dataesr/scientific-paragraphs-categorization huggingface dataset:
A databank of GMO coding sequences (CDSs) provided by the JRC and supplemented by insert CDSs of bacterial GMOs from the literature was created (list specified in Additional file 2, related literature references given in Additional file 3). This databank currently does not include GM CDSs of bacteria analysed in this paper. DUGMO requires paired Illumina sequencing data of a single bacterial species; it does not deal with metagenomic or multigenomic data. The JRC sequences included in the GM databank provided in this paper are obtained by PCR simulation screening of public nucleotide sequence databanks including patents and available whole plant genomes  [22].
Cleaning pipeline for raw data, host pangenome and known GMO databank The objective of this section is to describe the production of three sets of data: host genome CDSs, potential GM CDSs, and known GMO CDSs. To create these datasets, a pipeline was implemented to clean the raw data of the suspected GM bacterium by eliminating all sequences belonging to the reference genome. This step is frequently used in the processing of high-throughput sequencing data (e. g. , refer to  Baron et al. 2018 [23]  to remove chromosomal reads to assemble E. coli plasmids). This pipeline sorts CDSs into two categories: CDSs of potential GMO inserts and CDSs belonging to the wild type genome. Several steps are required (Fig. 1 ). First, the raw sequencing data of the potential GM bacterium are assembled twice with Shovill 1.
0. 4 [24]  with the trim option, once on the whole genome, and once focusing on plasmids (plasmid option of SPAdes 3. 13. 1  [25] ). Scaffolds with a coverage depth of less than 3 are eliminated; this threshold is intended to remove potential contamination of the bacterial genome, such as contamination by another sequenced library or by the matrix in which the bacteria are located. These two results of assembly are annotated with Prokka 1. 12  [26]  with the usegenus option to predict CDSs. These CDSs are merged and filtered out for duplicated sequences. In parallel, the raw sequencing data of the potential GM bacterium are "trimmed" using Trimmomatic software 0. 39  [27] , and aligned by Bowtie2 2. 3. 4. 3  [28]  with the reference genome. Then, the non-aligned reads (including any GMO inserts) are aligned with the CDSs predicted in the previous Prokka CDSs using BWA MEM 0.
The first BLASTN on pangenome CDSs removes CDSs belonging to the host genome that are present in potential GM inserts (for parameters see Additional file 1, section 4. 1). The second BLASTN on the pangenome aims to remove CDSs predicted by Prokka, but corresponding in our pangenome to non-coding sequences (for parameters see Additional file 1, section 4. 2). The CDSs aligned at least in one of the two BLASTN steps are added to the CDSs related to the host genome defined at the end of the cleaning pipeline. The non-aligned CDSs are kept as potential GM CDSs. After these two sorting steps, the databank of known GMO inserts is filtered to define a set of known GM CDSs used for distance calculation (section 2. 3). A CDS is qualified as GM only when considering it in a setting different from the genome in which it is naturally present.
When the aim is to identify whether a bacterium or an organism is a GMO or not, all CDSs naturally present in the genome of the species of the suspected strain are eliminated from our databank of GMO CDSs using a BLASTN alignment (for parameters see Additional file 1, section 4. 3). In addition, the parts of CDSs aligned during this BLASTN are also removed from the CDSs in the databank, leading to a filtered bank of GMO CDSs. We computed median for each of the three distances (P L3M1, P L4M2 and F L9M7 in section 2. 3) of each host genome CDS and the set of all host genome CDSs and called them respectively medL3M1, medL4M2 and medL9M7. Finally, CDSs of known GMO databank whose distances to the set of all host genome CDSs verify (P L3M1 < medL3M1 and P L4M2 < medL4M2 and F L9M7 < medL9M7) are discarded.
Bray-Curtis (BC) distances After comparing several alternative distances (Additional file 1, section 5), the most relevant Bray-Curtis dissimilarity was selected. This dissimilarity index does not verify the triangle inequality so it should not be called a distance. However, to facilitate understanding, the term "Bray-Curtis distance" will be used in this article. The Bray-Curtis distance is initially an ecological distance  [33]  to evaluate the dissimilarity between two given samples in terms of the abundance of available species. This distance is equal to 1 when the two compared datasets have no common point, and 0 when they have identical compositions. Our aim is to compare a sequence S and a set of sequences H based on their word compositions, via the following equation BC S; H ð Þ¼1- 2 P m∈M f m; S ð Þþ f m; H ð Þ ½ P m∈M f m; S ð Þþ f m; H ð Þ ½ ð1Þ where M is the set of the selected words of length l (in case of no selection, M is the set of all the 4 l possible words of length l in the alphabet {A,C,G,T}), m is one word in M, f is a function set either to F (for frequencies; defined in the following Eq.
Preliminary results In an initial step, the BC distances are computed both in frequencies and in proportions, with words of length l from 3 to 9, maximum Markov models of order k = l-2 and different ratios of selected words. These combinations of parameters are tested on our datasets Data1 and Data2, in which the GM bacterium status is known (section 2. 1) to better separate the host CDSs from the GM CDSs in terms of distance. Three different combinations are chosen to be used jointly in the machine learning step described below: (P L3M1): BC in proportions for all 3-letter words under Markov model of order 1. (P L4M2): BC in proportions for all 4-letter words under Markov model of order 2. (F L9M7): BC in frequencies for the 10% most over-represented 9-letter words under Markov model of order 7.
Methods Our aim is to target true GM insert CDSs. To this end, a large number of classification methods were applied, within a machine learning procedure (caret 6. 0_81 package in R 3. 5. 1  [34] ), and the most efficient method in terms of predicting GM CDSs was selected. The 12 classification methods used were: 4 parametric methods: Generalized Linear Model (logit), Stepwise Linear Discriminant Analysis (stepLDA), Stepwise Quadratic Discriminant Analysis (stepQDA) and Partial Least Squares (plsda); 8 nonparametric methods: Neural Networks (nnet), Support Vector Machines with Radial Basis Function Kernel (svmRadial), K-Nearest Neighbours (knn), as well as 5 based on decision trees: Classification Tree (Rpart), Random Forest (RF), Bagging Classification (Treebag), Extreme Gradient Boosting Tree (xgboost), and the supervised classification algorithm C5.
0. In this process, a K-fold cross-validation procedure was used. It consists in splitting the whole dataset into K subsets, namely a calibration set (consisting of K-1 subsets) and a validation set (the K remaining subset). In a first step, the calibration set is used to select the parameters of the model and its fitting ability. In a second step, this model is validated on the prediction set to get its prediction ability. This calibration and prediction processes were repeated K times. The parameters of each machine learning method were optimised following a 10-fold cross-validation procedure. The performances of these methods were compared on the basis of a 2-fold crossvalidation, in which data were previously stratified and centred. It is worth noting that stratified sampling provides homogeneous distribution of GM CDSs in the sampled data.
Data The learning data were the CDSs of the host genome and the databank of filtered known GMO CDSs (Fig. 2a  and b ). CDSs encoding RNAs were deleted from these learning data. The prediction data were the candidate GM CDSs (Fig. 2c ) that did not align with the pangenome (Section 2. 2). The associated variables were the Bray-Curtis distances in proportions L4M2, L3M1 and in frequencies L9M7, the length of the CDS, the average A H (S) of the exceptionality scores provided by R'MES in the host genome for L4M2 and L9M7 (see Eq. 4), the count density per nucleotide in the CDS for 4 and 9-letter words (sum of the counts C(m,S) of all selected words m, divided by the length of the CDS S) and the CDS GC content. The count density per nucleotide is a way of measuring the proportion of the host exceptional words in the CDS.
Application of DUGMO on real data DUGMO was developed using the data from B. subtilis (i. e. , Data1, Data2). Validation was conducted on E. coli genomes. Table  1  summarizes DUGMO results on the two B. subtilis genomes (i. e. , Data1, Data2) and on the three E. coli genomes. The GM CDSs on GM B. subtilis (Data1), correctly predicted by DUGMO, are the main genes located on the plasmids (riboflavin) and the gene conferring resistance to chloramphenicol. Combining the results of the RF and logit methods provides added value for accurately identifying potential GM inserts. At the end of the automatic learning step for the wild type B. subtilis (Data2), no CDS was predicted as a GM insert (see line GM B. subtilis and Machine learning column in Table  1 ). With the GM E. coli carrying genes of A.
Indeed, it does not have megablast  [37]  alignment with a better identity than 94. 7% in all genomes of actinomycetes found in NCBI wgs database (01/05/2020). Among the 45 synthetic GM tests, we obtained as few as one false positive, probably due to the open S. aureus pangenome and to the environmental and similar genome characteristics of the inserted C. jejuni gene (details in Additional file 1 section 8. 6). Then, we assessed the limits of the method with specific synthetic data. We evaluated the robustness of the method to dicodon optimisation. For this purpose, a B. subtilis gene encoding a riboflavin synthase, optimised for E. coli dicodon usage, was randomly inserted into the reference genome of E. coli. Illumina sequencing data were then generated from this genome with ART with the parameters previously described.
This (2) After filtering out CDSs of the known GMO databank that are too close to the host species (paragraph 3 of section 2. 2). (3) In "potential GM inserts". The "DUGMO final results" column details the results obtained after combining the results of the RF and logit methods, using the data from the learning data and prediction data columns. (4) Estimation of the maximum number of false negatives: the true number cannot be deduced because of the unknown origins of CDSs, potentially from the Bacillales family. (-) Does not apply was run 10 times. all DUGMO detected the optimised B. subtilis gene as a GM insert, thus proving the method's insensitivity to dicodon optimisation, therefore codon optimisation. To evaluate the proportion of substitution needed to make a wild type gene detectable by DUGMO, we artificially introduced substitutions in a wild type gene of B.
Discussion We propose a method to detect undescribed GM bacteria. DUGMO is intended to assess the presence of bacterial GM following purification of bacteria, as processed in Paracchini et al. [18]. Once a suspected GM is confirmed by DUGMO, PCR targeting the sequence identified by DUGMO can be employed for routine detection of GM in food or environment samples. DUGMO uses both pangenome and genome species properties to feed machine learning and distinguish GM CDSs from host CDSs. The robustness of the pangenome relies heavily on its completeness achievement, and also on accurate genomes attribution to their respective species when they are submitted to public databanks. Species attribution is most often deduced from 16S RNA leading to frequent errors in attribution and then resulting potentially in mixed species pangenomes  [38].
This CDS is present at positions 2652 to 2822 in pGMrib, included in GAY71_RS22250 (1 to 171 in the pGMBsub04 plasmid), and corresponds to a hypothetical protein of an Enterococcus faecalis plasmid (accession number AP018546. 1). In addition pGMrib (pGMBsub03 portion) has two additional non B. subtilis genes, part of GAY71_RS22445 (repB) and a shortened version of tet(L) (tetR) found GM in DUGMO results. The 53 kb chromosomal insertion (pGMBsub01 portion) carries genes of different origins (Bacillus amyloliquefaciens and Staphylococcus aureus). The S. aureus genes GAY71_RS12530 (bleR) and aadD1 (kanR) and the ribD, ribA, ribT and scpB CDSs of the B. amyloliquefaciens ribDEATH operon are correctly detected as GM CDSs by DUGMO. The gene of beta lactamase TEM-116 (ampR / ampiR, six loci in chromosome, one in pGMrib), ribH and scpA CDSs align with the pangenome CDSs in respect to BLASTN parameters, and are included in host CDSs learning data, showing the limits of CDS categorisation for closely related species.
For the other two E. genomes, carrying the genes of A. tumefaciens or M. tuberculosis, the arlS CDS is full length in the assemblies and is not detected by DUGMO. The truncated arlS CDS in E. coli carrying genes of S. pyogenes is localized at the end of a de novo contig, and the 15% missing part is found in another contig. Therefore, DUGMO is able to detect CDSs that may consist of a long deletion or insertion in a wild CDS. The mean coverage depth of data for this sample is 41, while the coverage depth mean of the other two E. coli strains is 63 and 68, respectively. This example indicates that the assembly with an average overlap depth of 41 is partial and leads to a false positive, due to the truncation of a CDS. On the other hand, data assemblies with a coverage depth mean greater than 60 do not have gene truncation problem in our datasets.
Availability and requirements Project  name: DUGMO (Detection of Unknown Genetically Modified Organisms ). Project home page: http://github. com /ANSES-Ploufragan/DUGMO. git. Operating system(s): Linux / Unix. Programming language: Python,  R. Other  requirements: conda, Python 3. 6 or higher. The other dependencies of the program are managed by the conda environment. License: GNU GPL. Any restrictions to use by non-academics: no.
The results of these experimental measurements have been used to compile a database of the reference gamma-ray lines of basic rock-forming elements that can be identified by means of a MGNS gamma-ray spectrometer based on a CeBr 3 scintillation crystal. This will be used to analyze and interpret gamma-ray data from the MGNS space-science experiments in ordbit about Mercury  (Kozyrev et al. 2018). Also, a comprehensive programme of numerical simulations of the energy spectra of gamma-rays will be performed for different types of the surface composition. The results of these calculations will be used for identification of detected nuclear lines and for estimation of abundance of corresponding elements in the shallow subsurface  (Masarik and Reedy 1996; Mitrofanov et al. 2020).
2019) , labradorite  (Helbert and Maturilli 2009) , and komatiite  (Maturilli et al. 2014) , although wavelength shifts in spectral absorptions are likely. Hence coupling the SIMBIO-SYS VIHI and MERTIS datasets will be pivotal for correct interpretation of Mercury's mineralogical surface composition. Together with the deblurring thanks to improved spatial resolution of elemental mapping that MPO will provide, we expect to develop a much better grasp of Mercury's mineralogy than before.
2020) , and appear less uniform and with an uneven distribution with respect to the Moon  (Fassett et al. 2011 ). In addition, the post impact deformation within basins, which appears more complex with increasing basin size, was deeply affected by global tectonism and the loading of adjacent or internal volcanic effusions  (Byrne et al. 2018a (Byrne et al. , 2018b)). Science goals of BepiColombo include: inferring the sites of old buried basins and their control on fault nucleation and propagation; determining the interplay of processes between impacts and volcanic effusion; and understanding how global geodynamic strain and volcanic effusion affected the post impact deformation within basins. These will be pursued by integrating SIMBIO-SYS, MERTIS and MIXS observations with geophysical data provided by MORE and ISA.
Analysis of the MESSENGER MDIS and MASCS data allowed a first characterization of Mercury's regolith thanks to the use of several photometric models adjusted to the data. Although similar in appearance, the surfaces of the Moon, asteroids, and Mercury are revealed to be actually quite different. Proximity to the Sun and faster collisional speeds both contribute to the expectation that Mercury's regolith should develop at a faster rate than its lunar equivalent  (Langevin 1997 ). Mercury's regolith seems to be smoother at micrometre scales, and made of finer particles (which can be produced by space weathering of the surface) compared to its lunar counterpart, with compositional differences yet to be explained  (Domingue et al. 2016 ). Mercury's regolith will be studied SIMBIO-SYS and MERTIS to derive its fundamental properties such as its thermal inertia (MERTIS/TIR), and grain size (SIMBIO-SYS/VIHI and MERTIS/TIR).
The MPO and Mio periherms will not be low enough in the initial phase of the mission to improve on the mapping based on MESSENGER data during its nominal mission. However, due to gravitational effects of Mercury together with the Sun, the periherm heights of MPO and Mio will decrease over time. Thus, it is possible that an improvement may be achieved during the closing phases of the mission. As well as refining the known mid-northern hemisphere magnetic anomalies, this may reveal hitherto unknown examples at other latitudes. It is uncertain for how long Mio will survive, which will probably be limited by its ability to maintain its spin axis direction. Fig. 12  The decreasing detectability of a crustal magnetic anomaly with orbital height above the surface How well will BepiColombo resolve a magnetic anomaly like the one associated with the Rustaveli impact crater? For this purpose, we make a simple model: At 0°latitude we put a magnetic dipole on the surface that causes a 10 nT magnetic field at 40 km altitude.
What Are the History and Mechanisms of Crustal Formation? So far, the best analytical evidence supporting the contention that Mercury's 'low reflectance material' represents exhumed remnants of a graphitic primary crust is from neutron spectrometry conducted at low altitude (<100 km) by MESSENGER  (Peplowksi et al. 2016). Data gathering at such a low altitude is not within BepiColombo's primary mission (though nor was it in MESSENGER's) and thus it may not be possible for BepiColombo to repeat, confirm and extend these neutron spectroscopy observations. However, as noted in Sect. 5. 1 it may be possible to detect and map carbon with MIXS or the MGNS gamma-ray spectrometer. As noted in Sect. 2, it has become widely accepted that most of Mercury's surface is 'secondary crust' emplaced largely by effusive volcanism (accompanied by an unquantified but probably significant amount of intrusive igneous activity;  Byrne et al.
Comparing the model results and current best surface elemental data, suggests that Mercury most likely lacks the putative iron sulfide layer at the core-mantle boundary  (Cartier et al. 2020). However, MESSENGER detection of Ti was close to the XRS detection limit, yielding only a global average of Ti/Si of 0. 0098±0. 0030 (Nittler and Weider 2019) , whereas BepiColombo's MIXS is expected to achieve an improved sensitivity for Ti due to the higher energy resolution of the detector, providing spatially-resolved measurements during active Sun conditions. Thus MIXS determination of Ti abundances will enable much more confidence to be placed in modelled results. Some MESSENGER XRS data on Cr concentration have suggested a heterogeneous distribution across Mercury's surface  (Nittler et al. 2018) , though coverage in the northern hemisphere was severely limited.
Conclusions A successful orbital mission by BepiColombo will greatly increase our knowledge of Mercury's surface and composition. This will elucidate more fully the planet's internal differentiation and its geological history. We note that definition of future landing missions (e. g. , Vander  Kaaden et al. 2020; Ernst et al. 2020 ) is driven by considerations of surface composition, and constrained by surface thermal and roughness properties, all of which will be better known as a result of successful BepiColombo orbital science. Most of the anticipated advances in understanding are likely to come from using data from multiple instruments, rather than from a single instrument in isolation.
Acknowledgements The work leading to this paper has been funded by many agencies in many countries over several years. Rothery,  Massironi  and several others are supported by  EU Horizon 2020  grant  776276  'Planmap', and the lead author was previously supported by various grants from the  Science and Technology Funding Council  and the  UK Space Agency. Several co-authors were supported by the  Italian Space Agency (ASI)  under  ASI-INAF agreement 2017-47-H. 0. Research at the  University of Helsinki  was supported, in part, by the  Academy of Finland  grant No. 1325805. Heyner was supported by the  German Ministerium für Wirtschaft und Energie  and the  German Zentrum für Luft-und Raumfahrt  under contract  50 QW 1501. The MGNS instrument team in Russia is funded by  Ministry of Science and Higher Education of the Russian Federation , grant  AAAA-A18-118012290370-6.
Political Campaigns Meet Digital Marketing Tools: The Rise of Influence Activities Political campaigners increasingly deploy digital marketing tools that lend themselves toward influence activities, and which have, in some cases, been demonstrably used for deceptive and coercive purposes. Across the past decade, digital marketing techniques have progressively supplemented the traditional focus of political campaigning on demographic market segmentation, opinion polling, targeted campaigning, and direct marketing. Increasingly, this involves a move to big data analytics to provide automated insights using data mining techniques and tools to discover hidden patterns in datasets. Drawing on a complex and opaque corporate ecosystem encompassing data brokers and data analytics companies, political campaigns now combine public voter files with commercial information from data brokers to develop highly granular and comprehensive voter profiles  (Bartlett et al.
By the 2016 presidential election, Trump's digital campaign manager claims that his team tested typically around 50,000-60,000 advert variations a day using Facebook's tool, Dynamic Creative, to find optimal combinations based on engagement metrics  (Beckett, 2017; Bartlett et al. , 2018, p. 33). Other digital marketing tools deployed by political campaigners include psychographic and neuromarketing tools combined with data and tracking methods (such as emotion-based profiling) to optimise the emotional impact of advertising messages to specific audiences' psychological needs  (Chester and Montgomery, 2017; Kim et al. , 2018; McStay, 2018). Psychographics, emotional testing and mood measurement have long been central to political campaigns  (Key, 1974; Jamieson, 1996) , but the rise of big data analysis and modelling has enabled access to psychological characteristics and political inferences far beyond the reach of traditional databases  (Tufekci, 2014).