dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, producing phylogenetic trees for a whole proteome or large gene set can be extremely time consuming, especially if the initial homology search retrieves numerous homologs for alignments and phylogenetic reconstruction. Because of their computationally demanding nature, phylogeny-based methods are currently hardly adapted to analysis of large genomes or to a high number of genes. Hence, AI or HGT-index based methods constitute an interesting method to rapidly identify candidate HGT and narrow down the number of genes to be phylogenetically analyzed afterwards. As far as we know, there is currently no publicly available web tool that allows to easily and rapidly identify HGT from large datasets directly from a BLAST result. Here, we propose Alienness, a user-friendly web application that requires no installation of any software and that is publicly accessible at http://alienness.
Input Data Typically, Alienness takes as input a compressed BLAST result (in. zip or. gz format) of a proteome performed against any NCBI protein database (Figure  1 ). Currently, the uploaded compressed BLAST result must not exceed a size of 500 Mb (ca. 2 to 2. 5 Gb uncompressed). The BLAST result must be obtained by the BLASTp program available at the NCBI FTP website in BLAST+ package. The required format is the tabular format with comment lines obtained by using the -outfmt 7 BLAST option. The user must also define the NCBI taxonomy identifier (TaxID) of the taxonomic group of interest (recipient group, e. g. , Metazoa, Viridiplantae) and one or several TaxIDs separated by a comma for the taxonomic groups to be ignored for the calculation of the AI (excluded group(s)). The latter parameter must at least include the TaxID of the query species used to produce the BLAST result to avoid self-BLAST results preventing the identification of putative HGT.
Taxonomic Assignement The taxonomic assignment of BLAST hit sequences is performed using the taxonomic databases available on the NCBI FTP site (ftp://ftp. ncbi. nlm. nih. gov/pub/taxonomy/). This repository contains the full taxonomy database along with flat files associating nucleotide and protein sequence records with their TaxIDs. The challenge lies in reconstructing the taxonomic lineage of species just from GenBank identifier (GI) extracted from BLAST results. To perform this taxonomic identification, we use the files 'gi_taxid_prot. dmp. gz' and 'taxdump. tar. gz', both available for download at ftp://ftp. ncbi. nlm. nih. gov/pub/taxonomy/. The first file provides, for each GI of protein record, its corresponding TaxID) The second file contains NCBI Taxonomy database dump files, and more.
Taxonomic Assignement The taxonomic assignment of BLAST hit sequences is performed using the taxonomic databases available on the NCBI FTP site (ftp://ftp. ncbi. nlm. nih. gov/pub/taxonomy/). This repository contains the full taxonomy database along with flat files associating nucleotide and protein sequence records with their TaxIDs. The challenge lies in reconstructing the taxonomic lineage of species just from GenBank identifier (GI) extracted from BLAST results. To perform this taxonomic identification, we use the files 'gi_taxid_prot. dmp. gz' and 'taxdump. tar. gz', both available for download at ftp://ftp. ncbi. nlm. nih. gov/pub/taxonomy/. The first file provides, for each GI of protein record, its corresponding TaxID) The second file contains NCBI Taxonomy database dump files, and more precisely nodes.
Calculation of an Alien Index To illustrate the calculation of an AI we will take as an example identification of candidate HGT of non-metazoan origin in a metazoan genome. The principle remains the same for identification of inter-phylum HGT to the genome of any species of interest from any donor (alien) taxonomic groups. Typically, Alienness takes as input the result of a BLASTp search of a whole set of predicted proteins of interest (e. g. , from a whole genome or a transcriptome) against the NCBI's NR library or any protein library available at NCBI. The BLAST result must be in tabular format and when performed against NR, we recommend using an E-value threshold of 1E -3 and no filtering for low complexity regions. Based on the BLAST hits GI accession numbers and on the NCBI taxonomy, Alienness extracts the best metazoan and best non-metazoan E-values to calculate an AI, first defined in  [9] , with the following formula.
Alienness Web Portal and Interface The Alienness web portal has been developed in HTML5 and CSS3 and launches a series of Perl programs via a CGI Perl module. Alienness is publicly and freely available at this URL: http://alienness. sophia. inra. fr and does not require creation of an account or downloading any software. Alienness just requires a pre-computed BLAST result in tabular format (-outfmt 7) against a protein library. The BLAST result file must be compressed in. gz or. zip format. The protein library must have GI or accession numbers that exist in the NCBI taxonomy database to retrieve the taxonomic information necessary to calculate AI values (i. e. , any downloadable BLAST protein library at the NCBI). For a better coverage of the biodiversity, we recommend using the NCBI's NR library.
For each category, a list of accession numbers from the query proteome as well as the AI value and the taxonomic category of the candidate donor are indicated. By clicking on any of these accession numbers, a color-coded BLAST result in. html format opens and allows exploring the results in more details. An example on a M. incognita protein (Minc14047b, a GH5 cellulase acquired by HGT  [37] ) is given below (Figure  4 ). In the BLAST result, the color code is as follows: hits that belong to the taxonomic group(s) to exclude are highlighted in blue. In this example, we chose to exclude the Tylenchida (TaxID: 6300) that include the root-knot nematodes and many other related plant-parasitic nematodes. The best 'alien' hit is indicated in green. In this example, we were interested in HGT of non-metazoan origin in a metazoan, so the alien taxon is anything but Metazoa.
Here, the best alien taxon is from the bacterium Cellulophaga algicola with an E-value of 8E -81 The best hit that belongs to the taxonomic group of interest (=recipient taxon, as defined in the field 3 of the form in Figure  2 ) is highlighted in red. In this example, the taxonomic group of interest is Metazoa and the best metazoan hit is from the phytophagous insect Apriona japonica with an E-value of 9E -68. These are thus the green (here best non-metazoan) and red (here best metazoan) E-values that are used to calculate the AI. Hits for which a taxonomic group could not be assigned are highlighted in light violet. This corresponds to protein that have either been removed from the NCBI or replaced by another protein with a different accession number. Hits that belong to the categories 'other' and 'unclassified' are highlighted in light grey.
Discussion Alienness allows rapid identification of candidate HGT from whole proteomes and provides accompanying taxonomic information as well as basic statistics for the possible donors. An advantage of providing scores such as an AI is that the user can define its own threshold of significance and partition its dataset according to the AI scores. A high AI is an indication for putative acquisition by HGT. Obviously; a careful phylogenetic analysis remains the gold standard to support the hypothesis of HGT. However, careful phylogenetic analysis is time-consuming, can be complicated and requires expert skills, which is not (or hardly) applicable to large datasets of thousands of genes. In this perspective, Alienness provides an efficient metrics to quickly reduce the number of candidates that can be further analyzed via phylogenetic analysis.
For instance, in the case of the plant-parasitic nematodes 20,359 and 14,309 proteins were present in the predicted proteome of M. incognita and G. rostochiensis, respectively. However, only 632 M. incognita and 519 G. rostochiensis proteins returned an AI > 0 and <70% identity to putative donors, which substantially narrows down the number of phylogenetic analyzes that must be undertaken. With an AI > 14, corresponding to low risk of false positives, the number of candidates is reduced to 165 in M. incognita and 136 in G. rostochiensis. Alienness also provides a list of putative contaminants that can be useful for the user to prune the original dataset or even to identify possible endosymbiont. Any protein with an AI > 0 and ≥70% identity is put apart in a category of possible contaminants.
In this framework, NLOCT automatically computes the R 2 rational terms and the ultraviolet counterterms used in the virtual amplitudes, and relies internally upon FeynRules  [47]  and FeynArts  [48]. The one-loop matrix elements are computed using the MadLoop module  [49] , which employs CutTools  [50]  and Ninja  [51, 52, 53]  for loop reduction at the integrand level and IREGI  [54]  for tensor integral reduction. All methods are complemented by an in-house implementation of the OpenLoops  [55]  algorithm. For the factorisation of the IR poles in the real-emission phase-space integrals, the resonance-aware MadFKS  [56, 57]  module is used. We work in the four-flavour scheme, where the bottom-quark mass regulates any soft or collinear divergence related to final-state bottom-quark emissions, making it possible to compute the total cross section without having to impose artificial cuts on the final state particles.
Acknownledgements We are indebted to  Fabio Maltoni  for his constant encouragement and his valuable suggestions. We would like to thank the  LHC Higgs Cross Section Working Group  for providing the motivation to perform this calculation. We are especially grateful to  Martin Flechl ,  Stephen Sekula  and  Michael Spira  for their comments on the manuscript. MZ would like to thank  Pietro Slavich  and  Tim Stefaniak  for discussions. CD is a  Durham International Junior Research Fellow ; the work of RF is supported by the  Alexander von Humboldt Foundation , in the framework of the  Sofja Kovaleskaja Award Project  " Event Simulation for the Large Hadron Collider at High Precision "; the work of VH is supported by the  Swiss National Science Foundation (SNF)  with grant  PBELP2 146525 ; the work of MW is supported by the  Swiss National Science Foundation (SNF)  under contract  200020-141360 ; and the work of MZ is supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodovska-Curie  grant agreement No  660171  and in part by the  ILP LABEX  ( ANR-10-LABX-63 ), in turn supported by  French  state funds managed by the  ANR  within the  "Investissements d'Avenir" programme  under reference  ANR-11-IDEX-0004-02.
This paper presents a model called HydroBioSed that can reproduce the millimeter-scale changes in seabed profiles of oxygen, nitrogen, and carbon, as well as the resuspensioninduced changes in seabed-water-column fluxes observed on the Rhône River subaqueous delta, by coupling hydrodynamic, biogeochemical, and sediment transport modules. This process-based numerical model was implemented for the Rhône River subaqueous delta and used to evaluate how episodic erosion and deposition affect millimeter-scale seabed biogeochemistry and overall oxygen consumption in a dynamic coastal environment. Specific research questions for this paper include the following: (1) how do erosion and deposition affect the timing and magnitude of seabed and bottom-water oxygen consumption? (2) What are the relative roles of local resuspension, organic matter remineralization, and oxidation of reduced chemical species in controlling oxygen consumption in the seabed and bottom waters? (3) How sensitive is oxygen consumption to resuspension frequency and magnitude, sedimentation rate, organic matter lability and availability, rate of diffusion within the seabed, and seabed nitrification rate? (4) What characteristics of the study site lead to the dependence of oxygen dynamics on local resuspension?.
Because erosion and deposition can co-occur, "erosional" and "depositional" time periods refer to times of net erosion, i. e. , when E ised -D ised > 0, and net deposition, i. e. , when E ised -D ised < 0, respectively. Previous CSTMS applications accounted only for inert particulates; however, here we adapted the model to link sediment transport and biogeochemical processes. In HydroBioSed, POM from the water-column biogeochemical module provides an additional source of particulates to the seabed (Sect. 2. 2. 3), and POM can be deposited, eroded, and buried along with the sediment in its seabed layer. Note that POM comprises only ∼ 3 % of the seabed by mass on the Rhône Delta and so it was considered negligible for calculating fluxes within the seabed layering scheme. Additionally, the seabed layering scheme of  Warner et al.
Water-column biogeochemistry module ROMS water-column biogeochemistry modules have typically included variables for multiple nutrient, plankton, and detrital classes and accounted for processes such as growth, grazing, and remineralization (e. g. ,  Fennel et al. , 2006). Here, the ROMS biogeochemical model from  Fennel et al. (2013)  was modified so that HydroBioSed converts some of the large detritus into faster-sinking aggregates in the water column. In  Fennel et al. (2013) , small detritus and phytoplankton in the water column may coagulate to form large detritus. HydroBioSed builds on the  Fennel et al. (2013)  framework by partitioning coagulated material into three types of particulate matter: (1) large detritus, (2) labile aggregates, and (3) refractory aggregates (Fig. 2b ). Based on estimates that roughly half of the deposited particulate organic matter is refractory in the Gulf of Lion  (Tesi et al.
Seabed biogeochemistry module A seabed biogeochemistry module  (Soetaert et al. , 1996a, b)  was added to ROMS to account for changes in oxygen, dissolved nitrogen, and POM due to remineralization, oxidation of reduced chemical species, and diffusion across the seabed-water interface. This model has performed well in many environments including areas near river deltas  (Wijsman et al. , 2002; Pastor et al. , 2011a) , on the continental shelf and slope  (Soetaert et al. , 1998; Epping et al. , 2002) , and in the deep ocean  (Middelburg et al. , 1996). To incorporate the  Soetaert et al. (1996a, b)  model into HydroBioSed, we used the code developed by  Wilson et al. (2013) , and adapted it for the ROMS framework and the Rhône Delta. Calculations use the first-order accurate Euler method. This seabed biogeochemistry model specifically tracks degradable particulate organic carbon (POC), oxygen, nitrate, ammonium, and oxygen demand units (ODUs), defined as the moles of reduced chemical species that react with 1 mole of O 2 when oxidized.
( 10 ) L tot , the non-dimensional sum of the limitation factors on remineralization processes, is as follows: L tot = O 2 O 2 + k O 2 + l O 2 O 2 + l O 2 NO 3 NO 3 + k NO 3 + l O 2_anoxic O 2 + l O 2_anoxic l NO 3_anoxic NO 3 + l NO 3_anoxic. ( 11 ) Adaptations to the  Soetaert et al. (1996a, b)  early diagenesis model, which were made to merge it with the CSTMS and Fennel modules, include neglecting seabed consolidation and temperature-induced changes to biogeochemical rates. Specifically, HydroBioSed neglects changes in porosity with depth in the sediment bed because this study focused on the frequently resuspended surficial centimeter of the seabed and seabed-water-column interactions. Also, we neglected the effect of temperature on remineralization and diffusion because temperature was held constant for this implementation of HydroBioSed (see Sect.
2. 3). Merging the  Soetaert et al. (1996a, b)  seabed biogeochemical model with the sediment transport and water-column biogeochemistry modules allows HydroBioSed to account for exchanges of biogeochemical tracers across the seabedwater interface due to deposition, erosion, and diffusion (Fig. 2b ). Upon settling to the seabed, phytoplankton, detritus, and labile aggregates are incorporated into labile seabed organic matter in the surficial seabed layer. Refractory aggregates are added to the pool of refractory seabed organic matter in that layer. Porewater in newly deposited sediments is assumed to initially have concentrations of dissolved nutrients and oxygen equal to those in the overlying water column. This material may be reentrained into the water column when bed shear stress exceeds the critical shear stress of the seabed.
Acknowledgements. Observations from the  Rhône River delta observatory (Mesurho)  were provided by  F. Toussaint  ( Laboratoire des Sciences du Climat et de l'Environnement ). R. Wilson  (formerly  Dalhousie University ) provided model code from Wilson et al. (2013). Feedback from  E. Canuel ,  C. Friedrichs  ( Virginia Institute of Marine Science; VIMS ), two anonymous reviewers, and  Biogeosciences Associate Editor Jack Middelburg  improved this paper. A. Miller ,  D. Weiss  (VIMS), and  E. Walters  (the  College of William & Mary ; W&M) provided computational support and access to W&M's computing facilities, which are funded by the  National Science Foundation , the  Commonwealth of Virginia Equipment Trust Fund , and the  Office of Naval Research. Funding was provided by the  US National Oceanic and Atmospheric Administration Center for Sponsored Coastal Ocean Research  ( NA09NOS4780229 ,  NA09NOS4780231 , NGOMEX contribution 217) (Moriarty,  Harris, Fennel, Xu ), VIMS student fellowships (Moriarty), and  MISTRALS/MERMEX-River  and  ANR-11-RSNR-0002/ AMORAD  (Rabouille).
Evaluating security vulnerabilities of software implementations at design step is of primary importance for applications developers, while it has received litte attention from scientific communauty. In this paper, we describe virtual prototyping of an implementation of Elliptic curve cryptography (ECC), aiming to make it secure against first-order horizontal and vertical side-channel attacks (SCAs). Reproducing information leakage as close to reality as possible requires bit-and clock-cycle accuracy, we got with Mentor Graphics Modelsim tool, simulating the execution of the ECC software implementations on PULPino, an open-source 32-bit microcontroller based on the recently released RISC-V instruction set architecture. For each clock cycle, we compute the number of bit toggles into microcontroller's registers, an image of the power consumption, and watch the program counter to identify the assembly instruction executed, then the corresponding C function.
EXECUTIVE SUMMARY H2020 Phenomenal e-infrastructure program aims to deliver a scalable, robust and standard-compliant infrastructure for clinical phenotyping by means of metabolomics techniques. The aim of this deliverable is to provide a landscape of data and metadata standards available in the field, as well as to gauge the awareness and usage of standards in the community of practitioners --primarily in Europe, but also beyond --to discover coverage gaps and generally deepen our understanding of the needs and practice. This is in keeping with making clinical datasets compliant with the FAIR principles 1  promoting data reuse and discoverability in light of reproducible research but also to ensure compliance with current regulations to meet patient protection and privacy. To gather the community needs we designed and launched a survey, which was advertised among the metabolomics peer.
The data gathered defined the landscape and community feedback which is essential for objectives 8. 1 and 8. 2 (see below), achieving MS8. 1 "Initial analysis of community standards finalised", as well as informing the directions that need to be taken in the data standards development tasks (T8. 2-8. 4) within this work package. We report the major findings of a survey commissioned by this work package within the EU Horizon2020 PhenoMeNal e-infrastructure project for the use of Metabolomics in clinical context. The study reveals a good awareness of standards for data acquisition but a fairly low rate standard compliant data deposition to public repositories. It also reveals that spread sheets are still the tool of choice for day-to-day data management for small and even large projects. Only a minority of users carry out encryption or integrity checks in spite of awareness of patient data confidentiality and privacy issues.
Finally, and most relevant to PhenoMeNal's mission, the survey highlighted a lack of modern tools to document computational workflows and computational tasks in ways conducive to ensuring reproducible practice. These results therefore reinforce the needs for an infrastructure such as PhenoMeNal while instilling in the PhenoMeNal development the urge to deliver pragmatic and practical yet regulation compliant and standard-aware toolkits to deliver effective and trustworthy data management solutions. Introduction With more than 950 registrants, the latest Metabolomics Society Meeting held in Dublin at the end of June 2016 was a resounding success, considering the attendance of scientific conferences. This highlights the prominence, progress and maturity of the field in the last few years. In stark contrast with genomics and transcriptomics, metabolomics can now process thousands samples at the fraction of the cost and considerably faster.
Both targeted and untargeted approaches have demonstrated their power as diagnosis or as biomarker discovery tools. Furthermore, owing the limited invasivity of sample collection methods and high detection sensitivity, metabolomics application to the clinics is greatly facilitating health services to patients. Metabolomics proves to be the tool of choice to probe the most dynamic components of the molecular responses, therefore offering unparalleled capabilities to probing biological systems in their homeostatic responses to environmental challenges and to draw a more accurate molecular portrait of pathological phenotypes. However, such growth in usage and application of high throughput analytics comes with a significant burden in data management. In particular, ensuring that datasets can actually meet critical criteria such of findability, accessibility, identifiability and reusability (FAIR principles, Wilkinson et al, 2014) is central for clinical data that has been approved for ELSI compliant secondary data usage.
Methods To collect information about clinical and metabolomics standards awareness in the field, a questionnaire hosted by surveymonkey. com was launched among the metabolomics community. The PhenoMeNal Metabolomics Data Survey was hosted at https://www. surveymonkey. co. uk/r/metabolomics-data Within the WP8 survey, we sought to engage with all stakeholders in the metabolomics community and gathered feedback on data standards usage, and needs from different user groups. To this end we designed the survey to serve three different broad user groups relating to PhenoMeNal: 1) Clinicians and clinical researchers 2) Data managers and curators 3) Data analysts and software, and e-infrastructure developers. The survey comprised 3 targeted tracks, engineered to target a cross-section of users involved in the whole data lifecycle.
Results and Discussion In all, we obtained 132 responses out of a potential catching population of more than 2400 email addresses from the Metabolomics Society mailing list. This amounts roughly to a 5 % rate of response, which is in the average for such unsolicited request for information and consistent with earlier surveys in the Metabolomics circles  13. The entire dataset is publicly available from the following link 14 and corresponds to an export of SurveyMonkey collected data. Of the 132 responses, 2 had to be excluded as clearly spurious while another 4 entries had to be merged as clearly corresponding to the same users who supplied information twice. A total of 128 responses suitable for exploitation were collected, which, given the targeted nature of the survey to metabolomics-related stakeholders, we believe is an excellent return.