min = 75. 11%, max = 90. 63%) of the DNA barcodes assigned reads were clustered into OTUs (Table  3 ). It was assumed that the small fraction of reads that was not assigned to OTUs contained non-informative (e. g. , nonspecific fragments, chimers) sequences that may have been generated during PCR amplification and were filtered out during clustering. For taxonomy prediction, OTUs were assigned to dataset sequences using BLAST when aligning with at least 98% sequence identity, a minimum of 90% query coverage, and an E-value of at least 0.
001. Generally, the best match ("top hit") is used as a best estimate of species identity. However, species identification using BLAST requires careful weighting of the evidence. To minimize erroneous taxonomic identifications, a more conservative guideline was used that allowed a species to be assigned only when the best 3 matches identified the species. If the bit scores do not decrease after the top 3 hits, or if other species have identical bit scores, then identification was considered inconclusive. In such cases, OTUs were assigned to higher taxonomic levels (genus, family, or order). All animal ingredients, except Parapenaeopsis sp. , could be identified at the species level with 1 was recently assigned to the genus Gonialoe  [55]. or more DNA barcode markers using the default settings of the CITESspeciesDetect pipeline (Tables  4  and 5 ).
The exception being Parapenaeopsis sp. (all mixtures), A. anguilla in EM6, and Cycas revoluta in EM8 and EM11. Careful inspection of the NGS data revealed that in nearly all cases OTUs related to Parapenaeopsis sp. , A. anguilla, and C. revoluta were present, but that these sequences had been filtered out by the CITESspeciesDetect pipeline because their cluster sizes did not fulfil the 0. 2% OTU abundance threshold. There appeared to be no trend as to the type and length of DNA barcode marker that had been filtered out by the CITESspeciesDetect pipeline. For instance, Parapenaeopsis sp. was detected below the OTU threshold with cyt b, mini-16S, COI, and 16S markers (not shown). Lowering the OTU abundance threshold, however, would lead to (more) false-positive identifications, and this was therefore not implemented.
After pre-processing, the samples contained on average 142 938 (pseudo-)reads. On average, 94. 66% of the reads (min = 88. 12%, max = 98. 02%) passed the quality filtering, indicating that the overall quality of the sequence data was consistently high across the 160 datasets. OTU clustering at 98% sequence similarity on average assigned 78. 14% of the pre-processed and DNA barcode assigned reads into OTUs (Additional file 1; Table  S7 ). Only 2 samples, both from the same laboratory, had a slightly lower percentage of the (pseudo-)reads assigned to OTUs (66. 02% and 66. 05%). This indicates that the pipeline correctly removed PCR artefacts in the clustering phase. For taxonomy prediction, an OTU would be assigned to a database hit if they aligned with ≥98% sequence identity and ≥90% query coverage and yielded an expected value (E-value) of at least 0.
Beta vulgaris in sample S6 could only be identified by 4 out of 16 (25%) laboratories. Beta vulgaris-specific sequences were present in all remaining datasets, but at very low read counts. So these clusters did not fulfil the 0. 2% OTU abundance threshold (Additional file 2; Table  S2 ). In order to provide insight into what alternative setting of the CITESspeciesDetect pipeline may have been better suited for identifying Beta vulgaris, 3 datasets with relatively low (S6 -laboratory 13), medium (S6laboratory 14), and high (S6 -laboratory 6) data volumes were Table  7 : Interlaboratory trial study: composition of the complex mixtures and taxa identified using the default setting of the CITESspeciesDetect pipeline. 16 ) 10% (  16 ) 10% (  16 ).
Aloe variegata (synonym Gonialoe variegata) was recently assigned to the genus Gonialoe  [55]. reanalysed using a range of different settings for the OTU minimum cluster size and OTU abundance threshold (Additional file 2: Tables  S3-S5 ). Setting the OTU minimum cluster size to 2, 4, or 6 has no effect on taxon identification, and Beta vulgaris is not identified at the species or higher taxonomic level in the datasets of laboratories 6 and 13. Setting the OTU abundance threshold to 0 allows the identification of Beta vulgaris in all 3 samples, but at the expense of many false-positive identifications. Applying an OTU abundance threshold of 0. 1% (default is 0. 2%) allows the identification of Beta vulgaris at the species or genus level irrespective of any differences in data volume between the 3 samples.
schrenckii (results not shown). Furthermore, Brassica rapa was identified by ITS2 in sample S4 by all 16 (100%) laboratories, instead of Brassica napus. We confirmed by Sanger sequencing rbcL and matK that our reference specimen is indeed Brassica napus, but that its ITS2 sequence is identical to Brassica rapa (LT718651). Finally, a taxon of the plant family Phellinaceae was reproducibly identified (by all laboratories) using the mini-rbcL marker in all samples containing L. sativa (S6, S7, S9, S10). Species of the family Phellinaceae and L. sativa both belong to the order Asterales. The evidence for Phellinaceae was not strong; i. e. , the family-level identification was based on a single NCBI reference sequence only (GenBank: X69748). We therefore suspect a misidentification during the interpretation of the BLAST results.
Furthermore, the developed DNA metabarcoding method includes a dedicated bioinformatics workflow, named CITESspeciesDetect, that was specifically developed for the analysis of Illumina paired-end reads. The developed pipeline requires skilled experts in bioinformatics and applies scripts for command-line processing. NGS data analysis pipelines may provide a lot of flexibility to the user as modifications are easily implemented by expert users. The design of the pipeline prevented cyt b and COI full-length barcodes from being separated from their corresponding mini-barcodes as they have identical forward primers. Since the 300 PE reads can read through the cyt b and COI mini-barcodes, and therefore contain both 5 primer and 3 primer information, separation should be feasible. To simplify the inter-laboratory validation of the pipeline, a user-friendly and intuitive web-interface with associated "Help" functions and "FAQs" was developed for the CITESspeciesDetect pipeline.
Alternatively, fulllength barcodes may be resolved using an advanced bioinformatics strategy (SOAPBarcode) to assemble Illumina shotgun sequences of PCR amplicons  [27]. Single barcodes in several cases failed to amplify or provide resolution. The latter is likely to be caused mainly by database incompleteness, lack of genetic variability within some loci/target sequences, and sample composition. However, combining multiple barcodes into a multi-locus metabarcoding method mitigated the problems observed for individual barcodes. A high degree of confidence in the taxonomic assignments based on the combined barcodes was therefore observed, providing for enhanced quality assurance compared to the use of single barcodes. While the use of well-characterized experimental mixtures allowed for an assessment of the performance of the DNA metabarcoding method under ideal conditions, the amplifiable DNA content of real-life samples encountered in routine diagnostic work is often of an unpredictable and variable quality.
An analysis of 2 authentic TM products seized by the Dutch Customs Laboratory demonstrated that few ingredients listed on the labels could be reproducibly identified. This does not mean that the undetected species were not used as ingredients. Ingredients may have been processed in such a way that the DNA is either degraded or effectively removed. This is, e. g. , the case with refined oils or cooked ingredients  [28]. A PCR-free targeted DNA capturing approach coupled with shotgun sequencing was recently proposed for biodiversity assessments, which may potentially also be suitable for enhancing species identification in difficult wildlife forensic samples  [27, 29]. The quality of the sequence reference database also strongly affects the ability to correctly identify species. Without correct references that also exhibit the necessary intraspecific variation, it is not possible to match and discriminate sequence reads correctly.
It is well known that accurate DNA barcoding depends on the use of a reference database that provides good taxonomic coverage  [6, 10]. The current underrepresentation of DNA barcodes from species protected by CITES and closely related species critically hampers their identification. We estimate that only 18. 8% of species on the CITES list contain 1 or more DNA barcodes (COI for animals, and matK or rbcL for plants). This will improve as DNA barcoding campaigns continue, in particular through initiatives such as the Barcode of Wildlife Project (BWP)  [30]. Only by expansion of the sequence reference database of endangered and illegally traded species can DNA barcoding provide the definitiveness required in a court of law. A noteworthy observation was that most species that were reproducibly identified did not appear on the ingredients lists on the labels of the analysed TMs.
Furthermore, the pipeline can be readily used to analyse different types of Illumina pairedend datasets, even the future Illumina datasets (read length > 300 nt). Additionally, the web-interface provides an opportunity for the global audience with limited expertise in bioinformatics to analyse their own data. It also provides the liberty to select different primer sets and customize the settings for the selected purposes. As a result, the range of potential applications of the method to identify plant and animal species is diverse and the pipeline is versatile and adjustable to the user's needs, thus providing a powerful tool for research as well as enforcement purposes.
Reference materials and preparation of experimental mixtures All reference specimens were obtained from a local shop in the Netherlands or provided by the Dutch Customs Laboratory (Additional file 1; Table  S2  and Table  S3 ). The reference specimens were taxonomically characterized to the finest possible taxonomic level. For each species, it was checked whether reference sequences were present in the NCBI GenBank. For taxonomic confirmation, standard COI barcodes for all animal specimens were generated and individually Sanger sequenced, then compared against the NCBI and BOLD nucleotide database. For plant species, the DNA barcodes rbcL and matK were Sanger sequenced to confirm species identity. For a number of plant and animal species, the generated barcode sequence information was deposited in the ENA under accession numbers LT009695-LT009705 and LT718651 (Additional file 1; Table  S1 ).
8. 1. 1861 [22]. Next, the merged pseudo-reads, unmerged forward reads, and unmerged reverse reads are processed separately during quality filtering using a sliding window method implemented in PRINSEQ (PRINSEQ, RRID:SCR 005454)  [36]. During this procedure, low-quality bases with Phred scores lower than 20 are trimmed from the 3 end using a window size of 15 nt and a step size of 5 nt. After PRINSEQ, reads with a minimum of 95% per base quality ≥20 are kept, while the remaining reads are removed using FASTX Toolkit v. 0. 0. 14  [37]. Then, reads are successively selected, trimmed, and sorted per DNA barcode marker using Cutadapt  [35]. The following steps are followed for each DNA barcode marker separately during this procedure. First, reads containing an anchored 5 forward primer or anchored 5 reverse primer (or their reverse complement) are selected with a maximum error tolerance of 0.
2 (=20%) and with the overlap parameter specified to 6 to ensure specific selection of reads. Also, reads ≤10 nt are removed. The anchored 5 primer sequences are subsequently trimmed. Second, primer sequences that are present at the 3 end of the selected reads are also removed. For each DNA barcode, the primer-selected and unmerged reverse reads are reverse complemented and combined with primer-selected merged and unmerged forward reads. The following procedure is used to cluster the quality trimmed reads of each DNA barcode into OTUs using the UP-ARSE pipeline implemented in USEARCH  [22]  with the following modifications: reads are dereplicated using the derep prefix command. Also, singleton reads and reads with minimum cluster size smaller than 4 are discarded. Representative OTUs are generated using an OTU radius of 2 (98% identity threshold) and 0.