dataesr/scientific-paragraphs-categorization huggingface dataset:
82 0. 03 IPR000884 Thrombospondin type 1 (TSP1) repeat - Cell interactions ORTHO03S004517 0. 79 0. 01 IPR004911 Gamma interferon inducible lysosomal thiol - Vacuolization reductase ORTHO03S004519 0. 79 0. 01 IPR016201 PSI domain - Cell adhesion ORTHO03S005547 0. 79 0. 01 IPR002477 Peptidoglycan binding domain - Digestive enzyme ORTHO03S002888 0. 77 0. 02 IPR011040 Sialidase GO:0004553 Digestive enzyme ORTHO03S003756 0. 76 0. 03 IPR021345 Protein of unknown function DUF2961 - Unknown function ORTHO03S004503 0. 75 0. 02 IPR012338 Beta-lactamase/transpeptidase like GO:0005576 Digestive enzyme ORTHO03S004518 0. 75 0. 01 IPR029787 Nucleotide cyclase GO:0007165 Signal transduction ORTHO03S004748 0. 75 0. 02 IPR036452 Ribonucleoside hydrolase GO:0016614 Digestive enzyme ORTHO03S005894 0. 75 0. 01 IPR008139 Saposin B type domain - Digestive enzyme ORTHO03S004453 0.
72 0. 05 IPR017920 COMM domain - Regulation ORTHO03S003676 0. 72 0. 03 IPR004007 Dihydroxyacetone kinase, subunit L GO:0004371 Signal transduction ORTHO03S005231 0. 72 0. 03 IPR004785 Ribose 5-phosphate isomerase B GO:0005975 Sugar metabolism ORTHO03S003865 0. 72 0. 04 IPR005524 Predicted permease DUF318 - Membrane transport ORTHO03S005235 0. 71 0. 02 IPR028730 Zinc finger FYVE domain-containing protein 26 GO:0061640 Cell division ORTHO03S005554 0. 71 0. 03 IPR029723 Integral membrane protein GPR137 - Transmembrane protein ORTHO03S005577 0. 71 0. 01 IPR009613 Lipase maturation factor - Lipid metabolism ORTHO03S005836 0. 71 0. 01 IPR001124 Lipid-binding serum glycoprotein GO:0008289 Lipid metabolism ORTHO03S005884 0. 71 0. 02 IPR002889 Carbohydrate-binding WSC - Cell interactions ORTHO03S005895 0.
1. DNA metabarcoding is becoming the tool of choice for biodiversity studies across taxa and large-scale environmental gradients. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines removing experimental noise have been designed to address this issue. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and the appropriateness of filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools. 2. Here, we present metabaR, an R package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, i.
e. reagent contaminants and tag-jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitate assessment of the appropriateness of data curation filtering thresholds. 3. metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre-analysed with any bioin-2.
This multi-fold information, often stored in separate tables, is not easily handled by most R packages for data analyses (but see e. g. phyloseq). As such, we currently lack effective tools to transition from bioinformatic pipelines to ecological R packages. To bridge this gap, we developed metabaR, an R package that enables the post-processing and filtering of DNA metabarcoding data already processed through bioinformatic pipelines so as to improve downstream ecological inferences. It is designed to take advantage of negative controls, positive controls and PCR replicates when available to efficiently flag and remove artefactual MOTUs or dysfunctional PCRs. It is implemented in the R statistical programming environment (R Core Team, 2020) which provides flexible analytical tools coupled with powerful graphical capabilities.
metabaR uses these properties to provide highly   The metabarlist object can be manipulated for different purposes. It can be subsetted with subset metabarlist with customisable criteria relating to any table of the metabarlist. The user can also aggregate read counts based on MOTU criteria with aggregate motus, such as for obtaining community data at higher taxonomic ranks than the OTU level. Similarly aggregate pcrs can be used to aggregate read counts based on PCR related criteria, typically to aggregate technical PCR replicates at the sample level. We also include two functions to facilitate the customisation of taxonomic information. The first, taxoparser is a simple tool that parses full or partial taxonomic paths generated during upstream bioinformatic processing. The function taxodecider enables users to process taxonomic assignment for the same MOTU from multiple databases.
For example, building a custom reference database is often recommended, since including species from the regional species pool increases the reliability of taxonomic assignments  (Taberlet et al. , 2018). However, these databases are often incomplete and it is common to run in parallel annotation tools with more generalist reference databases such as EMBL (https://www. embl. org/). Thetaxodecider function allows users to merge different annotations based on assignment scores and by giving priority to assignments from the user's preferred reference database, usually the one for which taxonomic and sequence information is the most reliable. Finally metabaR has different export tools. First, fasta generator exports sequences in the fasta format where the user is free to add any information from the metabarlist to the sequence header.
This function can be of use when following the implementation of metabaR functions, it becomes apparent that specific bioinformatic data curation pro-cedures require retuning. The package does not provide other export functions, since the metabarlist is a simple R list that can be directly exported with the R base function saveRDS. Alternatively, any table may be extracted from the metabarlistwith extracttable, before R's write. table function is used for export.
| Example dataset The package contains a dataset, named soil euk, which is a typical output of a DNA metabarcoding experiment. It is used in the package help and the vignette to illustrate the functions of metabaR. soil euk is a metabarlist and contains the abundance of 12,647 MOTUs obtained from 384 PCRs, corresponding to a total of 64 biological samples. The dataset also includes different information on MOTUs, PCRs, and samples. The dataset was generated from soil and litter samples collected in two tropical forests in French Guiana, from which a variable region of the 18S rRNA gene was amplified by PCR and sequenced on an Illumina platform. The data was then processed with the OBITools bioinformatic pipeline. Detailed information regarding the generation of this dataset is available in the soil euk help page.
| Visualisation Appropriately representing DNA metabarcoding data visually is a prerequisite to assessing the quality of the data or of the curation process. Such assessments require going beyond representing dataset characteristics such as sample sequencing depth or richness in MOTUs 10. CC-BY-NC 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted August 31, 2020. ; https://doi. org/10. 1101/2020. 08. 28. 271817 doi: bioRxiv preprint using standard boxplots and histograms. Here, we developed two functions, ggpcrplate and ggpcrtag, to represent data set characteristics in their experimental context, i.
e. the PCR plate. Their input consists of a metabarlist and a function pre-encoded in metabaR or designed by the user to be applied to the input metabarlist so as to enable the plotting of numerous dataset characteristics. Such visualisation can enable the identification of potential experimental problems, such as pipetting or tag/primer issues as exemplified in Fig. 2. 11 The taxonomic composition of DNA metabarcoding data is also often difficult to represent because taxonomic assignments are seldom available at a uniform taxonomic level. This problem usually results from either the incompleteness of reference databases, or as a result of the inherent variation of DNA markers in taxonomic/phylogenetic resolution across lineages. To facilitate the visualisation of the sample or experiment's community composition in this context, we developed the function ggtaxplot, dependant on the igraph R package  (Csardi & Nepusz, 2006).
The tagjumpslayer function aims to reduce this noise by removing a MOTU in a given PCR product when its relative abundance over the entire dataset is below a given threshold. This threshold can be empirically chosen by testing the effect of varying curation thresholds on the MOTU and read counts in the dataset in general and, when available, in the sequencing negative controls (i. e. unused tag or library index-combinations) in particular. The effect of these tag-jumps can complicate the detection of external contaminants, such as those occurring in laboratory reagents  (Salter et al. , 2014). An approach which only consists in the detection of MOTUs present in experimental negative controls would ignore tag-jumps, and can result in the removal of the most abundant genuine MOTUs from the dataset.
However, in negative controls, contaminants should be preferentially amplified in the absence of competing DNA, which is unlikely to be the case in biological samples. The contaslayer function relies on this assumption and detects MOTUs whose relative abundance across the whole dataset is highest in negative controls. Finally, the pcrslayer function aims to identify potential failed PCR reactions by comparing the dissimilarities in MOTU composition within a biological sample (i. e. between PCR replicates, hereafter dw) vs. between biological samples (hereafter db). It relies on the assumption that PCR replicates from a same biological sample should be more similar than two different biological samples (dw < db). A PCR replicate having dw above a given dissimilarity threshold, defined automatically by the function based on the distribution of dw and db, is considered to be an outlier.
| Conclusions The metabaR package provides a much needed tool at the interface between bioinformatic pipelines and ecological analyses to evaluate the quality of data and of the curation process, prior to offering further curation of commonly overlooked artefacts. We also provide a vignette along the package that constitutes for new users a good starting point to build their own quality assessment and filtering of DNA metabarcoding data: it highlights all the recommended steps and possible uses of experimental controls to clean the data. The metabaR package and its vignette will contribute in improving data quality standards in the field, ease the analysis of DNA metabarcoding data and will therefore help to broaden the use of eDNA-based analyses of biodiversity. 15.
Acknowledgements We are deeply indebted to  Eric Coissac  for stimulating discussions that led to the development of this package, and are also grateful to  Jerôme Chave  and  Wilfried Thuiller  for supporting this work. We thank  Pierre Taberlet  and  Heidy Schimann  for providing data, as well as  Irene Calderón-Sanou ,  Camille Martinez-Almoyna ,  Jerôme Murienne  and  Renato A. Ferreira de Lima  for practical discussions on -and/or testing of -earlier versions of the package. We also thank  Chris Bowler  for providing informatics equipment to ASB. The work was funded by the  METABAR  ( ANR-11-BSV7-0020 ) and  GlobNets  ( ANR-16-CE02-0009 ) projects, and has benefitted from " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  TULIP :  ANR-10-LABX-0041 ;  OSUG@2020 :  ANR-10-LABX-56 ).
Usually largely differ from any known sequence. Dysfunctional PCRs Any PCR product yielding a low amount of sequences or an irreproducible signal. Table 1 -Overview of DNA metabarcoding experimental artefacts customisable functions, as well as effective visualisation of DNA metabarcoding data in their experimental context. Hence, it is of direct use for any practitioner of DNA metabarcoding techniques with basic skills in R programming. 2 | Data structure, import/export, and manipulation metabaR performs the analysis of DNA metabarcoding data by taking into account its multidimensional nature. The central object of the package is a metabarlist, an R list composed of 4 interconnected tables (Fig. 1): (i) reads, a table that stores the read abun- dance of MOTUs in each PCR product, (ii) motus, a table which stores any information relative to each MOTU in the dataset (e.
Prada et al. (2016)  instead performs mesh and texture alignment at defined transitions points to ensure smooth blending. This overcomes the challenging problem of global mesh alignment and only considers alignment of geometry and texture where necessary. In contrast,  Boukhayma and Boyer (2017)  and  Casas et al. (2014)  leverage global alignment of the mesh sequence to obtain temporally consistent mesh connectivity from the volumetric video. This allows 4D motion graphs with mesh blending for high-level parametric control of the motion and smooth transitions between motions. In this paper we introduce Deep4D, a learnt generative representation of volumetric video sequences, presented in Section 3. Deep4D provides compact representation, which overcomes the memory and computation requirement of previous approaches to explicitly represent all captured sequences at run-time through the learnt parameters of the network.
Volumetric Video Pre-processing In the context of this work, 4D volumetric video represents 4D mesh sequences M s t , 2D textures T s t and 3D skeletal motion p s t computed from multiple view video capture. A 4D volumetric video dataset consists of N S sequences s [1. N S ] and each sequence consists of N s T frames at a time instance t 1. N s T. State-of-the-art volumetric performance capture of people with loose clothing and hair  (Collet et al. , 2015)  results in high resolution reconstructed shape and texture appearance. Raw volumetric video typically results in an unstructured mesh sequence where both the mesh shape and connectivity changes from frame-to-frame  (Prada et al. , 2016). Several approaches have been introduced for temporal alignment over short subsequences to compress the storage requirements  (Collet et al.
Training Details The network architecture used to regress 3D skeletal pose to 4D mesh shape is summarised in Figure  2. The network was empirically found to learn a good latent space distribution with accurate 4D shape generation using a training cycle of 10 4 epochs, which is optimised through validation data to avoid over-fitting with a learning rate of 0. 001. The datasets are split by randomly selecting frames from each motion sequence with ≈80% used for training and ≈20% used for validation. We set the prior probability over latent variables to be a Gaussian distribution with zero mean and unit standard variation, p (z) N (z; 0, I). We use Adam optimisation  (Kingma and Welling, 2013)  with a momentum of 0. 9 to optimise Eq. (  4 ) between the reconstructed and ground truth mesh vertices, and simultaneously the KL divergence of the 3D skeletal pose distribution.
DEEP4D MOTION GRAPHS The following section introduces Deep4D motion graphs, a novel approach to generate motion graphs  (Casas et al. , 2012; Casas et al. , 2013; Boukhayma and Boyer, 2015; Boukhayma and Boyer, 2019)  from the Deep4D representation introduced in Section 3. The motion graphs, animation and rendering blocks presented in Figure  5  are discussed in detail to demonstrate the steps taken to generate motion graphs capable of animating learnt characters from 4D volumetric video datasets. The goal is to merge the popular deep learning research field with traditional animation  pipelines to begin a new era for computer graphics, creating novel mechanisms to produce realistic human animations. Firstly, we discuss the input data to the animation framework along with the pre-requisites for initialisation.
RESULTS AND EVALUATION This section presents results and evaluation for the proposed Pose2Shape network, the Pose2Appearance network from motion, and their applicability using Deep4D motion graphs to generated realistic animations, introduced in Section 3. 2 and Section 3. 3. To evaluate the 4D animation framework we use publicly available volumetric video datasets for whole body and facial performance. The SurfCap dataset, JP and Roxanne characters, and Dan character  (Casas et al. , 2014)  are reconstructed using multi-view stereo  (Starck and Hilton, FIGURE 6  | Latent space interpolation, two frames with distinct motions are selected, on the left surrounded with a green box is the source, on the right surrounded with a red box is the target, in between is the interpolated results for 4D shape and appearance.
2007 ) and temporally aligned with SDSR  (Regateiro et al. , 2018)  which allows for surface pose manipulation. Martin dataset  (Klaudiny and Hilton, 2012)  consists of one sequence of temporally aligned geometry and texture appearance of a human face, and 3D facial key-points given by OpenPose  (Cao et al. , 2021). Thomas dataset  (Boukhayma and Boyer, 2015)  consists of four sequences of temporally aligned meshes and texture appearance. An overview of dataset properties is shown in  Table  2. Examples of character animation using Deep4D motion graphs are shown in Figures  9, 6. Results demonstrate that the proposed generative representation allows interactive character animation with seamless transitions between sequences based on interpolation of the latent space. The meshes are coloured to illustrate different motion sequences and interpolation between them when performing a blend transition.
Linear Blend Skinning Comparison This section includes a comparison of the proposed Pose2Shape network against linear blend skinning (LBS) techniques demonstrating the benefits of using the proposed network. LBS is a widely used approach in real-time character animation for deforming a surface mesh according to an underlying bone structure, where every bone contains a transformation matrix that affects a group of vertices. This relation is given by a weighting attribute that weights the  To compare the meshes, we use the Hausdorff distance metrics, discussed in Section 5. 1. Figure  12  illustrates the results for the first experiment using the Thomas  (Boukhayma and Boyer, 2015)  dataset. The top row represents the original sequence of walking motion, the source and target frames surround by green and orange boxes, respectively, represent the frames used for interpolation.
appearance, i. e. the topology, vertex connectivity and texture coordinates are constant across all frames and sequences. s t skeletal structure from a motion sequence, which contains N s {z s t , S s t }, where S s t is a T number of frames t 1. N s T , representative of the original motion dataset. Lastly, it is necessary to utilise the pre-trained mesh generator Q z s t | A z s t | Ts t from Section 3 to interpret each latent vectors z s Ms and the appearance generator t t stored as a motion frame in latent motion sequence F s t. The generative networks synthesise Ms t meshes and Ts t texture maps for every z s t ∈ F s t , which represents a temporally consistent 4D mesh and.
In this context, technology studies have been carried out by several groups and space agencies in order to refine technical requirements and IPDA mission concepts. For instance, the ASCEND program from NASA  [2]  or the A-SCOPE study from ESA  [4]  have driven technological developments and mission concepts for CO 2 probing. Space-based lidar concepts of other GHG have also been investigated: with proposals such as LiVE or WALES for water vapor  [11, 12]  and the French-German lidar mission MERLIN dedicated to methane (CH 4 ), that will be the first GHG DIAL mission in space  [1]. Despite the foreseen breakthroughs for space-based active GHG monitoring, significant technological hurdles in the development of stable, robust, and powerful laser sources in the relevant wavelength windows still need to be tackled in the prospect for future missions.
The following section specifies the instrument requirements derived from studies for CO 2 IP-DIAL measurement from space and briefly outlines the overall LEMON instrument concept. Section 3 is dedicated to the laser transmitter module. We present the optical setups of the pump laser, optical parametric oscillators (OPOs), and parametric amplification stages, along with preliminary experimental validation results and numerical performance simulations. Section 4 outlines the envisioned approach to measure and reference the emitted laser wavelength using a frequency comb technique. A summary of the so-far achieved instrument performances and perspectives for further development and testing in the prospect of an airborne demonstration is given in Section 5.
The measured tuning data correspond reasonably well with calculations using Sellmeier expansion from  [49]  and thermo-optic coefficients from  [50]. The effective nonlinearity of the crystals was estimated from the OPO threshold measurement. While scanning the crystal across the pump beam (1/e 2 radius of 350 µm), the OPO threshold was measured at each point. The effective nonlinearity was deduced from the analytic expression for the nanosecond SRO threshold according to Brosnan and Byer  [51]. The 2D map of the effective nonlinearity is shown in Figure  11. The apparent lower nonlinearity around the edges of the crystal is due to geometric pump beam clipping. The effective nonlinearity is homogeneously distributed and corresponds to the maximum value expected in PPKTP, indicating high quality periodically poled structure throughout the whole crystal volume.
Preliminary OPA Testing and Performance Simulations Similar to the approach used for the NesCOPO simulation study presented in Section 3. 1. 2, a reference OPA experiment was carried out to determine the parameters for further numerical calculations using the OPT simulation software. In the experiment, we used 10-20 µJ of idler single-frequency emission emitted by the PPLN-based NesCOPO specified in Table  2  and pumped an OPA line of three 12 mm long PPKTP crystals with up to 70 mJ at 1. 064 µm and a repetition rate of 150 Hz. The pump beam diameter (at 1/e 2 ) is 2. 8 mm. To avoid gain saturation, the idler beam was removed between the last two crystals, as suggested in  [52]. The measured signal output energy as a function of the pump energy is shown in Figure  12. The calculated output energy obtained by simulation with OPT using parameters adjusted to fit the experimental data is also shown.
The second part of the supercontinuum was filtered and spatially and temporally overlapped with the light from the lidar transmitter. The 2 µm OPA pulses were frequency-doubled (SHG: second-harmonic generation) to shift the detection to 1 µm, enabling the use of standard silicon detectors. With a fast analog-to-digital converter (ADC), the heterodyne beat signal between comb mode and SHG pulses was sampled. Together with a low-resolution wavemeter measurement, the control unit can determine the absolute frequency of the SHG pulse. The obtained frequency value was used for lidar data analysis and for providing an error signal for active stabilization of the lidar transmitter. The underlying functional operating principle of the FRUIT is depicted in more detail in Figure  17. The frequency of each mode of the frequency comb is determined by the two degrees of freedom of the comb: repetition rate and offset frequency.