dataesr/scientific-paragraphs-categorization huggingface dataset:
Because there is no publicly available A. lyrata TE annotation, we prepared custom-made catalogues of 53,089 A. lyrata and 17,009 A. thaliana repetitive elements (Additional files 3 and 4, respectively). Although the two species differed threefold in their TEs numbers, their spectra of TE families were similar (Additional file 5: Figure  S1 ). The multi-copy nature of many TEs hinders RNA-seq analysis using standard protocols. Therefore, we developed the COMparative EXpression of TEs (COMEX) method, which allows quantification of transcripts derived from individual TE copies and effective removal of the RNA-seq reads mapping across TE families (see "Methods;" Additional files 6 and 7). We found 197 and 132 significantly (adjusted P <0. 05; DESeq) upregulated TEs, representing 90 and 60 families (26 in common), after 6 h HS in A.
3c ; Additional file 5: Figure  S7 ). Sequence analyses of AlTERESTRAs revealed that all copies are full length, contain a tRNA primer binding site and a polypurine tract, suggesting their autonomy (Fig. 3c ). TERESTRA LTRs are relatively A-T-rich (69 %) and the consensus sequence contained only a small number of cytosines in symmetrical contexts (CG = 5, CHG = 0; H = A, T or C), which resembles LTR nucleotide composition of ONSEN  [18]. TER-ESTRA was missing in the A. thaliana Col-0 genome. Therefore, we extended our search to 50 A. thaliana accessions by genotyping them with TERESTRA-specific primers (Additional file 5: Table  S3 ). This screen also gave negative results and suggested the absence of TERESTRA in A. thaliana. However, we found TERESTRA TEs in Arabidopsis cebennensis (95 % identity; Additional file 5: Figure  S8 ) and Arabidopsis halleri (91 % identity; Additional file 5: Figure  S9 ) using the NCBI sequence database.
After two days of drug treatment, plants were heat-stressed for 0, 6, and 12 h and the amount of transcript analyzed by RT-qPCR (Fig. 3h-j ). Control DZNep and zebularine treatment increased COPIA37 and ONSEN transcript tenfold and fivefold, respectively (Additional file 5: Figure  S11 ), suggesting that both TEs can be weakly activated by TGS interference also without HS treatment. TERESTRA was not activated by the drug treatment. A combination of HS with drug treatments had strong additive effects in all cases, except for zebularine and HS-treated COPIA37 (Fig. 3h-j ). Both ONSEN and TERESTRA transcripts accumulated at much higher levels that were not decreasing at 12 h HS (Fig. 3h, i ). The effect was generally stronger for DZNep and weaker for zebularine. This suggests that the heat-induced TE transcript accumulation is rapidly suppressed by epigenetic means, in particular for TEs carrying lower affinity binding HREs.
4e ). GFP accumulated in the apical meristem after 24 h of HS recovery and remained detectable for at least five days (Fig. 4f, g ), although GFP transcript was not present anymore (Fig. 4e ). Heat-responsiveness of Silex transgene in the absence of ROMANIAT5-2 5' LTR suggested that the locus may be at least partially controlled by a bidirectional heat-responsive promoter activity of the 3' LTR. Indeed, we found putative 3P/gap HREs within the 3' LTRs (and also the 5' LTRs) of all heat-responsive AtRO-MANIAT5 TEs. However, transcription from the 3' LTR could result in ROMANIAT5-2 antisense transcript. To (See figure on previous page. ) Fig. 3  COPIA37 and TERESTRA are novel heat-responsive COPIA families. a In silico reconstruction of putative HREs in the 5' LTR of COPIA37 in different species.
A minimum of three adjacent (<5 bp) nGAAn motifs can form a basal HRE, whose activity will depend on their distance and the total number  [20]. Heat-responsive COPIAs featured the whole spectrum of HREs ranging from the 4P types in ONSEN and TERES-TRA, through 3P types in COPIA37 and ROMANIAT5 to a dozen of variable gap and step HREs in all these families. By comparing predicted HREs with transcriptional data, we conclude that gap and step HREs are mostly not sufficient to trigger HS-induced TE upregulation. This is congruent with their proposed low HSF binding efficiency  [36]. Predicted 3P HREs correlated with up to a hundred-fold (COPIA37, ROMANIAT5) and 4P HREs with up to a thousand-fold (ONSEN, TERESTRA) transcript accumulation upon HS. This suggests a strong correlation between putative HREs and the transcriptional response of the TEs.
COMparative EXpression of transposable elements (COMEX) Accurate quantification of TEs expression using short read sequences is hampered by high similarity of potentially many genomic copies. We developed a simple protocol called COMEX (https://github. com/bpietzenuk/ COMEX) that partially overcomes this problem and allows analysis of TE transcription from RNA-seq data. Out of >10 million reads per average sequencing library, 0. 12 % and 0. 73 % high-quality mappable reads corresponded to TEs within our custom made A. thaliana and A. lyrata, respectively, TE annotations. This suggests that TE expression analysis using RNA-seq can be made more sensitive by high sequencing depth. The reads were processed via a shell-script that merges the pipeline as follows. First, the binary mapping. bam file is converted into a readable.
py). Afterwards, UM and SMM are merged into a single. sam-file and converted into a binary. bam-file. Subsequently, the output file of the COMEX2. 0-pipeline (filename. output. final. bam) containing the number of SMM and UM reads from the same TE family was retrieved using a strand non-specific protocol in Qualimap. To avoid a bias by repeated counting of SMM reads, we used the proportional read count method that divides the power of a read by the number of mapped positions. This provided the number of reads per individual TE families and TEs, which were subjected to statistical analysis using the DESeq package in R software  [43]. To avoid considering potentially large number TEs with minimal transcriptional changes, which would be later difficult to validate experimentally, we considered only those which had at least 0.
In silico sequence analysis Sequences of interest were extracted from corresponding TE annotation files using bedtools  [45]. LTR reconstruction was carried out in LTR-Finder  [46]  or manually by pairwise and multiple alignments of the 3'end to the 5'ends of TE annotated regions using MUSCLE or multalin with the DNA 5-0 comparison table option. Structural analysis and annotation of TERESTRA was performed using LTR Finder and blastx using NCBI non-redundant protein sequences database. LTR_Finder was used in both analyses with the threshold option set to 2. 0 using the tRNA database of A. thaliana to predict PBS. The LTR length range was set from 100-3500 and the minimum LTR distance was set to 1000. Other parameters were left at default settings. Search for ONSEN sequences within genomes of various Brassicaceae was done using BLASTN within Phytozome 10  [47, 48].
Phylogenetic analysis To analyze the evolutionary distance of the Ty1/COPIA LTR-retroelements, multiple sequence alignments of the RT domains were performed using the genomic nucleotide sequences in MUSCLE  [49]. RT protein sequences used for construction of the network and the tree (Fig. 1j  and Additional file 5: Figure  S2 , respectively) are provided in Additional file 12. The evolutionary history was inferred using the Neighbor-Joining method (Kimura-2-Parameter method) with 1000 bootstrap replicates. Positions containing missing data and gaps were removed (pairwise deletion option) leading to a total of 862 position in the final dataset. The tree was visualized as an unrooted tree. Phylogenetic network of genomic RT domain sequences from Ty1/COPIA LTR-retroelements was constructed using Neighbor-Net  [50]  within the splitstree 4.
0 package  [51, 52]. The phylogenetic distances were calculated by LogDet-pairwise genetic distances using LDDIST  [53]  with imputed missing matrix entries. Multiple sequence alignments of CHS genomic sequences were performed using MUSCLE  [49]. The CHS phylogeny was inferred using the Maximum Likelihood tree based on the Kimura-2-parameter model with 1000 bootstrap replicates. CHS sequences were retrieved from  [25]. All positions containing gaps and missing data were eliminated. There were a total of 1267 positions in the final dataset. All phylogenetic trees were constructed within MEGA 7  [54]. Phylogenetic shadowing and analysis of motif conservation was performed with mVISTA  [55, 56]  using LTR consensus sequences of different species prepared in BioEdit  [57] , allowing fasta ambiguity codes for low conserved positions.
Acknowledgements We thank  J. de Meaux ,  E. Schranz ,  S. Woody , and  K. S. Schumaker  for seed material. We are grateful to  B. Eilts ,  R. Gentges ,  P. Pecinkova , and  D. Hamacher  for plant care and technical assistance,  A. Abdelsamad  for basic identification of TEs using RepeatMasker,  J. Ali  for preparation of the shell script,  O. Mittelsten Scheid  for critical reading of the manuscript, and  M. Koornneef ,  V. Cavrak ,  K. Schneeberger , and  J. Jimenez-Gomez  for numerous discussions.
Availability of data and materials RNA-sequencing data have been uploaded to the Gene Expression Omnibus and are available under accession numbers [GEO: GSE69077]. Lists of A. lyrata and A. thaliana TEs are provided in Additional files 3 and 4. Primers for PCR analyses are available in Additional file 5: Table  S4. The COMEX method is described in detail and is available to download from https://github. com/ bpietzenuk/COMEX; this software is licensed under GNU General Public License version 3. The seeds of plants used in this work can be requested from the corresponding author.
Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven Ligand Extractor called Knodle, a software library for the recognition of atomic types, hybridization states and bond orders in the structures of small molecules. We trained the model using an excessive amount of structural data collected from the PDBbindCN database. Accuracy of the results and the running time of our method is comparable with other popular methods, such as NAOMI, fconv, and I-interpret. On the popular Labute's benchmark set consisting of 179 protein-ligand complexes, Knodle makes five to six perception errors, NAOMI makes seven errors, I-interpret makes nine errors, and fconv makes thirteen errors.
On a larger set of 3,000 protein-ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI have a comparable accuracy of approximately 3. 9 % and 4. 7% of errors, I-interpret made 6. 0 % of errors, while fconv produced approximately 12. 8 % of errors. On a more general set of 332,974 entries collected from the Ligand Expo database, Knodle made 4. 5 % of errors. Overall, our study demonstrates the efficiency and robustness of nonlinear SVM in structure perception tasks.
Here, we introduce a new algorithm for the perception of chemical properties of a molecule from its 3D structure using a machine learning approach. More precisely, we use the multiclass support vector machine (SVM) method with a nonlinear kernel function to off-line train the models for the perception of atomic hybridization states and bond orders. The feature space for the SVM model is constituted by almost all available geometrical descriptors that also contain some chemical information. One of the main advantages of using SVM is that it is a convex optimization method with a unique optimal solution. Conversely, the commonly used methods for the minimization of a scoring function often roll the solution to one of the local minima. We implemented our algorithm in Knodle, a KNOwledge-Driven Ligand Extractor -a software library for the perception of bond orders and hybridization states of atoms of small molecules.
We chose the cutoff distance parameter of r c = 2. 5 Å according to the statistics of covalent bonds in the PDBeChem database  [10] , which does not contain any bonds, excluding the metallic ones, beyond this distance. A decision as to whether a bond of length l between two atoms exists is taken based on the probability of finding such a distance l in the statistical data for these two atoms. We collected this data from the PDBeChem  [10]  and PDBbind  [18]  databases. If we find more bonds with nonzero probability than an atom can form, the bonds with the lowest probability are rejected. Unlike common pairs of atoms, such as C-C or C-N, data for S-Se or P-S pairs contain statistical gaps. We use a linear interpolation where appropriate to fix the gaps. Some ligands in the databases contain unusually long bonds.
Classification The fundamental goal of this study is to demonstrate that machine-learning methods are suitable for some classification tasks in structural chemistry. More precisely, our practical goal was to predict atomic hybridization states and bond orders in an experimentally determined molecular structure starting from a set of its geometrical descriptors. For this purpose, we used support vector machines (SVM)  [24] , a convex optimization method, which has found many applications in text, speech and image recognition, bioinformatics, medical data analysis and various other fields due to its robustness and high performance. To solve the optimization problem, we used the LIBSVM library 1  -the multi-platform software for SVM classification  [6]. To predict atomic hybridization states and bond orders, we first split all the atoms and all the bonds into several classification groups that have corresponding SVM models.
Assignment of Atom Types and Bond Orders In the last steps of the ligand analysis, we set types corresponding to the functional groups. During this stage, we remove excessive double bonds and correct hybridization states when needed. As Knodle is primarily designed for the small molecules' type recognition, we aimed at a very accurate and exhaustive system of atomic types that would represent the corresponding chemical properties. Thus, we adapted the internal types system of the fconv library, which consists of 160 types that can fully describe chemical properties of all atoms in nonmetallic ligands. The assignment of atom types is based on common knowledge about properties of the functional groups, similar to the ones used in the fconv method and the Pluto  [8]  program for Cambridge Structural Database (CSD) structures analysis.
Computational Details The presented method is implemented using the C++ programming language and compiled with the gcc-4. 8 compiler on Linux and the clang compiler on Mac OS systems with -O3 optimization level. The test benchmarks were run on the following machines: a desktop with Linux and 3. 10 GHz Intel(R) Core(TM) i5-4440 CPU processor and 16 GB 1600 MHz DDR3 RAM, and a MacBook Pro late 2013 laptop with a 2. 6 GHz Intel Core i7 processor and 16 GB 1600 MHz DDR3 RAM. Knodle was tested with two versions of its SVM kernels, the slow exact one, and the fast approximate one. The user has the choice of these two options.
Results and Discussion We compared Knodle's performance with three popular methods for the molecular types recognition and format conversion, NAOMI  [22] , fconv  [20]  and I-interpret  [28]. To do so, we ran several series of tests. First, we assessed the accuracy of the methods on the Labute's benchmark set of 179 proteinligand complexes  [15]. Second, we assessed the accuracy of the methods on a part of the PDB-BindCN general data set (v2014) consisting of 3,000 protein-ligand complexes that were randomly excluded from the SVM model training. Third, we performed a more general evaluation of our method using 332,974 ligands extracted from the Ligand Expo database  [11]. These 332,974 ligands do not intersect with those ligands that were used for training. Finally, we compared the running times of the methods using 10,605 protein-ligand complexes from the PDBBindCN general data set (v2014), and 10,605 isolated ligands from the same data set.
Labute's Test Set Although the Labute's benchmark set  [15]  is nowadays far from being exhaustive or representative, we decided to include it for historical reasons, since most of the previously developed perception methods were validated on this set. For this set, we ran the four programs and manually compared the results with the reference structures, whose chemical schemes can be found in the RCSB PDB database  [5]. We must mention here that 59 of 179 complexes of the Labute's test set appeared to be in the training set, which can make this assessment somewhat biased. Therefore, we further assessed Knodle's accuracy on other two sets. As we can see from the table, most of the Knodle's errors are caused by ambiguous geometry of the ligands (1AAQ, 3FX2, 8XIA, 1NNB), and one error was made in a long retinol chain (1AQB).
PDBBindCN Test Set To further validate the results, we performed a series of tests on a part of PDBBindCN general data set (v2014)  [18]  that was not used for the training (3000 ligands). Here, we assessed the quality of Knodle (with fast kernels), fconv, NAOMI and Iinterpret. This set is more statistically significant than the previous one, but still allows a visual inspection of errors along with a comparison of mol2 types predicted by the four methods. We first obtained the results using an automatic parser of the mol2 files. The parser was designed to compare mol2 atom types and, consequently, hybridizations. Differences in the perception of tautomeric forms were not considered as errors. Some typical examples of the substructures where tautomerism occurs are guanidinium and amidinium groups, pyrimidines with amino substituents, pyrimidones and ketoenol groups.
Nitrogens in most of the nitrogencontaining groups from this list are usually planar trigonal and have N. pl3 SYBYL types, even if the bond is single, thus making such tautomeric states easily distinguishable at the parsing stage. We compared the results of the four tools with the mol2 ligand files provided by PDBBindCN. Afterward, we manually examined all the hybridization inconsistencies by visual inspection of ligand struc-tures and their SMILES notations from the RCSB PDB  [5]  and PDBeChem  [10]  databases. Some of the inconsistencies were not interpreted as errors because of different tautomeric states of the corresponding groups. We excluded several structures from the comparison as they contained a metal or metalloid atom that affected prediction of the correct bond orders and hybridizations in its vicinity.
Ligand Expo Test Set For a more general evaluation of our method, we ran Knodle on ligands in the pdb format from the Ligand Expo database  [11]. To construct the pruned test set for the assessment, we first removed all empty entries, monatomic ligands, and entries with disconnected components. We also ignored truncated ligands with the number of heavy atoms smaller than in the reference chemical formulas provided by Ligand Expo. Finally, we excluded from the comparison metallic ligands, as it is difficult to distinguish whether a perception error is caused by the vicinity of the metal or not, and intersections with the training set. To proceed with the evaluation, we ran Knodle to convert 332,974 ligands from the pruned Ligand Expo test set into sdf files. Then, we neutralized the predictions and converted them again to the canonical SMILES format.
Running Time Generally, for the structure perception algorithms, running time is not of the highest priority compared to the accuracy of the perception. Nonetheless, to complete the assessment of different methods, we performed a comparison of running times of Knodle, fconv, NAOMI and I-interpret methods using the PDBBindCN general data set (v2014) consisting of 10,605 full protein-ligand complexes and the corresponding ligands, converted to the pdb file for-mat. Table  8  lists the timings of conversion of isolated ligands measured on two platforms. Overall, we can see that when converting small files, all the four tested methods demonstrate a very similar performance, with fconv being the fastest one, which is however negated by its accuracy. Table  S3  from the Supporting Information also lists the timings of the conversion of full protein-ligand complexes.
Conclusion Here we presented Knodle, a software library for the recognition of atomic types for small molecules. We demonstrated that this task can be accomplished very efficiently using nonlinear support vector machines. To accelerate the computation of the nonlinear radial kernel functions, we approximated the most time-consuming of them using Maclaurin series expansions and third-order polynomials. The accuracy of the results and running time comparable with other popular methods show that SVM is a good alternative to existing approaches for the perception of atomic structures, being a more general and extendable tool. More precisely, on the popular Labute's benchmark set consisting of 179 protein-ligand complexes, Knodle made five to six perception errors, depending on the kernels chosen, NAOMI made seven errors, I-interpret made nine errors, while fconv made thirteen errors.
On a larger set of 3,000 protein-ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI had a comparable accuracy of 3. 9% and 4. 7% of errors, correspondingly, while Iinterpret made 6. 0% of errors, and fconv produced about 12. 8 % of errors. On a more general set of 332,974 entries from the Ligand Expo database, Knodle made 4. 5 % of perception errors. Overall, SVM demonstrated robustness, power, and ease in structure perception tasks studied here. In the future, when more structural data becomes available, it will be straightforward to retrain our SVM models. Knodle is available at https://team. inria. fr/nano-d/software/Knodle. A graphical user interface for Knodle will be made available at http://www. samson-connect. net.
Acknowledgement The authors thank  Dr. Damien Jouvenot  from the  University of Grenoble Alpes  for providing his expertise on electronic structure of small molecules. The authors also thank  Julie Bourget  from  Inria Grenoble  for her invaluable assistance with the project. This study was partially supported by the  Russian Federal Target Program 'Research and Development'  (contract  14. 587. 21. 0011,RFMEFI58715X0011 ) and the  5top100 program  of the  Ministry of Education and Science of the Russian Federation.
A new computer environment to perform simulations on synchrotron experiments has been designed. It performs ray-tracing simulations using the popular ray-tracing code SHADOW. With this new application one can define, in a very easy and elegant way, one or several optical systems (beamlines) and perform calculations of the propagation of the X-ray beam through it. Many complementary tools and supplementary calculations improve and extend the functionality of SHADOW to deal with complex optical system optimization, including compound optical elements, iterative calculations, some sample simulations, and implementing corrections for wave optics via a hybrid model.
, 2011)  version guarantees its easy installation, compilation, maintenance and use in any modern computer environment. It is relatively simple to use, documented, includes a Python application programming interface (API), and is freely available (open source). SHADOW is based on a geometrical ray-tracing approach, but also traces field amplitude with phase difference, and therefore is capable of including reflectivity and transmittance of optical elements calculated by models of physical optics. It may also incorporate wave features beyond the validity domain of geometric optics. In recent years we attended to a tremendous growth of techniques exploiting the coherence of the synchrotron beam (phase contrast imaging, coherent diffraction imaging, ptychography, etc. ) and this is also supported by new techniques exploiting the total coherence of X-FEL sources as well as in the much improved coherence of the synchrotron beams in the new storage rings.