dataesr/scientific-paragraphs-categorization huggingface dataset:
) The up-front planning during the development of the databases does not mean that annotations have not changed over the years. For many species, as we learn more about them, we have become better able to identify them, leading to increased recognition as we continue to encounter them. To derive the full benefit of our thousands of hours underwater, we would have to re-annotate previous dives in light of current knowledge. Similarly, it is only within the last 15 years that we have paused during dives to collect and identify certain species, as different researchers with different interests have begun working with us (Figure  3 ). Improved video resolution has allowed better identification of taxa that we were not able to see as clearly before, and this will continue to improve as we transition to 4k video.
The evolution of our annotation capabilities has resulted in a spurious increase in apparent abundance of previously unknown or unrecognized species over time in the database records, reflecting our ever-improving knowledge of the species we encounter. Some of the species we now see routinely, particularly among the ctenophores, have not been seen or collected since their original descriptions in the late 1800s or early 1900s (Figure  4 ). Future work therefore includes improving our imaging capabilities, digitizing and selectively reevaluating portions of past dives that preceded our current taxonomic knowledge, and continuing to.
, 2001; Dunn et al. , 2005b; Osborn, 2009; Hurt et al. , 2013). The resultant "trees" depicting the relationships between organisms even include evolutionary hypotheses that challenge long-held notions of the origin of animal life  (Dunn et al. , 2008; Ryan et al. , 2013) , transforming our understanding of basic biology. Critically, these molecular data sets are grounded by morphological observations so that they can contribute to a reference database that will allow accurate assignment of community diversity through such methods as bulk sequencing of environmental DNA and metagenomics. Having genetic studies conducted in concert with taxonomic expertise is critical to the success of these fields. However, because so much of the genetic diversity is uncategorized, there are no shortcuts yet that allow a nonspecialist to "barcode" their way to an in-depth understanding of this ecosystem.
Initially, a survey would be conducted on a dedicated cruise designed to study a particular process, with a surface ship tending the vehicle as it worked across a few kilometers of Monterey Bay. Now, the vehicles can be programmed to perform their missions routinely and return with a broad suite of data (Figure  8 ). Upcoming integration of bioluminescence sensors on long-range AUVs will allow continuous sampling of oceanographic bioluminescence for two weeks at a time, and longer-term observations are being conducted with light sensors deployed on the Monterey Accelerated Research System (MARS) underwater cabled observatory. These measurements provide a unique perspective on the composition of life in the sea, which is very different from the picture painted by chlorophyll fluorescence. The three known species (black labels) and two species recently described (purple labels at right) all use wriggling bioluminescent lures associated with the side branches of their tentacles.
Assessment of genome quality We assessed the quality of the assembly by using two independent sources of data. First, we used the SNP markers that were mapped on the previously mentioned integrated genetic linkage map to validate A r t i c l e s scaffold assembly. Of the 15,417 SNP probe sequences, we identified sequence homology in the GDDH13 genome for 14,732 of them. We then assessed their position on the scaffold assemblies by comparing their location on the integrated genetic linkage map. In total 14,117 of the mapped markers (95. 8%) were found to be located at their expected positions (Supplementary Note). To visualize these data, we plotted the genetic distance against the physical distance of the genetic markers for each chromosome (Supplementary Fig. 2 ); the data for chromosome (Chr) 11 is shown as an example in Figure  1c.
3a  and Supplementary Table  2 ). DNA transposons or class II elements (DNA transposons and Helitrons) make up 23% of the TE content (13. 4% of the genome assembly; Fig. 3a  and Supplementary Table  2 ). A complete list of identified TEs, their integrity and copy number can be found in Supplementary Table  3. table 1 comparison of the GDDH13 genome with previously published assemblies of the apple genome GDDH  13  Li et al. 33  Velasco et al. We ran the REPET  38  pipeline on the PacBio contigs, which allowed us to identify an additional hyper-repetitive consensus sequence (Genbank entry KX869746). This consensus sequence was automatically classified as a 9,716-bp LTR-RT with over 500 full-length copies, and it accounted for 3. 6% of the genome assembly (22. 3 Mb). We termed this TE consensus sequence HODOR (high-copy Golden Delicious repeat).
1 ), it is possible that through the consecutive steps of selfing, haploid development and chromosome doubling, some minor parts of the genome might have been lost or re-arranged. Thus, it is possible that some of the genome sequence might be missing in the GDDH13 assembly. Our gene prediction analysis reduced the estimated number of annotated genes in apple from 63,541 (Genome Database for Rosaceae, see URLs and ref. 6) to 42,140, which is much closer to the 42,812 genes that have been reported for pear  49  and the 45,293 genes that were identified after filtering out overlapping genes from the original apple genome annotation  49  (Supplementary Note). TEs also have an important role in structuring genomes. The indepth TE annotation we performed showed a major TE burst in apple that we estimated to have happened around 21 Mya.
The high-quality reference apple genome sequence reported here offers unprecedented insights into the genome dynamics of a tree and provides an important basis for future studies, not only in apple but also in other Rosaceae species. URLs. Structural and functional annotations are available through our genome browser: https://iris. angers. inra. fr/gddh13/. The Whole-Genome Shotgun project can be found in GenBank under: https:// www. ncbi. nlm. nih. gov/nuccore/MJAX00000000. 1 The REPET package v2. 5 used to detect TEs used in this study can be found here: https://urgi. versailles. inra. fr/Tools/REPET SynMap-CoGe: http:// www. genomevolution. org Genome Database for Rosaceae: http:// www. rosaceae. org.
ACkNoWLEDGMENTS We thank  M. Mirouze  and  C. Vitte  for their support with the TE analysis, and  T. Giraud  and  A. Cornille  for their insights on apple evolution. We are very grateful to the  Horticulture Experimental Unit of the Institut National de la Recherche Agronomique (Pays de la Loire ) for taking care of the apple trees used in this study. We thank the IMAC and ANAN platforms from the  Structure Fédérative de Recherche 'Qualité et Santé du Végétal' (SFR QUASAV ) for their technical support. This research was funded by the  EPICENTER ConnecTalent  grant of the  Pays de la Loire  (E. B. ) and supported by the  Provincia autonoma di Trento  (G. L. , L. B. , D. M. , R. V. and M. T. ), the  EU seventh Framework Programme  by the  FruitBreedomics  project no. 265582 : "Integrated approach for increasing breeding efficiency in fruit tree crops (http://www.
In addition, some purine(R)-purine(R) (denoted RpR) steps also show twist bimodality, but with a somewhat smaller range of twists. However, it was also noted that the sequences flanking such steps can significantly modify the degree of bimodality and also the preference for a low or high twist state. It is clear that such behavior may play an important role in how DNA reacts to torsional strain and the aim of this study is to address this question. In order to do this, we have developed a new structural constraint, based on the Curves+ conformational analysis methodology  (29) , which controls the total helical twist between two chosen base pairs, allowing both under-and overtwisting to be imposed. This restraint has been implemented within PLUMED free energy library  (30)  and can thus be used in conjunction with a variety of standard all-atom MD software packages.
Initial trials showed this to be true to within a few tenths of a degree. If it is necessary to control the total twist of longer DNA fragments, several restraints acting on short segments can be chained together. The new twist restraint was implemented within version 2. 2. 1 of the PLUMED free energy library environment  (30). This allows it to be used in combination with a variety of MD software packages. The C++ code and a user guide for the twisting constraint can be found here: https://github. com/annareym/PLUMED DNA-Twist.
Molecular dynamics umbrella sampling simulations The DNA oligomers studied were initially constructed in standard B-DNA conformations using the nucleic acid modeling program JUMNA  (32). Unrestrained MD simulations followed by umbrella sampling simulations were both performed using the GROMACS MD software package, version 5. 1  (33). Simulations were carried out using the latest AMBER all-atom nucleic acid force field Parmbsc1  (31). Note that this parameter set avoids artefacts that, after a few tens of nanoseconds of simulation, caused a build-up of unusual backbone conformations involving the ␣ (P-O5 ) and ␥ (C5 -C4 ) dihedrals and resulted in a steady loss of helical twist. The parmbsc1 force field has been the subject of extensive testing and has been shown to accurately reproduce a wide range of experimental data on DNA  (31).
Role of individual base pair steps in the response of DNA to torsional stress In order to understand the detailed behavior of DNA with respect to changes in twist, we have to look at what happens to individual base pair steps as our twist restraint is applied. These data are presented in Figure  3. In passing, we remark that measuring the average twist over the restrained regions with conformational analysis program Curves+ leads to a value that is very close to the value imposed by our restraint confirming its validity. If we now consider the behavior of individual base pair steps, we see that they do not respond equally to the imposed changes in twist. The most interesting steps are those whose slope deviates most from average twist per step, shown by the black line in each panel of Figure  3.
Our simulations enable the relatively slow equilibration of BI/BII states to occur. The fact that these states are particularly sensitive to the base pairs flank- ing a given dinucleotide step explains why certain YpR steps become unable to respond to torsional stress (such as the CpG step in the ACGT oligomer), or may only be able to efficiently absorb undertwisting (CpG in ACGA), or overtwisting (CpG in CCGA). The fact that the behavior of a given base pair step under torsional stress is influenced by its sequence environment has recently been illustrated by the structural analysis of a 2D triangular self-assembled matrix of DNA, where one branch was constructed with one less base pair than would be necessary assuming a canonical B-DNA conformation (PDB ID: 5EOS)  (47). The resulting imposed underwinding, of 4 • per base pair step on average, was absorbed in a very heterogeneous way with two YpR steps being strongly underwound (TpG 25.
Thus, linkages such as Figure  19   The simulation of the spectra with ACD/I-Lab yields a value of 41. 8 ppm for the reacted amine, and 38. 7 ppm for the unreacted amine. The online software at nmrdb. org  [15]  yields 44. 0 ppm for the covalently reacted amine formed and 41. 9 ppm for the nonreacted amine. The superposition of the pTSA-catalysed spectra at 185 and 100 °C shown in Figure  20  indicates that the formation of the amine is indeed occurring. Furthermore, on the pTSA-catalysed 185 °C spectrum, a rotation band at 13 ppm occurs. If the rate of rotation that has caused it is subtracted, a well-defined peak at 133. 8 ppm is obtained. This peak is hidden by other species. This chemical shift can also be simulated with an amination of the -OH group on the C3 of catechin and corresponds to the to the shift of the carbon in C1′.
Moreover, the disappearing of the peak at 68 ppm observed on the pTSA-catalysed 100 °C spectrum and absent The simulation of the spectra with ACD/I-Lab yields a value of 41. 8 ppm for the reacted amine, and 38. 7 ppm for the unreacted amine. The online software at nmrdb. org  [15]  yields 44. 0 ppm for the covalently reacted amine formed and 41. 9 ppm for the nonreacted amine. The superposition of the pTSA-catalysed spectra at 185 and 100 • C shown in Figure  20  indicates that the formation of the amine is indeed occurring. This being a rather unexpected occurrence. The first question to be asked is then: are covalent bonds with the amine formed also with the carbons of the B-ring, as interpreted from the oligomers representation shown in the interpretation of the MALDI spectra? The answer to this question is clearly yes, as the covalent bonds are also formed at 185 °C on the Bring as the 145-146 ppm peak belonging to the aromatic B-ring carbons carrying the phenolic -OH groups is also markedly smaller in all the catalysed and uncatalysed spectra at 185 °C.
Moreover, the covalent bond formed transmit at 134-135 ppm, indicating that the 138-139 ppm peak belongs to both the covalently reacted A-and B-rings of catechin. Thus, linkages such as Figure  19   The simulation of the spectra with ACD/I-Lab yields a value of 41. 8 ppm for the reacted amine, and 38. 7 ppm for the unreacted amine. The online software at nmrdb. org  [15]  yields 44. 0 ppm for the covalently reacted amine formed and 41. 9 ppm for the nonreacted amine. The superposition of the pTSA-catalysed spectra at 185 and 100 °C shown in Figure  20  indicates that the formation of the amine is indeed occurring. Furthermore, on the pTSA-catalysed 185 °C spectrum, a rotation band at 13 ppm occurs. If the rate of rotation that has caused it is subtracted, a well-defined peak at 133. 8 ppm is obtained.
A. Tasks description It has been decided to divide the challenge into 2 independent tasks (see Fig. 1 ), each focusing on a non-overlapping split of the dataset. This decision is meant to open the competition to a larger audience and also to include teams that only have a partial working system. Indeed, the possibility to bypass the first task can be supportive as the dataset is relatively noisy and thus could potentially lead to discouraging scores during the training phase. The two tasks of 1) detection and 2) correction are described below. Task 1 -Detection of OCR errors: Given the raw OCR-ed text only, the participants are asked to provide the position and also the length of the suspected erroneous tokens. The length information is non-trivial; it is necessary in the case of words that are wrongly split (e.
B. Dataset The proposed dataset  [1]  has been built within the AM ÉLIOCR project 1 on OCR post-correction. It accounts for 12M OCR-ed characters along with the corresponding Ground Truth (GT), with an equal share of English-and Frenchwritten documents (see Table  I ). The documents come from different digital collections available, among others, at the National Library of France (BnF) and the British Library (BL). The corresponding GT comes both from BnF's internal projects and external initiatives such as Gutenberg, Europeana Newspapers, IMPACT and Wikisource. Degraded documents sometimes result in highly noisy OCR output and thus cannot reasonably be fully aligned with their GT. The unaligned sequences have not been included in the presented statistics (e. g. number of characters and error rates).
Error rates vary according to the nature and the state of degradation of the documents. Historical newspapers for example, due to their complex layout and their original fonts have been reported to be especially challenging for OCR engines with up to 10% of wrongly detected characters in some documents. A first part of the dataset (80%) was provided to the participants for training and testing purposes, and the rest (20%) has been used by the organizers for evaluation. Moreover, as the input of "Task 2) correction" contains the answers of "Task 1) detection", each task was assigned to a different subset of the corpus. Fig. 2  illustrates on a sample file the format provided to the participants. Tokens are simply space-separated sequences, with no restriction on punctuation (examples of tokens: "i", "i'am", "football?", "qm87-7lk.
univlr. fr/gchiro01/icdar2017). It computes the metrics presented above over either the training set or the full dataset, with the assumption that the input files are correctly formated (see Fig. 3 ). The choice of using a structured format key(pos,lenght) / value(candidates,weights) rather than asking fully corrected sequences has been motivated by the bias that would have implied any further alignment process between the participants results and the corresponding GT. Miss-aligned tokens (see "#" signals) are ignored for the computation of the different metrics. Also, given the complexity of dealing with hyphen correction, it has been decided to ignore the tokens containing an hyphen through the evaluation. Thus, whether such errors are corrected does not impact on the final result.
D. Modalities and timeline of the competition The competition was run in open mode (submission of the results but not the executables). We have relied on the scientific integrity of the authors to follow the rules of the competition. The authors were free to participate in one or both tasks, even on subparts of the dataset. The training set was made available mid-March 2017. The test set (without the GT), used for evaluating the different methods was made available on the 27th of June with 2 days given for the teams to submit their results.
CLAM -Team from IITB 8 -Monash Research Academy, India The proposed method relies on the Character Level Attention Model (CLAM) with beam search used at decoder's output. We used the open source system OpenNMT (http://opennmt. net) also used in  [12] ). Regarding the training phase: to take care of real word errors as well as non-word errors, at network's input we used the characters (with space as delimiter) from input OCR word o t along with characters from few words (with $ as delimiter) on its left (o t-l:t-1 ) and right (o t+1:t+r ). At network's output we used the characters (with space as delimiter) from the Ground Truth word g t corresponding the input OCR word o t. l = 4,r = 1 gave the optimized results for first 3 datasets, and l = 6, r = 1 for last. Regarding the testing phase: we expect the model to jointly learn the language as well as error patterns in OCR output.
The dataset, given its important size and its nature (manually annotated, OCR/GT automatically aligned) is obviously imperfect. Those inaccuracies, although rare, can still cause harm both during the training (by misleading the final model) and the 10 https://github. com/tung18tht/ICDAR-2017-Post-OCR-Correction 11 To the benefit of participants who wrongly formated their results, manual shifts of 1 or -1 on the offsets were applied when observing a major improvement evaluation phase (by wrongly considering a right correction). Major issues, such as inconsistent hyphenations (e. g. line breaks), were handled directly in the evaluation script, and as mentioned earlier related word corrections were therefore not taken into account for the evaluation.
V. CONCLUSION This paper describes the first ICDAR competition on post-OCR text correction. The challenge consisted of two independent tasks: 1) error detection and 2) error correction on an original dataset of 12M OCR-ed symbols along with an aligned ground truth. The data come from sources of different natures (newspapers and monographs) and target 2 different languages. This competition demonstrated, through formatted results provided by the participants, the performance of their systems exposed to this specific dataset and to the custom metrics described in this paper. Concerning the first task (error detection), the WFST-PostOCR method performs the best. Its authors proposed an application of the noisy channel model  [13]  where probabilistic character error models were estimated from the training corpus using longest common subsequence alignments of tokens and compiled into weighted finite-state edit transducers.
Methods Sediment core and age model. Marine sediment core GeoB4905-4 was recovered at 2°30. 0´N, 09°23. 4´E from 1328 m water depth offshore Cameroon  67. The age model of the core is based on 12 radiocarbon ages  68, 69  that have been re-calibrated using the Marine13 curve with a reservoir age of 0. 4 ± 0. 1 kyr. The age-depth relationship was constructed using the software BACON 2. 2  70  and represents the median of 10,000 iterations (Supplementary Fig. 6 ). The mean age uncertainty (1σ) over the last 25 ka is ± 0. 3 kyr. n-Alkane extraction and purification. Extraction and purification were performed at MARUM-Center for Marine Environmental Sciences, Bremen. Sediment samples of 10 ml were taken from core GeoB4905-4 with syringes, which yielded up to 9 g of dry sediment. Samples were oven dried at 40 °C, homogenised and squalane internal standard was added before extraction.
Investigations of the source of the atmospheric δD p signal were performed with a transient run of the intermediate complexity isotope-enabled climate model iLOVECLIM  [80] [81] [82]. We studied the last 25 kyr of a 150 kyr simulation, which was run with the atmosphere at 5. 6°resolution and used accelerated forcing (irradiance, GHGs and ice sheets were updated with an acceleration factor 10)  83. Intermediate complexity models such as this have difficulty reproducing precipitation, but have the advantage of producing a continuous transient simulation of water isotopes for comparison with proxy data. The iLOVECLIM source code is based on the LOVECLIM model version 1. 2, whose code is accessible at http://www. elic. ucl. ac. be/modx/elic/index. php?id=289. The developments on the iLOVECLIM source code are hosted at https://forge.
Acknowledgements J. A. C. was supported by the  Helmholtz Postdoc Programme  ( PD-001 ) and the  Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven. E. S. , B. B. and M. P. were supported by the  German Science Foundation (DFG)  within the  Priority Programme (SPP) 1266"Interdynamic " (Sche903/9;  PR1050/4 ) and the  DFG Research Center/Cluster of Excellence  "The Ocean in the Earth System" at  MARUM-Center for Environmental Sciences. T. C. and D. R. are supported by  CNRS-INSU. C. S. was partly supported by a Grant from the  French government through Agence Nationale de la Recherche (ANR)  under the ' Investissements d'Avenir' programme , reference  ANR-10-LABX-19-0. CCSM3 model experiments were performed on the HLRN supercomputer. We thank  Raquel Nieto  for her assistance with the FLEX-PART computations.
In order to obtain a general view about the expression profile of these genes in miRNA-machinery mutants, we isolated total RNA from two tissue types: buds (enriched in meiocytes) and leaves. Most of the genes analyzed displayed unexpectedly high mRNAs levels in both organs. hst-21 and ago1-52 had quite similar mRNA profiles and overexpressed all the genes analyzed in buds, and many of them in leaves (Fig. 4 ). Several miRNAs have been detected in the male germline of Arabidopsis 8. To investigate if the above-mentioned genes are miRNA targets, we used the psRNATarget Analysis Server (http://plantgrn. noble. org/psRNATarget/)  23. Among 337 miRNAs in the psRNATarget database, miR5021 and miR161. 2 were predicted to cleave the mRNA of TOPII, although the two target sites for miR5021 mapped at the 5′ UTR.