dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of proteins of interest from proteogenomics and DDA analysis In the shotgun experiment, an average of 46,068 MS/MS spectra were recorded per sample, from which 27-29% were attributed to peptide sequences after querying the GFOSS proteogenomics database. These peptide-to-spectrum matches (PSMs) allowed the identification of 5,298 protein sequences, which were functionally annotated through BLAST searches and used as the starting point for the selection of the candidates for the targeted assay. The in silico mining of this large list of proteins allowed to select a panel of 263 proteins according to the criteria described in section 2. 9. These proteins are involved in key physiological functions such as reproduction, immunity, homeostasis, and detoxification / defence mechanisms, and are therefore susceptible to be disrupted by exposure to toxic compounds.
The authors thank the  French Ministry of Higher Education, Research and Innovation (Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation)  for the PhD fellowship of  Julien Faugere. This work was supported by the  French National Agency Research  ( Young investigator Grant   ANR-18-CE34-0008  and Grant  ANR-18-CE34-0013 ), the  "Office Français de la Biodiversité ", and the  Ecophyto National Action Plan. The authors also benefitted from the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches. The authors are grateful to  Véronique Gouy  (INRAE),  Matthieu Le Dréau  (INRAE),  Christine Combe  ( Water Department of the City of Lons Le Saunier ) for their help in the definition of the field deployment study, and to  Anaïs Valada-Mennumi  for her help on the sample preparation and data analysis.
Practical considerations for using OCIM Besides its computational efficiency, OCIM has several other features that facilitate its use by nonexperts. A few of these features are: 1. OCIM is lightweight and memory-efficient. The OCIM takes up only several MB of disk space and RAM. This contrasts with typical GCMs which require GB of storage and memory space. 2. The native language of OCIM is MATLAB, which is commonly used throughout the scientific community, and easily learned by those without prior programming experience (though it is a proprietary language and thus the AO is not fully open-source). This contrasts with typical GCMs that are written in Fortran or other expert-level programming languages. 3. OCIM can be run on a laptop computer. This contrasts with typical GCMs that must be run on supercomputers or large computer clusters, requiring significant IT infrastructure.
The AWESOME OCIM Here we describe a new model, A Working Environment for Simulating Ocean Movement and Elemental cycling within the Ocean Circulation Inverse Model; the AWESOME OCIM, or the AO for short. This model is intended to put the tools of the OCIM into the hands of observationalists and others with little or no prior experience in ocean modeling. Ultimately, we hope that this can provide an alternative to the simplified box models or 1-d models often now used for interpretation in observation-focused papers. In order to achieve this goal, we focus on three specific criteria: 1) Code is written as transparently as possible, so that users can more easily understand the code they are running and more easily modify that code for new purposes. Variable names are meant to be easily understood and interpreted, extensive commenting is used to describe the purpose of each line of code, and all mathematical operations are performed as intuitively as possible, even at the expense of less succinct code and sometimes slightly longer computational times.
2) The model is written in a modular format, so that users can easily add new biogeochemical processes. Each biogeochemical process is handled in a separate Matlab function. This way, users can model an element which is affected by several biogeochemical processes by running several functions in series. New biogeochemical processes can be incorporated into the model in a similar fashion, and shared between users as self-contained functions. 3) The model comes pre-loaded with observational datasets and model output. In order to streamline work with the AO, the model comes pre-loaded with a variety of observational datasets (e. g. 2017 GEOTRACES Intermediate Data Product and 2009 World Ocean Atlas) stored in the AO model grid, as well as important output from other models (e. g. predictions of global ocean productivity and dust input).
In this manuscript we describe the mathematical basis of the AO and work through a few examples of how the model might be used in research. An appendix provides additional detail, briefly discussing each of the biogeochemical processes and datasets which come with the pre-packaged model. An explicit goal of the AO is to make global ocean modeling tools available to scientists with little prior modeling experience. This manuscript is therefore written with that audience in mind, prioritizing detailed and simple explanations which will be useful for scientists without much prior experience in linear algebra mathematics, ocean modeling, or coding. The code is available at https://github. com/hengdiliang/AWESOME-OCIM-v1. 1 and http://www. mtel. rocks/mtel/awesomeOCIM. html, and in the supplementary materials.
However, as shown in Figure  2 , the model becomes constrained as additional first-order terms are incorporated into A and zero-order terms (i. e. fixed sources and sinks) are incorporated into b to represent biogeochemical processes. Having incorporated all additional processes into A and b, the full physicalbiogeochemical system can then be solved by matrix inversion: (9 ) where e is a vector containing all the values of E in each box: (10) The AO code follows a similar approach, with the main program calling individual functions which each add a representation of an individual process to A and b, then solving for the distribution of the hypothetical element E using matrix division. In this fashion the AO includes functions to represent a wide variety of biogeochemical processes, including sources such as dust and hydrothermal vents, internal cycling processes such as scavenging and biological uptake and remineralization, and sinks such as radioactive decay and sedimentary burial.
Examples Three examples discussed below illustrate different ways in which the AO can be used in scientific research, with a focus on functions that are easy to implement with minimal modifications of the AO code as it is originally released. These examples demonstrate ways that "off-the-shelf" AO simulations might be incorporated for simple hypothesis testing into data-oriented papers, highlighting ways in which certain processes either can or cannot explain the observations (e. g. John et al 2018 using a preliminary version of the AO). Alternatively, more extensive work with the AO might form the basis of a modeling-focused manuscript aimed at synthesizing a global view of an element cycle, in which the user might develop new functions to address specific aspects of that element's biogeochemical cycle.
Using the AO to model simple processes Even without modifying the AO to create a realistic global model of an element, there can still be value in comparing data to a simple model of one or two processes. For example, a plume of Fe-rich waters has been observed emanating from the Peru margin on the US GEOTRACES GP15 transect (Fig. 3 ). The most obvious source of Fe on the margin would be reducing sediments, yet the depth of the plume is much deeper than the oxygen minimum  (John et al. , 2018). Alternatively, it could be that the Fe plume is created by non-reductive dissolution of Fe from oxic sediments underneath the oxygen minimum, for example in nepheloid layers. To quickly test this second idea, a model can be run with a source of Fe in nepheloid layers and a sink through first-order removal (modeled using the code for radioactive decay) with a residence time of 50 years (consistent with the timescale at which dissolved Fe is lost by particle scavenging in the oceans)  (Tagliabue et al.
Conclusions Directly "off-the-shelf", the AWESOME OCIM provides a simple interface for users to test how various biogeochemical processes influence large-scale tracer distributions in a model with realistic global circulation. We hope that such tools provide users with a way to develop better intuition for oceanographic processes, aiding data interpretation. We further hope that the simplicity of the model will make it appropriate for use in classes, both as an introduction to oceanographic processes and as an introduction to ocean modeling. We anticipate that most research applications of the AO will require further modification by the user. Specifically, we imagine that the biogeochemical functions included here will be a good starting point for modeling most elements in the ocean, but that every element will be affected by a few processes which are not considered here, and thus new code will have to be written to reflect that biogeochemistry.
The OCIM is actively maintained, developed, and improved, and new versions are released periodically at https://tdevries. eri. ucsb. edu/models-and-data-products/. The AO is a library of MATLAB codes that run linear biogeochemical models, using a transport matrix derived from a particular version of the OCIM (DeVries, 2014) to ensure high-fidelity tracer transport. The AO could easily be run with transport matrices derived from other models, and a major goal of future versions of the AO is the incorporation of new circulations, particularly those reflecting the past and future ocean states. By doing this, we hope that the AO can become a tool for paleoceanographic and geological research, and for climate science. In this way we hope that the AO can become a tool not only for modern oceanographic research but also for exploration of past and future Earth conditions.
The maps of aerosol input are 3dimensional model output from a variety of types of aerosol, contributed by the lab of Natalie Mahowald, as described in  Brahney et al. (2015)  and  Chien et al. (2016). hydrothermal adds E to the ocean according to predictions of submarine hydrothermal 3 He input stored in HEFLUX. mat (Fig. 7 ). Helium fluxes are based on seafloor spreading rates, as originally outlined by  Farley et al. (1995)  and further modified in an OCIM framework by allowing for regionally variable differences in the relationship between spreading rate and 3 He  (Holzer et al. , 2017). The HEFLUX data already accounts for the volume of the grid cell into which hydrothermal fluids are injected, so that the input of element E is given by: ,-. /01 ℎ82/-3ℎ1/956. : ⋅ ; %<=> where hydrothermal.
, 2018). The data has been re-gridded onto the AO grid, where all data points within a grid cell were averaged together to produce the final concentration. This folder contains data for 79 parameters (of the total 470 parameters included in the 2017 IDP) including many of the most commonly studied dissolved trace metals. GLODAP is a folder containing data on global ocean alkalinity and DIC from the GLODAP data product  (Lauvset et al. , 2016; Olsen et al. , 2016). GLODAP data is not currently used by any functions in the AO, but it is never the less included here so that it may be used in future work. WJ18 is a folder containing model output from a global nutrient cycling model. The particular model used for the AO is described in  Weber and John et al. (2018) , though similar models of nutrient cycling have been constructed for previous work.
The model minimizes misfit between observed phosphate from the 2009 World Ocean Atlas and the model output phosphate (PO4_WJ18) (Fig. 9 ). Other concentrations reported from the model include dissolved organic phosphorous (DOP_WJ18) and particulate organic carbon (POC_WJ18), which is derived from particulate organic phosphorous assuming a 106:1 C:P ratio in sinking organic matter. Rates derived from the nutrient cycling model and included with the AO include the phosphorous uptake rate in the surface ocean (P_UP_WJ18) and the phosphorous remineralization rate in the deep ocean (P_REM_WJ18). WOA09 is a folder containing information about the properties of seawater worldwide based on a compilation of numerous datasets, often referred to as a 'climatology'. Data is taken from the 2009 World Ocean Atlas  (Levitus et al.
, 2010) , averaged to produce an annual mean, and re-gridded for the AO. Datasets include nitrate (WOANO3), oxygen (WOAO2), phosphate (WOAPO4), salinity (WOASAL), silicate (WOASI), and temperature (WOATEMP). ao is a structure containing information about the AO grid, including information such as which grid cells contain ocean, the latitude and longitude of the grid cells, and masks for different ocean basins and GEOTRACES sections (Fig. 10 ). A full list of the ao parameters and a complete description of them is included as Appendix II. water_transport contains the ocean circulation transport matrix. The standard transport matrix is derived from the CTL version of the OCIM as described in DeVries (2014). While OCIM is built on a square grid, the circulation is not as simple as having each box exchange water with only the six neighbors sharing a face.
AIII. Using the AO AIII. 1 Running the AO The AO can set up and run using the script setup_single. This script creates first creates a structure do, which tells the model what you would like it to do (a structure is a variable which can contain sub-variables). The do structure enables one or more biogeochemical processes in your model in the form do. function. var, where function is the name of a function which changes the A or b matrix to reflect a biogeochemical process stored in the srcsnk folder (e. g. bioredfield), and var is the variables needed to run that function (e. g. R, the Redfield ratio to apply). If you wish to run a function to enable biogeochemistry within your model, you must set do. function. on to a value of 1, in order to turn that functionality on. If you don't wish to enable a function, you can either set do.
function. on to 0, or simply don't include any reference to that function in your do strutcture. If the user wishes write their own new biogeochemical functions, the function must be saved to the srcsnk folder, and the function can then be called from do in setup_single. A graphical user interface (GUI) provides a simple way to begin exploring the AO without modifying any code at all. The GUI can be launched by running the script launchGUI, after which the user can select various processes to model, or can plot the output from earlier models in a variety of different ways.
Figure8. The intensity of nepheloid layers can be modeled in two ways. Input of tracers can be scaled to a global compilation of particle maximum concentration observations interpolated onto the AO grid (a). Alternatively, input can be scaled to satellite observations of surface eddy kinetic energy (EKE; b). These datasets are converted to input fluxes by dividing the total particle quantity or EKE energy by the volume of the bottom box where input occurs (c, d).
Form Figure  19  it can be seen that the mean value of the air gap between stator 1 and the rotor was higher than that between stator 2 and the rotor, and the smaller airgap value means higher flux density values and, hence, higher EMF. Therefore, this may be the main reason causing the two stators to generate slightly different EMF values. The phases' terminals from the two stators were meant to be connected in series; hence, the difference will not have any effect when the terminals are connected together. Experimental data were collected from a digital oscilloscope. The curves of the EMF of the three phases of each stator of the machine and the curves of the EMF when the stators' terminals are connected in series are shown in Figure  20. The curves have the shape a pure sinusoidal wave.
The phases' terminals from the two stators were meant to be connected in series; hence, the difference will not have any effect when the terminals are connected together. Experimental data were collected from a digital oscilloscope. The curves of the EMF of the three phases of each stator of the machine and the curves of the EMF when the stators' terminals are connected in series are shown in Figure  20. The curves have the shape a pure sinusoidal wave. There was a slight voltage difference between the voltages of stator 1 and 2. Although both stators were constructed to be identical, the rotor, due to the large axial forces, suffered from a slight deflection, causing the airgap width to have different values across the circumference. The airgap width was measured on the left and right sides of each rotor tooth where the left-side width represents the air gap thickness between the rotor and stator 2, and the right-side width represents the air gap thickness between the rotor and stator 1 (Figure  19 ).
This is particularly the case when image sharpness is essential, as for instance in the context of studying Multiple Sclerosis (MS) lesions, whose volumes scale from a few tens to thousands of cubic millimetres  (7, 9). Both the small numbers of available images and different contrasts in an ihMT experiment limit the application of standard retrospective motion correction methods. In this work, we thus propose an efficient and robust motion correction framework adapted to the ihMT images features. The method is compared to the Motion Correction function FMRIB's Linear Image Registration Tool (MCFLIRT) available in the FSL package (4). Both rigidbased approaches are evaluated before and after motion correction. The proposed algorithm implementation and tools adapted for ihMT imaging are made freely available at: https://crmbm.
Multi-Contrast Motion Correction (MC-MoCo) The proposed spatial motion correction method, called MC-MoCo thereafter, was compared to the freely available Motion Correction algorithm from the FMRIB's Linear Image Registration Tool (MCFLIRT) (4) from the FSL package (version 5. 0. 8; available at: https://fsl. fmrib. ox. ac. uk/), using the averaging option for target building. Both MC-MoCo and MCFLIRT rely on rigid-body transformations (6 degrees of liberty) since no non-linear deformations are expected between images based on the same readout in a single subject. Since native images (M 0 , MT + , MT +/-, MT -and MT -/+ ) have distinct contrasts, a mutual information metric  (12)  was systematically employed, and images were linearly resampled upon transformation. Figure  1  illustrates the proposed MC-MoCo method.
The whole process relies on functionalities provided in the Advanced Normalization Tools (ANTs) registration package (5) (version 2. 1. 0; available at: http://stnava. github. io/ANTs/) and can be summarized as follows: (i) Nick's nonparametric nonuniform normalization (N4) bias-field correction  (13)  and skull-stripping of the native images, (ii) target building based of the MTw images, (iii) registration of each native skull-stripped images onto the targset, (iv) application of the estimated transformations to each native image. A rationale for steps (i) and (ii) is given: (i) In order to enhance the sensitivity of the transformation estimation to brain structures, a skull-stripping step was first performed. This allowed minimizing contributions of out-of-brain features (e. g. various signal intensities in the scalp across MTw and reference images) throughout the registration process.
Statistical analyses Based on the previously-defined motion quantification, statistical analyses were performed to evaluate the performance of MC-MoCo and MCFLIRT, and to verify that the behavior of both methods was constant across landmarks and between the two groups of subjects. For these analyses, we used a mixed-effect linear model with fixed effects for the motion correction method, the landmark, and the subject group, and with a random effect for the subject to account for the non-independence of the observations within a subject. Shapiro-Wilk and Mann-Whitney tests were used to test for normality and median difference of distributions, respectively. The analysis code based on R is available at https://github. com/lamyj/mcmoco-data (commit 9e0555b was used for the presented results).
Acknowledgements This work was supported by the  SATT Sud-Est (France) , the  French Association pour la Recherche sur la Sclérose En Plaques (ARSEP) ,  Roche Research Foundation (Switzerland)  and  French National Research Agency ,  ANR  [ ANR-17-CE18-0030 ]. The authors thank  V. Gimenez ,  P. Viout ,  L. Pini  and  C. Costes  for technical support and management, as well as  Dr. Fanny Munsch  for the careful manuscript review.
1 ). Percentage identity matrix was calculated between this two dnmt3 related sequences in ciona and tetrapods, which showed that ciona sequences seemed no more related to dnmt3a than to dnmt3b (Supplemental Table  S1 ). In order to clarify the specific timing for when dnmt3a and dnmt3b genes arose, we did a dnmt3 syntenic analysis in chordates using Genomicus software v01. 01 (www. genomicus. biologie. ens. fr, Supplemental Fig. S4 ). We showed that the synteny around dnmt3 genes in amphioxus and ciona was not well conserved except for col21a1 and akap7, which were found on reftig_1 and reftig_44 in ciona, respectively. In vertebrates, dnmt3a and dnmt3b genes were included in two distinct syntenic groups: ak7-abhd1-col21a1-aka p7-epb41l2-sl3a1-zbtb24 and pcsk2-dusp15-pofut1-b4galt5-zbtb46, respectively.
We then investigate the evolutionary history of dnmt3 in vertebrates. In jawless vertebrate, lamprey, two dnmt3 genes annotated as dnmt3a (ENSPMAG00000007710 and ENSPMAG00000006895) were identified in the Ensembl database. However, we identified a 3 rd sequence in the lamprey genome assembly in NCBI (NCBI, GCA_002833325. 1). We identified this sequence (W1 scaf_00017) as a novel dnmt3 in lampreys. This new dnmt3 gene and ENSPMAG00000007710 in lamprey have all the conserved domain features for dnmt3, whereas the ENSPMAG00000006895 has no PWWP domain (Supplemental Fig. S3 ). Percentage identity matrix (Supplemental Table  S1 ) and phylogenetic analysis (Fig. 1 ) confirmed that all these three dnmt3 related genes in lamprey shared higher conservation with dnmt3a gnathostoma genes than with dnmt3b.
Syntenic analysis results also showed a partial syntenic conservation of the first group cited above around dnmt3 genes found in lampreys, confirming that these genes were related to the dnmt3a gene in other vertebrates (Supplemental Fig. S4 ). We also verified this result in SIMRBASE. Noteworthy, there is another gene PMZ_0040501-RA annotated as dnmt3b in this database. However, we found that this latter sequence contains only a FYVE domain, and clustered with the mammalian DNMT3L orthologs (Data not shown). In jawed vertebrates, only one dnmt3a gene was identified in tetrapods, shark, coelacanth and spotted gar, but dnmt3a pseudogenes were identified in humans (ENSG00000224071, chr. 2) and mice (ENSMUST00000192011. 1, chr. 3, Supplemental Fig. S4 ). By contrast, two dnmt3a genes were found in most of the teleost species (Table  1 ), which were well grouped as two independent clusters, dnmt3aa and dnmt3ab, in phylogenetic tree (Fig.