dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we thus applied the pipeline from García et al. [27]  to extract the surface rotation period of the 1,014 stars. We analyzed the three datasets corresponding to the three filters as described in Section 2. 1. First we selected the stars where all the methods and all the filters agreed within 2 σ, where σ = σ 2 1 + σ 2 2 with σ i being the uncertainty obtained by each method (except the ACF for which we do not compute any error). We also applied the criteria on the height of the ACF and the Composite Spectrum as described in Ceillier et al. [15]. We then visually checked the remaining stars that were not selected according to all these criteria. We also analyzed the PDC-msMAP lightcurves to check for pollution as we use customized apertures with KADACS, which are more likely to be polluted.
According to the DR25, 22 of these stars are subgiants so this could be a valid explanation for detecting their p modes (their intrinsic oscillations amplitudes are higher than during the main sequence). For the remaining 19 stars, the metallicity ([Fe/H]) obtained by the Kepler Follow-up Observations Program  [25]  also used in the DR25 stellar properties is super-solar for 12 stars. We still have 7 stars that have a high S ph value, low [Fe/H] but for which we could detect solar-like oscillations. This sounds reasonable given that for the Sun the amplitudes decrease by only around 12. 5% [e. g. 38] so the amplitudes could still be large enough to be detected even for stars with a < S ph > value above < S ph, ,max >. We see that below the S ph at minimum solar activity, there are around 100 stars with rotation and magnetic activity detection whether they have detected p modes or not.
Metalicity effect Many of the non-oscillating stars have spectroscopic data obtained with the DR14 Apache Point Observatory Galactic Evolution Experiment survey [  APOGEE 44, 33]  and the DR2 Large Sky Area Multi-Object Fiber Spectroscopic Telescope survey [  LAMOST 21, 43] , providing metallicity values more precise than photometric observations. After cross-checking our sample of stars with A max,pred > 0. 94 with the sample of these surveys we find that 158 stars have high-resolution spectroscopic observations from APOGEE and 326 stars have low-resolution spectroscopic data from LAMOST. We note that there could be an overlap between these two samples. Figure  14  shows the [Fe/H] histograms for APOGEE (top panel) and for LAMOST (lower panel) for all the non-oscillating stars that have a rotation and metallicity measurement (black solid line).
Inclination angle effect The APOGEE spectroscopic observations also provide a measurement of the v sin i for some of our non-oscillating stars. By combining these values with the surface rotation periods measured with the Kepler data and with estimates of the radii from the DR25 stellar properties, we can estimate the inclination angles of a subsample of 162 stars selected to follow criteria  (3). We obtain a reliable inclination angle, i, for 90 stars. For the other stars, the sin i is larger than 1. This could be due to a non-agreement between the rotation period and the v sin i or a less reliable radius, as the radii of these stars were mostly derived from isochrone fittings, hence less precise than asteroseismology. The typical uncertainties of the inclination angles are around 25 degrees.
CONCLUSIONS We have revisited the analysis of the 2,576 stars observed during the survey phase of main-sequence solar-like pulsating stars of the Kepler mission with one month of short-cadence data and followed for four years in long cadence. After removing polluting stars (red giants, classical pulsators, stars with already detected solar-like oscillations, new detection of solar-like oscillations, probable pollution), we have a sample of 1,014 main-sequence and subgiant solar-like stars. We measured the surface rotation period, P rot , and the photometric magnetic activity index, < S ph > and obtained reliable values for 684 stars, representing a yield of 67. 4%. As expected the hot stars rotate in general faster than the cool dwarfs. Our sample of subgiants is too small to conclude anything about them.
Introduction Ribonucleic acid (RNA) molecules are involved in most steps of the genetic expression including catalysis of central cellular functions  [1, 2, 3]. RNA function depends crucially on the specific tridimensional folding of the molecule which in turns depends on the sequence and on the way the bases pair through hydrogen bonds (secondary structure)  [4, 5]. High-resolution RNA structures can be determined experimentally by NMR spectroscopy  [6] , X-ray crystallography  [7] , and more recently using cryo-electron microscopy  [8]. However, obtaining high-resolution tridimensional structures through these techniques is still a challenging task as shown by the relatively small number of structures deposited in the Nucleic Acid Data Bank (NDB)  [9, 10]. One of the difficulties arise from the conformational flexibility of RNA molecules that allows them to adopt distinct conformations and results in rough energy landscapes  [11].
All-Atom Molecular Dynamics simulations Molecular dynamics simulations were performed with the GROMACS 5 package  [53, 54, 55, 56, 57]  using the Amber ff99+parmbsc0 force field for RNA molecules  [58]. To take in account the ligand in the binding site of the RNA molecule (like adenine in the adenine riboswitch), we performed DFT calculations to parameterize their atomic charges using Gaussian 16  [59]. We used the RESP procedure  [60] , which is part of the AMBER10 package to derive atomic charges  [61]. Atomic coordinates and electrostatic potential of the ligand were obtained by DFT with the B3LYP functional and the 6-31G* basis set  [59]. For the other parameters, we used the Generalized Amber Force Field  [62]. The molecular systems were placed in cubic box and solvated with TIP4P-EW water molecules  [63].
Conventionally, sugar ring puckers are divided into 10 families described by the atom which is most displaced from the mean ring plane (C1 ′ , C2 ′ , C3 ′ , C4 ′ or O4 ′ ) and the direction of such displacement (endo for displacements on the side of the C5 ′ atom and exo for displacements on the other side). Using the Curves+ program  [78]  for each simulation trajectory, for each nucleotide we computed the percentage of appearance for each family. In order to understand the interplay between the sugar conformation and the chemical reactivity, we grouped the sugar puckers into two large families. The sugar puckers C1 ′ -exo, C2 ′ -endo, C3 ′ -exo, C4 ′ -endo belong to the B-like family, while C1 ′ -endo,C2 ′ -exo, C3 ′ -endo, C4 ′ -exo belong to the A-like family. Helicoidal parameters. In order to decompose the movement between two nucleotides, we adapted the definition of inter-base parameters, as described in Curves+  [78]  and largely used for double-stranded nucleic acids, to the case of single-stranded RNA.
RNA structures and experimental data used as benchmark In our study, we considered six RNA molecules of different size and function, for which both experimental reactivity data and 3D structures are available (see Figure  3 ). We performed all-atom molecular dynamics (MD) simulations for each RNA molecule starting from experimental structure and we computed the fluctuations of the parameters previously presented and their correlation coefficients with the chemical probing reactivities. The stability of RNA structures was assessed via Root-Mean-Square-Deviation and hydrogen bond analysis (see Appendix Appendix B for more details). SHAPE and CMCT/DMS data for all systems except the ribozyme (PDB ID: 4P8Z) were taken from available data-sets  [84, 85, 86, 87, 88]. For the lariat capping ribozyme, the reactivity data were obtained for this study as previously described  [89]  (see Appendix A for details)  [52, 90, 91, 52].
PDB ID Looking in more detail at the relationship between fluctuations of distances and reactivity, the largest correlation is found between 1M7 reactivity and the distance C2 i -C2 i+1 (ρ P = 0. 6. ). However, also the fluctuation of the distance between consecutive C1 ′ atoms has a correlation value above 0. 5. For several angles under investigation, the Pearson correlation coefficient is above 0. 5 and it is significant (C2 i -C4 ′ i -P i+1 , C1 ′ i -P i+1 C1 ′ i+1 and C2 i -P i+1 C2 i+1 ). Although for the other angles we obtained low/moderate correlations coefficients, p-values lower than 0. 05 assure that these correlations are also significant. To provide a full picture of the flexibility of the nucleotide, we also analyzed the helicoidal parameters. Figure  5  shows the average of the Pearson and Spearman correlation coefficients of helicoidal rotational parameters (Tilt, Roll, and Twist) and 1M7 reactivity.
5-D. 10). For example fluctuation of the distance between consecutive C2 atoms can range from 0. 84 (strong correlation) to 0. 26 (weak correlation). The weak correlation can be due to several reasons such as a sampling problem during the MD simulations (multiple conformations and size limit), a difference between the crystallographic structure and the RNA molecule in solution (moreover for 3OFC the structure were taken from the whole ribosome, so the presence of proteins and other RNA molecules can influence the structure of the native RNA molecule), or other structural parameters having an impact on the chemical reactivity, such as stacking or accessibility. C 1 i -C 1 i + 1 C 2 i -C 2 i + 1 C 1 i -C 4 i -P i + 1 C 1 i -P i + 1 -C 1 i + 1 C 2 i -C 1 i + 1 -P i + 1 C 2 i -C 4 i -P i + 1 C 2 i -P i + 1 -C 2 i + 1 O 2 i -P i + 1 -O 5 i + 1 O 3 i -P i + 1 -O 2 i + 1 O 5 i -P i + 1 -O 2 i + 1 T i l t R o l l T w i ′ i -C4 ′ i -P i+1 , C1 ′ i -P i+1 -C1 ′ i+1 , C2 i -C1′ i+1 - P i+1 , C2 i -C4′ i+1 -P i+1 , C2 i -P i+1 -C2 i+1 , O2 ′ i -P i+1 -O5 ′ i+1 , O3 ′ i -P i+1 -O2 ′ i+1 , O5 ′ i -P i+1 -O2 ′ i+1 ; In order to better understand the meaning of correlation coefficients in this context, Figure  6  shows the RMSF, the fluctuation profile of the distance C2 i -C2 i+1 and the angle C2 i -C4 ′ i+1 -P i+1 and the 1M7 reactivity for the M-box Riboswitch RNA (PDB ID: 3PDR) and the 5SrRNA (PDB ID: 3OFC).
These results are in agreement with previous attempts to understand the origin of the SHAPE reactivity where several simple rules were proposed based on the geometry of the ribose  [46]. 3 : Multiple correlation coefficient between a set of parameters computed along the MD simulations and the 1M7 reactivity calculated by using eq. (  4 ). M 1 : fluctuations of distance C2 i -C2 i+1 and the angle PDB ID M 1 M 2 M 3 M 4 M 5 2L1B 0. C1 ′ i -P i+1 -C1 ′ i+1. M 2 : fluctuations of distance C2 i -C2 i+1 and of the angle C1 ′ i -C4 ′ i -P ′ i+1. M 3 : fluctuations of distance C2 i -C2 i+1 and of the angles C1 ′ i -C4 ′ i -P ′ i+1 , C1 ′ i -P i+1 -C1 ′ i+1 , C2 i -C1 ′ i -P i+1 , C2 i -C4 ′ i -P i+1 , C2 i - P i+1 -C2 i+1. M 4 : fluctuations of distance C2 i -C2 i+1 , of the angle C1 ′ i -C4 ′ i -P ′ i+1 and the rotational helicoidal parameters (roll, tilt and twist).
58 0. 61 0. 66 0. 66 0. 49 0. 64 0. 54 0. 63 1EHZ 0. 80 0. 73 0. 72 0. 76 0. 65 0. 75 0. 57 0. 75 3OFC 0. 64 0. 76 0. 60 0. 67 0. 72 0. 80 0. 62 0. 74 3PDR 0. 74 0. 75 0. 74 0. 71 0. 73 0. 75 0. 72 0. 77 4P8Z 0. 71 0. 82 0. 78 0. 76 0. 64 0. 77 0. 64 0. 74 TableD. 12: Spearman correlation coefficient between the fluctuation of pucker phase and the fluctuation of the angles C1 ′ i -C4 ′ i -P i+1 (a 1 ), C1 ′ i -P i+1 -C1 ′ i+1 (a 2 ), C2 i -C1′ i+1 -P i+1 (a 3 ), C2 i -C4′ i+1 -P i+1 (a 4 ), C2 i -P i+1 -C2 i+1 (a 5 ), O2 ′ i -P i+1 -O5 ′ i+1 (a 6 ), O3 ′ i -P i+1 -O2 ′ i+1 (a 7 ), O5 ′ i -P i+1 -O2 ′ i+1 (a 8 ). *: p-value higher than 0. 05. PDB ID a 1 a 2 PDB ID d 1 a 3 a 4 d 2 a 5 d 3 a 6 d 4 a 7 a 8 2L1B 0. 51 0. 48 0. 42 0. 55 0. 44 0. 50 0. 33 0. 48 2L1B 0. 57 0. 49 0. 65 0. 32 1Y26 0.
32 0. 30 0. 26 0. 33 0. 31 0. 32 0. 27 0. 32 1Y26 0. 52 0. 41 0. 74 0. 30 1EHZ 0. 41 0PDB ID Shift Slide Rise Tilt Roll Twist 1EHZ 0. 56 0. 60 0. 74 0. 25 3OFC 0. 73 0. 78 0. 68 0. 26 3PDR 0. 70 0. 71 0. 79 0. 47 4P8Z 0. 75 0. 65 0. 77 0. 46 PDB ID a 1 a 2 a 3 a 4 a 5 a 6 a 7 a 8 2L1B 0. 24* 0. 44 0. 21* 0. 65 0. 43 0. 35 1Y26 0. 45 0. 49 0. 58 0. 55 0. 47 0. 54 1EHZ 0. 28 0. 34 0. 21* 0. 24 0. 28 0. 34 3OFC 0. 29 0. 38 0. 29 0. 29 0. 27 0. 31 3PDR 0. 62 0. 60 0. 41 0. 69 0. 67 0. 71 4P8Z 0. 38 0. 29 0. 39 0. 37 0. 34 0. 37 PDB ID a 1 a 2 a 3 a 4 a 5 a 6 a 7 a 8 2L1B 1Y26 PDB ID Shift Slide Rise Tilt Roll Twist 0. 66 0. 54 0. 43 0. 45 0. 66 0. 31* 0. 27* 0. 32* 0. 43 0. 77 0. 60 0. 59 0. 69 0. 40 0. 69 0. 64 PDB ID Shift Slide Rise Tilt Roll Twist 1EHZ 0. 34 0. 35 0. 38 0. 46 0. 44 0. 45 0.
Using the aforementioned criteria, these strains are congeneric, and can be assigned to three new hypothetical species represented by strain PMC 1075. 18, strains PMC 1068. 18 and 1076. 18, and strains PMC 1050. 18 and 1051. 18, respectively (Table  1  and Table S1   18  was closely related (0. 5% sequence dissimilarity) to a sequence from an uncultured Nodosilinea sp. CENA 322, isolated from leaves of the mangrove tree Avicennia schaueriana in Brazil  [11]. Sequence from strain PMC 1064. 18 was highly similar (0. 5% dissimilarity) to sequences from two unpublished strains of Limnothrix. Strain PMC 1052. 18 displayed at least 7. 4% dissimilarity to all other sequences in databases, and represents a new species within a new undescribed genus. Finally, strains PMC 1069. 18 and 1070. 18 from Canal des Rotours were the only two Nostocales, clustering together and representing a single new hypothetical species belonging to the genus Scytonema.
Moreover, MS/MS data constituting the entire molecular network were searched against the GNPS spectral libraries  [13]  and yielded only 9 hits (triangle shapes on the network), including a nucleotide, diketopiperazines and phosphocholines (https://gnps. ucsd. edu/ProteoSAFe/result. jsp? task=9581427a15b7422d8bd2b3b4b086189e&view=view_all_annotations_DB). To further expand the annotation coverage, we applied DEREPLICATOR, a recently developed dereplication algorithm that enables high-throughput peptide natural products (PNPs) identification from their tandem mass spectra  [14] [15] [16] [17]. Interestingly, this tool allows to putatively identify an unknown PNP from its known variants (for example, with a substitution, a modification or an adduct) through the so-called variable dereplication process (as opposed to the strict dereplication when a PNP is described in the database).
Sampling and Strain Isolation Green biofilms, presumed to contain abundant cyanobacteria, were sampled in July 2018 from distinct locations in Guadeloupe, namely two stations located in the Manche-à-Eau lagoon close to red mangrove trees (Rhizophora mangle,  [25] , five strains), the Marina Bas-du-Fort (one strain), and the Canal Des Rotours, a 6-km long canal build between 1826 and 1830 connecting the city of Morne-à-l'eau to the sea on three stations, representing a transition between the coastal mangrove (stations 5 and 6 with six and three strains, respectively) and freshwater (station 7 with five strains, Table  1 ). In accordance with Article 17, paragraph 2, of the Nagoya Protocol on Access and Benefit-sharing, a sampling permit was issued and published (https://absch. cbd. int/database/ABSCH-IRCC-FR-246959).
geneious. com/), and compared to the GENBANK database using BLAST. Sequences are deposited in GENBANK under accession numbers MN823169 to MN823186; MN824246 and MN824247. A dataset was built consisting of sequences from the 20 isolated strains, their best BLAST hits, and representatives of major cyanobacterial lineages. Sequences from genus Gloeobacter were used as an outgroup. Sequences were aligned using the secondary structure-aware Infernal Aligner v. 1. 0 tool available on the Ribosomal Database Project website  [29]. Alignment was controlled to remove ambiguously-aligned zones. Phylogenetic tree was reconstructed using the software MEGA7  [30]. Relationships were inferred using a Maximum Likelihood approach using a General Time Reversible model (5 categories and invariants), and 1280 nucleotide positions.
We quantified the program ability to recover high-quality metagenomeassembled genomes (MAGs) from metagenomic assemblies generated directly from the meta3C libraries. Whereas nine high-quality MAGs are identified in the 148-Mb assembly generated using a single meta3C library, MetaTOR identifies 82 high-quality MAGs in the 763-Mb assembly generated from the merged 20 meta3C libraries, corresponding to nearly a third of the total assembly. Compared to the hybrid binning softwares MetaBAT or CONCOCT, MetaTOR recovered three times more high-quality MAGs. These results underline the potential of 3C-/Hi-C-based approaches in metagenomic projects.
, 2014; Press et al. , 2017; Stalder et al. , 2019)  and phage-host infection spectrum  (Marbouty et al. , 2017). These studies suggest that meta3C and similar approaches hold the potential to 1) accurately bin genomes and episomal DNA molecules and 2) assign episomal DNA molecules to their respective hosts. However, comprehensive, end-to-end computational pipelines to process raw meta3C datasets remain sparse  (Marbouty et al. , 2017; DeMaere and Darling, 2019). Most analyses so far have focused on single mock communities, and quantifiable metrics are lacking to see how meta3C-like approaches truly compare-and possibly complement-traditional binning methods, notably regarding the quality, completeness, and accuracy of retrieved bins. To address this need, we developed MetaTOR (Metagenomic Tridimensional Organisation-based Reassembly), a lean and scalable tool to investigate single or multiple proximity-ligation (i.
e. , 3C or Hi-C libraries) metagenomic experiments, from raw 3C reads and assembly to bins. MetaTOR was applied on 20 meta3C libraries of mouse gut samples collected over time. This first dynamic meta3C study allowed us to reconstruct dozens of complete genome sequences, and to compare the genomic bins recovered using MetaTOR with bins generated by binning software MetaBAT  (Kang et al. , 2015)  and CONCOCT  (Alneberg et al. , 2014). MetaTOR compared favorably with respect to the number of high-quality genomes recovered  (Bowers et al. , 2017)  and the amount of binned sequences. In addition, 3C-based binning was less dependent on the quality of the metagenome assembly (in terms of fragmentation-i. e. , contigs' mean size, N50). Overall, MetaTOR is a robust tool to process proximity-ligation sequencing data, regardless the number of samples processed.
Algorithmic Principles Underneath the MetaTOR Pipeline MetaTOR (https://github. com/koszullab/metaTOR) aims at providing the most accurate overview of genome content of a population, starting from as little as one meta3C library, while taking full advantage of additional libraries if available. It's structured around four main steps: alignment, partition, annotation, and binning (Figure  1 ). MetaTOR was purposely designed to maintain a high level of modularity and flexibility, so that users can supply their own intermediary inputs and tweak parameters to their liking at every step. This can save both time and resources. If starting from the raw data, all needed is the meta3C PE files and an assembly of the microbial community obtained either directly from the meta3C reads (as described in this work and in  Marbouty et al.
Construction of meta3C Libraries and Generation of Metagenome Assemblies To validate and compare the pipeline to classical metagenomic binning algorithms, we investigated the gut microbiota of various mice using meta3C libraries. Feces were sampled from three groups of two mice from the Institut Pasteur animal facility, over 20 days (Materials and Methods) (Supplementary Figure  1 ). Twenty meta3C libraries (three from cage n°1, nine from cage n°2, and eight from cage n°3) were then generated as described  (Marbouty et al. , 2017)  (Materials and Methods) using HpaII as restriction enzyme. Libraries were sequenced using PE Illumina 2x75 bp Kits (Table  1 ) (NCBI BioProject PRJNA542645). After trimming and quality filtering, between 25 and 100 million PE reads were recovered for each of the samples (~813 million PE reads total).
The mean completion and contamination rates of bins characterized by MetaBAT using the 20 libraries were slightly better (respectively, 74% and 1. 7%) than the ones obtained using MetaTOR (respectively, 65. 8% and 2. 4%) (Figure  3 ), but this could be due to the greater number of bins (>500 kb) obtained using MetaTOR (MetaBAT = 172; MetaTOR = 271) (Table  4 ). To compare further the output of MetaTOR and MetaBAT and their ability to reconstruct genomes from different phyla, we analyzed the taxonomic annotations of assembly #3 with the taxonomy of all the bins above 500 Kb retrieved for this assembly (Supplementary Figure  2 ). The bins generated by both softwares were highly consistent with the assembly annotation suggesting that they do not present particular taxonomic bias in their binning process.
FUNDING LB is supported by an  AMX  fellowship from the  French Ministry of Higher Education, Research and Innovation. TFR is supported by an  ENS  fellowship by the  French Ministry of Higher Education, Research and Innovation. This research was supported by funding to RK from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  260822 ) and from the  Agence Nationale pour la Recherche  ( JPI-EC-AMR STARCS ANR-16-JPEC-0003-05 ).
Time-lapse microscopy The higher resolution live-cell images of Figure  3A  were acquired using an inverted Spinning Disk microscope (Nikon Eclipse Ti-E-ad and Yokogawa CSU-X1-A1) equipped with a perfect focus system, a 100x APO TIRF/1. 49NA immersion objective, a Prime 95B camera (Photometrics) and a Live-SR module (Roper). The system is piloted by MetaMorph software (Molecular Device). Cells were grown exponentially in synthetic medium and plated on synthetic medium 1% agarose pads before imaging at 30°C. GFP and mCherry images were acquired sequentially with an exposure of 50 msec using 491 nm (Cobolt Calypso, 100 mW) and 561 nm (Cobolt Jive, 150 mW) lasers, filters used were  [BP 470/40, DM 495 nm, BP 520/35 (Nikon)  and BP 560/40, DM 595 nm, BP 630/60 (Nikon) and dichroic triple BP 405/491/561 nm (Semrock)].
Further processing was done using ImageJ software (National Institute of Health). Live-cell images of Figure  4  were acquired using a wide-field microscope based on an inverted microscope (Leica DMI-6000B) equipped with adaptive focus control to eliminate Z drift, a 100×/1. 4 NA immersion objective with a Prior NanoScanZ Nanopositioning Piezo Z stage system, a CMOS camera (ORCA-Flash4. 0, Hamamatsu), and a solid-state light source (SpectraX, Lumencore). The system is piloted by the MetaMorph software (Molecular Device). Cells were grown exponentially in synthetic medium, and 50 µL of the culture was loaded in microfluidic plates (Y04C plates, ONIX platform, CellASIC) equilibrated at 30°C for 2 hours. GFP and mCherry images were acquired sequentially for each Z section with an exposure time of 10 msec using solidstate 475-and 575-nm diodes and appropriate filters (GFP-mCherry filter, excitation: double BP, 450-490/550-590 nm, and dichroic double BP, 500-550/600-665 nm, Chroma Technology Corp.
Acknowledgments We thank  Angela Taddei  for lacI, lacI** and lacO array plasmids and suggestions,  Frank Uhlmann  and  Thomas Kuilman  for the G20 plasmid,  Helle Ulrich  for the AID tool kit,  Didier Busso  and  Eléa Dizet  ( CIGEX platform ) for the Rap1 sites plasmids,  Pascale Lesage  for the anti-Dps1 antibody,  Rémi Montagne  for assistance with the HiC data,  Romain Le Bars  (IMAGE-GIF platform) and  Lamya Irbah  ( IRCM microscopy platform ) for assistance with higher resolution microscopy,  Dan Throsby  for text editing,  John Marko ,  Damien D'Amours ,  Sarah Lambert ,  François-Xavier Barre ,  Pablo Radicella ,  Eric Coïc ,  Laurent Maloisel ,  Paul-Henri Roméo ,  Mathias Toulouze  and  Maoussi Lhuillier-Akakpo  for fruitful discussions and suggestions. This work was supported by funding to SM from  Fondation ARC, EDF  and  ANR  ( DICENs - ANR-14-CE10-0021-01 ), to K.