dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS To test the accuracy of the pipeline across a diverse set of ingredient types and sequencing methods, validation was done using 5 simulated and 11 experimentally sequenced datasets of expected composition. Among these sequenced datasets were four preliminary factory ingredient samples: two biological samples for which both RNA and DNA sequencing was completed. The pipeline was additionally applied to 31 commercial raw material samples of a high protein powder factory ingredient to test the authentication of food ingredients and detection of contaminants. Determining the number of reads required for species detection Choosing the number of reads to be sequenced for a specimen occurs upstream of the FASER pipeline and is typically limited by sequencing cost. Our underlying assumption is that due to laboratory costs associated with sequencing, the number of sequenced reads is desired to be as low as possible, while still enabling detection of species that are present above a desired threshold, e.
g. , above 1% of the sample total. The time and cost of bioinformatics processing is low (in the order of hours for data analyzed in this paper) compared with sequencing more reads of a sample, or sequencing replicates of the same sample. Details on the compute environment and timing of the FASER pipeline are included in Supplementary Methods. In order to identify a breadth of eukaryotic species without prior knowledge of the expected ingredient(s), a large reference database containing the appropriate representative sequences must be used; however, querying millions of sequencing reads (as is common in a single sequencing run output) against such a database will be very slow and may not be necessary for food matrix authentication. In the All-Food-Seq pipeline, Ripp et al. 4  used subsets of 500,000 reads (from 16 million sequenced reads) for food matrix authentication.
We ultimately chose to use a similar number of reads, but also demonstrated using a mathematic model how subsampling reads for increased speed of matrix authentication affects the species detection capability and accuracy. The modeled read subsampling and probability of species detection capability are illustrated in Fig. 2. For a dataset with 300 million reads, such as the high protein powder dataset analyzed in this paper, 450,000 reads represent 0. 15% of the total sequencing information available from a sample. Based on Eq. (  1 ), this subsample size supports detection of at least 100 sequencing reads from any species with frequency as low as 0. 032% in the full sample with high probability (P ≥ 0. 9999), thus allowing for sensitive detection of constituent species. Supported by this calculation for species detection, we analyzed 0.
15% of the sequencing reads for the high protein powder samples, resulting in a similar number of reads as Ripp et al. 4  We found that fewer reads were sufficient to test both the species detection hypothesis as well as to quantify the species proportions in the in silico datasets and in single ingredient public datasets. Thus, as few as 150,000 reads for the simulated datasets (Fig. 3 , Tables  1  and 2 ) and the single ingredient datasets (Table  3 ) was used to test the detection and accuracy limits of the pipeline on low pass sequencing data.
Species-level proporƟons Blast database of 6,160 organisms (158 GB) Fig. 1  Pipeline applied to food sample sequencing data to determine matrix species and their relative proportions. In the taxonomic assignment step with exemplary diagram, reads are placed on the lowest common ancestor (LCA) of the nodes that they hit, in case of multiple hits per read. In the relative quantification step the read counts at internal nodes are re-assigned to the species at the leaf nodes observed with e-value parameters of 10 -10 and 10 -40 (Table  1 ). The 10 -40 threshold yielded fewer false positive hits in simulated testing as compared with 10 -10 while improving computational efficiency due to a smaller output size without compromising the results. Therefore, this BLAST parameter was chosen for further use in this study.
We considered that if the vast majority of the alignments assigned to species S are equally good to one or more other species, then the reads aligning with S may actually come from other species causing a false positive result. Accordingly, we developed a downstream promiscuity filtering process to further reduce the number of false positive species. The promiscuity filtering of taxa improved accuracy for Bos taurus (cattle) (Table  2 ) in the case where phylogenetically similar species may occur in the database, such as Bos mutus (wild yak), Bos indicus (zebu), Bubalus bubalis (water buffalo), and Bison bison (bison). With promiscuity filtering, only alignments to B. taurus were reported, consequently increasing the relative frequency of B. taurus in the results from 92. 55 to 100% (matching the expected composition).
In addition, we found that the required minimum threshold of 10% unique alignments specifically allowed unique identification of each species and allowed more reads to be unambiguously assigned to a species. Accuracy evaluation with in silico simulated data Three in silico constructed datasets were evaluated using known compositions of (i) single food sources, (ii) only microbes, and (iii) complex microbiomes with plants, animals, and microbes to Fig. 2  Illustration of the minimum size of a subsample to obtain a desired limit of detection. The required number of reads is shown as a function of frequency of species S (in the full sample). In this example with a total number of N = 300 million reads, we desire with high probability P (here P ≥ 0. 9999) to have limit of detection at least L = 100 sampled reads coming from species S when S is present.
For example, when frequency of S is 0. 1% (x = 0. 001), a subsample of 141,499 reads from the total 300 million reads is required (marked with a square). When frequency is S is 2% (x = 0. 02), fewer than 10,000 reads are required (marked with a circle)    6  examine the accuracy of matrix authentication from sequencing data. These datasets were all processed with FASER without a priori assumptions of the samples' content. In the first simulated experiment, datasets were generated to model common food ingredient species related to the collection of 31 high protein powder samples (see the section 'High protein powder factory ingredient sample collection and sequencing'). The product specifications indicated that the ingredient's primary animal source was chicken (Gallus gallus). However, other livestock species could be unintentionally present due to challenges in the supply chain.
We examined the rate with which microbial reads were falsely assigned to eukaryotic plant and animal genomes. For this we used simulated reads from a microbial reference genome database (NCBI RefSeq Complete, 15 ~7800 microbial genomes) and processed them with FASER. From 150,000 simulated microbial reads, 204 (0. 14%) were assigned to a matrix species. Chiru (Pantholops hodgsonii) was identified by 0. 04% of the total input reads and was the largest proportion of false positive matrix content reported from the microbial content. No other matrix species had more than 50 BLAST alignments assigned. We further confirmed that with 300 and 450 K simulated microbial reads the observed matrix species hits had the same low rate (0. 14%). Based on these results, when microbial reads comprised a minor proportion of the total sequencing reads (e.
g. , <1%), the expected false positives from microbes was <0. 0014%. In the third simulated experiment, FASER was evaluated on two in silico food microbiomes where the matrix content was constructed from multiple sources e. g. , chicken (G. gallus), pork (S. scrofa), and soy (Glycine max) with the microbiome representing 0. 11-0. 19% of total sequences. The tests were designed to model realistic food microbiomes and evaluation of the data Left: after filtering, right: before filtering demonstrated the accuracy of FASER (Fig. 3 , details of the simulation are given in the section 'Constructing simulated datasets'). The median absolute difference was 0. 84% between the expected and observed proportions for each input species. In order to compare the expected and observed compositions, we used the χ 2 test.
The test showed no significant difference (P ≥ 0. 99; data not shown) between the expected and observed species compositions indicating that FASER indeed accurately identified the expected contents. Accuracy evaluation on single species sequencing data The accuracy of species assignment was tested on real mRNA sequencing data from single species experiments retrieved from NCBI Sequence Read Archive (SRA)  16  (chicken (G. gallus) embryo, pork (S. scrofa) ovary, tuna (Thunnus albacares) muscle, carp (Cyprinus carpio) spleen, rice (Oryza sativa) root, and maize (Zea mays) leaf tissues; see Supplementary Table  1  for dataset information). For all datasets, >96% of the species-matching reads identified the expected matrix species (Table  3 ) and there were no false positive species representing >0.
5% of the reads except in carp spleen and chicken embryo. A small fraction of chicken embryonic mRNA reads matched to turkey (1. 02%) and quail (0. 43%), as was also detected in the simulated data experiments (Table  1 ). Note that the chicken sequences were directly sampled from fertilized eggs and had no turkey or quail RNA present. This demonstrated that the real data results closely matched the in silico observations, thus validating the simulation framework for modeling real sequencing data. In the carp spleen sample, 3. 26% of the reads were assigned among three other fish species in the genus Sinocyclocheilus that belongs to the same family as carp, Cyprinidae. In addition, in the tuna sample, the expected species was T. albacares (yellowfin tuna), but FASER reported 99. 8% T. orientalis (bluefin tuna).
The analysis was correct in assigning tuna but was unable to differentiate yellowfin from bluefin as only bluefin tuna was present in the BLAST database. The carp and tuna results highlight the need for a diverse reference database that includes the relevant species of interest to achieve the most accurate identification. Overall the results in Table  3  demonstrated high accuracy, 0. 36% median absolute difference of observed from expected species content, and agreed with the observations from simulated data. The difference between all expected vs. observed values was not significant (χ 2 test P ≥ 0. 99; data not shown), indicating FASER accurately identified the expected contents.
Accuracy evaluation on a complex food metagenome of known composition The closest pipeline for food metagenomes analysis found in the literature is All-Food-Seq. 4 This pipeline was published with an application on DNA metagenome reads from a raw sausage mixture, matched against a small reference database (19 genomes, Table  S1  in Ripp et al. 4  ). The sausage contained a known composition, by weight in grams: 55% sheep, 35% beef, 9% pork, and 1% horse meat. In addition to the raw meats, the sausage mixture also contained material from 11 plant species at trace amounts to test the pipeline's ability to detect low abundance contaminants. The highest divergence from the target proportion observed by All-Food-Seq was for pork (-1. 79%). We analyzed all the available 409,616 paired reads (MiSeq dataset SRR1745838 from NCBI) to compare FASER with All-Food-Seq on the same input data.
FASER correctly identified the main ingredients and their relative proportions: sheep, beef, and pork, and additionally found 1% horse meat, with a median absolute difference of 0. 44% (Table  4 ). The highest divergence from the target proportion in our analysis was also pork (-2. 25%), followed by beef (+0. 44%). Of unexpected matrix components, we observed goat reads at 1. 83%. Goat was not included in the database used for All-Food-Seq; therefore, not present in their results and a false positive in this analysis. The simulated testing supported a small fraction of cattle (Table  1 ) and sheep reads (Supplementary Table  2 ) matching goat better than the originating genomes. Since the All-Food-Seq publication reported results on plant contaminants only at family level, we compared them with FASER results after summing up the counts at family level (Supplementary Table  3 ).
Summary of accuracy evaluation In summary of the above described validation work, we applied FASER to eleven experimental (Tables  3  and 4 ) and five in silico validation datasets (Fig. 3  and Table  1 ) representing single and mixed ingredient foods with expected compositions and demonstrated accuracy achieving a 0. 4% median absolute difference between observed and expected relative proportions of the true positive species (mean 1. 0%, std. 1. 4%, min. 0. 0%, max. 5. 9%). On average, the combined relative content of true positive species proportions per sample was over 99% (mean 99. 1%, std. 1. 3%, min. 96. 4%, max. 100. 0%), and the median number of false positive species observed was 1 (when considering species with relative proportions >0. 1%). Comparing all expected vs. observed values by the χ 2 statistic, no significant differences (P ≥ 0.
99) were observed across all detected species. Authentication of ingredients in high protein powder sequencing data With the robust results from multiple in silico and public dataset analyses, we progressed to analyze high protein powder samples obtained from the food supply chain to examine ingredients that were labeled to be from a single animal source. FASER was first applied to four preliminary samples: paired DNA and total RNA sequenced from the same biological samples, to test the pipeline consistency across different types of sequencing. The samples were collected from raw factory ingredients that were stated to contain chicken and beef high protein powders (Supplementary Table  4 ): MBMB-03 RNA and MFMB-08 DNA for poultry meal, MFMB-02 RNA and MFMB-06 DNA for meat and bone meal. Both the DNA and RNA samples were found to contain >99% of the expected ingredient for chicken meal and meat and bone meal, with RNA yielding 0.
2% fewer false positive hits than DNA (Table  3 ). A set of 31 high protein powder samples (MFMB-04 and MFMB-17 through MFMB-99) derived from poultry were subsequently used for deep total RNA sequencing with >300 million reads per sample. These sequences were then examined to determine the ingredient composition and detect possible contamination in a single analysis to replicate an industrial use case. Using the developed sequencing approach and analysis pipeline we observed that in 28 of 31 samples 99. 7-99. 9% of the speciesassigned sequence alignments matched chicken, the stated ingredient source (Fig. 4  and Supplementary Data). Sequence alignments matching other avian species were detected (0. 08-0. 26% assigned to turkey and quail) in all samples, which was expected based on the results from in silico simulations (Table  1 ) and chicken embryo sequencing data (Table  3 ), suggesting that conserved sequence content may account for the assignments to the other avian species.
DISCUSSION Bridging the gap between current targeted tests, such as PCR or ELISA,  17  and high-throughput sequencing for use in regular testing processes and standards is an emerging challenge for the food industry. Current molecular methods for detecting food contamination include restriction fragment length polymorphism (PCR-RFLP), random amplified PCR, multiplex-PCR, DNA hybridization, and DNA barcoding. 3 Each of these methods have shortcomings that include quantification accuracy, amplification bias, the necessity of prior knowledge of a contaminant for use with a pre-defined target sequence for primer development, and experimental inactivity due to common food additives and secondary metabolites. 3 Analytical fingerprinting techniques such as mass spectrometry  18  and chromatography  19  are also being explored for food authentication, with their associated challenges including the expense of building and curating databases of reference materials.
Accuracy of data analytics is of the utmost importance because use of inaccurate results could lead to incorrect conclusions and harm consumer safety or initiate unnecessary regulatory action. Bioinformatic accuracy of food matrix authentication and microbial profiling continue to hinge on the completeness of existing reference databases and proper calibration of existing and new tools against this type of data. Comprehensive use of improved public reference data and careful bioinformatics for interpreting the sequence data are both required for accurate validation of food matrix composition. In highly processed commercial food products, observed matrix species' DNA content linearly correlates with the mass of that species. 22 This shows promise for translating the resulting sequencing read proportions into ingredient weight proportions in a food sample.
Normalization by genome size or using an experimentally determined normalizing factor per species may be   required to accurately quantify ingredient proportions in complex food samples. Encouragingly, relative DNA contents per ingredient directly match their content by weight in the example of the raw sausage meat calibration data provided by All-Food-Seq publication. 4 ere, we define a matrix authentication pipeline, FASER, that is motivated by applications in food safety yet is relevant and useful for any intact eukaryotic matrix signal where the sample composition may be unknown or requires confirmation. With such pipelines, automated testing for food composition as well as detection of contaminants and adulterants becomes feasible. The ability to detect expected food ingredients and unexpected content depends on a comprehensive database.
The reference used in this paper contained over 6,000 plant and animal sequences. The pipeline achieved relative quantification of single and multi-ingredient samples with 0. 4% median absolute difference between observed and expected species relative proportions when tested with 16 simulated and experimental datasets. In each dataset, the sum of the true positive species' proportion was >96% (Tables 1, 3 and 4, and Fig. 3 ). The expected vs. observed compositions were also similar according to the χ 2 statistic. The observed low abundance false positives, such as turkey and quail in chicken data and goat in the sausage mixture, could be explained by conserved content between the sequenced genome and others in the database, deviation of the sequenced genotype from the reference genome, as well as sequencing errors.
In ongoing work we are examining the microbial content of the 31 samples whose eukaryotic composition was analyzed here. Achieving species-level accuracy is critical for food safety and quality as it will enable detection of contaminants even if present in trace quantities. We demonstrated these abilities with public raw sausage meat data 4 with a known composition, detecting 6 out of 11 of added very low abundance plant species (some with expected quantities of 0. 003% of total sample) (Supplementary Table  3 ). We showed how food matrix components from mixed plant and animal origins can be identified and quantified using short sequencing reads in the few hundreds of thousands range. This is in agreement with suggested practices by the All-Food-Seq publication where <500,000 paired sequencing reads were used to identify species in the sausage data.
4 Compared with All-Food-Seq, FASER employed a more comprehensive database (19 vs. 6,160 eukaryotes) and reported results at species level in a single analysis across the entire species collection without prior knowledge of the expected ingredients or contaminants (Table  4 ). All-Food-Seq only reported family-level results for the low abundance plants included in the sausage calibration experiment, while FASER reported species-level proportions. In addition, FASER automatically handled reads aligning to multiple taxa using a known taxonomy, unlike All-Food-Seq that required manually defining related sequence groups on a case-by-case basis. Taken together, these results support the utility of FASER as a robust pipeline for eukaryotic species identification that enables simultaneous detection of multiple contaminants and authentication of ingredients from food nucleotide sequencing.
2 g of powder was added to 2 mL of Trizol LS (Ambion by Life Technology, Carlsbad, CA). 5] [26] [27] [28] [29]  Total RNA purity (A 260/230 and A 260/280 ratios ≥ 1. 8) and integrity were confirmed with Nanodrop (Nanodrop Technologies, Wilmington, DE, USA) and BioAnalyzer RNA Kit (Agilent Technologies Inc. , Santa Clara, CA, USA). 23 Subsequently, cDNA was constructed using RNA (4-15 µg total input) and SuperScript Double Stranded cDNA Synthesis kit (Invitrogen, Catalog no. 11917-020, Life Technology, Carlsbad, CA). Sequencing libraries using HyperPrep Plus (Kapa BioSystems, Wilmington, MA, USA) cDNA were constructed as described previously  23, 30, 31  with an insert size between 300 and 400 bp. Library quantification was done using qPCR (Library Quantification kit catalog #KK4824, Illumina, San Diego, CA) prior to submission for sequencing at BGI@UC Davis (Sacramento, CA).
The Illumina HiSeq 4000 (San Diego, CA) was used with 150 paired-end chemistry for each sample except the following: HiSeq 2000 with 100 paired-end chemistry was used for the four preliminary samples, and HiSeq 3000 with 150 paired-end chemistry was used for MFMB-04 and MFMB-17. All of the sequences generated in this study are available via the 100 K Pathogen Genome Project BioProject (PRJNA186441) (see Supplementary Table  4  for a complete list of accession numbers for each sample).
Constructing a comprehensive database of plants and animals Sequences from NCBI's RefSeq 81 genomic collection  15  representing vertebrates and plants were downloaded on March 13, 2017. Organisms labeled "vertebrate_mammalian", "vertebrate_other", and "plants" within the NCBI RefSeq collection were selected, at that time comprising 11,623,393 sequences from 6160 unique organisms (taxonomic identifiers). 15 In addition, we identified as missing from RefSeq and subsequently added genome assemblies of three major food organisms (tuna, barley, wheat) and two potential food contaminants (cockroach, drain fly) to the plant and animal database (from NCBI or Ensemble databases; details in.
Summary The UWGeodynamics module facilitates development of 2D and 3D thermo-mechanical geodynamic models (Subduction, Rift, Passive Margins, Orogenic systems etc. ). It is designed to be used for research and teaching, and combined the flexibility of the Underworld Application Programming Interface,  (Moresi, Dufour, & Mühlhaus, 2002 , Moresi, Dufour, & Mühlhaus (2003) ,  Moresi et al. (2007) ) with a structured workflow. Designing geodynamic numerical models can be a daunting task which often requires good understanding of the numerical code. UWGeodynamics provides a simple interface with examples to get you started with development of numerical models. Users can start designing their models without any pre-existing knowledge of programming. Expert users can easily modify the framework and adapt it to more specific needs.
The code can be run in parallel on multiple CPUs on personal computers and/or High Performance Computing systems. Although UWGeodynamics has been primarily designed to address geodynamic problems, it can also be used to teach fluid dynamics and material mechanics. UWGeodynamics uses the flexibility of the Python language and the Jupyter Notebook environment, which allows leveraging the wide range of scientific libraries available from the Python community. It also facilitates the coupling with existing scientific Python modules such as Badlands  (Salles, Ding, & Brocard, 2018). The functionalities include: • Dimensional input values, using user's choice of physical units. • Automated and transparent scaling of dimensional values. • Sets of predefined geometries that can be combined to define the initial geometry of a model.