dataesr/scientific-paragraphs-categorization huggingface dataset:
We acknowledge the  PICT-IBiSA@Pasteur Microscopy Facility (Institut Curie)-BioImaging  for assistance with microscopy. This work was supported by  la Ligue Nationale contre le Cancer (E ´quipe Labellisee Ligue) , the  European Commission Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ),  ERC  Advanced Grant  2009-AdG_20090506  ''Eccentric,'' the  European Commission Large-Scale   Integrating  Project  FP7_HEALTH-2010-259743  '' MODHEP,'' ANR ''ChromaTin ''  ANR-10-BLAN-1326-03 ,  ANR-11-LABX-0044_DEEP ,  ANR-10-IDEX-0001-02   PSL*, ANR ''CHAPINHIB ''  ANR-12-BSV5-0022-02 , and  Aviesan-ITMO Cancer Project ''Epigenomics of Breast Cancer. ''H. T. was supported by the  Japan Society for the Promotion of Science , and S. M. was supported by  Marie Curie/Nucleosome 4D  and  La Fondation pour la Recherche Medicale.
In the writing of this paper both Sellafield and the NDA (now RWM Ltd) have had input and provided permission to publish. Competing Interests: The materials and additional financial support for this project has come from Sellafield Ltd as part of a platform grant associated with the Centre of Expertise for Uranium and Reactive Metals. In the writing of this paper both Sellafield and the NDA (now RWM Ltd) have had input and provided permission to publish. Co-author (John Jowsey) is employed by Sellafield Ltd. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. conditions of 100% relative humidity and 100°C exhibited the same initial rapid reaction and decrease in reaction rate; however, after 40-50 hours, the evolution of hydrogen appeared to cease after 83% conversion of UH 3 to UO 2.
3, was used for the 10 month examination, which had a slightly reduced resolution of 1 pixel = 1. 3 x 1. 3 μm with the Module 4 camera. It must also be noted that during this particular beam time, Diamond Light Source had a reduced beam current of 136 mA, approximately half of that used in the two previous examinations, and hence the quality of the tomography was slightly reduced. Avizo was used to produce 3D visualisations of the tomography. For the purpose of data reduction, calibration of the detector and x-ray beam energy was determined from diffraction patterns of a cerium dioxide (CeO 2 ) standard (NIST-Standard Reference Material 674b) recorded at multiple detector positions  [14]. For the initial examination of the sample, the XRPD x-ray energy (114. 08 keV) was lower than chosen for XRT (115.
97 keV) as for this experiment the resolutions of the diffraction peaks were better at this lower energy, and the XRT tomographs were clearer at the higher energy. However, due to the ongoing development of the beam line the same conditions were not possible to replicate for later examinations, and hence energies of 113. 32 and 115. 23 keV were used for both measurements for the 3 and 10 month analysis respectively. 2D XRPD data were recorded using a flat panel Pixium RF4343 (Thales) in high resolution mode (2880 x 2881 pixels). This detector has a pixel size of 148 x 148μm and beam size of ~340 x 340 μm.
Using Avizo software, the total volume of corrosion products calculated after 3 months was ~0. 242 mm 3 (34. 2% of total sample volume measured) and, over the following 7 month period, the corrosion products increased to 0. 282 mm 3 (38. 7% of total sample volume measured). A volume increase would be expected from the corrosion of uranium to either UO 2 or UH 3 , as these corrosion products are almost half the density of the metal  [17]. Further measurements of the oxide layer at 70 random locations on all three tomography reconstructions away from hydride blisters revealed an average oxide thickness of 10. 1, 12. 8 and 13. 9 μm for the as-formed, 3 and 10 month reconstructions respectively indicating a 2. 7 and 1. 1 μm growth on the surface of the uranium between each time period (Table C in S1 Table ).
Acknowledgments The authors would like to thank  Diamond Light Source  for access to beamline I12 (Experiments  EE9127-1 ,  EE9127-2  and  EE10569-1 ) that contributed to the results presented here. Also to the beam line staff,  Michael Drakopoulos ,  Christina Reinhard ,  Robert Atwood ,  Michael Hart ,  Thomas Connolley  and  Nghia Vo  for their technical and moral help throughout each set of beam time. This work was funded by the  EPSRC  and the  UK Nuclear Decommissioning Authority's Radioactive Waste Management Directorate (NDA RWMD)  under the  GEOWASTE program  and in association with the  Sellafield Ltd. Centre of Expertise in Uranium and Reactive Metals. Since April 2014 , the RWMD has become a wholly-owned subsidiary of the NDA, Radioactive Waste Management Limited (RWM).
However, the expression of KCNJ10, KCNQ1 and pendrin was not altered. Overall, our results demonstrate an essential role of KCNK5 in the maintenance of hearing. Our data support the hypothesis that KCNK5 in outer sulcus cells play an important role in K þ recycling. Other mechanisms mediated by KCNK5 channels including endolymphatic pH-buffering, K þ secretion and/or leakage and reabsorption may also contribute to the present findings.
Observations and photographs were made with either an Olympus BX50 or a Zeiss ApoTome (1D-SIM) AxioObserver ZI microscope equipped with both epifluorescence and a high-resolution digital camera either an Olympus DP50 (Olympus Optical, Germany) or a Zeiss AxioCam MRm (Carl Zeiss MicroImaging, Germany), or with a Laser Scanning Confocal Microscope (LSM510; Carl Zeiss MicroImaging). The figures were prepared with Photoshop software (Adobe Systems Inc. ) and both brightness and contrast of the images were adjusted. Statistics. Data were expressed as mean values ± s. e. m. from n observations and compared using either a paired t-test (two groups) or an analysis of variance (one way) followed by Dunnett's test correction for multiple comparisons. A linear regression analysis was used to test the age effect on EP values recorded in Kcnk5 À / À mice.
Acknowledgements We thank  Vanessa Tillement  for her assistance in confocal microscopy. We also thank  Jonathan Ashmore  for careful proofreading of the manuscript. This work was supported by grants  ANR Noise-Addprotect  (to Y. C. ),  ANR RESPITASK  (to J. B. and C. G. ) and  ANR-11-LABX-0015-01  (to J. B. ) from the  French National Agency for Research.
Loci-producing siR-NAs in a PolIV-dependent manner where retrieved from  [50]. Young miRNAs were defined as DCL1-independent miRNA: we downloaded the sRNAs produced by  [51]  and discarded all of those miRNAs that contained at least one read from this data set. The data reported in this paper will be deposited in the Gene Expression Omnibus (GEO) database, www. ncbi. nlm. nih. gov/geo (accession no. GSE49866). Plant protein sequences homologous to Arabidopsis RTL1 were retrieved using TblastN. DCL sequences were discarded based on the presence of RNA helicase and PAZ domains upstream of the RNaseIII and DRB domains. In each species, the RTL1 ortholog was identified based on the presence of a single RNaseIII domain and a single DRB domain. Proteins were aligned using MultiAlign.
Acknowledgments We thank  Olivier Voinnet  for providing P38 and  P38GA2 ,  Shou-Wei Ding  for providing 2b and  P69 , and  Shih-Shun Lin  for providing P1/HC-Pro. We thank  Taline Elmayan  and  Olivier Voinnet  for helpful discussions,  Bruno Letarnec ,  Hervé Ferry  and  Patrick Grillot  for plant care, and  Anne de Dures  for technical assistance. We also thank  Florian Bardou ,  Martin Crespi , and  Nathalie Bouteiller  for their help with the CRISPR technology.
The full mission includes 29 months of scientific data taken by the HFI (High Frequency Instrument) covering five frequency bands centered about 100 GHz, 217 GHz, 353 GHz, 545 GHz, and 857 GHz using highly sensitive bolometric detectors cooled down to 100 mK and 48 months of data taken by the LFI (Low Frequency Instrument) covering the three channels at 30 GHz, 44 GHz, and 70 GHz using coherent amplification based on HEMT technology. The first cosmological results from Planck were released in 2013 and included only the nominal mission (consisting of about half the data) and used only the temperature data. The second release in February 2015 included the full mission data and also the high polarization data (as well as the 70 GHz low polarization). (See Fig. 1  for a sky map of the CMB as seen by Planck produced by taking linear combinations of the various frequency channels in order to isolate the CMB and exclude galactic and other foreground contaminants.
For the temperature maps the power spectrum is denoted as C T T , and when the E mode of the polarization is included, C T T , C T E , and C EE. Fig. 2  shows these power spectra as reported in the Planck 2015 release using the full set of data collected and including the high-polarization  [10, 5]. Similarly the theoretically predicted power spectra are the only quantities needed for comparing model predictions to the CMB observations under the hypotheses of statistical isotropy and Gaussianity. Probably the most important conclusion from Planck 2013 and Planck 2015 analyses is that but for a few anomalies at modest statistical significance (described in more detail below), a simple six-parameter cosmological model 3  suffices to account for the current observations. This is the same conclusion as that reached by the WMAP team in 2003, which John Bahcall aptly summed up at the WMAP press conference in the following way: "The biggest surprise is that there is no surprise.
This discrepancy could have a number of possible explanations. The clusters are characterized in the catalogue in terms of their integrated y spectral distortion, denoted as Y; however, simulations predict abundances in terms of an appropriately defined cluster mass M, so a relation between the two must be established. In the 2013 Planck analysis, X ray data interpreted under the assumption of hydrostatic equilibrium was used to calibrate the Y-M relation, and a bias estimated using simulations was applied to correct for the non-thermal pressure (e. g. , turbulent pressure, bulk velocities). An underestimate of cluster masses could reconcile the two data sets. In the 2015 Planck analysis lensing data was also used to help calibrate the mass determinations. Other proposals such as large neutrino masses have been proposed to resolve the discrepancy  [16].
5 m in diameter) prevents Planck from measuring the power spectrum at very large. By contrast, the ACT and SPT telescopes, located in the Atacama desert in Chile and at the South Pole, provide measurements of the power spectrum on smaller angular scales owing to their mirror sizes of 6m and 10m, respectively. ACT  [19]  and SPT  [20]  probed the C T T power spectrum in the damping tail on very small angular scales beyond the resolution of Planck and found that the spectrum is consistent with the predictions of the concordance model. The multi-frequency data from ACT and SPT also proved invaluable for validating the Planck modelling of high-foregrounds. Another important Planck result with profound implications for inflationary model building was the constraints on primordial bispectral non-Gaussianity.
An independent analysis cast doubt on the BICEP2 claim  [27]. In September 2014 the Planck team published a paper on the level of polarized dust emission measured across the whole sky, and in particular in the BICEP2 field  [28]. This work also extrapolated the polarized dust signal seen in the Planck 353 GHz map (a frequency in the Wien tail of the CMB blackbody where dust dominates) down to 150 GHz and reached the conclusion that the BICEP B mode signal could be entirely explained by polarized dust emission although a primordial B mode contribution could not be ruled out. Subsequently, a cross-correlation study was carried out jointly by the Planck collaboration and the BICEP2 team, which also included data from the Keck Array. The result of this study is an upper limit on the tensorto-scalar ratio r with r < 0.
These techniques have widely been applied to extract and exploit background knowledge from biomedical texts. Among many tasks, a crucial issue is the annotation of a large amount of genetic information. NLP, and Information Extraction (IE) in particular, aim to provide accurate processing to extract specific knowledge such as named entities (e. g. , gene, protein) and relationships between the recognized entities (e. g. , gene-gene interactions, biological functions). Databases such as BioGRID  [2]  or STRING  [3]  store a large collection of interactions derived from different sources and indicate which gene interacts with a specified gene. However, these databases do not support more complex requests such as: which genes inhibit gene X? what is the biological context (e. g. , organism, biological information) associated to a genegene interaction? what is the kind of interaction between genes X and Y ? what is the modality associated to the extracted information (related work, experimental result, etc.
The patterns output convey a model of the interactions that are enhanced with semantics information (modalities, biological contexts, interactions types). Only limited resource is required: the text collection (used as a training corpus) which only contains sentences with interactions and where only gene names are tagged but not the interaction. In particular, terms and patterns are automatically discovered from texts without other resources. To the best of our knowledge, there are few methods that extract the interactions and also provide associated semantics information on the extracted interactions thanks to the discovered patterns which are understable and can be manually modified by a human expert. In addition, we propose to use background knowledge, a well-established biomedical corpora and a gene interaction database, in order to assess the relevance of patterns, used as linguistic rules, and to help an expert a to select them.
Background: sequential pattern mining Sequential pattern mining is a well-known technique introduced in  [24]  to find regularities in database of sequences, and for which there are several efficient algorithms (e. g. [26] [27] [28] [29] [30] ). A sequence as used in our method is an ordered list i 1. i m , where the elements of the list i 1. i m are called items b. A sequence A tuple (sid, S) contains a sequence S α , if S α is a subsequence of S. The support of a sequence S α in a sequence database SDB, denoted sup(S α ) is the number of tuples in the database containing S α. For example, in SDB 1 sup( b d ) = 2, since sequences 1 and 2 contain b d. Notice that for notational convenience, sometimes the relative support is used. In this case, the support sup(S α ) is the relative number of tuples in the database that contain S α , sup(S α ) = |{(sid,S) | (sid,S)∈SDB∧(S α S)}| |SDB|.
Extraction of sequential patterns in texts For the extraction of sequential patterns from biological texts, we use a training corpus which is a set of sentences that contain interactions (but not annotated) and where the genes are identified. In this paper we consider sentences containing interactions and at least two gene names to avoid problems introduced by the anaphoric structures c  [31]. The training corpus, with tagged gene names, is selected by an expert. The items are combinations of lemma and POS tags d. POS tag information is important to disambiguate words (e. g. , "form" the noun vs "to form" the verb). The sequences of the database are the interaction sentences where each word is replaced by the corresponding item. The order relation between items in a sequence is the order of words within the sentence.
For example, let us consider two sentences that contain gene interactions: "Recent studies have suggested that c-myc may be vital for regulation of hTERT mRNA expression and telomerase activity". and "Injection of frpHE mRNA in Xenopus embryos inhibited the Wnt-8 mediated dorsal axis duplication". All gene names are replaced by a specific item, AGENE, and the other words are replaced by the combinations of their lemma and their POS tag. An excerpt of the database that contains the sequences associated to those two sentences is given in Table  1. The sequential patterns are extracted from this database. The choice of a support threshold minsup is a wellknown problem in data mining. With a high minsup, only few very general patterns can be extracted. With a low minsup, a lot of patterns can be found.
Recursive mining Even if the new set of sequential patterns, SAT(C G ), is significantly smaller than the initial set of all extracted sequential patterns without constraints, it can still be too large to be analyzed and validated by experts. To find a limited number of patterns corresponding to general structures among the whole pattern collection, we use the recursive mining technique of  [33]. The key idea of this post-processing is to reduce the size of the output by successively repeating the mining process on the patterns themselves in order to extract the structure shared by the patterns. More precisely, at each step, the previous set of sequential patterns is used as a new sequential database, and a new extraction is made. The process stops when no.
Training corpus Genes can interact with each other through the proteins they synthesize. Moreover, although there are conventions, the same word can represent a gene name and the protein synthesized by the gene. Biologists know from the context if the sentence is about protein or gene. To discover the linguistic patterns of interactions between genes, we merge two different corpora containing genes and proteins, to create the training corpus. The first corpus contains sentences from PubMed abstracts, selected by Christine Brun e as sentences containing gene interactions. It contains 1,806 sentences. That corpus is available as a secondary data source for the learning tasks "Protein-Protein Interaction Task (Interaction Award Sub-task, ISS)" from BioCreAtIvE Challenge II  [8]. The second corpus  [37]  contains 2,995 sentences mentionning interactions between genes selected by an expert.
Data mining task As previously mentionned, the extraction of sequential patterns from the training corpus needs the computation of POS tags. For this task, we use the treetagger tool  [38]. In addition, for the data mining task, minsup is set to 10. It means that a sequential pattern is frequent if it appears in at least 10 sentences (i. e. in more than 0. 2% of sentences). Indeed, with that threshold some irrelevant patterns are not taken into account while many patterns of true gene interactions are discovered. Note that other experiments, not reported here, have been conducted with greater minsup values  (15 and 20). With those greater minsup, some relevant patterns for interaction detection are lost. More than 32 million of frequent sequential patterns are extracted, with minsup equals to 10.
Testing corpora and evaluation criteria Testing Corpora We have considered three well-known testing datasets (cf Table  2  and Table 3 ): AIMed  [43] , BioInfer  [44] , HPRD50  [45]  and a fourth testing corpus extracted from PubMed [1] (more information is given in the next section). Note that, in AIMed, BioInfer and HPRD50, the names of genes are already identified and tagged. More information about those corpora can be found in  [46].
Construction of the PubMed corpus In order to test the sequential patterns extracted in the previous section as linguistic extraction rules to characterize interactions, we need a testing corpus. We have built a testing corpus that is a subset of abstracts from the PubMed database. It is built in two Table  2  gives the list of the four testing corpora used to evaluate the proposed approach, and the results of the evaluation. The meaning of the columns is: the name of the corpus, the number of the sentences in the corpus, the recall score of the proposed approach applied on the corpus, the precision score of the proposed approach applied on the corpus, thef -score of the proposed approach applied on the corpus. The last column indicates the range of the f -scores presented in  [11]  with also a cross-corpus validation.
steps which are described below. The first step is the selection of abstracts from PubMed. In the PubMed database, each paper has an identifier called PMID (PubMed IDentifier). For each official acronym of gene in the HUGO  [47]  dictionary, a request is sent to PubMed in order to get all PMID of papers that contain the gene. An index of genes and their associated papers is thus created. Then the inverted index is computed, i. e. the index that associates to each PMID the list of genes. From that second index, the PMIDs that do not have at least two gene names in their list are pruned. Indeed, as we are looking for interactions between genes, it implies that at least two genes are mentioned in the text. There remains 624,519 PMIDs. The second step is the named-entity recognition. Sometimes, the gene name used to index an abstract and the gene name that appears in the abstract text are different.
Second, a gene interaction database is used as an oracle to assess the patterns. In our method, the rule validation corpus comes from the PubMed papers and the gene interaction database is BioGRID. Our idea is that the relevant patterns, when applied on the validation corpus, retrieve interactions that must be consistent with the gene interaction database. An interaction detected by a sequential pattern is considered as a false positive if the interaction does not exist in the gene interaction database, else it is a true positive (same gene names and same PMID) j. A pattern with a high number of true positives is likely to be interesting. Table  4  gives an excerpt of the information provided for each pattern. It contains the number of interactions.
The discovered interaction in the sentences in paper 10204582 is thus unexpected in BioGRID. The pattern also detects that in this paper, an interaction between genes CBL and CRKL is mentioned, and indeed, according to BioGRID, there is an interaction between CBL and CRKL mentioned in paper 10204582. It is interesting to note that the three genes involved harbour all three similar biological functions (they are all signalling molecules) and that their association is fully relevant as exemplified by the strong functional connectivity detected in the STRING database between those three genes. Therefore the extracted interaction between genes SHC1 and CRKL is fully relevant even if it does not appear in BioGRID. Of course, more systematic studies should be undertaken to ascertain this, but this is beyond the scope of the present paper.
Availability of software and supporting data The extracted gene interactions from PubMed are available at https://bingotexte. greyc. fr/. The evaluation corpora from PubMed are available at https://cremilleux. users. greyc. fr/jbms/. The software (SMBio) that allows sequential patterns of gene interactions to be extracted is available at https://bingo2. greyc. fr/?q=node/22. The list of the 182 official acronyms identified as common English words is available at: https://bingotexte. greyc. fr/ambig_ names.
The sequence S 1 is called a subsequence of S 2 , and we note S 1 S 2. For example, we have b d a b c d. A sequence database SDB is a set of tuples (sid, S), where sid is a sequence identifier and S a sequence. For instance SDB 1 = {(1, a b c d ), (2, b d e ), (3, a c d e ), (4, a d c b )} is a database of four sequences.
than k patterns are obtained by the extraction, where k is a parameter set by the user. Our target is to identify at least one pattern by verb or noun that appears in the patterns in SAT(C G ). So, for each verb or noun denoted X i , that appears in SAT Table1shows an excerpt of a sequence database which contains two interaction sentences: S1: "Recent studies have suggested that c-myc may be vital for regulation of hTERT mRNA expression and telomerase activity. " and S2: "Injection of frpHE mRNA in Xenopus embryos inhibited the Wnt-8 mediated dorsal axis duplication. ". more.