dataesr/scientific-paragraphs-categorization huggingface dataset:
1. Actual cost and time involved with the preparation of data presented in this manuscript are provided, along with a list of reagents and product numbers for reference in Supplementary Table  1. Several types of IFC are available, but the basic design features two sets of input wells that can be filled with extracted DNA samples and PCR reagents on one side and PCR primers on the other. Once loaded, the IFC is 'primed' by pushing the fluids through a series of microtubes that intersect, forming a central matrix of thousands of reaction chambers. The IFC used with the Access Array contains 48 DNA sample wells and 48 PCR primer wells that interact to serve as the template for 2,304 isolated combinations of sample and reagent. Using the Juno system, the scale of library preparation can be further increased with IFCs that contain 192 DNA sample wells and 24 primer wells.
These instruments allow for simultaneous amplification of thousands of amplicons in miniaturized, parallel PCRs. This system is a cost-effective approach to amplify different target regions from multiple samples as it not only reduces the amount of reagent used in HTS library preparation and targeted amplification of barcode loci, but also decreases instrument and sample handling and technician time. The comparative cost, reagent use, and estimated time involved with various PCR-based methods of DNA barcoding approaches compared with those of MEBarcoding are shown in Fig. 2. The cost and time estimates provided here come from real costs involved with the data presented in this paper for both Sanger sequencing and the 48. 48 Access Array microfluidic PCR approach. Other costs have been estimated based upon listed reagent costs, direct experience with these methods, and consultation with colleagues and core facilities.
It is important to note that costs vary depending on equipment, location, and resources available to given laboratories -these costs are provided as estimates only, but reflect the actual cost and time associated with these methods investigated by the authors. One technician can produce 96 barcode sequences during a 40-hour work week using traditional PCR and Sanger sequencing protocols, compared to 192 with recently published Illumina  21  and PacBio 22 methods. Using the MEBarcoding approach described here on the Access Array or Juno systems plant DNA barcodes could be generated for 384 or 768+ samples, respectively. An important assumption in the estimation of costs presented here is that the laboratory already has access to equipment required for MEBarcoding (approximately $40,000 for the Access Array and $100,000 for the Juno) or can utilize this instrument at one of many core facilities that make this instrument available to researchers.
Results Microfluidic PCR. All primer pairs tested in our primer validation produced amplicons as anticipated and were used in our Access Array workflow. 96/96 samples tested for MEBarcoding produced at least one amplicon that was successfully sequenced. On average, samples in this study amplified 3. 18 loci (Fig. 3 ). Forty-two samples amplified all four plant DNA barcoding loci, 32 amplified three loci, 19 amplified 2 loci, and 3 amplified only one locus. Sequences produced by the MEBarcoding approach were compared to sequences produced by Sanger sequencing and are summarized in Table  3. Sequence read characteristics. Our dataset yielded 15,737,842 sequence reads following sequence quality filtering and adapter trimming. Sequencing produced an average of 163,936 reads per sample, with a minimum of 119 and a maximum of 932,436 reads (Fig.
4 ). All raw sequence reads are deposited in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA: PRJNA389125). Statistics for each marker are summarized in Fig. 5  and Table  3. After clean up, only three samples failed to produce more than 1,000 reads (Table  S1 ). The number of sequence reads was uneven across loci. Our results reveal amplification bias toward certain loci (rbcL and trnH-psbA) and samples using this method. On average, we recovered close to 229,000 reads (163,935 after adapter and quality trimming) and 3. 1 loci from each sample. ITS and matK were problematic in bryophytes, ferns, and lycophytes; and trnH-psbA in Gymnosperms (Fig. 5 ). The underrepresentation of these loci in such groups is not particularly surprising as a growing body of literature has reported similar results for these three markers  [40] [41] [42] [43] [44].
The maximum sequencing depth was from nrITS, with an average of 78,518 reads per sample, followed by trnH-psbA and rbcL close behind with 62,158 and 72,131, respectively (Fig. 5 ). The lowest sequencing depth came from matK, which averaged 8,489 reads per sample (among samples for which this marker was successfully assembled). Barcode sequence comparison and validation. On average, sequences produced by MEBarcoding were 98% similar to their Sanger sequence counterpart. rbcL, matK, trnH-psbA, and ITS each shared 99. 59%, 99. 3%, 97. 56, and 95. 57% average pairwise identity, respectively. Twenty-two sequences produced with MEBarcoding were removed from the analyses after comparison to BLAST results produced dubious results and are not included in the results we report here. Of the sequences that were excluded, ten did not produce data with Sanger sequencing either, nine produced very low number of reads (<100; <50X coverage), and three were of poor quality (high proportion of ambiguities) (Table  S1 ).
00/ hour. Multiple amplicon high-throughput sequencing (HTS) is meant to reflect methods that use a combination of traditional PCR with HTS (e. g. 21, 24 ). Genome skimming time and cost estimates are based upon lowest current market rates for library preparation from kits and core facilities and personal communication with genomics core facility lab technicians. Like most methods at the cutting edge of molecular biological technology, MEBarcoding has enormous and rapidly evolving potential. Results in this study show the capacity of a first-generation microfluidic instrument, the Fluidigm Access Array. With the ability to prepare 192 barcode libraries in approximately 5 hours on the Juno instrument, output can be increased fourfold. We estimate that a single lane of Illumina MiSeq using v3 chemistry could produce sufficient barcode sequence data for approximately 500 pooled samples (10 × 48.
48 IFCs or 2 × 192. 24 IFCs). Furthermore, the proof of concept and workflow provided here demonstrates that MEBarcoding is not only practical for plant DNA barcoding studies, but potentially any DNA barcoding study that targets multiple regions, particularly the rapidly growing field of metabarcoding  45. One of the biggest challenges to any DNA barcoding study is the continued lack of barcode reference sequences for all taxa of interest in existing databases 4 (Table  2 ). However, as barcode databases expand, the capacity of DNA barcoding to accurately identify an unknown sample increases. MEBarcoding would facilitate and accelerate the expansion of such databases by generating sequence data for multiple loci in a single reaction. If sampling depth was increased by 100-fold -to 960 samples -on this platform (and if sequence read efficiency scaled linearly), the expected average coverage would be 84X from even the lowest performing plant DNA barcode locus (matK).
Therefore, we expect this method would scale remarkably well with increased sampling despite the difference in relative sequencing success between some markers. In many ways, the hindrances to MEBarcoding are similar to those for traditional PCR-based plant DNA barcoding; but the cost and time necessary to carry out research projects with high sample volumes is lower using the MEBarcoding approach. This technique does, however, involve two limitations that are unique and involve (1) a high initial equipment cost, (2) comparatively lower sequencing success to Sanger methods. First and perhaps the most immediate limitation is the high initial cost if a researcher does not have access to an instrument capable of performing microfluidic PCR. The instruments discussed in this   manuscript (Access Array and Juno) come with a price tag between $40,000 and $100,000, respectively, which may be a prohibitively expensive upfront cost; however, if this is cost-prohibitive there are several core facilities in the United States that offer library preparation services with one of these instruments (e.
g. , ibest, https://www. ibest. uidaho. edu/). Second, overall sequencing success is marginally lower than that of Sanger sequencing according to results in Table  3  (95% vs. 97% for rbcL, 60% vs. 74% for matK, 87% vs. 92% for trnH-psbA, and 67% vs. 70% for ITS). It should be noted that all Sanger sequences were run twice, in the event that a first sequence attempt failed Zúñiga, pers. comm. ); however, MEBarcoding sequences were not rerun. The benefits of this system should be balanced with the costs for researchers to ensure their program is leveraging the cost-effectiveness of MEBarcoding with high-volume sample processing. As with traditional plant DNA barcoding, longer loci, such as matK, present several limitations, which we address in three ways. First, longer PCR amplicon targets (i.
This technique provides a template for massive, worldwide plant DNA barcoding that can help advance the priorities of the global barcoding community rapidly and therefore facilitate downstream applications that depend upon diverse and high-quality barcode reference sequence databases. Besides expediting the construction of plant DNA barcode libraries, we envision MEBarcoding as particularly valuable for DNA metabarcoding and its application to biodiversity research  48  ; wildlife forensic identification  49  , and food and natural products and authentication  4, 16, 50, 51  , see Kress, 2017 for review of plant DNA barcoding applications. It would also be an effective method for evaluating the utility of different DNA barcodes for undersampled taxa (e. g. algae and other microbial eukaryotes  52  and optimizing DNA barcoding primers for a set of taxa  53  ).
We identify three principle challenges to metabarcoding: first, insufficient global databases for barcode sequence-based identification; second, repeatability of community studies; and third, sufficient affinity of primers for diverse community samples at varying scales of taxonomic diversity. We anticipate the first challenge, regarding sequence databases to be resolved over time as barcoding databases are continuously updated. The second challenge, posed by repeatability in samples is unique and will not necessarily be ameliorated by growth in scientific knowledge; several studies have attempted to overcome this by advocating for multiple replicates per sample in their metabarcoding studies. Multiple replicates have been used to demonstrate robustness in several recent studies  59, 61, 62  , however best practices should be a priority for the metabarcoding research community.
For each replicate, reagent and handling costs increase, respectively and as the number of samples increases, these expenses can quickly render large scale studies cost-prohibitive. The third challenge, posed by primer specificity, depends upon the scale of the research project being undertaken. A recent attempt by Brown et al. (2016) to improve fungal metabarcoding has optimized primer combinations using microfluidics on the Fluidigm Access Array platform. A strategy employing a set of optimized primer sets that target a broad range of eukaryotic and prokaryotic lineages on microfluidic PCR platforms will allow for improved scaling of metabarcoding approaches that reveal community diversity across the tree of life. Such an approach follows a global need for improved standards  63, 64  that seek to unify best practices by implementing proven, standardized barcode markers that are being utilized in molecular biodiversity reference databases  5.
Materials and methods Taxonomic sampling. 96 samples were selected from across the vascular plant tree of life (Supplementary Table  2 ) representing 78 families and including all major lineages of land plants (e. g. , Bryophytes, Ferns and Lycophytes, Gymnosperms, and Angiosperms). All samples were collected as per Funk et al. (2017) as part of the Global Genome Initiative for Gardens program  65  , and are publicly searchable through the Global Genome Biodiversity Network 66 (GGBN) web portal (http://www. ggbn. org/ggbn_portal/). These have been sequenced using traditional PCR and Sanger chemistry for plant DNA barcode loci in another publication  67. This set of samples was selected specifically so that a direct comparison could be made between traditional barcode sequencing methodology and the MEBarcoding approach presented here and for representation across plant groups.
PCR primer design and validation. The twelve different PCR primer pairs employed in this study were selected from the literature and from the BOLD primer dataset platform (http://www. boldsystems. org/index. php/ Public_Primer_PrimerSearch) to target the four most commonly cited plant DNA barcoding loci (rbcL, matK, trnH-psbA, and ITS) (Table  1 ). Primers were validated according to the primer validation protocol described in the Fluidigm User Guide (Fluidigm PN 100-3770, San Francisco, California, USA), using the FastStart High Fidelity PCR System (Sigma-Aldrich). Amplicons were visualized using gel electrophoresis on an agarose gel (1. 5% agarose, 90 V for 45 minutes). All primer pairs used in the validation protocol produced amplicons as anticipated and were retained for use on the Access Array.
High-throughput sequencing. The cleaned, pooled library was sequenced on an Illumina MiSeq (Illumina, Inc. San Diego, California, USA) instrument using v3 chemistry (2 × 300, 600-cycle) sequencing kit at LAB. Custom sequencing primers, from Exiqon, Inc. (Woburn, Massachusetts, USA), were used in the sequencing reactions according to the Fluidigm Access Array User Guide (Fluidigm PN 100-3770). Sanger sequencing. Sanger sequence data were generated for each of the 96 samples utilized by Zúñiga   et al. (2017). This study produced 62, 67, 79, and 83 barcode sequences for ITS, matK, trnH-psbA, and rbcL, respectively. Prior to this study, plant DNA barcode sequences had never been produced for these loci in these taxa. When a sequence was generated by both methods, we compared sequences produced by the MEBarcoding approach to the previously generated sequences using pairwise distances.
When novel barcode sequences were produced by the MEBarcoding approach, but not the traditional approach, we confirmed the sequence identity based on comparative metrics from a nucleotide BLAST search (when available). Sequence read processing. Illumina MiSeq reads were quality trimmed and filtered using a custom script in CutAdapt 1. 8. 1  68. This script is available in Dryad (https://doi. org/10. 5061/dryad. ps8ng8g). Reads were filtered according to the method outlined by Gostel, et al. 38  , which includes removal of ends with quality scores <Q20, removal of reads shorter than 60 bp, trimming poly-N tails ≥ 6 bp, and trimming all Illumina adapter sequences from the ends of reads. Mapping of sequence reads followed two approaches; first using a combined approach with the software package BWA v 0.
7-17  69  and -for read files that did not successfully map to a reference using the BWA approach -using the "Map to Reference" feature in the software package Geneious v 11. 1. 2  70. Read mapping in BWA was performed using the BWA-MEM setting  71. We defined a reference index (using the "index" command) from a set of barcode reference sequences representing phylogenetically similar taxa, including 20 taxa from across the plant tree of life (Reference sequence dataset in Dryad, https://doi. org/10. 5061/ dryad. ps8ng8g). All SAM files generated by BWA were converted to BAM files, using a script in samtools (Dryad, https://doi. org/10. 5061/dryad. ps8ng8g) and the resulting BAM files were imported into Geneious to visualize and review each locus assembly. Not surprisingly, the BWA approach was not able to reliably assemble 100% of our sequenced barcode data.
In some cases, it is likely that a barcode sequence was not able to be amplified and sequenced for one or more markers in some samples (see Discussion); however, in other cases it may be that read mapping was not possible because the BWA algorithm is not optimized for short reference sequences (i. e. 500-600 bp barcode sequences), but rather complete genome reference sequences. To rescue barcode sequence data from samples that were not successfully assembled with BWA, we used a second approach, described below. All BAM files generated by the BWA assembly were imported into Geneious v 11. 1. 2 (henceforth, "Geneious") and a 50% (strict) majority consensus file was exported as a. fasta file for each locus. Before computing the consensus sequence, we deleted all annotations that corresponded to soft-clipped sequences.
Any sample (and corresponding barcode locus) that did not produce assemblies in BWA was analyzed separately in Geneious using the "Map to Reference" feature. Cleaned. fastq read files were loaded into Geneious and grouped into a folder for each sample. In each folder, we also imported a. fasta reference sequence file that contained nucleotide sequences for each of the four barcode loci (or their primer sequences) included in this study. Assembly was carried out using the "Map to Reference" option in Geneious using default settings under the "highest sensitivity" option. 50% (strict) majority consensus sequences resulting from reads that mapped to reference sequences using this approach (but were not successful using the BWA approach) were then exported as a. fasta file and saved for subsequent analysis with the corresponding BWA-assembled consensus sequences for each locus.
Comparison of Sanger sequencing and the MEBarcoding approach. The consensus reads obtained through MEBarcoding were loaded into Geneious, and BLAST searches were conducted using the blastn algorithm and limiting search results to 100 hits. All barcode sequences previously generated for these samples through Sanger sequencing were already discoverable on GenBank at the time the BLAST searches were carried out. Pairwise distances between sequences generated through the Sanger and MEBarcoding approaches were recorded if both were available; as well as the taxonomic level of the BLAST match; and whether the corresponding Sanger sequence was among the top ten BLAST results or not among the results altogether (Supplementary Table  2 ).
Acknowledgements This material is based upon work supported by the  Global Genome Initiative  under Grant No. ( GGI-146-2016 ). Thanks to the collectors who helped MRG and VAF supply samples for this study as well as GGI-Gardens partner institutions who allowed collecting on their grounds or in their facilities ( US Botanic Garden, US National Arboretum, Smithsonian Gardens, City of Alexandria ) without such assistance this study would not have been possible (see Supplementary Table S2 for details). The authors also acknowledge  Dr. Marc Allard  and  Sabina Lindley  at the  United States Food and Drug Administration  for their generous contribution of in-kind support for the development of this project. We are grateful to the  Center for Conservation Genomics ,  Smithsonian's National Zoo and Conservation Biology Institute , especially  Nancy Rotzel McInerney  for providing access to their facilities that contributed to the success of this project.
Data availability All read data has been stored in the NCBI Sequence Read Archive PRJNA389125. All scripts used to analyze and assemble sequence data as well as a. fasta file containing the consensus sequence for each sample -organized by locus -generated in this study have been submitted to Dryad (https://doi. org/10. 5061/dryad. ps8ng8g). Complete voucher information (including locality, genbank accession numbers, and biorepository ID) for each sample accession used in this study is provided in Supplementary Table 2.
Following this, COT and CER were retrieved from the AHI data, with ice cloud parameters being retrieved from an extended Voronoi ice crystal scattering database and water cloud parameters being retrieved from the Mie-Lorenz scattering model. The retrieved COT and CER for water clouds were compared well with MODIS collection 6 cloud property products, with correlation coefficients of 0. 77 and 0. 82, respectively. The COT of ice cloud also shows good consistency, with a correlation coefficient of 0. 85. Finally, the SSR was calculated based on the SSR LUT and the retrieved cloud optical parameters. The estimated SSR was validated at 122 radiation stations from several observing networks covering the disk region of Himawari-8. The root-mean-square error (RMSE) at CMA (China Meteorological Administration) stations was 101.
, 2015). The EarthCARE is to be launched in 2021. It was demonstrated that the Voronoi model can effectively retrieve the ice cloud properties from satellite measurements  (Letu et al. , 2016). In this study, a new LUT-derived algorithm was developed to calculate all-sky SSR, based on the H-8/AHI data, which have high spatiotemporal and spectral resolutions. Specifically, the Voronoi ICS database was extended for the purpose of accurately deriving the ice cloud optical parameters, and their estimates were evaluated through comparing them with MODIS products. The forward RTM, together with the ICS database, was used to build the LUT utilized in the SSR estimation, and the SSR estimates from AHI were validated against widely-distributed station data.
The RSTAR model The RSTAR model is used to establish the LUTs for the purpose of retrieving the SSR from AHI measurements. The RSTAR model is a package of numerical models specializing in atmospheric radiative transfer that is applicable to the plane-parallel atmosphere, and the calculated wavelengths can cover 0. 17-1000 μm. The presumed planeparallel atmosphere can be divided into 50 layers, from the sea surface    level to the highest altitude of 120 km, and 6 atmosphere models can be selected. The plane-parallel atmosphere includes 11 classes of aerosol models that are composed of seven basic components  (Sekiguchi and Nakajima, 2008). The cloud particle scattering model in the RSTAR is composed of water and ice clouds. The ice cloud model further consists of various ice scattering kernels, such as spherical and ellipsoidal shapes and hexagonal columns.
Extension of Voronoi ICS database The conventional Voronoi ICS model consists of single-scattering properties (e. g. phase function, single-scattering albedo, asymmetry factor, absorption efficiency, extinction efficiency) at 86 spectral wavelengths that were selected to retrieve the optical properties of the ice clouds  (Letu et al. , 2018). The selected wavelengths in the Voronoi ICS model are determined by the variation of the refractive index of ice in the satellite observation bands. They can satisfy the requirement of accurate estimation of ice cloud parameters from satellite measurements, e. g. MODIS, AHI, SGLI and MSI  (Letu et al. , 2018) , but need an extension in order to estimate SSR accurately. Fig. 3  shows the solar spectral irradiation (SSI) at the wavelengths of the Voronoi ICS model calculated using the refractive index data provided by  Warren and Brandt (2008).