dataesr/scientific-paragraphs-categorization huggingface dataset:
4 GHz cores and 128 GB of RAM. although it remained faster that Daccord's DALIGNER. Minimap2's memory consumption, however, was higher than that of MECAT's overlapping strategy, especially on the C. elegans datasets. Compared to both FLAS and CONSENT, MECAT displayed lower throughputs on all the datasets. As for FLAS, this can be explained by the fact that it was designed as a MECAT wrapper, allowing to retrieve additional overlaps, and thus correct a greater number long reads. As a result, since it relies on MECAT's error correction strategy, it displayed highly similar memory consumption. Runtime was however higher, due to the additional steps allowing to retrieve new overlaps, and to the resulting higher number of reads to correct. Throughputs and error rates of FLAS and CONSENT were highly similar on all the datasets, also it slightly differed on the C.
Comparison on real data We then evaluated the different correction methods on larger, real, Oxford Nanopore datasets. For these datasets, we not only evaluate how well the corrected long reads realign to the reference genome, but also how they assemble. For the alignment assessment, we report how many reads were corrected, their throughput, their N50, the proportion of corrected reads that could be aligned, the average identity of the alignments, as well as the genome coverage, that is, the percentage of bases of the reference genome to which at least a nucleotide aligned. For the assembly assessment, we report the overall number of contigs, the number of contigs that could be aligned, the NGA50 and NGA75, and, once again, the genome coverage. We aligned the long reads to the reference with Minimap2, and obtained statistics by parsing the output SAM file.
We performed assemblies using Minimap2 and Miniasm  (Li, 2015) , and obtained statistics with QUAST-LG  (Mikheenko et al. , 2018). Results are given in Table  5  for the alignment assessment, and in Table  6  for the assembly assessment. Runtimes and memory consumption of the different methods are also given in Table  5. As for the simulated data, we report runtime and memory consumption of the overlapping and correction steps apart, when possible. All the correction experiments were run on cluster node equipped with 28 2. 39 GHz cores and 128 GB of RAM. On these two datasets, Daccord failed to run, as DALIGNER could not perform alignment, for the same reason as for the simulated C. elegans 60x dataset. CONSENT corrected the largest number of reads, and reached the highest alignment identity on the two datasets.
Its N50 was also higher than that of all the other methods, except for Canu on the D. melanogaster dataset. CONSENT also reached the highest throughput, and the largest genome coverage, for the two datasets. When it comes to runtime and memory consumption, MECAT once again outperformed all the other methods, as in the experiments on simulated data. Moreover, it reached the highest proportion of aligned reads, on the two datasets. CONSENT was however really close, as only 0. 36-0. 61% less reads could be aligned. Moreover, on the H. sapiens dataset, CONSENT was the only tool able to deal with ultra-long reads. Indeed, other methods reported errors when  Table  5. Statistics of the real long reads, before and after correction with the different methods. /A N/A N/A N/A N/A N/A Canu _ _ _ _ _ _ _ _ _ _ _ Daccord _ _ _ _ _ _ _ _ _ _ _ _ FLAS 1 670, 1 Reads longer than 50kbp were filtered out, as ultra-long reads caused the programs to stop with an error.
There were 1,824 such reads in the original datasets, accounting for a total number of 135,364,312 bp. Daccord could not be run on these two datasets, due to errors reported by DALIGNER. Canu stopped with an error on the H. sapiens dataset, both with and without the long reads > 50kbp. attempting to correct the original dataset. As a result, in order for those methods to work, we had to remove the reads that were longer than 50kpb. There were 1,824 such reads, accounting for a total number of 135,364,312 bp. However, even after removing these ultra-long reads, Canu still failed to perform correction, and reported an error. The assembly yielded from Canu corrected reads outperformed all other assemblies in terms of NGA50, NGA75, and genome coverage, on the D. melanogaster dataset. However, it produced a higher number of contigs than the assemblies yielded from FLAS and MECAT reads.
The assembly obtained from CONSENT corrected reads contained the largest number of contigs, but outperformed the assemblies obtained from FLAS and MECAT reads in terms of genome coverage and NGA50. NGA75 of the CONSENT assembly was also larger than that of FLAS, but slightly shorter than that of MECAT. The error rate per 100 kbp of the CONSENT assembly was also lower than that of FLAS, and slightly higher than that of MECAT. On the H. sapiens dataset, the assembly obtained from CONSENT corrected reads outperformed the assemblies produced by both FLAS and MECAT corrected reads, in terms of number of contigs, NGA50, and NGA75. In particular, the NGA50 of the CONSENT assembly was more that 1 Mbp larger than that of other assemblies. However, 5 of the contigs of the CONSENT assembly could not be aligned to the reference, likely due to misassemblies by Miniasm.
Contig polishing As an additional feature of CONSENT, we also allow to perform contig polishing. The process is pretty straightforward. Indeed, instead of computing overlaps between the long reads, as presented in the previous sections, the long reads used for the assembly are simply mapped to the assembled contigs. The rest of the pipeline remains the same. We present contig polishing results on the simulated E. coli, S. cerevisiae, and C. elegans 60x datasets, as well as on the real D. melanogaster and H. sapiens datasets, and compare to RACON  (Vaser et al. , 2017) , a state-of-the-art contig polishing method, on Table  7. These results show that CONSENT outperformed RACON in terms of quality of the results, especially dealing better with errors, and greatly reducing the error rate per 100 kbp, on the the E.
coli, S. cerevisiae, and C. elegans datasets. Moreover, the NGA50 and NGA75 of CONSENT were highly similar to those of RACON on these datasets. RACON however covered a slightly larger proportion (0. 11%) of the reference genome on the C. elegans dataset. For the larger, eukaryotic D. melanogaster dataset, RACON outperformed CONSENT in terms of error rate and genome coverage, but the NGA50, NGA75 of the two methods remained comparable, the NGA50 of CONSENT even outperforming that of RACON. Moreover, after polishing with CONSENT, one more contig could be aligned to the reference, compared to RACON. Additionally, on all the datasets, CONSENT was 2x to 16x faster than RACON. This contig polishing feature, being straightforward, raises the question as to how other correction methods should propose such an option.
Discussion and future works Experimental results on the human dataset are particularly promising. Indeed, CONSENT is the only method able to scale to the ultra-long reads contained in this dataset. As such reads are expected to become more frequent in the future, being able to deal with them will soon become a necessity. In addition, judging from the memory consumption of the error correction step, it seems like CONSENT could easily scale to the error correction of a complete human dataset, and further experiments should therefore head in that direction. However, the overlapping step, performed by Minimap2, tends to display higher memory consumption than the overlapping steps of other methods. In addition, the runtime of the correction step also tends to be higher. We discuss how to further reduce these computational costs below.
The memory consumption, for the overlapping step, can be explained by the fact that, in our experiments, we voluntarily set Minimap2 index size to 100 GB, so the index would not be split when processing large datasets. This can be explained by the fact that, in order to create the alignment piles, CONSENT needs all the alignments of a given read to directly follow each other in the Minimap2 output PAF file. Allowing to split the index would invalidate this property, as each part of the index would be processed separately by Minimap2, resulting in multiple, independent alignment files, that would then be concatenated into the final result file. As a result, if overlaps for a given read A are found in different parts of the index, the final result file would contain multiple, independent sets of lines concerning read A.
Our experiments show that CONSENT compares well, or even outperforms other state-of-the-art methods in terms of quality of the results. In particular, CONSENT is the only method able to scale to a human dataset containing Oxford Nanopore ultra-long reads, reaching lengths up to 340 kbp. Although very recent, such reads are expected to further develop, and thus become more accessible in the near future. Being able to deal with them will thus soon become a necessity. CONSENT could therefore be the first self-correction method able to be applied to such ultra-long reads on a greater scale. The contig polishing feature that was added to CONSENT also seems to offer promising results. As the processes of long reads correction and contig polishing are not so different from one another, one can wonder why more error correction tools do not offer this feature.
Morisse et al. Overlapping Correction Total Dataset Corrector Throughput (Mbp) Error rate (%) Deletions (%) Insertions (%) Substitutions (%) Runtime Memory (MB) Runtime Memory (MB) Runtime Memory (MB) Original 140 12. 2862 2. 6447 8. 7973 0. 8442 N/A N/A N/A N/A N/A N/A 30x E. coli Canu Daccord FLAS MECAT 130 131 130 107 0. 2508 0. 0219 0. 2077 0. 1649 0. 0636 0. 0034 0. 1490 0. 1328 0. 2001 0. 0090 0. 0741 0. 0459 0. 0102 0. 0115 0. 0043 0. 0018 _ 1 min _ 25 sec _ 6,813 _ 1,600 _ 13 min _ 1 min 14 sec _ 639 _ 1,083 19min 14 min 12min 1 min 39 sec 4,613 6,813 1,639 1,600 CONSENT 130 0. 2013 0. 0944 0. 1095 0. 0163 22 sec 2,390 16 min 48 sec 132 17 min 10 sec 2,390 Original 279 12. 2788 2. 6437 8. 7919 0. 8432 N/A N/A N/A N/A N/A N/A 60x coli Canu Daccord FLAS 219 261 260 0. 5211 0. 0175 0.
97 95. 66 _ _ 4,404 3,781 CONSENT 182 177 2,663,412 439,178 90. 49 4,543 Dataset Method Contigs Aligned contigs NGA50 NGA75 Genome coverage Errors / 100 kbp Runtime (CPU seconds) Memory (MB) Original 1 1 4,939,014 4,939,014 99. 91 10,721 N/A N/A E. coli 60x RACON 1 1 4,663,914 4,663,914 99. 90 499 5,597 643 CONSENT 1 1 4,637,939 4,637,939 99. 91 78 334 1,648 Original 29 29 579,247 456,470 96. 14 10,694 N/A N/A S. cerevisiae 60x RACON 29 29 539,472 346,116 96. 09 637 14,931 1,703 CONSENT 29 29 532,258 334,560 96. 15 208 1,616 7,073 Original 47 47 5,495,235 2,656,350 99. 71 10,611 N/A N/A C. elegans 60x RACON 47 47 5,073,456 2,349,027 99. 72 819 136,325 14,288 CONSENT 47 47 5,019,450 2,286,190 99. 61 330 30,907 85,486 Original 423 408 864,011 159,590 83. 19 10,690 N/A N/A D. melanogaster RACON 422 416 693,918 287,386 92.
The CaM-binding domain is located in the C terminus of ceQORH To establish the location of the CaM-binding domain of ceQORH, we first performed successive deletions of the ceQORH protein (Fig. 2A ) and expressed these constructs in E. coli (Fig. 2B ). Using these experiments, we were able to demonstrate that this CaM-binding domain is located in the C terminus of ceQORH (Fig. 2 , B and C) between residues 229 and 280 of the protein. In this region, we searched for a putative CaM-binding site within this domain using the CaM prediction program  (23) , which identified (see Fig. S1 ) the following candidate motif: 255 AMWTYAVKKITMSKKQLVPLLL 277. CaMbinding peptides have the potential to fold into a basic, amphiphilic ␣-helix (see Ref. 24 ) in which hydrophobic and charged residues are predicted to be essential for formation of this helix.
e. 16 kDa). This high-molecular-mass signal was also enriched in the membrane fraction of epidermal cells (Fig. 5B ), whereas the soluble fraction contained CaM isoforms of lower molecular mass. Finally, this high-molecular-mass CaM signal was 10 -20 times more enriched in epidermal cells (ϳ5 ng CaM in 4 g of crude epidermal extract, i. e. 1/1000 of total epidermal proteins) compared with crude leaf extract (ϳ1 ng of CaM in 20 g of crude leaf extract, i. e. 1/20,000 of total leaf proteins) (Fig. 5C ). In screening databases for a membranebound CaM-like isoform of higher molecular mass than classical (short) CaM isoforms, we identified CaM53 from petunia (CALM3_PETHY), a CaM isoform that contains an additional C-terminal sequence compared with short CaM isoforms (e. g. CaM1, AT5G37780) (Fig.
5D ). A close homolog of this CaM53 protein from petunia is present in Arabidopsis, and this isoform is named CaM5 (AT2G27030. 3). Compared with other CaM isoforms, this CaM5 isoform contains an additional C terminus sequence that is highly conserved (Fig. 5D ) with CaM53 from petunia (CALM3_PETHY). Notably, using AT2G27030 as a query in the Uniprot database, only a classical (short) version (149 amino acids, molecular mass of 16,820 Da) of CaM5 is referenced (Q682T9 or TCH1 or CALM5_ARATH). However, in the TAIR database, a query using the term AT2G27030 results in the gene model AT2G27030. 3, which encodes a 181amino acid protein with a molecular mass of 20,575. 9 Da (i. e. similar to CaM53 from petunia).
This work was supported by the  French National Research Agency. This work was supported in part by Fellowships  ANR-06-GPLA-0003 ,  ANR Génomique Végétale-Genoplante 2010 Glyco-chloroplast ;  ANR-2010-BLAN-1610-01 ,  ANR Blanc Chloro-Pro ; and  ANR-10-LABX-49-01 , Labex GRAL from the  French National Research Agency  (to L. M. ). This work was also supported in part by  Ph. D. Fellowship   ANR-10-LABX-49-01  ( Labex GRAL ) from the  INRA Plant Biology and Breeding Division  and from the  French National Research Agency  (to I. B. ) and by a Ph. D. fellowship from the  CEA  (to S. M. ). We also acknowledge  ANR  Project Grant  ANR-15-IDEX-02. Additional support was provided by specific  ANR Labex GRAL, CNRS , and  INRA  funding and by access to the local IRIG-CEA facilities for confocal microscopy.
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.
Data Acquisition The cardiac CT/CTA data were acquired from two state-of-the-art 64-slice CT scanners (Philips Medical Systems, Netherlands) using a standard coronary CT angiography protocol at two sites in Shanghai, China. All the data cover the whole heart from the upper abdomen to the aortic arch. The in-plane resolution of the axial slices is 0. 78×0. 78 mm, and the average slice thickness is 1. 60 mm. The cardiac MRI data were obtained from two hospitals in London, UK. One set of data were acquired from St. Thomas Hospital on a 1. 5T Philips scanner (Philips Healthcare, Best, The Netherlands), and the other were from Royal Brompton Hospital on a Siemens Magnetom Avanto 1. 5T scanner (Siemens Medical Systems, Erlangen, Germany). In both sites we used the 3D balanced steady state free precession (b-SSFP) sequence for whole heart imaging, and realized free breathing scans by enabling a navigator beam before data acquisition for each cardiac phase.
The data were acquired at a resolution of around (1. 6 ∼ 2) × (1. 6 ∼ 2) × (2 ∼ 3. 2) mm, and reconstructed to half of its acquisition resolution, i. e. , about (0. 8∼1)×(0. 8∼1)×(1∼1. 6) mm. Both cardiac CT and cardiac MRI data were acquired 200 in real clinical environment. The pathologies of patients cover a wide range of cardiac diseases, including myocardium infarction, atrial fibrillation, tricuspid regurgitation, aortic valve stenosis, Alagille syndrome, Williams syndrome, dilated cardiomyopathy, aortic coarctation, Tetralogy of Fallot. The subjects for MRI scans also include a small number of normal controls. All the CT and MRI data have been anonymized in agreement with the local regional ethics committee before being released to the MM-WHS challenge. In total, we provided 120 multi-modality whole heart images from multiple sites, including 60 cardiac CT and 60 cardiac MRI.
Manual labeling was adopted for generating the gold standard segmentation. They were done by clinicians or by students majoring in biomedical engineering or medical physicists who were familiar with the whole heart anatomy, slice-by-slice using the ITK-SNAP software  (Yushkevich et al. , 2006). Each manual segmentation result was examined by a senior researchers specialized in cardiac imaging with experience of more than five years, and modifications have been take if revision was necessary. Also, the sagittal and coronal views were visualised simultaneously to check the consistency and smoothness of the segmentation, although the manual delineation was mainly performed in the axial views. For each image, it takes approximately 6 to 10 hours for the observer to complete the manual segmentation of the whole heart.
Participants Twelve algorithms (teams) were selected for this benchmark work. Nine of them provided results for both CT and MRI data, one experimented only on the CT data and two worked solely on the MRI data. All of the 12 teams agreed to include their results in this paper. To simplify the description below, we used the team abbreviations referring to both the teams and their corresponding methods and results. The evaluated methods are elaborated on in Section 3, and the key contributions of the teams are summarized in Table  3. Note that the three methods, indicated with Asterisk (*), were submitted after the challenge deadline for performance ranking.
The Chinese University of Hong Kong, Method No. 1 (CUHK1) Yang et al. (2017b) apply a general and fully automatic framework based on a 3D fully convolutional network (FCN). The framework is reinforced in the following aspects: First, an initialization is achieved by inheriting the knowledge from a 3D convolutional networks trained on the large-scale Sports-1M video dataset  (Tran et al. , 2015). Then, the gradient flow is applied by shortening the back-propagation path and employing several auxiliary loss functions on the shallow layers of the network. This is to tackle the low efficiency and over-fitting issues when directly train the deep 3D FCNs, due to the gradient vanishing problem in shallow layers. Finally, the Dice similarity coefficient based loss function  (Milletari et al. , 2016)  is included into a multi-class variant to balance the training for all classes.
CCA(S) = {S 11 , • • • , S nm | ∪ S ij = o}& {S 11 , • • • , S nm | ∩ S ij = φ}, (1) where S indicates the segmentation result. The differences between the reliable and unreliable regions are used to guide the reliability of the segmentation process, namely the higher the difference, the more reliable the segmentation. 3. 6. The Chinese University of Hong Kong, Method No. 2 (CUHK2) Yang et al. (2017c) employ a 3D FCN for an end-toend dense labeling, as Fig. 3  shows. The proposed network is coupled with several auxiliary loss functions in a deep supervision mechanism, to tackle the potential gradient vanishing problem and class imbalance in training. The network learns a spatial-temporal knowledge from a largescale video dataset, and then transfer to initialize the shallow convolutional layers in the down-sampling path  (Tran et al.
Shenzhen Institutes of Advanced Technology (SIAT) Tong et al. (  2017 ) develop a deeply-supervised endto-end 3D U-Net for fully automatic WHS. The training dataset are artificially augmented by considering each ROI of the heart substructure independently. To reduce false positives from the surrounding tissues, a 3D U-Net is firstly trained to coarsely detect and segment the whole heart structure. To take full advantage of multi-modality information so that features of different substructures could be better extracted, the cardiac CT and MRI data are   2018 ) design a pixel-wise dilated residual networks, referred to as Bayesian VoxDRN, to segment the whole heart structures from 3D MRI images. It can be used to generate a semantic segmentation of an arbitrarysized volume of data after training.
Results Table  4  and Table  5  present the quantitative results of the evaluated algorithms on CT and MRI dataset, respectively. For the CT data, the results are generally promising, and the best Dice score (0. 91±0. 09) was achieved by GUT, which is a DL-based algorithm with anatomical label configurations. The DL-based methods generally obtained better accuracies than the MAS-based approaches in terms of Jaccard, Dice, and SD metrics, though this conclusion was not applied when the HD metric is used. Particularly, one can find that the mean of HD from the two MAS methods was not worse than that of the other eight DL-based approaches. For MRI data, the best Dice score of the WHS (0. 87 ± 0. 04) was obtained by UB2 * , which is a DL-based method and a delayed submission; and the best HD (28.
Progress and challenges The MM-WHS challenge provides an open access dataset and ongoing evaluation framework for researchers, who can make full use of the open source data and evaluation platform to develop and compare their algorithms. Both the conventional methods and the new DL-based algorithms have made great progress shown in this paper. It is worth mentioning that the DL models with best performance have demonstrated potential of generating accurate and reliable WHS results, such as the methods from GUT, UB1 * and UB2 * , though they were trained using 40 training images (20 CT and 20 MRI). Nevertheless, there are limitations, particularly from the methodological point of view. Table  6  summarizes the advantages and potential limitations of the benchmarked works. WHS of MRI is more arduous.