dataesr/scientific-paragraphs-categorization huggingface dataset:
As each sample is cut 8-12 times it could be possible that the raw data acquired using REIMS is analogous to that of liquid chromatography-mass spectrometry (LC-MS), a 'classical' technique often used when carrying out metabolomic profiling experiments. Perhaps from an analytical variability standpoint (QC pooled samples) LC-MS is more suited towards such metabolomic profiling experiments  (De Vos et al. 2007). But, in a real-world situation where species identification is both desired and needed rapidly (fishery, port loading dock, etc. ) LC-MS cannot compete with the REIMS technology. The mislabelling of the six 'haddock' samples signifies the vast time comparisons that exist between PCR and REIMS. Whereas the REIMS technology in conjunction with the prototype recognition software provided a result for each sample burn within seconds (including sample preparation), PCR analysis of the six samples took 24 h, including time taken for sample preparation.
However, because a greater majority of the cuts were identified as an outlier and not coley, the statistical validation of all 14 samples gave a 100% correct classification as the software uses one average spectrum of all the cuts for each sample. Along with the validation of the speciation models and the PCR testing of the six suspect 'haddock' samples, the classification of the 14 seabass and seabream samples as outliers further illustrates that fish speciation is very achievable using REIMS with fast and accurate results attainable. Compared to PCR, the coupling of the REIMS source to a XEVO G2-XS QTof mass spectrometer does result in large cost differences. However, in this study only a few aspects of the QTof mass spectrometer were utilised; the time-of-flight (Tof) tube and the detector.
As well as this, REIMS has shown to be able to analyse multiple aspects of fish fraud through the separation of line and trawl caught haddock samples and it may well be that there are other aspects such as geographic origin and wild/farmed which can be differentiated, further issues which genomic profiling is ill-equipped to do. Fig. 4  Method to identify ions which are found predominately within pollock compared to that of the other four species of fish; a a PCA loading plot identifying the average position of each species of fish (blue markers) and the relevant ions (green markers) that contribute most to their positioning; b a S-plot of pollock v the other species of fish identifying the ions that are found predominately in pollock; c a VIP graph of all 701 ions analysed in the multivariate dataset.
The three ions identified within the loading and S-plots (red) have great significance (VIP > 1, S-plot |p| > 0. 03 and S-plot |p(corr)| > 0. 5) towards the dataset and explain the separation of pollock from the other four species of fish within the PCA score plot. Based on MS/ MS fragmentation, two of the three ions (m/z 629. 5 and 667. 5) could not be assigned a putative identification. However, m/z 655. 5 [2M-H] -was identified as a dimer of docosahexaenoic acid (DHA) (m/z 327. 21 [M-H] -). Sciences, 360, 1847 -1857. Xiong, X. , Guardone, L. , Cornax, M. J. , Tinacci, L. , Guidi, A. , Gianfaldoni, D. , & Armani, A. (2016). DNA barcoding reveals substitution of Sablefish (Anoplopoma fimbria) with Patagonian and Antarctic Toothfish (Dissostichus eleginoides and Dissostichus mawsoni) in online market in China: How mislabeling opens door to IUU fishing.
All this contributes to an ever growing visibility of the photogrammetric tools across various fields of science and engineering, the growing market interest, and subsequently a multitude of photogrammetric/computer vision libraries and software solutions, be it commercial or free/open-source. MicMac -together with Bundler, PMVS, VisualSfM, openMVG, OpenCV and othersbelongs to the open-source solutions 1. This publication aims at familiarizing the reader with the philosophy behind MicMac, some of its crucial algorithmic aspects, the software architecture and the pool of available tools.
Historical notes MicMac has been developed at the National Institute of Geographic and Forestry Information (IGN) and the National School of Geographic Sciences (ENSG), since 2003. Initially, the software tools were developed having in mind exclusively the IGN's cartographic production. The independent tools were interfaced in 2005 via an XML framework, allowing the user to freely parametrize the calculations at all processing stages. In 2007, IGN began to freely distribute MicMac under the CECILL-B license that is a version of the L-GPL license adapted to the french law. Until 2008, the dense image matching of already oriented images was possible only with the IGN's internal image file format. In the same year the Apero tool was added to the sofware kernel, offering from now on the possibility to estimate camera exterior and interior orientations, with no restriction on the image format.
In 2010, the XML interface was replaced by a simplified command line. This evolution contributed to an improved accessibility, diffusion and subsequently a better visibility of the software in the scientific communities, and the general public. Since 2010 MicMac has been undergoing a significant evolution due to its implication in many french and european projects. Besides the contributions to the source code, new software distributions under Windows, Mac OSX, as well as the GPU processing for certain tasks became available.
MicMac vis-à-vis other tools From the architecture point of view, the unique feature of MicMac with respect to its alternatives is that the user can operate on different software levels, cf. Fig. 1. The least experienced user can steer the processing by a simple command line where little or no parameters must be set. An experienced user can operate from the same command line by adapting the parameters typically filled with default values. An expert in photogrammetry can access any parameter at any processing step via a set of XML files (cf. Fig. 1 ). On top of that, developers and scientists can use MicMac as a library, implement their own algorithms, and share them for the benefit of photogrammetric community. This said, MicMac was designed primarily as a tool for professionals and consequently is far from a model design in the sense of HCI 2.
Software organisation MicMac is organised in a number of modules, accessible through a common command mm3d. Execution with a unique command has a twofold advantage: (a) it allows to factorize certain developments while keeping the binary files compact, and from the user point of view, (b) it is easy to remember and gives instantaneous access to all modules (cf. Fig. 2 ). Invoking a selected module is followed a b Fig. 1  The simplified architecture of the core MicMac modules. a the low-level and high-level modules dependencies, b the processing workflow. Marked in red are the outcome products, in green are the direct georeferencing inputs Fig. 2  Each module is executed with some obligatory and optional parameters. Adding -help at the end will print out the expected input arguments by a number of parameters.
Unlike Tapas, Campari is a BBA module adapted to handle heterogeneous observations. Besides the tie points, it works with GCPs, the GNNS data and can impose various constraints on the solution. This module is typically executed once a good relative orientation is established, and the cameras are moved two a coordinate system (CS) consistent with that of auxiliary observations, e. g. the GCPs. The latter is performed with any Bascule tool, and it is a spatial similarity transformation (cf. Fig. 1 ). Both Campari and all variations of the Bascule can be regarded as georeferencing tools. Since recently, MicMac provides the user with the internal accuracy estimates of the BBA parameters (their standard deviations and correlations) derived from covariance matrices.
Fig. 3a ). On the other hand, it is known that matching in image space with a master and a set of slaves images is more reliable, especially in close-range photogrammetry applications and for small datasets. The disparity space ε px in the image geometry is then either the depth along the ray or the respective disparity in image space (i. e. the image geometry; cf. Fig. 3b  and c ). It is up to the user which geometry to employ. MicMac offers a very flexible formulation of the matching cost function as far as its terms are concerned, the E(F px ) = T A(x, y, F px (x, y)) + ∇(F px ) reg (1) ∇(F px ) reg = α 1 * |∇ F px 1 | + α 2 * |∇ F px 2 | (2) where A(x, y, F px (x, y)) is the measure of similarity between pixels with A = 0 when they are identical; ∇(F px ) reg a norm on the gradient, which is used as a regularity criteria (it penalizes the variation of F px ); α 1 is the regularization on first component of disparity and α 2 the regularization on the second component (equivalent of matching in the direction transverse to the epipolar line).
Interactive tools Although MicMac by default is based on simplified command lines, for some processes -i. e. image measurements, visualization of the results -a graphical user interface (GUI) is available. Besides this, for multiple commands there exists a GUI that replaces the standard command line. To run it, the user should simply type the name of the command preceded by the letter 'v'. For example, in the GrShade command, the GUI is called by typing vGrShade (cf. Fig 9). All results produced by MicMac are exclusively in open data formats (tif, xml, ply) and as such it opens up the possibility to interact with the processing chain at any desirable stage. For instance, the user can import interior and exterior image orientation data and proceed with the dense matching and orthophoto generation.
Discussion MicMac is an open-source project for photogrammetry. As a research implementation platform, it serves the IGN's internal needs as well as aims at bringing the state-ofthe-art 3D image processing algorithms to a wider audience, be it universities, public institutions or enterprises. Like any other open-source project, MicMac authorizes the volunteers to contribute or change the source code according to their needs. For more information please refer to: • http://micmac. ensg. eu, as the software's reference webpage; • http://forum-micmac. forumprod. com/, for technical support; • https://github. com/micmacIGN/, for the source code. The high priority on-going developments and shortterm perspectives are those concerning effective processing of big datasets. The developments pertain to (a) the tie points extraction, (b) the SfM, as well as (c) the storage of 3D data.
Regarding the tie points, very precise tie point detectors and detectors invariant to affine distortions are being developed. Within the SfM, global, structure-less methods  [12] [13] [14]  are under development. As a further perspective, adequate methods for storing very large 3D point clouds will be conceived. Lastly, as there has been numerous demands to create a GUI, computer programmers willing to contribute are strongly encouraged to contact the team of developers. A GUI in form of a stand-alone application or a GUI integrated with other open-source software tools, e. g. QGIS, are possible.
Results Sequencing of the Neotropical rainforest soil samples resulted in 132. 3 million cleaned V4 reads (Supplementary Table  1 ). Of the 50. 1 million reads assigned to the protists, 75. 3% had a maximum similarity of < 80% to references in the Protist Ribosomal Reference database (PR 2 ) 12 (Fig. 1  and Supplementary Fig. 1 ). By contrast, our re-analysis of 367. 8 million reads from the hypervariable V9 region of the 18S rRNA from the open oceans 11 produced only 3. 1% reads < 80% similar to the PR 2 database, and most of our new V4 and V9 reads from European near-shore marine environments were likewise highly similar to the PR 2 database (Fig. 1  and Supplementary Fig. 1 ).
Reads < 80% similar to known references are often considered spurious and removed in environmental protist sequencing studies  13. Three-quarters of our rainforest soil protist data would be discarded if we applied this conservative cleaning step. Furthermore, PR  2  and similar databases are biased towards marine and temperate references. To solve these problems, reads were dereplicated into 10. 6 million amplicons (strictly identical reads to which an abundance value can be attached), and subsequently placed using the Evolutionary Placement Algorithm (EPA)  14  , as implemented in RAxML  15  , onto a phylogenetic tree inferred from 512 full-length references from all major eukaryotic clades (Fig. 2  and Supplementary Fig. 2 ). We conservatively retained operational taxonomic units (OTUs) constructed with Swarm  16  (Supplementary Fig.
7 ). Although algae are thought to affect the net uptake of carbon in some terrestrial ecosystems  23  , the contribution of these soil-inhabiting algae to the carbon cycle in Neotropical rainforests is unknown. Only 131 OTUs were assigned to the Oomycota. Oomycota are efficient parasites with flagellated stages that disperse through soils. Using the best BLAST hits to GenBank references, we inferred close relationships between many of these Oomycota OTUs and taxa that infect either animals or plants (Supplementary Fig. 8 ). Non-metric multidimensional scaling (Supplementary Fig. 9 ) and Bray-Curtis dendrograms (Supplementary Fig. 10 ) showed that the protist community composition was slightly more similar among the samples from Costa Rica and Panama than those from Ecuador, reflecting similar diversity patterns seen in animals and plants in Central and South American forests  24.
The samples were mostly taken at least 100 m apart. DNA from every two consecutive samples was combined in equal concentration to reduce costs; many of these samples failed to amplify and only those that worked are shown here. The combined samples were amplified with a high fidelity polymerase for the V4 region of 18S rRNA following ref. 52 with universal eukaryotic V4 primers (TAReuk454FWD1 and TAReukREV3)  13. This primer pair has not shown a preference for amplifying Apicomplexa, and the V4 primers were able to amplify all Oomycota in GenBank in an in silico analysis. After PCR cleanup, a second PCR with primers containing sample-specific tags and the Illumina MiSeq sequencing adapters was performed following the manufacturer's instructions. Amplicons were gel size-selected for appropriate length, and library quality was assessed using a Bioanalyzer 2100 (Agilent) and Qubit 2.
Marine samples. Reads from the open oceans came from the Tara Oceans consortium  11. Starting with the Tara Oceans raw reads (Sequence Read Archive accession numbers: PRJEB6610 and PRJEB7315) allowed us to treat the sequencing data with the same bioinformatic pipeline. Our pipeline's goal was to improve the sensitivity of OTU detection by applying most quality-and frequency-based filtering steps after clustering instead of applying them before. Removal of low-abundance OTUs was performed on the combined data and not at the sample level. We also replaced the paired-read merger FLASH (Fast Length Adjustment of SHort reads)  54  with the newer tool PEAR (Paired-End reAd merger)  55  , which is advertised as yielding higher-quality results. After a careful investigation, it turned out that the large increase in OTU number we observed compared with de Vargas et al.
Taxonomic assignment. Taxonomic assignment used VSEARCH's global pairwise alignments with the PR 2 database 12 v203 (based on GenBank release v203). Using Cutadapt, the PR 2 database was extracted for just the specific regions that were amplified and sequenced to allow for comparisons using a global pairwise alignment. Amplicons were assigned to their best hit, or co-best hits, in the reference database as reported by VSEARCH. The different steps of that taxonomic assignment strategy are grouped in a pipeline called Stampa  60. Low-abundance OTUs were removed from the combined dataset only if they included ≤ 2 reads, were found in only one sample and were < 99% similar to an accession in PR  2. Stampa plots. To globally assess the results of Stampa, our taxonomic assignment pipeline, we produced 'Stampa plots'  60.
Stampa plots are simple distribution plots showing the number of reads per similarity value, where the similarity value is the best match between environmental and reference sequences. Stampa plots are a visual assessment of the taxonomic coverage of our reference database sequences: if most environmental reads or OTUs have high similarity values with references, then the coverage is good. Low similarity values indicate a lack of coverage, which can be a sign of amplification-sequencing artefacts (chimeras for instance), the presence of known taxa without reference sequences, the presence of new taxa, or if the coverage deficit is very large, a previously unexplored environment (such as Neotropical rainforest soils).
Phylogenetic placement pipeline. To put the 50,118,536 soil protist reads into a phylogenetic context, they were dereplicated into 10,567,804 strictly identical amplicons and placed onto a comprehensive eukaryotic reference tree. The corresponding multiple sequence alignment used to build this tree contained 512 full-length sequences from all major eukaryotic clades (see Supplementary Data 1 and 2 for GenBank accession numbers, sequences and clade assignments), based on the taxon sampling as summarized in a range of pan-eukaryotic phylogenomic studies reviewed in refs  61, 62  , with a bias towards lineages known to occur in soils from environmental sequencing studies; for example, refs  35, 37, 38. To reduce phylogenetic artefacts, only high quality, full-or near-full length 18S rRNA reads were selected, and reads that have previously been observed to form long branches were omitted.
It is acknowledged that single gene trees are unable to resolve the backbone of the eukaryote phylogeny; however, this was not the intended purpose of this taxon selection or the phylogenetic analyses. The intent was to produce a comprehensive eukaryote sample tree on which to place the V4 reads generated by this study. Phylogenetic placements of query OTU representatives that were taxonomically assigned to the Apicomplexa were conducted using a full-length reference multiple sequence alignment and corresponding phylogeny comprising 190 taxa from all major Alveolate clades (see Supplementary Data 3 and 4 for GenBank accession numbers, sequences and clade assignments). The taxon sampling was collated from a range of publications relating to eukaryotic diversity in general and Alveolata diversity and phylogeny in particular; for example, refs  20, [63] [64] [65] [66] [67] [68] [69].
14 ). Phylogenetic placement of Haptophyta. For the 28 Haptophyta OTUs, we first downloaded 106 GenBank accessions following a previously described taxon-sampling method  22, 72, 73  , and aligned them in MAFFT 74 v7. OTU representatives were then added to this alignment using the MAFFT 'add-in' function, and the tree was inferred with RAxML, using the substitution model GTR-G-I, the new rapid hill-climbing algorithm and 100 bootstrap runs. Oomycete analyses. To tentatively assign functional roles of the Oomycota OTUs, we first compared each individual OTU with related sequences in GenBank using BLAST  75  for a taxonomic affiliation. In cases of identical BLAST hits, we proceeded to a quick phylogenetic neighbour joining analysis to determine as closely as possible its affiliation. Functional affiliation of the OTUs was then determined mostly based on Lara et al.
Statistical analyses. We analysed frequency count data derived from OTU clustering to estimate the total (observed + unobserved) OTU richness in the population from which the observed samples were drawn. These data consisted of the number of OTUs with one representative in the sample (the 'singletons'), and then the number with two representatives, the number with three, and so on. We used two sets of statistical procedures. The first was implemented in the software package, Breakaway 77 v2. 0. This method estimates a family of statistical models, known as Kemp-type distributions, from the frequency count data, by fitting ratios of successive frequency counts via nonlinear regression; an optimal selected model is returned, along with error and goodness-of-fit assessments. In the case of the combined (global) OTU data the lowest-order model was selected, which is equivalent to fitting a weighted linear regression to the ratios of successive frequency counts.
The second was implemented in the software package CatchAll  78. This method fits a family of mixed-Poisson models to the frequency data, as well as calculating a suite of nonparametric procedures. It returns an optimal selected model with errors and goodness-of-fit assessments, and comparisons of parametric and nonparametric analyses. CatchAll selected a mixture of three exponential-mixed Poisson models. In all cases, the results were virtually identical, showing almost no diversity extant in the population beyond that observed in the sample. The R package vegan  79,  was used to analyse frequency count data derived from OTU clustering. Different functions of Vegan were called to randomly subsample our samples (rrarefy function) and to estimate and compare species compositions, using Bray-Curtis distance and NMDS ordination (monoMDS function).
Figure1| Similarity of protists to the taxonomic reference database. In contrast to marine data, most of the reads and OTUs from the Neotropical rainforest soils were < 80% similar to references in the PR 2 database. Only 8. 1% of soil reads had a similarity ≥ 95%, whereas 68. 1% of the marine reads from the Tara Oceans study of the world's open oceans had a similarity ≥ 95%.
Acknowledgements We thank  T. Stoeck ,  L. Katz ,  H. Kauserud ,  O. T. Lewis  and  D. Montagnes  for helpful comments. Funding primarily came from: a  Deutsche Forschungsgemeinschaft  grant  DU1319/1-1  to M. D. ; the  Klaus Tschira Foundation  to L. C. , A. S and A. K; and the  French Government's 'Investissements d'Avenir' program OCEANOMICS  ( ANR-11-BTBR-0008 ) to C. d. V. , C. B. and S. R. Funding also came from: the  Natural Environment Research Council  grants  NE/H000887/1  and  NE/H009426/1  to D. B. ; the  National Science Foundation's International Research Fellowship Program  ( OISE-1012703 ) and the  Smithsonian Tropical Research Institute 's  Fellowship program  to J. M. ; the  Swiss National Science Foundation  ( 310003A 143960 ); and the  Swiss Federal Office for the Environment and Swiss National Science Foundation  to E.
The orientation of the β1-β2 loop in these structures is similarly influenced by the presence of calcium (Supplementary Fig. 8 ). Interestingly, the NMR structure of native PulGp is more similar to the X-ray structures of EHEC and V. cholerae pseudopilins obtained with calcium (DALI server Z score of 14. 4 and 13. 4 over 107 aligned residues) than to its own X-ray structure (Z score of 11. 8). While the structural effect of calcium is reversible, calcium depletion and Ala substitutions of calcium-coordinating residues in vivo led to PulG degradation and abolished T2SS function. These data support an important regulatory role of calcium levels, which are known to influence expression or activity of many bacterial virulence-related secretion systems16-18 and adherence factors19,20, during adaptation to specific niches or lifestyles21.