dataesr/scientific-paragraphs-categorization huggingface dataset:
For each model consisting of an E. coli metabolic network and a random biomass, we classified the metabolic reactions according to their functionality: blocked (flux values are always equal to 0 through the reaction), alternative (flux values are positive or equal to 0 through the reaction) and essential (flux values are always positive through the reaction). We implemented the classification of metabolic reactions within TOMLAB environment  [45] , using Gurobi as the underlying linear programming solver. Then, for the three E. coli models iJR904, iAF1260 and iJO1366, we randomly removed 10%, 20%, 30% or 40% of metabolic reactions, with 10 different replicates by percentage. The same percentage of the three types of reactions was removed in each degradation process. The degraded networks were chosen such that none of the 90 random biomass functions could be produced by any of the 40 degraded networks.
All together, we obtained a set of 10,800 metabolic networks which were not functional with respect to the production of their associated biomass. E. siliculosus draft metabolic network. EctoGEM 1. 0 is based on genome data downloaded from the ORCAE website (http://bioinformatics. psb. ugent. be/orcae/overview/Ectsi) [46]  on June 21, 2013; the Ca. P. ectocarpi genome used for network reconstruction is available from the European Nucleotide Archive (accession HG966617). Both networks were mapped against MetaCyc 19. 0 as a reference database. The construction of the set of targets for Meneco relied on the dataset of 90,637 ESTs published in the Ectocarpus genome paper (  [34] , EMBL accessions: FP245546-FP312611). E. mutabilis draft metabolic network. Sequences of E. mutabilis transcripts were obtained from de novo transcriptomic experiments published in  [18].
The sequence data are publicly available in the Sequence Read Archive with the SRA accession numbers ERS358279 and ERS358280. The transcripts assembled with newbler were annotated by tblastn searches using reference enzyme sequences in MetaCyc 17. 5 as queries. A reaction was included in the network if at least one of the corresponding reference enzyme sequences had a blast hit in the transcript set with an expect value lower than 10 -10 and at least 25% identity over 80% of the length of the reference sequence. Generic reactions in MetaCyc 17. 5 involving generic carbohydrates, amino-acids, nucleotides or NAD-P-OR-NOP (standing for NAD or NADP) were replaced with the appropriately balanced instances involving the corresponding metabolites present in the E. mutabilis draft network. Transport and ambiguous non-informative reactions such as 3.
Respiration was added as a single reaction. When a reference enzyme sequence corresponding to an added reaction was available in the MetaCyc database, this sequence was used as a query to identify potential homologs in the E. mutabilis transcriptome by tblastn. Transcripts with an expect value lower than 10 -3 were extracted and used to search the protein nr database with blastx on the NCBI web server. The presence of the reaction was validated if the search yielded significant hits (e-value < 10 -3 ) in the Conserved Domain Database (CDD) and with sequences of enzymes that were consistent with the tested reaction. After each step, if the added reactions produced new metabolites previously absent from the network, the spontaneous reactions described in MetaCyc consuming those metabolites were also added to the network.
The appropriate instances were manually added for the generic reactions corresponding classes of the newly added metabolites. Functional studies E. coli degraded networks after their completion. The Flux Balance Analyses studies were performed using the python-based toolbox COBRApy (version 0. 3. 2)  [47]  and the GLPK solver. Flux Variability Analysis was performed using both COBRApy in association with GLPK, and homemade scripts within the TOMLAB environment using the Gurobi solver. E. mutabilis metabolic network. The biomass reaction was defined according to  [48]  and based on the composition of E. gracilis as determined by  [49] [50] [51] [52] [53] [54] [55]. The fatty acid composition of the E. mutabilis membrane adapted to arsenic contamination was obtained from  [36]. The proportions of amino-acids and nucleotides in proteins and nucleic acids was estimated from the composition of transcripts and their translation.
After completion with Meneco, the network was tested with COBRA findBlockedReactions() command in Matlab R2015b. The dead-end metabolites blocking the production of biomass or of the experimentally determined targets  [19]  were manually identified and their boundaryCondition property was set to true. As transcripts corresponding to reactions that could regenerate reduced flavodoxins, oxidized ferredoxins, and thioredoxin, could be identified, the corresponding reactions (FLAVONADPREDUCT-RXN, RXN0-882, RXN-9944) were added to the network. The resulting network was eventually tested in Flux Balance Analysis with COBRA in Matlab R2015b, using the BIOMASS-RXN reaction as the objective function, and the mineral seed metabolites listed in S3 Files as entries. Culture in the dark was simulated by setting the boundaries of the |Light| entry reaction (RXN_in_Light) to 0.
siliculosus and Ca. P. ectocarpi networks is provided in a separate file. Finally, detailled examples of false positive predicted interactions and their explanation are provided in a separate pdf file. (ZIP) S3 Files. E. mutabilis networks. Both draft and functional reconstructed metabolic networks for E. mutabilis are provided here. The folder also provides the SBML file describing the repair database (MetaCyc 17. 5 modified as described in the Material and Methods) required to run the Meneco tool. Files corresponding to targets and seeds are also provided. The file seed_mi-nimum_medium_Emutabilis. sbml describes light and the 8 mineral nutrients of the minimal growth medium for E. mutabilis. As described in the main text, the network is not functional with only these seeds as reactions in cycles are blocked.
4. 14. 10-RXN (POLY-PEPTIDE + WATER ! Peptides + TRIPEPTIDE) were discarded. The Meneco completion of the resulting draft metabolic network was performed with the modified version of MetaCyc 17. 5 as the repair database and the 71 target metabolites listed in S3 Files. The version 17. 5 of MetaCyc was used since it was the version available at the time this work was done, including manual addition of instances of generic reactions. Even though new versions of MetaCyc had been released during network reconstruction, we proceeded with version 17. 5 for the sake of consistency.
A deep understanding of RNA modification functions requires large and global analysis datasets for other important RNA species, namely for transfer RNAs (tRNAs), which are well known to contain a great variety of functionally-important modified residues. Here, we evaluated the application of the RiboMethSeq protocol for the analysis of tRNA 2 -O-methylation in Escherichia coli and in Saccharomyces cerevisiae. After a careful optimization of the bioinformatic pipeline, RiboMethSeq proved to be suitable for relative quantification of methylation rates for known modified positions in different tRNA species.
Introduction RNA modification is a complex step in the post-transcriptional maturation of cellular RNAs and includes numerous chemical alterations of initially incorporated parental nucleotides. Among these various chemical reactions, the most frequent is the transfer of a methyl (CH 3 ) group from a methylation donor (generally S-adenosyl-L-methionine, abbreviated as SAM or AdoMet) to different positions on nucleobases or to the 2 -OH of the ribose  [1] [2] [3] [4] [5]. Methylated nucleotides are found in every studied RNA species, and their presence allows fine modulation (tuning) of RNA properties in tRNA reference numbers are from tRNA database (tRNAdb). U*: unknown modified uridine; Q: queuosine; I: inosine; ψ: pseudouridine; N: unknown residue; other modified nucleotides abbreviated according to their conventional names.
tRNA reference numbers are from tRNA database (tRNAdb). U*: unknown modified uridine; Q: queuosine; I: inosine; ψ: pseudouridine; N: unknown residue; other modified nucleotides abbreviated according to their conventional names. Trm: tRNA methyltransferase; aTrm: archaeal tRNA methyltransferase; hTrm: human tRNA methyltransferase; sRNA: small RNA; aFib: archaeal fibrillarin. In other bacteria, enzymatic tRNA modification systems targeting the ribose-2 -OH are only partially characterized. Recently, a tRNA MTase TrmJ in Pseudomonas aeruginosa PA14 was found to be involved in resistance to oxidative stress. TrmJ catalyzes the formation of Cm34, Um34 and, in contrast to E. coli, an additional Am34 in tRNA Pro (GGG)  [27].
The necessity for optimization is related to short and quite similar tRNA genes (transfer DNA, tDNA) sequences used for alignment, to the very irregular alkaline cleavage profile of these small and strongly-structured RNA molecules and certainly to the presence of numerous other modified nucleotides with RT-arresting properties, decreasing even further the number of available sequencing reads at certain tDNA regions. Taking into account all of these difficulties, we proceeded with the optimization of all treatment steps (Figure  3 ), the most important concerning the used read length, selection of only uniquely aligned reads, simultaneous usage of 5 -and 3 -end information, as well as the calculation of scores for shorter neighboring regions and, finally, a careful optimization of the reference tDNA dataset.
Construction of the Reference tDNA Dataset Initial tDNA datasets for alignment were constructed from sequences of modified tRNA species deposited in tRNA database (tRNAdb)  [66] , converted to DNA sequences in Fasta format. Inspection of the sequencing reads alignment to this tDNA reference revealed that, even at a minimum length of 17 nt, a significant number of reads mapped to multiple locations in tRNAs (Figure  4 ). This was mostly due to almost identical tDNA species present in the dataset and also to the presence of highly repetitive regions. In order to obtain better certitude in read mapping, we first decided to reduce and simplify the tDNA reference datasets for analysis. sequencing reads at certain tDNA regions. Taking into account all of these difficulties, we proceeded with the optimization of all treatment steps (Figure  3 ), the most important concerning the used read length, selection of only uniquely aligned reads, simultaneous usage of 5′-and 3′-end information, as well as the calculation of scores for shorter neighboring regions and, finally, a careful optimization of the reference tDNA dataset.
Construction of the Reference tDNA Dataset Initial tDNA datasets for alignment were constructed from sequences of modified tRNA species deposited in tRNA database (tRNAdb)  [66] , converted to DNA sequences in Fasta format. Inspection of the sequencing reads alignment to this tDNA reference revealed that, even at a minimum length of 17 nt, a significant number of reads mapped to multiple locations in tRNAs (Figure  4 ). This was mostly due to almost identical tDNA species present in the dataset and also to the presence of highly repetitive regions. In order to obtain better certitude in read mapping, we first decided to reduce and simplify the tDNA reference datasets for analysis. In order to identify closely-related tDNA sequences, the Levenshtein distance was calculated for the complete tDNA dataset for known modified tRNA species, obtained from tRNAdb.
Distances between tDNA sequences are given in Figure  S1 ; the calculation was performed for the dataset before and after collapsing of closely-related sequences. tDNA sequences with Levenshtein distance <4 (corresponds to a maximum of three nucleotide replacements) were collapsed into one unique sequence, and an undefined nucleotide "N" was placed at the divergent positions. Other closely-related pairs were retained in the dataset. At the same time, observed sequencing non-conformities and errors were corrected, and instances of the A-derived, but C-base In order to identify closely-related tDNA sequences, the Levenshtein distance was calculated for the complete tDNA dataset for known modified tRNA species, obtained from tRNAdb. Distances between tDNA sequences are given in Figure  S1 ; the calculation was performed for the dataset before and after collapsing of closely-related sequences.
tDNA sequences with Levenshtein distance <4 (corresponds to a maximum of three nucleotide replacements) were collapsed into one unique sequence, and an undefined nucleotide "N" was placed at the divergent positions. Other closely-related pairs were retained in the dataset. At the same time, observed sequencing non-conformities and errors were corrected, and instances of the A-derived, but C-base pairing modified nucleotide inosine (I) present in some tRNA species were replaced by a G in the reference tDNA dataset. The same strategy was applied to E. coli and S. cerevisiae tDNA datasets. Details of the adaptation of both tRNA datasets are given in Tables  S1  and S2.
Alignment and Analysis For alignment to optimize the tDNA dataset, raw read trimming was performed to keep all sequences >8 nt. Of note, this length, while unsuitable for large genomic alignments, is still appropriate for a limited size tDNA "genome". In order to gain additional information on both the 5 -and the 3 -end positions for every aligned read, only trimmed (with adapter removed) sequences were retained for subsequent alignment. This reduced the number of usable reads, but still provided some gain in information, since both 5 -and 3 -end locations were used. Bowtie2 alignment parameters were adjusted for short input sequences (L = 10) in end-to-end mode. In order to avoid contamination by ambiguously (multiply) mapped sequencing reads, only uniquely-aligned sequences were taken for further analysis.
Details for the number of retained reads at every step are given in Table  S3. The positions of the 5 -and 3 -ends of every read were extracted after conversion of the aligned *. sam file to bed format with bedtools. Biomolecules 2017, 7, 13 10 of 22 pairing modified nucleotide inosine (I) present in some tRNA species were replaced by a G in the reference tDNA dataset. The same strategy was applied to E. coli and S. cerevisiae tDNA datasets. Details of the adaptation of both tRNA datasets are given in Tables  S1  and S2. The positions of the 5 -and 3 -ends were cumulated together to obtain the global end-coverage profile, and these data were used to calculate RiboMethSeq scores (ScoreMean (derived from ScoreMax) and ScoresA, B and MethScore(C)) as described previously  [63, 65]. Since the length of tRNA sequences is rather limited and the cleavage profile of tRNAs is highly irregular, only two neighboring nucleotides on each side were taken into account (instead of six in the standard RiboMethSeq approach).
Sequencing Library sequencing was performed using the Illumina HiSeq 1000 sequencer in a single read mode for 50 nt with SBS kit v3. Primary analysis of sequencing quality was done with Illumina RTA 2. 12 software, to insure a >Q30 quality score for >95% of the obtained sequences. Following the SR50 sequencing run, demultiplexing was done with Illumina BclToFastq v2. 4; reads not passing the quality filter were removed.
Data Analysis Raw reads after demultiplexing were trimmed to remove the sequence of the Illumina 3 adapter. Trimming was performed with Trimmomatic v0. 32  [67]  with the following parameters: -phred33 /adapters/TruSeq3-SE. fa:2:30:10 LEADING:30 TRAILING:30 SLIDINGWINDOW:4:15 MINLEN:8 AVGQUAL:30. Tests with a training dataset demonstrated that with stringency parameter = 10, Trimmomatic removes the adapter with a minimal size of 16 nts. Thus, only trimmed and adapter-free reads of <35 nt were taken for alignment. Alignment to the reference tDNA sequences was performed with Bowtie2 ver2. 2. 4  [68]  in end-to-end mode using the following parameter set: -no-1mm-upfront -D 15 -R 2 -N 0 -L 10 -i S,1,1. 15. Preliminary tests demonstrated that the use of soft trimming during alignment (-local settings) is not recommended, since the 5 -and 3-end may be incorrectly determined.
Conclusions The alkaline hydrolysis-based RiboMethSeq method (Figure  3 ) can be successfully applied to the analysis of known tRNA 2 -O-methylations and their dynamics or variations under different conditions. Quantification of the modification level is possible with careful calibration, obtained either with unmodified tRNAs extracted from a relevant KO strain or, if not available, with a synthetic RNA transcript with an identical sequence. The candidates for novel modified positions revealed by this high-throughput technique should be further validated by orthogonal approaches, either by physico-chemical methods, like HPLC-MS/MS analysis or by primer extension at low dNTP concentrations, an approach specific for RNA 2 -O-methylations. In the case of Gm18 in E. coli tRNA Met (ac 4 CAU), where we have exercised such a validation, the RiboMethSeq method proved accurate and led to the correction of the data from the literature.
Acknowledgments: This work was supported by  Agence Nationale de Recherche HTRNAMod   ANR-13-ISV8-0001  grant to Y. M. Part of this work was supported by grants of the  German Research Foundation :  Da592/5  to A. D. and by the  DFG   SPP1784 ,  MH3397/12-1 ,  MH3397/14-1  to M. H.
At first the reaction of such walls to analytic ground motions of altered amplitude and frequency content oriented at changing directions with respect to the wall trend is tested. Thereby, the influence of the wall height to wall width ratio (h/w-ratio) is taken into account; however, no vertical movements are applied. In a second series of tests, selected measured strong ground motions of earthquakes are used as boundary conditions. In this case, the full 3D ground motion is applied and again the h/w-ratio is varied. The discrete element models have been created with the Unity3D software development tool which utilizes the physics engine PhysX (see data and resources), a combination that has been previously applied in archaeological studies (ONeill, http://www. sas. upenn. edu/ancient/againstgravity_abstracts.
html, last accessed May 2016). For the current project, scripts in C-Sharp language were written to facilitate automation of scanning projects with easy handling of parameter changes. In this way, several thousand of dynamic test calculations were conducted and individual blocks monitored within reasonable amounts of modeling times. Energy dissipation during impacts between blocks was controlled by a coefficient of restitution of 0. 86. To detect potential impacts of blocks a contact offset of 0. 02 was chosen; this means when blocks come closer than 0. 04 m they are monitored for a possible impact. The collision shape was defined by a mesh following the graphic shape of each block. For solving the equations of motion a semi-implicit Euler algorithm was used.
Earthquakes A set of 24 earthquake strong motion records was selected from the Center for Engineering Strong Motion Data (CESMD) and the European Strong-Motion Database (ESD) (see data and resources). In order to cover a sufficiently broad range of ground motion characteristics, no limitations were set for the source mechanism and the subsurface condition at the recording sites, with the exception that only free field station data were used. We selected records with PGAs of the horizontal ground motion component ranging from 1. 0 m/s 2 to 8. 0 m/s 2 in steps of 1. 0 m/s 2. If available from the databases, the corrected displacement records were used; otherwise, displacement was calculated from the acceleration records af-ter a standard baseline correction. Figure  4  shows the elastic acceleration response spectra of all 24 earthquake records for 5% of the critical damping.
Data and Resources Strong motion data used in the simulations were retrieved from the Center for Engineering Strong Motion Date (CSEMD, http://www. strongmotioncenter. org/, last accessed May 2016) and the European Strong-Motion Database (ESD, http://www. isesd. hi. is/ESD_Local/, last accessed May 2016). Discrete element calculations were made with the UNITY (Unity Technologies Ltd. , v. xxx) under use of the physics engine PhysX  (Nvidia Corporation, v xxx). Photo credit of the Inca-type wall is to ITW: Martin St-Amant -Wikipedia -CC-BY-SA-3. 0, all other photos were taken by the first author. Part of the strong motion data processing was made with SeismoSignal (http://www. seismosoft. com, last accessed May 2016).
Acknowledgements. We thank  H. Kehmeier  and  I. Schwellenbach  for the efforts to create some early versions of the discrete element models, G. Schweppe helped to solve software problems,  K. Kemna  assisted with some data handling. We are grateful to  S. K. Reamer  for comments on an early version of the manuscript and editing of the final submission. Two anonymous peers helped to improve the paper during the review process and we thank the editors for handling the special volume.
Calculation of AI scores for whole proteomes had previously been implemented in a software called AlienG  [23]. It was used to highlight the importance of HGT in the colonization of land by plants  [8]  and in several other studies of HGT across different lineages  [24] [25] [26]. In the original AlienG, publication, an AI > 30 was deemed good indicator of acquisition via HGT. However, as far as we know, this software is neither publicly available for download nor deployed on a web server. Hence, no user-friendly web tool or downloadable software is available, so far to compute AI scores directly from BLAST results. Furthermore, extracting the taxonomic information from BLAST results against the National Center for Biotechnology Information´s (NCBI) (Bethesda, MD, USA) protein libraries can be a long and difficult task, which prevents popularization of such methods.
A refined scoring method has been recently proposed that not only takes into account sequence similarity between receiver and candidate donors, measured in BLAST bit score, but also taxonomic distance measured as the number of step in the NCBI's taxonomic lineage to reach the common ancestor of the query and subject species  [30]. The software is publicly available and has been initially developed to identify HGT in fungal genomes. However, this requires local installation of voluminous data, manual configuration, and expert skills are required to tune and adapt the method to identify HGT in other phyla. Similarly, a method called DarkHorse and initially developed to identify HGT in bacteria is available as a software that can be downloaded and installed locally  [31]. This method proposes a lineage probability index score (LPI) based on the taxonomic ranks of top hits to identify candidate HGT from BLAST against protein databases.
Here again, the method can be generalized to other taxa of interest. However, the installation is restricted to users with computational skills on a Unix-based platform and requires installing, configuring and managing a MySQL database as well as downloading the whole NCBI's NR database and the NCBI's taxonomy. Phylogenetic methods that compare a gene or protein tree to a reference species tree and identify inconsistencies are the gold standard to predict HGT. While such methods exist  [32] [33] [34] [35]  they need to be implemented by expert users. These phylogeny-based methods require a reference species tree for comparison. However, such reference trees are not always available for the group of species of interest but some methods propose to generate a reference species trees as well as computing individual gene trees in parallel  [36].