dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  Achal Rastogi  ( E ´cole Normale Superieure), Neal Clarke (Yale University ),  Michael Melkonian  ( University of Koln ),  Gane Ka-Shu Wong  ( University of Alberta ) and  Jun Yu  ( Beijing Institute of Genomics ) for early access to sequence data used in this study, and  Catherine Cantrel ,  Anne-Flore Deton-Cabanillas ,  Zhanru Shao ,  Leı ¨la Tirichine  and  Javier Paz-Yepes  ( E ´cole Normale Superieure ) for assistance with generation of transgenic expression constructs for Phaeodactylum tricornutum. Funding is acknowledged from the  ERC Advanced Award ''Diatomite'' , the  Louis D Foundation of the Institut de France , the  French Government ''Investissements d'Avenir'' programmes MEMO LIFE  ( ANR-10-LABX-54 ) and  PSL* Research University  ( ANR-11-IDEX-0001-02 ), and the  Gordon and Betty Moore Foundation  (all to CB), and from  FP7  (grant number  2007-2013 Grant Agreement 615274 , to EPB).
RGD is supported by an  EMBO   early career fellowship  ( ALTF 1124 ( ALTF -2014 )). DJR was supported by a postdoctoral fellowship from the  Conseil Regional de Bretagne  and the  French Government 'Investissements d'Avenir' programme OCEANOMICS  ( ANR-11-BTBR-0008 ). CB was the Grass Fellow at the  Radcliffe Institute for Advanced Study, Harvard University  during the 2016-2017 academic year. The authors would like to thank the handling reviewer and two anonymous editors for constructive comments on the manuscript.
(g) Phenotypes of 3-weekold wild-type, sur2-1gl1, 2035 and csn4-2035 mutant plants. Seeds were sown in vitro in light conditions and were subsequently transferred to soil where the plants were grown for 3 weeks in a controlled environment, as described in the Methods. Bar, 20 mm. For (c to e) at least 50 seedlings of each line were analyzed and the experiments were repeated three times and the data pooled. Error bars indicate standard error. One-way ANOVA combined with Tukey's multiple-comparison post-test revealed that the values indicated by different letters were significantly different from each other (P < 0. 05; n > 150). (h) Free IAA content was quantified on apical parts (cotyledons+hypocotyls) and on roots of seedlings grown in vitro under light for nine days; samples were extracted, purified, and analyzed by GC-MRM-MS as described by  55.
The conditions in the controlled environment chambers were as follows: 130 μE m -2 s -1 irradiance on average, 16/8 light/dark cycle, 22/15 °C, 60% relative humidity. Plates were scanned before and after transfer of the seedling on auxin containing medium. Root length was measured using the ImageJ software package (http:// rsb. info. nih. gov/ij/index. html) and ref. 52. Percentage inhibition was calculated by dividing the average growth on 2,4-D containing medium by the average growth on control medium and subtracting this ratio from 100% (n > 25). Complementation test and genotyping the csn4 alleles. For complementation analyses, homozygous 2035 plants were crossed with heterozygous csn4-1 and csn4-2, respectively. F1 progeny was phenotyped and subsequently genotyped, using allele specific PCR primers, as follows.
The csn4-1 mutation was genotyped using CSN4-FW1 and CSN4-RV1. 1 to test for the presence of the wild-type gene, and LBb1. 3 and CSN4-RV1 to test for the presence of the T-DNA. The csn4-2 mutation was genotyped using CSN4-FW2 and CSN4-RV2 to test for the presence of the wild-type gene and LBb1. 3 and CSN4-RV2 to test for the presence of the T-DNA. To genotype the csn4-3 point mutation, newly derived cleaved-amplified polymorphic sequence primers were designed using the dCAPS Finder 2. 0 software (ref. 53, http://helix. wustl. edu/dcaps/dcaps. html). Two mismatches (underlined) were introduced in the F primer to incorporate a restriction site in the PCR product of one allele. After amplification, the PCR products were digested with HpaI (Fermentas Fast Digest) following the manufacturer's recommendations and separated on a 4% agarose gel.
1; 0. 4; 2; 10 and 52 μmol m -2 s -1 irradiance of cB light, respectively. For dark growth conditions, five plates/chamber/replicate were wrapped in three layers of aluminum foil and placed vertically. After 7 d, the seedlings were bended on the surface of the medium, the plates were photographed and hypocotyls measured using ImageJ software  52  (http:// rsb. info. nih. gov/ij/index. html). All measurements were performed on three independent biological replicates with a minimum of 40 seedlings each. Real-time PCR experiments and data analysis. Transcript abundance was assessed by quantitative real-time (RT) PCR as previously described  22. All quantifications were repeated with three independent biological replicates, using the following standard thermal profile: 10 min at 95 °C, followed by 40 cycles at 95 °C for 10 s, 60 °C for 15 s (except for GH3.
Acknowledgements The authors would like to thank  Dr. Claus Schwechheimer  ( Technische Universität München TUM, School of Life Sciences, Weihenstephan/Munich, Germany ) for providing the seeds of csn4-1 ( SALK_043720 ), csn4-2 (SALK_053839) segregating lines, csn5a-1 ( SALK_063436 ), csn5a-2 ( SALK_027705 ) and csn5b-1 (SALK_007134);  Dr. Johannes Stuttmann  ( Institute of Biology, Martin Luther University, Halle-Wittenberg, Germany ) for providing the seeds of csn2-5; and  Dr. William M. Gray  ( College of Biological Sciences, University of Minnesota, USA ) for providing the seeds of csn1-10 and csn3-3. This work was supported by the  Swedish Natural Sciences Research Council (VR) , the  Swedish Research Council for Research and Innovation for Sustainable Growth (VINNOVA) , the  Wallenberg foundation , the  Carl Trygger foundation  ( CTS08: 298 ) to CB, and the  Erasmus Programme  to AMP.
Novel or rare variants in mitochondrial tRNA sequences may be observed after mitochondrial DNA analysis. Determining whether these variants are pathogenic is critical, but confirmation of the effect of a variant on mitochondrial function can be challenging. We have used available databases of benign and pathogenic variants, alignment between diverse tRNAs, structural information and comparative genomics to predict the impact of all possible singlebase variants and deletions. The Mitochondrial tRNA Informatics Predictor (MitoTIP) is available through MITOMAP at www. mitomap. org. The source code for MitoTIP is available at www. github. com/sonneysa/MitoTIP.
Design and implementation A database of reference benign and pathogenic variants (S1 Table ) was created from a comprehensive PubMed search and from publicly available information accessible through MITO-MAP  [6]. The MITOMAP analysis of sequence diversity is drawn directly from GenBank full sequence data. Interspecific comparison was adapted from Mamit-tRNA and included sequence for all species from the superorder Euarchontoglires  [7]. Given the large number of mitochondrial sequences now available from GenBank (n = 37,545 accessed June 2017), we inferred that a lack of observed variation represents a requirement for sequence conservation and we penalized these unobserved variants accordingly. These information sources were combined to provide a variant history and conservation score for each variant (see S1 Fig for complete scoring algorithm).
Results and discussion In order to optimize the system and confirm the validity of pathogenicity prediction we tested it by re-evaluating reference pathogenic (n = 38) and benign (n = 651) variants. To provide an effective test of its ability to discriminate pathogenic and benign variants, we removed available data on each variant iteratively, and examined the ability of the system to predict the effect of each variant naively. The algorithm was modified by altering scaling factors using a differential evolution optimization program (SciPy) that maximized the sensitivity and specificity of the detection system (S2 Table)  [8]. The final scoring system provided good discrimination of known pathogenic and benign variants (Fig  2A ). Using a single point pathogenicity score cutoff, the system had a sensitivity and specificity of 74% (Fig  2B ).
We compared our system to a more limited set of pathogenic and benign positions that were used to test both the machine learning predictive system and the interspecific homology system proposed by Kondrashov and our system provided superior sensitivity and specificity (Table  1 )  [3, 5]. As another demonstration of the specificity of this method, we evaluated scoring at positions associated with haplogroups, as haplogroup-associated variants would generally be presumed to be non-pathogenic. We identified all haplogroup-associated polymorphisms that have been sequenced a minimum of 10 times in GenBank (n = 619). Only three of these exceeded our combined threshold value for pathogenicity, and all of these had been linked to diseases in published studies (see S3 Table ). For example, m. 5628T>C, which is found in individuals from multiple haplogroups, has been associated with chronic progressive external ophthalmoplegia (CPEO) and hearing loss  [9, 10].
The reports of pathogenicity in this case are sufficient to inflate the MitoTIP score into the pathogenic range, whereas the same variant analyzed without these reports return a score of possibly benign. This highlights a disadvantage of MitoTIP's use of databases. The m. 5628T>C variant was initially reported as a heteroplasmic variant with a 40% mutation load causing late-onset CPEO A second study reports the same variant as being a phenotypic modifier for hearing loss in a family that was homoplasmic for the m. 5628T>C variant, but there is no mention of CPEO  [10]. This casts doubt on the first study reporting disease association and suggests that this variant may be wrongly classified as pathogenic in both studies. MitoTIP is designed for the analysis of novel variants, where previous data confirming pathogenicity is unavailable.
Similarly, high-frequency variants (>1% of all GenBank sequences or >10% in any single major haplogroup division) are listed as "frequent polymorphism. " The use of the MITOMAP platform simultaneously directs users to underlying literature supporting the assignment of variants. For the target novel mutations, which could all be considered variants of uncertain significance, the pathogenicity prediction is provided by percentile (ranging from 1-99%). Conveniently, the optimal point of the sensitivity/specificity curves is at the 51st centile for pathogenicity scoring. We have chosen to provide an interpretation with four categories (likely pathogenic/possibly pathogenic/possibly benign/likely benign) based upon the quartile scored. We have done this to generally conform with ACMG recommendations for the description of sequence variants  [11].
The underlying subpart scoring is also available to interested users. We have not incorporated the heteroplasmy of a variant into our scoring. It is widely accepted that heteroplasmic variants are more likely to be pathogenic and low-penetrance variants that are homoplasmic are less common. The pathogenicity scoring from MitoTIP for newly observed variants can and should be evaluated by the end user in the context of the actual patient heteroplasmy and the heteroplasmy seen in affected and unaffected family members. MitoTIP places considerable reliance on databases, which provides important advantages and disadvantages. Full sequence entries used to infer normal human variation might have been obtained from patients with mitochondrial disorders. The underlying calls of pathogenicity represent a best effort at identifying all legitimate reports of mitochondrial variants but may have missed some reports.
In addition, pathogenic variant databases may fall out of date or contain errors, as described above for m. 5628T>C. The possibility exists that the associations made between haplogroup-defining variants and disease states are incorrect and are due to the coincidence of maternally inherited mitochondrial variants and unmeasured nuclear variants that are actually responsible for the heightened risk of common phenotypes. Providing MitoTIP data in the context of access to these studies will allow users to integrate multiple sources of information when assessing unfamiliar variants. The use of databases is advantageous because it allows MitoTIP scoring to be easily updated when new information is incorporated into MITOMAP. The system will improve in sensitivity and specificity over time as more sequences are available in MITOMAP and more reports of pathogenic mutations are made.
S1 Fig. Pathogenicity scoring algorithm. The MitoTIP score has three main components: the variant history and conservation score, the position score, and the secondary structure score. The variant history and conservation score is derived from the history of previously reported pathogenic and benign variants, and interspecies sequence conservation. The variant history and conservation data are imported from MITOMAP and Mamit-tRNA, respectively. In benign variants, the GenBank population frequency is calculated and the variants are categorized by percentile rank to generate the pop score. Pathogenic variants from the database are stratified by heteroplasmy and whether pathogenicity is confirmed to generate the path score. The conservation data for species in the superorder Euarchontoglires was evaluated using a logarithmic function that quantifies each position's deviation from complete conservation to generate the cons score.
Finally, a base_scal variable controls the base score that is applied to novel variants with no previous variant history. In order to optimize these variables we sought to maximize the sensitivity and specificity of MitoTIP at classifying known pathogenic and benign variants (S1 Table ) using a take-one-out approach. The SciPy package for python was used to perform differential evolution optimization, which seeks to find the minimum for a multivariate function. The MitoTIP algorithm modified to take the 6 variables as input and output single value that captures the performance of the algorithm. This value was calculated as 2-((sensitivity + specificity)-Abs(sensitivity-specificity)), and is at a minimum when both sensitivity and specificity are maximized. The solution provided by the differential evolution algorithm varies each time that the algorithm is run.
ARS2 depletion affects the levels of promoter-proximal RNAPII To interrogate the genome-wide effect of ARS2 on transcription, we conducted RNAPII ChIP-seq analysis of chromatin derived from duplicate HeLa cell samples depleted by RNA interference (RNAi) of ARS2 (siARS2), ZC3H18 (siZC3H18) or RRP40 (siRRP40) (Supplementary Figure  S1A  and Table  S1 ). As control, ChIP data were collected from cells treated with siRNAs targeting Firefly Luciferase RNA (siFFL). All replicate experiments were generally well correlated (Supplementary Figure  S1B ). In parallel to the ChIP-seq analysis, HeLa cells subjected to ARS2 or ZC3H18 siRNA-mediated depletion (Supplementary Figure  S1C ) were subjected to triplicate rRNAdepleted total RNA-seq. Here, RNA samples from cells administered with siRNAs against EGFP (siEGFP) were used as controls.
Protein-coding TUs While reverse-oriented PROMPT transcription from mammalian gene promoters is usually terminated rapidly, forward (e. g. mRNA) transcription is generally more elongation competent, which has been attributed to a decreased density of pA-sites downstream of mRNA compared to PROMPT TSSs  (12, 14, 35, 36). Moreover, 5 splice site (5 SS) sequences, capable of binding U1 snRNA and suppressing pA site usage  (37) , are over-represented in promoter proximal regions downstream of mRNA TSSs largely due to the relatively short length of first exons. However, despite these precautions to delimit transcription termination within pc TUs, cryptic pA sites are to some extent still being utilized  (12, 37) , giving rise to the detection of exosome-sensitive RNA 3 ends  (14). Hence, we decided to examine this phenomenon in more detail using our new datasets.
Yield function The model described above contains a number of parameters that ought to be constrained from experimental data. The yield cap can be constrained from the extensive dataset of  Rutter et al. [2009]  on intact and dehydrated blocks of lizardite. The data used are shown in Figure  2. For simplicity, we use a modified Cam-clay yield surface, given by f (p , τ ) = τ -C (b + p )(p * -p ) = 0, ( 14 ) where C is the critical state line ratio, b is the tensile strength, and p * is the compaction yield pressure (following the notation of  Wong and Baud [2012] ). Specific values of C, b and p * for intact, partially and fully dehydrated serpentinites are reported in Figure  2. In accordance with observations on porous sandstones, the critical compaction pressure p * decreases with increasing porosity.
Obviously, the yield cap described by Equation 14 is not appropriate as the porosity approaches zero, since in that case p * diverges. However, the focus of this work is the description of rocks that are already undergoing dehydration, i. e. , in which the porosity is never exactly zero. Furthermore, in absence of a more complete dataset on dehydrated and partially dehydrated serpentinite, the Cam-clay yield surface is one of the simplest yield criterion which is closed at high pressure (i. e. , the material can fail by pure hydrostatic compaction). Hence, our choice for the yield function should be viewed as a first order approximation which incorporates the essential qualitative elements of the behaviour of dehydrating serpentinite: a yield cap that is closed at high pressure and that shrinks with increasing porosity.
Numerical tests We performed a series of numerical computations in order to explore further the effects of potential nonlinearities associated with the variations of mechanical, hydraulic and chemical parameters during deformation and reaction. We included a power-law dependence of the permeability on porosity, k ∝ ζ 3 (where we recall that ζ is the porosity of the rock minus its poroelastic variations), and accounted for depletion of antigorite by using a simple first-order approximation for the reactant surface area, A rlm /A 0 rlm ≈ (1 -ξ) , in Equation (  7 ). The numerical method is described fully in Appendix D, and the Matlab R implementation and source code is available online at http://github. com/nbrantut/Compaction_Dehydration. The solution for pore fluid pressure, strain, reaction progress and porosity is computed within a layer of width L, with periodic boundary conditions, and an initial sinusoidal infinitesimal pore pressure perturbation is added to the homogeneous initial conditions.
At 4 GPa and 625 • C, the enthalpy change of the reaction is 442 kJ per mole of antigorite (computed from the database of  Holland and Powell [1998] ), i. e. , around 2. 5 × 10 2 MJ m -3. For a reaction rate of the order of 10 -8 s -1 , the rate of heat absorbed by the reaction is of the order of 2. 5 J m -3 s -1. The rate of work of the volumetric strain is given by ˙ p , and for an effective stress p of around 100 MPa and a characteristic strain rate of the order of 10 -8 s -1 , the D R A F T July 10, 2017, 12:11pm D R A F T rate of heat generated by inelastic deformation is of the order of 1 J m -3 s -1. Although the exact quantities are only approximate, the rate of heat absorbed by the reaction and released by deformation are of the same order of magnitude, and might compensate each other.
The Australian marine context Australia is a marine nation; more than 85% of its population live within 50 km of the coast, and its surrounding oceans have a strong impact on terrestrial climates (NMSC, 2015). Australia's 'marine economy' is projected to grow three times faster than its national gross domestic product over the next decade (AIMS Index of Marine Industry, Australian Institute of Marine Science in  NMSC, 2015). The Australian Government's National Marine Science Plan 2015-2025 highlights seven challenges for the next 10 years: marine sovereignty and security; energy security; food security; biodiversity conservation; sustainable urban coastal development; climate change adaptation; and resources allocation. It emphasizes the need to develop and refine decision-support tools that translate knowledge and data into useful informationincluding realistic projections-for effective decision-making in relation to these challenges.
Review of integrated modelling in Australia A review of integrated modelling in Australia was conducted through joint processes of seeking written contributions from invited experts followed by a 2-day workshop (twenty-seven experts from ten Australian research organisations and representing the following fields: physical and climate modelling, biogeochemical modelling, marine ecosystem modelling, socioeconomic modelling, observing systems and databases-eighteen of the twentyseven contributors attended a workshop in Hobart, Tasmania on 25th and 26th August 2015). Experts were invited to provide 300 word contributions via a wiki (www. soki. aq) that addressed: (i) the development and/or use of models and model output, (ii) integrating different methods (including any standards used for coupling and comparing models), and (iii) suggestions on supporting networks for integrated modelling in Australia, including connecting existing modelling efforts.
Operational needs While the key directions discussed above contain recommendations for extending the scope and skill of the existing suite of integrated models in Australia, experience to date has also allowed us to identify a set of priority operational needs for improving capability in integrated modelling for marine social-ecological systems in Australia (Table  2 ). These priorities are: The use of standardised scenarios (e. g. shared socioeconomic pathways;  Schweizer and O'Neill, 2014; O'Neill et al. , 2015)  and, where possible, standardized forcing data (e. g. datasets in the ISI-MIP project;  Warszawski et al. , 2014). Recognition of the ongoing need for human expert skill/ knowledge as a component of integrated modelling toolboxes, and some formalisation around the degree of engagement of experts that might be needed for a particular problem or in a particular context (see Figure  4  and discussion in text below).
g. coding conventions that are difficult to maintain across model platforms). Moving towards the point where it is standard practice to publish code and supporting data will help address this issue. We suggest that an Australian facility for integrated modelling could not only support standards for documentation and archiving, as discussed above and identified as a priority in the recent National Marine Science Plan, but might also help minimize needs for re-investment since existing model components could be strategically re-deployed for different scales and applications (with the caveats related to model testing described above). Such a facility would usefully be linked to existing observing systems and data portals for the region; namely IMOS (Integrated Marine Observing System) and SOOS (Southern Ocean Observing System).
Acknowledgements. The authors extend special thanks to  Isabelle Sacareau  ( Passages Laboratory of the CNRS and Montaigne University of Bordeaux, France ), coordinator of the Preshine Project. They are also grateful to the hydrometry team and the administrative staff of the  Laboratoire Hydrosciences Montpellier, France , the hydrologists of the  Institut des Géosciences de l'Environnement in Grenoble, France , the meteorologists of the  Centre National de la Recherche Météorologique in Toulouse and Grenoble, France , the  Association Ev-K2 CNR  and the  Pyramid Laboratory staff in Bergamo, Italy, and Kathmandu and Lobuche, Nepal , and the  Vice-Chancellor of the Nepalese Academy of Science and Technology. The authors are particularly grateful to  Keith Beven  from  Lancaster University (UK)  and to  Patrick Wagnon  from the  Institut de Recherche pour le Développement.