dataesr/scientific-paragraphs-categorization huggingface dataset:
Provirus expression is tissue-specific Four proviruses lacking full-length env ORFs dominate expression in our MCL cell lines (accounting for 36-71% of reads): 1q21. 3, 1q22, 1q23. 3 and 3q12. 3 (Figs. 3a  and 4 ). Provirus 1q21. 3 is missing most of env, 3q12. 3 has multiple premature stop codons, and both 1q22 and 1q23. 3 are Type 1 HML-2 s  [20]. The similarity between JVM2 and G519 cell lines is also found in whole transcriptome RNA-Seq datasets downloaded from the Short Read Archive at https://www. ncbi. nlm. nih. gov/sra (Additional file 2), although these are based on far fewer matching reads than our analyses, and might reflect them both being derived from early stage tumours. The unique high expression of 1q32. 2 in REC1 is puzzling and discussed in Additional file 6. Three of our four more highly expressed proviruses were also found to dominate RNA expression in an earlier study of healthy lymphocytes  [40]  (Fig.
Bioinformatic pipeline After sequencing, short-read Quality Control (QC) was performed using the CLC Genomics Workbench software with low quality (Quality score < 0. 05) and abnormally long (> 200 bp or > 400 bp depending on the sequencing kit used) or abnormally short reads (< 50 bp) excluded from each dataset. As mentioned in the Overview, reads were mapped to HML-2 proviruses in a 'faux' genome consisting only of concatenated HML-2 sequences using Bowtie2  [55] , run within Tophat2  [65] , and counted using Cufflinks  [56]  (all with default settings) and reporting the RPKM values (to take into account variation in provirus length and total number of reads). Almost identical results for the 87 proviruses in the reference genome sequence were obtained using HML-2 coordinates in the hg19 assembly rather than building a faux genome (not shown).
We also used another method to quantify the relative expression of proviruses, based on k-mers pseudoalignments, implemented in the software package kallisto  [69]. The reference sequences were transformed into indexes with k-mer length 31 and quantification was done with the default parameters. The reads' abundance values in TPMs produced by the program were then further normalized with variance-stabilizing transformation (DESeq2 Bioconductor package). This approach gave broadly similar results to those presented in Figs. 3a  and 4  but, similar to using featureCounts in default mode, it also appears to underestimate the contribution of more similar proviruses (Additional file 10).
e. their alignment requires no insertions of gaps or nucleotide substitutions). We counted the number of uniquely mapping reads using featureCounts (in default mode) for each Env-encoding provirus (Table  3 ). We then searched for unique SNPs in the env sequences (only) as follows and added them to this table: firstly, we selected all unique SNPs (single nucleotide polymorphisms) from the alignment in Additional file 9; secondly, we checked that these were unique among all known HML-2 sequences by searching in a FASTA file of these sequences with a 31 nt long sequence that spanned the SNP (15nts either side) using a grepping procedure carried out in a Python script; finally, exact matches to each of these 31 sequences in our FASTQ files were then found and counted using the above grepping procedure (we ignored REC1 because of the small number of mapping reads).
In a few instances, e. g. T(1677)C in 5p13. 3, the coordinate represents one of two SNPs within the 31 nt sequence, the combination of which is unique. We repeated the above analysis for long reads except for excluding poorly mapping reads (Q = 20) in feature-Counts and in the SNP counting we used a 17 nt rather than a 31 nt long sequence. Both of these adjustments were necessary because of the much higher error rate with long-read sequencing. We also wrote a python script to detect long reads that contained multiple unique SNP alleles (in this case using an 11 nt long sequence). Allowing for mismatches in the regions flanking the SNP did not provide additional insights.
Acknowledgements We thank  Neeru Bhardwaj  for sending us the sequences of their newly discovered HML-2 proviruses,  Richard Billington ,  Hannah Thompson  and  Afak Zaidi  for cell cultures,  Gkikas Magiorkinis  and  Timokratis Karamitros  for comments on the manuscript,  Lynn McCallum  and  Sylwia Ammoun  for help in the laboratory, and  Agilent  for help designing the enrichment probe.
With all these comparisons, it is possible then to cluster coins that have the same die. Registration algorithms are algorithms which align 3D point cloud and are usually used for reconstruction, SLAM (Simultaneous Odometry And Mapping) or even 3D object tracking  (Vongkulbhisal et al. , 2017). But aligning partially scanned scenes or 3D models and aligning patterns are different. Indeed, registration algorithms usually align shapes, whereas in our problem, we want to align patterns in arbitrary shapes. That's why we need to adapt regis- tration algorithms to perform this task. Our contribution is the following: • We propose a method that performs accurate coin die clustering with small annotated dataset In order to realize this objective, we adapted the registration algorithm for the pattern alignment, and we also define a metric to know whether two patterns are similar.
Pattern recognition applied on point cloud Researchers like  (Zambanini et al. , 2014 , Zambanini, Kampel, 2013 , Schlag, Arandjelovic, 2017 , Salgado, 2016)  usually work with images because it is easier to acquire images and pattern recognition techniques work very well on images. According to  (Salgado, 2016)  and to our knowledge, no work has been published on 3D scans of coins for classification or clustering. Moreover, researchers usually try to get a rough classification of the very different patterns. But in our dataset, coins have the same class of patterns and we want to know if they come from the same die or not (a sample of our dataset can be found in Figure  1 ). In our problem, it is much harder to use RGB images. In Figure  2 , we can see rust in the coins and other color artifacts.
For the 3D descriptors, FPFH descriptors can be used for example. Then, we can compute the closest feature to obtain matches, and then filter the matches using the reciprocity test. With the matches, a robust method such as RANSAC can be used to estimate the transformation and filter the correct matches. For 2D descriptors, we can project the point cloud into plane, then SIFT descriptors (Lowe, 2004) can be used. As we will see in the experiment section, descriptors did not work on our dataset.
Dataset The coins have been scanned with the AICON Smartscan (with surface measurement distance error around 50 µm). 3D Point clouds have around 300k points for one side of the coin. 881 coins have been scanned but only a small part of that dataset is annotated. For 66 coins, we have all the pair transformations. We have therefore N (N -1) 2 comparisons ie 2145 comparisons for the metric learning. To evaluate the whole pipeline, we have 90 coins (we just need the labels to evaluate the coin die clustering). Because annotating this treasury manually is complicated and tedious (we remind the readers that only an expert can annotate the data), we could not evaluate our method on the whole dataset.
Metric learning evaluation We have seen in the previous section that the decision whether two coins have the same die is a problem of binary classification. To correctly evaluate this part, we have aligned by hand the patterns, then we perform an ICP to refine and then, we can test if two patterns are similar or not. As it said in previous section, the dataset is composed of 2145 comparisons so 2145 histograms. In our dataset, we have 1436 negative labels and 709 positive labels. We divide our dataset randomly in a training set and a test set: half of our dataset is used for training, the other half is used for testing. We can simply compute the accuracy on the test set to see if our method is correct or not. When the patterns are correctly registered using ICP, the accuracy is 98% on the test set.
But if we registrate the patterns by hand the accuracy drop to 72% on the test set. It shows that ICP allows a better repeatability in decision. We can see that the method is very effective when the patterns are well aligned. When patterns are the same, the histograms of distances are good features to see if the patterns are similar or not. But it will fail when the coins are not well aligned. We have also shown that the metric learning part doesn't need a huge dataset to learn an appropriate metric.
CONCLUSION AND DISCUSSIONS This work introduces the challenging problem of coin die clustering and a method to solve it on real data. To our knowledge, no automatic method exists in the literature to cluster patterns on 3D point cloud that only experts can identify. To do so, we can perform coin registration no matter the shape of coins. Then, with small annotated dataset, we can learn an accurate metric which gives a probability that two coins have the same die. Finally, we can obtain clusters of coins and, they are relevant for archaeologists. This work will allow them to analyze automatically treasuries and to know how many dies have been used at that time. In future work, we are interested in generalizing pattern recognition on every kind of shapes and not just coins.
In Sect. 3, we detail the dust model we use, THEMIS, as well as the local dust emission tool, DustEM. We also present the effects of variations in dust properties on its emission in the optically thin case with DustEM in order to disentangle variations in the dust spectrum due to changes in dust properties and those due to radiative transfer effects. In Sect. 4, we present SOC, the 3D radiative transfer code we use, as well as the effect of variations in the dust parameters on dust emission in the optically thick case, in the case of the Horsehead. In Sect. 5, we compare our model with the observations and present the best parameters we obtain. In Sect. 6, we discuss our results and propose a scenario of dust evolution in the Horsehead. Finally, we present in Sect. 7 our conclusions.
Observations with Spitzer and Herschel We use Spitzer and Herschel observations (see Appendix B) of the Horsehead in 10 photometric bands from 3. 6 µm to 500 µm, which cover nearly the entire dust spectrum. The processing of the Spitzer maps is detailed in  Bowler et al. (2009). Data were processed in the HIPE environment, with standard Herschel corrections for instrumental effects and glitches. PACS 70 µm and 160 µm maps were obtained after the superposition of two observations with a scan speed of 20 /s whose directions were perpendicular to one another. The overall duration of these observations is 4122 seconds and cover 8. 8 × 4. 5 of the Horsehead. Concerning SPIRE 250 µm, 350µm and 500 µm, they were obtained after the superposition of two observations with a scan speed of 30 /s whose directions were perpendicular to one other.
The overall duration of these observations is 1341 seconds and they cover 8 × 8 of the Horsehead. Striping induced by offsets in the flux calibration from one detector to another was removed using the Scan Map Destriper module included in the HIPE environment. We study the observed emission profiles through three different cuts across the Horsehead (see Fig. 1 ). The calibration uncertainty in the IRAC bands (IRAC 3. 6 , IRAC 4. 5 , IRAC 5. 8 and IRAC 8. 0 ) is 2 %  (Reach et al. 2005) , 4 % in MIPS 24  (Engelbracht et al. 2007) , 5 % in PACS 70  (Gordon et al. 2007) , 12 % in PACS 160  (Stansberry et al. 2007 ) and 15 % in the 3 SPIRE bands  (Swinyard et al. 2010). In this study, we considered all these errors to be independent of the wavelength to first order. Also, we consider that the emission in all of these 10 bands is coming from dust, which is not completely the case in IRAC 3.
Radiative transfer code : SOC SOC is a 3D Monte Carlo radiative transfer code, parallelised using OpenCL libraries  (Juvela 2019) , that computes dust emission and scattering. SOC has been benchmarked with other radiative transfer codes in  Gordon et al. (2017)  and used in  Juvela et al. (2018a Juvela et al. ( ,b, 2019)). The radiation field corresponds to that of a blackbody at 34 600 K produced by a star to which a dilution factor has been applied to obtain G 0 = 100 at the Horsehead edge. This radiation field is estimated on a logarithmic grid of 334 frequencies that extends from 3×10 9 Hz to 3×10 16 Hz. As the Horsehead edge is located outside the HII region, there are no photons above 13. 6 eV hence we applied the Lyman cut to the radiation field that is 2 DustEM is available here : http://www.
Acknowledgements. We would like to thank the  CNES  and the  P2IO LabeX  for supporting Thiebaut Schirmer PhD work. This work was supported by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES. HIPE is a joint development by the  Herschel Science Ground Segment Consortium , consisting of  ESA , the  NASA Herschel Science Center , and the  HIFI, PACS  and SPIRE consortia. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT , LENS, SISSA.
In silico approaches contribute to drug discovery  [24] [25] [26] [27] [28] [29] [30]  but many algorithms, often not yet integrated in commercial packages, could be very valuable for a given project but can be difficult to install, redevelop and/or use. To improve the usability of such computational resources, new in silico approaches are often implemented online. These web applications and/or databases help not only wet-lab researchers but also computational experts to quickly integrate many different types of data and advanced drug design tools in their everyday research tasks. Some online tools allow users to create an account and manage their data. On most systems, data are automatically deleted within a few days. It should also be born in mind that uploading highly confidential data online can be risky and as such users may have to check with the web developers for potential security issues.
Indeed, these recent years, one observes that new databases and algorithms that attempt to solve new questions and better address known or emerging drug discovery challenges are reported almost every week  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44]. Of importance, these resources can also assist teaching, for example chemistry databases, online tools and freely available software packages are being used in different universities for that purpose  [44] [45] [46] [47]. In the present communication, among the different computational approaches that can be used in drug discovery, we essentially focus on recently reported and easily accessible virtual screening web servers. The first part of the review introduces the field of virtual screening and some related computational approaches that might be required prior to or after virtual screening experiments.
In addition, several other computational approaches (a. k. a. in this review named 'related tools') can be used prior to or after in silico screening (e. g. investigation of absorption, distribution, metabolism, excretion and toxicity-ADME-Tox-properties, predictions of binding pocket, evaluation of flexibility via molecular simulations. ) (Fig. 1 ). For the first category (LBVS), 2D or 3D chemical structures or molecular descriptors of the known actives are used to retrieve other ('similar') compounds of interest in a database using different types of similarity measures or by seeking a common substructure or pharmacophore between the query molecule and the scanned libraries. The methods rely on the knowledge of small molecules (the queries) that bind to the target(s) of interest (for example obtained after mining annotated compound collections or after a first round of experimental screening).
The main tools that are used in ligand-based screening are 2D molecular similarity approaches, 3D similarity searches (pharmacophore, molecular shapes colored or not by physicochemical properties, energy fields around the molecules such as electrostatic properties) and 2D/3D QSAR (quantitative structure-activity relationship) modeling. In 2D molecular similarity methods, the molecular fingerprint of known ligands that bind to a target is used to find molecules with similar fingerprints in the electronic libraries  [69] [70] [71] [72] [73]. Various types of similarity or distance metrics can be used among which the Tanimoto coefficient values between 0 and 1. Here, the higher the threshold, the closer the compounds in the database are to the input query  [74]. In ligandbased pharmacophore modeling (a pharmacophore is a molecular framework that defines the essential features responsible for the biological activity of a compound), usually performed in 3D, common and key structural features of the ligands that bind to a target are identified and used to do develop a model and to screen a compound collection with that model such as to identify compounds that match the model requirements  [75] [76] [77].
Predictive machine learning models (e. g. classification, regression and clustering) can thus provide a list of compounds that is different or that complements the one obtained with, for instance, 2D or 3D similarity search  [17, 97]. Of importance, different types of metrics can be used to evaluate statistical models and explanations about the applicability domain have to be provided to users  [13, 86, 96]. As described in this section, most of these approaches are available online and in some cases, the same web servers can indeed offer different types of computations. For the second category (SBVS), compounds (or fragments) from a database are docked into a binding site (or over the entire surface or in several binding sites or taking into account distance constraints such as in covalent docking) and are ranked using generally one (e.
Along the same line of reasoning, databases of comparative structure models of drug-target interactions can be built  [118]. The structure-based methods are applicable when the 3D structure of the target has been determined with biophysical methods such as X-ray, cryo-electron microscopy and nuclear magnetic resonance, but homology models can also be used  [119] [120] [121] [122] [123] [124] [125]. Molecular dynamics (or related approaches) are frequently used in structure-based screening to explore the likely poses of a compound, to rescore molecules, to investigate the importance of water molecules, the pathways of interaction (e. g. kinetics of binding, unbinding events) and to investigate target flexibility, explore binding pockets and/or discover cryptic pockets and to rationalize allosteric events  [126] [127] [128] [129] [130] [131].
This is often referred in the literature to as target-fishing or in silico profiling. The popular approaches to perform target-fishing involve ligand-based or structure-based approaches. The simplest methods for target prediction are based on chemical similarity and make use of the known bioactivity of millions of small molecules stored in databases. In this case, the putative targets of a molecule can be predicted by identifying proteins with known ligands that are highly similar to the query compound. 2D similarity search, 3D search and machine learning approaches are available. It is also possible to dock a query compound into a small or large database of prepared receptor 3D structures. This process of target-fishing relies on inverse or reverse docking methods. A first step prior to virtual screening computations usually involves gathering information about targets and compounds.
Numerous databases are freely available and can be mined (e. g. target already drugged, novel targets, search for approved drugs, commercially available compounds, medicinal chemistry aware virtual compounds, natural products, small macrocycles, short peptides, toxic compounds, compounds reported in patents. ). Three very recent reviews on chemistry databases  [42, 142]  and Omics and target databases  [143]  have just been reported and cover all the major databases that most users will need to embark in a virtual screening project. Further, as mentioned above, for most receptor-based screening experiments, it is important to define (or refine) the likely binding pocket for the putative ligands. Often, this zone is known from the literature (e. g. a catalytic site) but, if not or in case the researchers need to screen outside these well-known binding cavities, different types of computational methods have been developed.
2D similarity search utilities on some selected well-known databases As mentioned above, we will not cover compound databases in the present review. We here highlight some well-known services where it is possible to carry out ligand-based similarity search. Users can usually upload or draw a compound of interest, for example known to be active on a given target and search for similar compounds present in the investigated database (e. g. ChEMBL  [168] , SureCheMBL  [169] , PubChem  [170] , DrugBank  [171] , hmdb  [171] , SuperDRUG  [172] , ZINC  [173]  etc. ). Most often, one or several structural fingerprints are available to carry out the search  [174, 175]. Fingerprints in general encode the presence of substructural fragments. A similarity measure can be obtained by determining the numbers of chemical substructures in common between the query compound and the molecules in the database.
ChemMine tools This is an online service for small molecule data analysis  [185]. The primary functionalities of this service fall into five major application areas: data visualization, structure comparisons, similarity searching, compound clustering and prediction of chemical properties. Users can upload compound data sets and use utilities such as compound viewing, structure drawing and format interconversion. Pairwise structural similarities among compounds can be quantified. Interfaces to ultrafast structure similarity search algorithms are available to mine the chemical space in the public domain databases (PubChem or ChEMBL). The service also includes a clustering toolbox to enable systematic structure-and activity-based analyses. Further, physicochemical property descriptors of compound sets can be calculated and, for instance, use for QSAR studies.
Macromolecule extended atom-pair fingerprint Seven million of the currently 94 million entries in the Pub-Chem database break at least one of the four Lipinski constraints for oral bioavailability while 183 185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets can be of interest in many projects. The macromolecule extended atom-pair fingerprint (MXFP) web-based application has been developed to explore larger molecules and to position users' compounds in this chemical space  [188]. MXFP is a 217-D fingerprint tailored to analyze large molecules in terms of molecular shape and pharmacophores. The approach can perform MXFP nearest neighbor searches in the NLP and NLC space.
Pharmit This tool provides an online, interactive environment for the virtual screening of large compound databases (over 200 million compounds in total) using pharmacophores, molecular shape and energy minimization  [193]. Users can import, create and edit virtual screening queries in an interactive browser-based interface. Queries are specified in terms of a pharmacophore, a spatial arrangement of the essential features of an interaction, and molecular shape. Search results can be further ranked and filtered using energy minimization. In addition to nine prebuilt databases of popular compound libraries (e. g. ChEMBL, molecules from chemical vendors. ), users may submit their own compound libraries for screening. The Pharmit web service was, for example, used to search for novel cholinesterase inhibitors, molecules that could be used in the treatment of Alzheimer's disease  [194].