dataesr/scientific-paragraphs-categorization huggingface dataset:
This was done with the skbio python package (v4. 2) (http://scikit-bio. org/). For diversity analysis, we rarefied the data, i. e. , we generated a count matrix where each column had the same sum of reads by randomly sampling the original data. This procedure can result in a waste of significant data, and alternatives have been designed to deal with this problem  44. Yet, in this work samples were of very similar sizes (by design), so rarefaction can be done without much loss of information. The count matrix was rarefied with the"rarefy" function of the vegan package in R  45. The richness of a sample was defined as the number of different contigs (with at least three ORFs). Alpha-diversity was defined as the average of the richness of different samples for a given condition. Beta-diversity within a set of samples was calculated as: - = - - - Beta diversity Gamma diversity Alpha diversity 1 ( ) where gamma-diversity is the total number of contigs present in the samples (with a minimum of one read that mapped on the contig).
Identification of phage contigs. Putative phage contigs were detected with Virsorter v1. 0. 3 (option db2 and -virome 1). Given the small size of contigs, we used putative prophages from all categories given by the software. Additionally, we searched for genes that could constitute markers of the presence of phages. HMMER (v3. 0, option-cut_ga)  46  was performed on proteins with HMM phage profile from PFAM (30. 0) and TIGRFAM (15. 0) with a keyword like "capsid, portal, protease, replication, tails" (details of the profiles are given in supplementary data, Table  S2 ).
Identification of bacteria contigs and non-bacterial contamination contigs. To identify bacterial DNA, we searched for the presence of specific bacterial markers in the contigs. We used 128 out of 139 HMM profiles (Table  S2 ) of bacterial conserved marker genes  47  using HMMER (v3. 0, option-cut_ga). We removed 11 profiles from the original dataset because they were matched by some proteins in the phage Refseq database (indicating that they are not specific to bacteria) (Table  S2 ). We also searched the contigs for similarity with a 16 S HMM profile using HMMER (v3. 0, option -E 0. 1). To identify contigs representing non-bacterial contamination (human, archaea and eukaryotic virus DNA), we used Blastn (v2. 6. 0, options -evalue 0. 00001 -max_target_seqs. 10 -task megablast, select only match with a coverage higher than 90%) to search for homologs of the contig genes in the NCBI-nt catalogue (last accessed September 2017).
We classed the contigs with hits using the NCBI taxonomy, attributed using the python package taxabd (https://github. com/HadrienG/taxadb). Classification of phage contigs. We computed the wGRR between all contigs with more than three ORFs and between them and phages from the three phage databases (see above). The contigs were sorted with the following data: phage family (i. e. , Myoviridae, Podoviridae, Siphoviridae, Inoviridae, Microviridae), lifestyle (virulent or temperate), and bacterial host (phyla) associated with the phage with highest wGRR value. However, since these values were sometimes very low, we defined a statistically meaningful wGRR threshold for each type of information. First, we computed all wGRR values between all pairs of phages (or prophages) in each database. We calculated the wGRR cut-off between two elements of known classification so that if one of the elements had a given classification, the other had at least a probability of 95% of having the same classification.
This method provides wGRR thresholds to class phages given the classification of the best hit (i. e. , given the known phage with highest wGRR). To apply the previous method to metagenomics, we had to devise a way of dealing with phage sequence fragmentation in small contigs. We recalculated the wGRR cut-off in the same way, except that we made the analyses on fragments of known phage genomes. We used fragments of different sizes (5, 10, 15, 20, 30, 40, 50, and 60  genes). This allowed adjusting the wGRR cut-off to the size of the smallest element (the contig in the real analysis). For each database and for each type of information (family, lifestyle, host) we fitted a negative exponential curve in the data using curve fit function from Scipy package (v1. 0. 1). This equation has the form: α γ = × + β - Y e X where Y is the wGRR cut-off and X is the number of proteins encoded in the fragment of the phage, α, β and γ depends of the database used and the information searched.
We then computed the best wGRR between the contig and known phages from the phage Refseq database. The contig was classed using the information of the best hit among the phages if the wGRR is higher that the cut-off determined with the equation above. We used the wGRR values to classify the phage contigs in terms of taxonomy. The classification of the lifestyle depended on the number of proteins encoded on the contig. We used the PHACTS software prediction for contigs with more than 19 proteins (4 categories: virulent/temperate confident or virulent/temperate non-confident). For shorter contigs, PHACTS provided unreliable classifications, and we used the wGRR approach. For host phyla attribution, we used the wGRR calculated in comparisons with three different phage/prophage databases (see section genome sequences and prophage identification).
Acknowledgements We thank  Stevenn Volant  for his help in count matrix normalization,  Remi Denise  and  Amandine Perrin  for their help in script optimization. We thank  Etienne Ruppé  for insightful comments on a previous version of this manuscript. High-throughput sequencing was performed on the  Institut Pasteur Genomics Platform , member of " France Génomique " consortium ( ANR10-INBS-09-08 ). We thank two anonymous reviewers providing helpful comments on earlier drafts of the manuscript. This work was supported by a grant from the  "Agence Nationale de la Recherche " ( ANR-16-CE15-0022 ). It was also partially supported by a grant from the  "Fondation pour la Recherche Médicale " to ED (Equipe FRM 2016, grant number  DEQ. 20161136698 ).
Conclusions and Outlook In this article, a new OTR modular vehicle concept is presented, comprising an electrified and autonomous driveboard and separated low-cost capsules that can be easily switched during operation. This model enables new unique operational strategies for urban logistics. Furthermore, a possible business model is described as an example. However, there are still challenges on the way to realizing this vehicle concept, whereby technical solutions are being developed in an ongoing project. In addition, the business models have to be quantified for a real use case and initial estimates have to be verified. This creates a reliable database that allows for meaningful comparisons with existing concepts. For the OTR modular approach, an open platform seems appealing: the capsule's external dimensions and interface requirements are predetermined, while stakeholders suggest designs and applications.
For example, daily AOD products are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)  (Levy et al. , 2013)  sensor on board Terra and Aqua LEO satellites: AOD products at 10 km resolution (MOD 04 and MYD 04 products) or at 1 km resolution (MAIAC product). Sensors on geostationary orbit satellites can continuously scan one-third of the Earth's surface much more frequently than low Earth orbit satellites. The SE-VIRI (Spinning Enhanced Visible and Infrared Imager) sensor, aboard MSG (Meteosat Second Generation), is an example of a GEO sensor providing information on aerosols. Different AODs are retrieved over lands from SEVIRI data in the VIS0. 6 and VIS0. 8 channels, respectively centred at 0. 635 µm (0. 56-0. 71 µm) and 0. 81 µm (0. 74-0. 88 µm). AOD products are retrieved following different methods.
Desert dust is emitted using wind speed, soil moisture and surface characteristics based on  Marticorena and Bergametti (1995) , which give the total emission mass, which is then distributed in each bin according to  Alfaro et al. (1998). Secondary inorganic aerosols are included in MOCAGE using the module ISORROPIA II  (Fountoukis and Nenes, 2007) , which solves the thermodynamic equilibrium between gaseous, liquid and solid compounds. Chemical species are transformed by the RACMOBUS scheme, which is a combination of the RACM scheme (Regional Atmospheric Chemistry Mechanism;  Stockwell et al. , 1997)  and the REPROBUS scheme (Reactive Processes Ruling the Ozone Budget in the Stratosphere;  Lefèvre et al. , 1994). Dry and wet depositions of gaseous and particulate compounds are parameterized as in  Guth et al.
For this computation, the control variable x is also converted into all MOCAGE aerosol bins according to the local fractions of the total aerosol mass in the model background. The AOD is computed for each model layer to obtain a sum of the AOD of the total column. The optical properties of the different aerosol types are issued from a look-up table that is computed from the Mie code scheme of  Wiscombe (1980 Wiscombe ( , 1996) )  for spherical and homogeneous particles. The refractive indices come from  Kirchstetter et al. (2004)  for organic carbon and from the Global Aerosol Data Set  (GADS, Köpke et al. , 1997)  for other aerosol species. The hygroscopicity of sea salts and secondary inorganic aerosols is taken into account based on  Gerber (1985). While the observation operator is designed to assimilate the AOD of any wavelength from the UV to the IR, the assimilation system MOCAGE-PALM cannot assimilate data on several wavelengths simultaneously  (Sič et al.
Data availability. The data and software used in this work can be accessed through the following links. AqeR data: http://www. eea. europa. eu/data-and-maps/data/aqereporting-8 (EEA, 2019). AERONET data: http://aeronet. gsfc. nasa. gov/ (NASA, 2019). LibRadTran software: http://www. libradtran. org/doku. php (Mayer et al. , 2019). RTTOV software and data: https://www. nwpsaf. eu/site/software/rttov/ (EUMETSAT NWP SAF, 2019). The MOCAGE data produced are available on request to the contact author.
We, therefore, use the strategy described in Section 2. 1: For each sequence pair, the Hamming distance and the evolutionary time separation are calculated. Times are binned (in the simplified data generation times are actually discrete), and average Hamming distances are computed. The resulting data are fitted against the theoretical result in Equation (  10 ) to obtain the effective mutational parameter to be used in the phylogenetic inference. Results for µ L ∆t = 3 are shown in Supplementary Figure  S1 , choosing ∆t = 0. 3 without loss of generality.
Results on Protein Data The main application of DCA-like methods so far has been their ability to predict contacts in the three-dimensional protein structure. Strong couplings between two sites in the Potts model are a good indication of the corresponding amino acids being in contact in the protein fold. As, in the case of artificial data, couplings are inferred more accurately when frequencies are corrected for phylogeny (Figure  5 ), it is natural to ask whether this translates to improved contact predictions for actual protein data. To assess the performance of our correction scheme on actual protein data, we evaluated the PPV of DCA contact predictions on five protein families(cf. Supplementary Material S1 for details). Those families were chosen from the families used in  [8]  on the basis of having short enough sequences for our pairwise phylogenetic correction to be tractable in reasonable time, and to have potentially stronger phylogenetic correlations than current Pfam data, which are based on representative proteomes, i.
e. , which have undergone already some phylogeny-based sequence pruning. In contrast to the artificial data, the phylogenetic tree is not a priori known, and we have applied FastTree  [30, 31]  for each family for tree inference. Next, for each family, three DCA models were inferred: a "naive" model based on completely uncorrected statistics; a model based on frequencies corrected by the reweighting scheme, which is the one used in common DCA implementations; and a model based on frequencies corrected by our pairwise phylogenetic inference scheme. Contact prediction was done using the standard procedure of plmDCA  [20]. Figure  8  shows representative results for two of the five families. In the case of PF00013, our phylogenetic correction clearly performs worse than both reweighted and uncorrected DCA for the first 100 predictions.
Note that the reweighting method does not lead to any improvement either, suggesting that the phylogenetic bias may be weak for this family, and the potential benefit of the correction is overcome by problems due to the approximations used. The picture changes for PF00046, where both correction methods-reweighting and phylogenetic inference-improve significantly over the uncorrected DCA model. Reweighting outperforms our method for the prediction corresponding to the strongest coupling, having a fraction of true predictions of ∼0. 7 versus ∼0. 5 for the first ten predictions. However, for a large number of predictions, the phylogenetically informed DCA model tends to have an enriched fraction of contacts among its couplings when compared to the reweighted model. This observation fits well with results on artificial data, showing an overall increase in the accuracy of inferred couplings.
However, as applications of DCA usually rely on the very strong couplings only, this long-term increase in accuracy remains of limited practical interest. Results for three other families can be seen in Figure  S4. Over all the five investigated protein families, our phylogenetic correction only shows improvement with respect to an uncorrected model for two of them: PF00046 and PF00111. In both cases, it is outperformed by reweighting in the first predictions.
We show that our phylogenetic correction method combined with these approximations is efficient in the case of artificial data. When data are generated by a known Potts model using a sensible but simple evolutionary process on a known tree, our method is able to efficiently correct single-site and pairwise statistics, including connected correlations as intrinsically pairwise quantities. This, in turn, results in an improved inference of the Potts model in all tested aspects: individual coupling and field parameters are more accurate, the inferred Potts probability distribution is closer to the real one, contact prediction is more precise, and prediction of local energy changes from mutations is improved. In the case of actual protein families however, results are at best mitigated. For only two of the five investigated families (PF00046 and PF00111), our method does improve the accuracy of contact predictions with respect to uncorrected data, whereas it has a negative effect on these predictions for two of the other families (PF00013 and PF00014).
Furthermore, even in the positive cases, it is still outperformed by the simpler empirical method of reweighting sequences according to simple sequence-similarity measures. In this regard, it is important to note two things: The first is that for the two families for which our method fails, the reweighting technique leads to very marginal improvements in terms of contact prediction. This seems to indicate that our method does perform reasonably well only in the case of strong phylogenetic biases. It also suggests that phylogeny does not affect contact prediction to a noticeable degree in some families. The second is that in the cases of protein families for which our method does provide an improvement, it outperforms reweighting in the "long run", e. g. , for more than ∼100 predictions for PF00046.
Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples.
In this way, displacement continuity can be explicitly ensured between adjacent elements by the shared nodes of the FE mesh  [8]. Nowadays, several DIC software solutions are available, both commercial and open source (see  Section 2). However, the use of commercial software applications may be very expensive and they cannot be modified to fit specific requirements  [11, 12]. Alternatively, open source software can remarkably reduce costs and can be tailored to user needs  [11, 12]. For all these reasons, several research DIC codes were developed and made freely available to the scientific community. Among these research DIC software packages, a new, free and open source software (FOSS) for local 2D DIC, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome (http://github.
com/Geod-Geom/py2DIC/). Implemented in Python, the software can estimate 2D displacement and strain fields from an input set of images. In this paper, by continuing the work started in References  [11, 13] , py2DIC potentialities were evaluated. In particular, two different sets of experimental images were processed to estimate displacement and strain fields; the accuracy of the results was evaluated by comparing the estimated displacements with those computed from the strain values independently measured by a strain gauge fixed to one of the samples. Furthermore, the obtained displacements were also compared with those obtained through three renowned software applications: the well-known and mature open source software packages Ncorr and DICe and the commercial software Vic-2D, developed and sold by Correlated Solutions Inc, USA.
Commercial and Open Source DIC Software Nowadays, different companies sell 2D and 3D DIC commercial software applications. Among these, Correlated Solutions, HOLO3, Dantec Dynamics, GOM, Image Systems, Imetrum, LaVision and Match ID are the most important ones  [11]. At the same time, though, several research DIC codes are now made freely available to the scientific community: Ncorr (Georgia Institute of Technology, Atlanta, Georgia), DICe (Sandia National Laboratories, Albuquerque, New Mexico) and YaDICs (Laboratoire de Mécanique de Lille, Lille, France) are among the most popular ones  [11] , whereas several new Python applications, such as py2DIC (University of Rome La Sapienza, Rome, Italy), pyxel (Institut National des Sciences Appliquées de Toulouse, Toulouse, France), pydic (University of Limoges, Limoges, France) and dolfin_dic (École Polytechnique, Palaiseau, France) have been developed more recently.
Commercial Software Correlated Solutions, Inc. offers Vic-2D  [14, 15] , a user friendly commercial software that uses optimized correlation algorithms to provide full-field displacements and strains for mechanical testing on planar specimens. Specifically, 2D displacements and strains are evaluated at every pixel subset within the AOI  [16]. HOLO3 developed a licensed DIC software named CorreliSTC following the specifications defined by Airbus Group Innovations. The package can be used to measure 2D and 3D DIC displacements and strains and it is mainly applied in the aeronautics, automotive and energy industrial sectors  [17]. Dantec Dynamics was founded during the 1950s and it is specialized in instrumentation for flow measurement and particle characterisation. Among the optical measurement systems it supports, it offers Q-400 to perform 3D DIC measurements  [18].
GOM is an industrial manufacturer specialized in developing, producing and distributing optical measurement solutions and technologies for 3D coordinate measurement and deformation analysis. Among the developed technologies, ARAMIS software provides a non-contact and material-independent measuring system and solutions for full-field analysis  [19]. Image Systems was born in 1999 and it offers a software named TEMA which allows for 2D and 3D measuring of full-field displacements and strains  [20]. Imetrum is a company composed by a group of experts in non-contact measurements who operate in the field of rail bridge, road, geotechnical and structural monitoring. Among the products it sells, the Video Gauge TM software offers the possibility to perform DIC to identify areas of high stress, crack opening or other discontinuities  [21].
LaVision was founded in 1989 as a spin-off from Max Planck Institute and Laser Laboratory in Gottingen and it is focused on imaging systems, smart optical sensors, measurement technologies and software tools. It offers the 2D and 3D DIC system StrainMaster, a non-intrusive optical tool for shape and deformation analysis which combines the DIC algorithms with high quality hardware to offer an easy to use instrument for material analysis  [22]. MatchID was founded by experts in the field of image correlation and it offers 2D and 3D DIC software  [23].
Open Source Software Ncorr  [24]  is an open source, freely available 2D subset-based Digital Image Correlation software. It was developed at the Georgia Institute of Technology and it is implemented in Matlab  [24]. The software has a Graphical User Interface (GUI) and it provides plotting tools for figure creation  [25]. It is used to estimate 2D displacement and strain fields starting from a set of speckle images  [11]. DICe  [26]  is an open source DIC software developed by the Sandia National Laboratories, capable of computing full-field displacements and strains from a sequence of digital images. It is cross-platform and easy to use; package installers are available for Windows and Mac OS and instructions are provided to build the software on Linux. It has an intuitive GUI to perform 2D and 3D DIC.
Furthermore, three analysis options can be selected: the subset-based full-field mode for local DIC, the global mode for finite element-based DIC and the tracking mode for trajectory tracking. The displacement and strain fields are not directly shown at the end of the analysis but the results can easily be post-processed in Paraview, a data analysis and visualization application  [27]. YaDICs  [28] [29] [30]  was developed at the Laboratoire de Mécanique de Lille and runs on Linux operating systems. It is implemented in C++ and it is used for 2D and 3D solid and fluid kinematics field measurements. It allows the use of local and global methods by combining them at different pyramidal scales. pyxel  [31, 32]  is an open source global 2D DIC library for experimental mechanics applications. Developed at the Institut National des Sciences Appliquées de Toulouse, it is implemented in Python and is freely available for research and teaching.
py2DIC py2DIC is a free, open source and cross-platform local 2D DIC software based on the well-known template matching method. The software computes 2D displacements and strains of a sample by comparing one or more image pairs of its surface acquired in different steps of the deformation process. Implemented in Python, py2DIC has a GUI (Figure  1 ) and leverages the functionalities of OpenCV  [34]  computer vision library. py2DIC template matching method involves different steps. At the beginning, a grid is defined to divide the AOI of the reference image into smaller areas. Then, the normalized cross-correlation index is computed through a convolution of a portion of the reference image (the reference template) with the corresponding larger subregion (the search window) in the search image (Figure  2 ); the sub-pixel resolution is reached by oversampling both the reference template and the research window using a bicubic interpolation.