dataesr/scientific-paragraphs-categorization huggingface dataset:
An initial set of 50 models was created, and the structure with lowest empirical energy was selected for MD simulations. A model of the variant IR complex was constructed in two stages. Preliminary MD studies of the variant hormone-IR complex were first performed using a coarse model of the modified side chain within the GROMACS package (version 4. 6. 1 (104)) with OPLS-all atom force field (96, 105) as described below. The lowest energy model emerging from this simulation then provided a starting point for QM-parameterized MD as outlined above. Initial MD simulations of the variant IR complex exploited an approximate model of 3-I-Tyr in which a virtual site was placed near the iodine atom to mimic the -hole. This site's position was determined by minimizing the error of the fit of the atom-centered charges to the molecular electrostatic potential for 2-iodo-4-methylphenol, calculated at the HF/6 -311G(d,p) level; the optimal position of the virtual site was 1.
5 ϫ 10 Ϫ5 bar. The time step was 2 fs. Simulations were performed with a single non-bonded cutoff of 10 Å and neighbor-list update frequency of 10 steps (20 fs). The PME method modeled long-range electrostatics (108); the grid width was 1. 2 Å with fourth-order spline interpolation. Bond lengths were constrained using LINCS  (109). The MD protocol consisted of an initial minimization of water molecules, followed by 100 ps of MD with the protein restrained to permit equilibration of the solvent. Calculations were continued for 200 ns from the geometries obtained after initial positionally restrained MD at a temperature of 300 K. Database Deposition-Atomic coordinates of the crystal of 3-[iodo-Tyr B26 ,Nle B29 ]insulin have been deposited in the Protein Data Bank (code 5EMS). Supplemental Information-The supplemental Table  S1  provides interaction energies contributing to dimerization.
B chain residues B8 -B19 are shown as a black ribbon and the A chain as a yellow ribbon; residues A1-A3 are concealed behind the surface of ␣CT. Key contact surfaces of ␣CT with B24 -B26 are highlighted in magenta, and L1 with B24 -B26 are highlighted in cyan; L1 and ␣CT surfaces not in interaction with B24 -B26 are shown in lighter shades. E, orthogonal view to D, showing interaction of the side chain of Phe B24 with the nonpolar surface of the L1-␤ 2 sheet. Tyr B26 is hidden below the surface of ␣CT. Engagement of conserved residues A1-A3 against the nonpolar surface of ␣CT is shown at top. F, environment of Tyr B26 within site 1 complex (stereo). Neighboring side chains in L1 and ␣CT are as labeled. Coordinates were obtained from PDB code 4OGA (39). G, model of wild-type insulin in its receptor-free conformation overlaid onto the structure of the insulin-bound IR (71).
3-[iodo-Tyr B26 ,Nle B29 ]Insulin and the WT R state: main-chain r. m. s. d. ϭ 0. 55(Ϯ0. 08) Å and sidechain r. m. s. d. ϭ 1. 94(Ϯ0. 23) Å. These values are similar to those observed among a collection of independent WT R state protomers 13 (main-chain r. m. s. d. 0. 68(Ϯ0. 26) Å; side-chain r. m. s. d. 1. 14(Ϯ0. 34) Å). Within the crystal structure of 3-[iodo-13 WT R state coordinates were obtained from PDB entries 1TRZ, 1TYL, 1TYM, 1RWE, 1DPH, and 1ZNJ.
The arrows on the right assist in identifying the direction of the respective polypeptides within each subpanel. Chains within each subpanel correspond (from left to right) to chains B, D, F, H, J, and L (respectively) within each structure. Overlaid on the three subpanels in A is A -weighted (2F obs Ϫ F calc ) difference electron density contoured at the 0. 75 level and masked to within 2. 5 Å of the side-chain atoms of Phe B25 and its symmetry-related equivalents. The values displayed under the respective chains within the subpanels of B correspond to the side-chain occupancies of the Phe B25 and its respective non-crystallographic symmetry equivalents within PDB code 1ZNJ. The side chain of Nle B29 is not shown.
Average r. m. s. d. across the WT-T 2 , 3I-Tyr B26 T 2 , and R 2 dimer interface from 20 ns of MD simulation and from the three dimers (D1, D2, and D3) taken from the 3-I-Tyr B26 R 6 crystal structure, with respect to WT (PDB code 1DPH). All structures are aligned with respect to the backbone atoms of residues B24 -B28 and B24Ј-B28Ј. Values reported are r. m. s. d. of the side chains of residues B24 -B26 and B24Ј-B26Ј.
Acknowledgments-We thank  Drs. Q. -X. Hua ,  W. Jia ,  L. Whittaker ,  S. H. Nakagawa , and  Z. -L. Wan  for advice regarding experimental procedures. We thank  N. Rege  for assistance with manuscript preparation and journal editors  C. Goodman  and  M. Spiering  for helpful suggestions. We also thank  Profs. P. Arvan ,  T. L. Blundell ,  F. Ismail-Beigi ,  M. Karplus ,  P. G. Katsoyannis , and  M. Liu  for general discussion.
Materials and methods ORHEO was a large observational, noninterventional, longitudinal, national, multicenter study (NCT02140736). 12 nclusion criteria included patients at least 18 years of age presenting with CIA (irrespective of chemotherapy cycle) associated with solid tumors, lymphomas, or myelomas and eligible for treatment with an epoetin alfa biosimilar. Patient characteristics were recorded at baseline, along with anemia-related information, such as observed and target hemoglobin (Hb; as chosen by the treating clinician), blood count, and iron-load profile; brand and dose of epoetin biosimilar prescribed; details of any other treatments; and adverse events. Patients were followed for 6 months over three visits: inclusion visit, 3-month follow-up visit, and 6-month end-of-study visit.
statistical methodology All statistical analyses were performed on the per protocol population. This excluded patients with missing baseline Hb values, patients for whom age was not recorded, patients who had not initiated chemotherapy at baseline, patients who were not receiving an epoetin biosimilar at baseline, and patients who switched to another epoetin treatment between baseline and month 6. In addition, patients with a disease other than those described in the protocol were excluded. The rates of Hb responders were calculated at each visit with 95% confidence intervals using SAS software (9. 2 release).
17, 19 his subanalysis was designed to assess the clinical profile of biosimilar epoetin as part of an ongoing commitment to postmarketing observation. The results demonstrated that biosimilar epoetin is as effective in elderly patients as in younger patients. Safety profiles were similar, except for a higher incidence of thromboembolic events and a lower frequency of infections among the older patients; however, numbers were small. 15 here was no evidence of unexpected treatment-related adverse events. Due to the methodology design of an observational trial, no samples for analysis of antibodies to erythropoietin receptors were collected. The results of this subanalysis support previous research on the effectiveness and tolerability of biosimilar epoetin in treating patients with CIA. 20 he strengths of this study include the following: 1) data specifically evaluating the safety and effectiveness of biosimilar epoetin in the elderly are lacking, 2) this subanalysis involved a large number of patients, 3) the methods were as close to current, "real-life" conditions as possible, and 4) it is unlikely that future observational studies will focus on the elderly population.
Acknowledgments Assistance with data analysis was provided by  Karine Andre (Clinact) , funded by  Hospira Ltd. Medical writing and editorial support was provided by  Anne Gentry, PharmD  ( Gentry Medical Communications LLC ), funded by  Hospira Inc , a Pfizer company. This study was funded by  Hospira SAS France , which was acquired by  Pfizer  in September 2015. The abstract of this paper was presented at the 2013  SIOG Conference in Copenhagen, Denmark  as a poster presentation. The poster abstract was published in the Journal of Geriatric Oncology (2013; 4 Suppl 1:S82 ).
Diffraction stress analysis X-Ray diffraction. Stress analysis can be carried out by X-ray diffraction using crystalline planes as a strain gauge. The sin method is usually chosen in case of polycrystalline and multiphase materials. residual stress are only accessible in case of a multiphase (phases  i ) material without unstressed reference (axis are defined in Fig. 1 ). X-ray elastic constants (XEC) of considered phases must be determined from elastic constants of a single crystal and the orientation distribution function (ODF) of polycrystalline aggregates in case of textured sample. Fig. 2  gives the evolution of the Young modulus E of Fe 3 C orthorhombic single crystal in spherical coordinates (data from  [11] ). The elastic anisotropy of Fe 3 C cementite is highly marked, E 319 GPa and E 65 GPa.
Phase Texture Observation and EBSD acquisition. Light micrographs were obtained using a Leica® Aristomet device coupled with a Nikon® digital acquisition system. Surface was etched with a 10 % Nital solution. SEM observations were carried out on a JEOL® 7001F device operating at 15 kV using backscattered electrons. For EBSD analysis, sample was tilted at 70° using hkl® device. Basic EBSD analysis and stress analysis (cross correlation method) were performed using Channel 5 and CrossCourt® softwares respectively. X-ray diffraction. Residual stress and texture analyses of cementite were performed on Seifert® XRD 3003 PTS and Siemens® D500 devices using position sensitive detector (PSD). Acquisition parameters are given in Tab. 1. Fig. 3  gives the X-ray diffraction spectrum of the analysed surface.
Introduction Viruses are found in all three domains of life, from higher animals to tiny prokaryotes  [1] , and some viruses even infect other viruses (i. e. , virophages)  [2]. Given the rapid increase of sequenced viruses infecting diverse hosts, it is becoming increasingly possible to study viruses from the wider taxonomic perspective of global interaction networks of both viruses and hosts. The first complete genome ever sequenced was in fact a viral genome, that of bacteriophage ΦX174 (5375 nucleotide circular ssDNA, infecting Escherichia coli) determined in 1977 by Frederick Sanger and his colleagues  [3]. Currently, 6544 complete viral genomes are recorded in the National Center for Biotechnology Information (NCBI) Reference Sequence Database (RefSeq release 72)  [4]. Given this large number of viral genomes, comparative genomics is a potent approach to studying viruses with the aim of uncovering shared functional features, deducing ancient evolutionary histories, or predicting host ranges for known and novel viruses.
Being typical of free text annotation fields, these host names suffer from many cases of ambiguities or typos that make reliable automated mapping to TaxIDs difficult. For example, "Sida sp. " is provided as the host information for Sida mosaic Sinaloa virus (NC_008059) in RefSeq, but "Sida" could refer not only to a genus of angiosperms (the true host group, TaxID = 108335) but also to a genus of crustaceans (non-host, TaxID = 77655). UniProtKB  [9]  does provide machine readable TaxIDs for viruses and hosts, but this linking information is only assigned for 20% of viral genomes in RefSeq. To our knowledge, there is no comprehensive resource that organizes robust machine readable taxonomic links between viruses and their hosts. Consequently, even such a simple query as "How many archaeal virus genomes are recorded in RefSeq?" is not easy to answer for non-specialists, and is completely out of reach for automated software methods.
Materials and Methods The GenomeNet Virus-Host Database  [10]  organizes TaxID based links between viruses and their hosts. We first extracted natural host and laboratory host information from RefSeq viral genome entries ("source" features) and from protein sequence entries in UniProtKB ("OX-organism taxonomy cross-reference" and "OH-organism host" entry lines). RefSeq free text to TaxID mapping was manually curated (error correction and disambiguation), and for viral genome entries that lacked relevant host data, we collected host information by surveying the literature. In some cases, we referred to viral species names (or virus names) to reach host information as some of these names contain host names (e. g. , Apple mosaic virus). Finally, the Virus-Host Database provides links to external reference resources such as ViralZone  [11] , the NCBI taxonomy database, the Kyoto Encyclopedia of Genes and Genomes database  [12] , and the International Committee on Taxonomy of Viruses database  [13].
Results and Discussion Currently, about 38% of the total viral entries in the Virus-Host Database are manually curated. These curated entries can be distinguished from automatically created entries by the "Evidence" line in the individual viral entry page of the database. Manually curated entries contain "Literature" and/or "Other" keyword depending on the type of evidence for known virus-host relationships, whereas automatically created entries contain "RefSeq" and/or "UniProt". The number of viral genomes, host taxonomy and host genome sequence availability are summarized in Figure  1. To illustrate how the paired taxonomic information could be used in computational genome analysis, here we present two broad scale analyses spanning the full range of sequenced prokaryotic viruses with known hosts.
945). Viruses 2016, 8, x 3 of 6   Viruses 2016, 8, 66 4 of 6 Second, we used the paired taxonomic information to assess a computational method for host prediction. Genomic features of phages and their hosts can be used to predict phage-host pairs  [16, 17]. Here, we analyzed the relationship between the host taxonomic similarity and phage inter-genome sequence similarity. The underlying assumption is that if two phages have similar enough genomes in terms of both sequence and nucleotide composition similarities, then the two phages may be evolutionarily highly related and thus may share the same or related hosts. From the Virus-Host Database, we extracted a set of 1,057 genomes of phages that are known to infect 107 distinct bacteria. As a proxy for overall phage genome sequence similarity, we used the logarithm of the sum of the TBLASTX scores  [18]  between the two phage genomes.
98%) but with a lower recall (0. 76%) than our method. Therefore, the presented approach holds a potential (which will grow as reference databases are enriched) for predicting hosts of unknown phage sequences (such as those obtained by metagenomics projects [20]) solely based on their similarity with known viral genomes. Second, we used the paired taxonomic information to assess a computational method for host prediction. Genomic features of phages and their hosts can be used to predict phage-host pairs  [16, 17]. Here, we analyzed the relationship between the host taxonomic similarity and phage inter-genome sequence similarity. The underlying assumption is that if two phages have similar enough genomes in terms of both sequence and nucleotide composition similarities, then the two phages may be evolutionarily highly related and thus may share the same or related hosts.
From the Virus-Host Database, we extracted a set of 1,057 genomes of phages that are known to infect 107 distinct bacteria. As a proxy for overall phage genome sequence similarity, we used the logarithm of the sum of the TBLASTX scores  [18]  between the two phage genomes. For compositional similarity, we used one divided by the Euclidean distance [19] based on tetramer frequencies. As shown in Figure  3 , phages showing high inter-genomic similarity by both measures tend to infect similar hosts (i. e. , same host taxonomy at genus level). By setting appropriate thresholds for sequence and compositional similarities (>3. 75 and >93, respectively), we were able to predict pairs of phages infecting hosts of the same genus with a precision of 95. 42% and a recall of 11. 5%. This method solely relies on the genomic data of phages (i.
e. , sequence and nucleotide composition similarities between phage genomes) and correctly predicted 4582 phage pairs with the same host genus (between 759 distinct phages). In comparison,  Roux et al. [20]  previously reported a prediction method based on nucleotide composition similarities between phage and host genomes with a higher precision (98. 98%) but with a lower recall (0. 76%) than our method. Therefore, the presented approach holds a potential (which will grow as reference databases are enriched) for predicting hosts of unknown phage sequences (such as those obtained by metagenomics projects) solely based on their similarity with known viral genomes. The GenomeNet Virus-Host Database provides machine readable taxonomic links between completely sequenced viral genomes and their hosts.
The taxonomic links are extracted from existing databases and literature. The database is updated upon each new RefSeq release with its content being continuously improved/enriched by manual curation. We also welcome users to provide feedback on the functionality and contents of the database. The Virus-Host Database is accessible through a user friendly web interface and as a downloadable file. The GenomeNet Virus-Host Database provides machine readable taxonomic links between completely sequenced viral genomes and their hosts. The taxonomic links are extracted from existing databases and literature. The database is updated upon each new RefSeq release with its content being continuously improved/enriched by manual curation. We also welcome users to provide feedback on the functionality and contents of the database.
Acknowledgments: This work was in part supported by  JSPS KAKENHI  (Grant number  26430184 ),  Canon Foundation  (Project number  203143100025 ) and the  Collaborative Research Program of Institute for Chemical Research, Kyoto University  (Grant number  2015-125 ). Computation time was provided by the  SuperComputer System, Institute for Chemical Research, Kyoto University. Acknowledgments: This work was in part supported by  JSPS KAKENHI  (Grant number  26430184 ),  Canon Foundation  (Project number  203143100025 ) and the  Collaborative Research Program of Institute for Chemical Research, Kyoto University  (Grant number  2015-125 ). Computation time was provided by the  SuperComputer System, Institute for Chemical Research, Kyoto University.
In this paper, we address the problems of Arabic Text Classification and root extraction using transducers and rational kernels. We introduce a new root extraction approach on the basis of the use of Arabic patterns (Pattern Based Stemmer). Transducers are used to model these patterns and root extraction is done without relying on any dictionary. Using transducers for extracting roots, documents are transformed into finite state transducers. This document representation allows us to use and explore rational kernels as a framework for Arabic Text Classification. Root extraction experiments are conducted on three word collections and yield 75. 6% of accuracy. Classification experiments are done on the Saudi Press Agency dataset and N-gram kernels are tested with different values of N. Accuracy and F1 report 90.
Synonyms are also widespread in Arabic language. The third challenge is the lack of publicly available free Arabic corpora for evaluating ATC systems. Much work was done on manually obtained datasets. This lack should be fulfilled over time with standard and benchmark corpora. In the next paragraphs we will give more details about these challenges. Morphological analysis is the study of internal word structure  (Habash, 2010). Morphologically, the Arabic language is the most complicated and rich language. Many words can be formed using the same root, a few patterns, and a few affixes. One of the challenges in a root extraction is that words in Modern Standard Arabic are free of diacritics, which makes them more ambiguous. For instance, the two words ( , He wrote) and ( , Books) are originated from the same root but has different meanings when vocalized.
The first parameter is the FST archive (data. far) and the second one (3gram. kar) is the resulting kernel archive. OpenKernel library includes a plugin for the LibSVM implementation  (Chang and Lin, 2011). This enables us to do training, predicting and scoring on our dataset. Training command creates a model on the training set (line 5), where the first argument -k specifies the kernel format, the second one (-K) specifies the N-gram kernel archive. The first parameter specifies a correctly classified subset of the training set, the second parameter is the resulting model. In this command, cul. train contains a correctly labeled sub set of training documents belonging to Cultural class. Having a model, we can use it to classify documents of the testing dataset with the command svm-predict (line 6), where the first parameter specifies a correctly classified subset of the testing set, the second parameter is the resulting model from the previous command.
ATC results Experiments are performed on the Saudi Press Agency (SPA) dataset  (Althubaity et al. , 2008)  for training and testing the ATC system. As detailed in Table  7 , this dataset contains 1526 text documents belonging to one of the six categories: culture, economic, social, general, politics and sport. As mentioned earlier, stop words, non Arabic letters, symbols and digits were removed. We have used a stratified sampling with.
Acknowledgements We thank  Gemma Richards  for critical reading of an earlier version of the manuscript;  Evelyn Houliston  for helpful comments on the manuscript;  Bård Steinar Giezendanner  and  Justine Diaz  for maintaining the Nematostella culture;  Eric Röttinger  for plasmids and discussions;  Jennifer Croce  for discussions and advice on experiments; and  Athula Wikramanayake  for sharing data prior to publication.
Funding This work was funded by the  Sars Centre  core budget provided by the  University of Bergen  and the  Research Council of Norway (Norges Forskningsråd)  (to F. R. ). L. L. was supported by a grant from the  Agence National de la Recherche  [ MEDUSEVO -ANR-13-PDOC-0016 ] during the final part of the study. Deposited in PMC for immediate release.
S. mansoni has just two PLK genes, Smplk1 and Smsak (Smplk4) (GenBank IDs AAV49163 and GU084154, respectively), which is in contrast to the five found in humans  [37] [38] [39]. PLKs are a family of conserved serine/threonine kinases, which, in humans, are involved in cell division, including G2/M transition, centrosome maturation, formation of bipolar spindles, cytokinesis and regulation of the spindle assembly checkpoint  [40] [41] [42] [43]. Plk1 is the best characterized member of the family and is vital to normal mitotic progression  [40, 41, [44] [45] [46]. Its overexpression in human tumors  [47] [48] [49]  has identified this kinase as a selective target for anti-cancer drugs. In S. mansoni, SmPLK1 is expressed in sporocysts (asexually dividing stages parasitizing the snail vector) and in adult worms, particularly in their reproductive organs, suggesting a contribution by this kinase to cell division  [39].
RNA isolation and gene expression analyses These analyses were performed as described  [20, 57, 60]. For RT-qPCR, total RNA was extracted using TRIzol reagent (Invitrogen) and the High Pure RNA Isolation Kit (Roche) following the manufacturer's instructions. Complementary (c) first-strand DNA was synthesized using the SuperScript III First-Strand Synthesis kit (Invitrogen). cDNA was then used as a template for PCR amplification using the Light Cycler 480 SYBR green I Master mix (Roche) and a Mx3005P qPCR detection system (Stratagene). Specific primers for each of the regulatory and kinase domains of SmPLK1 and SmSAK, and for mCherry were designed using the Primer Express Program (Applied Biosystems; see S2 Table ). Primers were experimentally validated and S. mansoni cytochrome C oxidase I (GenBank AF216698) was used as the reference transcript.