dataesr/scientific-paragraphs-categorization huggingface dataset:
Vocabulary setup and governance The FCM vocabulary is hosted on the NERC (Natural Environment Research Council) Vocabulary Server (NVS, https://vocab. nerc. ac. uk). The NVS is a dedicated semantic repository for terminologies used in the marine and related domains. It is managed by the British Oceanographic Data Centre (BODC) at the National Oceanography Centre in the United Kingdom. The NVS publishes terminologies like the FCM vocabulary, in standardized formats using the World Wide Web Consortium's (W3C) Simple Knowledge Organization System (SKOS, https://www. w3. org/2004/02/skos/) to represent knowledge in a format that is understandable by both computers and humans. As part of the SeaDataCloud project, the terms and definitions needed to represent the commonly agreed FCM groups were converted into SKOS concepts and added to a SKOS collection.
For the FCM concepts, a new collection had to be created. It was given its collection identifier (F02) to form its unique Uniform Resource Identifier (URI) https://vocab. nerc. ac. uk/collection/F02/. Within the collection, the SKOS concepts are made of an identifier, a mandatory preferred label, an optional alternative label, and an optional description field. The SKOS identifier enables each concept to be individually and uniquely accessible through its URI. The technical governance of the FCM vocabulary is entirely managed by the NVS team at BODC while the content governance is currently coordinated by the authors of this paper. A GitHub repository (https://github. com/nvs-vocabs/ F02) was set up to enable any interested person to propose changes, or additions or ask questions about the new vocabulary in a publicly open forum.
Discussion Large and curated datasets of fundamental ecological traits and abundances of marine pelagic microorganisms should be made available worldwide, given their relevance in balancing Earth's climate, controlling food webs, and affecting the ecological status of the oceans. FCM is a powerful tool to provide such datasets as it allows fast and reliable access to phytoplankton and heterotrophic prokaryotes abundance with a relatively good separation into important ecological traits such as size and pigment classes. Consequently, a wider scientific community and managers became interested in implementing FCM data more routinely in monitoring programmes (e. g. ,  Benedetti-Cecchi et al. , 2018; Delpy et al. , 2018; Eriksen et al. , 2019)  increasing the FCM datasets in different pelagic habitats.
However, homogenisation of datasets is as far as we know, not existing, preventing integration of knowledge at large scales. These datasets are undervalued and seldom integrated into global studies, due to a lack of sharing facilities and interoperability solutions. Without establishing a standardization framework also referred to as Ocean Best Practices  (Pearlman et al. , 2019) , the FCM datasets cannot be shared worldwide, preventing their fundamental contribution to Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) for calculating indicators of biodiversity and ecosystems status and trends. During the last 10 years, steps towards best practices and quality control procedures for sample collection, calibration, standardization procedures and data management (including international ISO standards) have been started and discussed in national and international programs such as SOMLIT (https:// www.
somlit. fr/en/), Dymaphy (http://www. dymaphy. eu/), SeaDataCloud (H2020 EU project, ID: 730960; https://www. seadatanet. org/About-us/SeaDataCloud), JERICO-NEXT and JERICO-S3 (H2020 EU projects, ID: 654410/871153; https:// www. jerico-ri. eu/) by a network of experts. Besides building best practices to make the handling of the flow cytometers similar between users, historically widely discussed and treated in the frame of immunology and medical use  (Pachoń et al. , 2012) , one of the first outcomes of these collaborative efforts is the establishment of the 13 groups of autotrophic (including mixotrophic) and heterotrophic microorganisms categorized and quantified based on a common non-taxonomic vocabulary. A second reason for building such a vocabulary is the continued development of machine learning and other techniques for interpreting the ever-growing number of studies involving multiparametric FCM analysis of marine samples, especially with the increasing use and diversity of autonomous high frequency sensors.
Indeed, inter-laboratory data analysis based on manual classification of the FCM groups can be affected by strong variability, especially for the dimmest and the least concentrated groups such as the RedPicoProk, the HsNano or the RedMicro and OraMicro groups  (Fuchs et al. , 2022) , and for data generated by different instruments  (Peperzak et al. , 2020). Annotation of groups based on a consensus definition will facilitate the process of building the workflow from the original dataset to the interoperable database. It will also strengthen and guide the users, by inferring and assessing the expected outcomes from marine water analyses. This step is one of several to proceed toward FAIR data, and it also paves the way for European (EMODnet; https://www. emodnet-biology. eu/) and global (OBIS; https://obis.
org/) databases to consider trait-based biological information from FCM with high spatial and temporal resolution as valuable as and complementary to taxonomical data for developing EBVs for future policy requirements. However, by applying standards and trained neural networks dedicated to the recognition of the described groups presented in this manuscript, we advise it could hide some interesting groups not specifically recognised by the training set. Indeed, rare events or sub-groups should find their place in one of the 13 groups described here, still, it will belong to the scientist dealing with his instrument to make the choice on which level of complexity its study should focus on. A similar effort on inter-laboratory validations and guidelines to ensure optimization of the settings for an instrument in observing marine microorganisms is urgently required.
Funding Funding originated from the  CHROME Project (Excellence Initiative of Aix-Marseille University -A*MIDEX , a  French "Investissements d'Avenir" program ), the  SeaDataCloud  ( H2020   EU  project, ID:  730960 ) and the  JERICO-NEXT  and  JERICO-S3  ( H2020  EU projects, ID:  654410/871153 ) projects,  HMS  gratefully acknowledges funding from the  U. S National Science Foundation  ( OCE-1655686 ) and the  Simons Foundation  ( 561126 ).
Data description In total, for the audio-visual runs (155 pulses), 76 runs were collected in 12 sessions for monkey T and 65 runs in 9 sessions for monkey S. For the pure visual runs (155pulses), 13 runs were collected in 8 sessions for monkey T and 12 runs in 5 sessions for monkey S. Based on the monkey's fixation quality during each run (85% within the eye fixation tolerance window), we selected 60 runs from monkey T and 59 runs for monkey S in total, i. e. 10 runs per task, except for one task of monkey S for audio-visual runs and 11 pure visual runs for S and 13 for T.
Data analysis Data were pre-processed and analysed using AFNI  (Cox, 1996 ), FSL (Jenkinson et al. , 2012; Smith et al. , 2013) , SPM software (version SPM12, Wellcome Department of Cognitive Neurology, London, UK, https://www. fil. ion. ucl. ac. uk/spm/software/), JIP analysis toolkit (http://www. nitrc. org/projects/jip) and Workbench (https://www. humanconnectome. org/software/get-connectome-workbench). The T1-weighted and T2-weighted anatomical images were processed according to the HCP pipeline  (Glasser et al. , 2013; Autio et al. , 2020)  and were normalized into the MY19 Atlas  (Donahue et al. , 2016). Functional volumes were corrected for head motion, slice timed referred on the middle image of the run and skull-stripped. They were then linearly realigned on the T2-weighted anatomical image with flirt from FSL, the image distortions were corrected using nonlinear warping with JIP.
Acknowledgements S. B. H. were funded by the  French National Research Agency (ANR) ANR-16-CE37-0009-01  grant and the  LABEX CORTEX funding  ( ANR-11-LABX-0042 ) from the  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We thank  Fidji Francioly  and  Laurence Boes  for animal care,  Franck Lamberton  and  Danièle Ibarrola  for their MRI methodological support and  Holly Rayson  for their help on visual and auditory stimuli collection.
There is no FUCCI staining in early S, while lamin B is not detectable at M. In the following analysis, early and late S nuclei were pooled together. (B) Optical cross section of 72, 96, and 116 h AEL wing discs expressing E2F1-RFP and CycB-GFP (same preparation as those shown in Figure  1 ). 1B , 3B-3D, S1, and S3: the same dataset of 11 discs was used in these figure panels. For the 96h and 116h AEL wing discs, the genotype was nubbin-Gal4/UAS-FUCCI and for the 72h AEL wing discs, it was pdm2 R11F02 -Gal4/UAS-FUCCI. Pdm2 R11F02 -Gal4 is a pouch marker which is stronger at 72h AEL than nubbin-gal4. 53 igure  S1B : tub-G4/UAS-CD8-GFP.
Image analysis Before segmentation, the region of interest was manually cropped using FIJI. 50 For eight of the eleven discs analysed, nuclei were segmented using the Nessys module of PickCells. 51 For the 3 remaining discs a machine learning algorithm (see below) was used to generate a binary mask of the segmented nuclei. This binary mask was then fed into Nessys to segment individual nuclei. Nessys then calculated the center of mass, volume, length of the longest axis (l max ) as well as the mean fluorescence values in the different channels (E2F1 and CycB) inside each segmented nuclei. To rotate the sample, a custom-made python code using the Numpy and Scikit-image libraries was used to define a plane based on three points manually picked and located in the most apical part of the disc.
QUANTIFICATION AND STATISTICAL ANALYSIS Data for the sample number (number of wing discs, nuclei or simulations), statistical significance (represented as * p<0. 05, ** p<0. 01, *** p<0. 001) as well as dispersion measures (standard deviation) is given in the figures and the figures legends. All statistical tests were performed using the stats module from the SciPy python library. A Wilcoxon signed rank sum test was used after testing for normality using a Shapiro-Wilk test. There was no blinding performed. The graphs in Figures 1B, 2H, 3C, S1D, S1F-S2H, S3B, S3C, and S4E were performed using the python libraries Seaborn and MatplotLib. The graphs in Figures 2B-2G, 4B, S2B-S2G, S4A-S4D, and S4F-S4M were performed with MATLAB.
(C) Percentage of nuclei in G1, S, and G2 at different stages. Note the increase of G2 nuclei and decrease of S nuclei, as predicted by the model. The same discs as those used to generate Figures 1A, 1B, and S1 were used (72 h AEL: 4 discs, 836 nuclei. 96 h AEL: 4 discs, 2,562 nuclei. 116 h AEL: 3 discs: 5,889 nuclei). Error bars represent standard deviation. (D) Distribution of nuclei along the apical-basal axis (mm). Nuclei in G1, S, and G2 were counted in slices of 5 mm (half the average spherical diameter of G1 nuclei) and normalized over the total number of nuclei. The relative increase of G2 nuclei at late stages is consistent with ''congestion'' impairing apical-ward movement. Each dot is an average from 4 discs (72 and 96 h AEL) or 3 discs (116 h AEL). (C) and (D) were generated from the same dataset.
ACKNOWLEDGMENTS This work was supported by core funding from the  Francis Crick Institute  ( FC001204  to J. -P. V. and  FC001317  to G. S. ), a  Wellcome Trust   Investigator award  ( 206341/Z/17/Z  to J. -P. V. ), the  Engineering and Physical Sciences Research Council  ( EP/M006883/1  to P. D. ), the  Royal Society  and the  Wolfson Foundation  through a  Royal Society Wolfson Research Merit Award  ( WM130048  to P. D. ), and the  National Science Foundation  ( RNMS11-07444  ( KI-Net ) to P. D. ). S. H. was supported by a joint  Imperial College-Francis Crick Institute  PhD studentship. D. S. was supported by a Francis Crick PhD fellowship. G. P. -M. is the recipient of a postdoctoral fellowship from  EMBO  ( ALTF 238-2018 ). S. M. -A. is supported by the  Vienna Science and Technology Fund (WWTF)  with a  Vienna Research Groups for Young Investigators grant   VRG17-014.
This analysis recapitulated the identification of several known targets of the Polo kinase Cdc5, including Slk19, Spo13, Net1 and Sgs1  (Grigaitis et al. , 2020; Matos et al. , 2008; Park et al. , 2008; Shou et al. , 2002; Figure   4B , dark blue dots) , validating our approach. Notably, it also led to the identification of various putative novel targets of Cdc5 (Figure  4B , dark gray dots). The proteome-wide dataset identified 88 phosphorylation sites among 16 nucleoporins from various NPC subcomplexes (Figure  4C ; Table  S6 ). All the basket nucleoporins displayed prominent phosphorylation (Table  S6 ). However, only one of the sites, Nup60 S89, had increased phosphorylation in response to CDC5 induction with high statistical confidence (p<0. 01; Figure  4C-D , Table  S6 ). Consistent with Nup60 being a target of Polo kinase, we observed higher mobility species of Nup60 during a wild type meiosis at time points when the Cdc5 protein was expressed (Figure  4E ).
Importantly, Polo kinase activity was both necessary and sufficient for Nup60 phosphorylation (Figures  4F-G  We note that the SWATH-MS dataset offers a valuable tool to identify previously uncharacterized Polo kinase targets in meiosis (Table  S6 ; see additional notes regarding data interpretation in Materials and Methods). As a proof of concept, we confirmed that putative targets Swi6 and Slk19 were modified in a Cdc5-dependent manner during meiosis. We predict that the SWATH-MS dataset described here will serve as a broad resource for the study of various other processes regulated by the Polo kinase during meiosis.
Nup60 detachment To determine the extent to which the Cdc5-dependent phosphosite Serine 89 contributes meiotic Nup60 phosphorylation, we generated strains expressing a phosphorylation-resistant mutant, NUP60-S89A-9myc, at the endogenous locus. The Nup60 S89A mutant protein displayed a reduced yet detectable mobility shift (Figure  4H ), indicating that S89 is phosphorylated but that additional residues may be concurrently modified during meiosis. To identify these additional sites, we took advantage of a second SWATH-MS dataset that characterizes the phosphoproteome during the entire budding yeast meiotic program  (Wettstein et al. , in preparation). This new dataset has comprehensive coverage of the NPC, with a total of 155 phosphopeptides and 106 individual phosphosites for nucleoporins including 20 phosphosites in Nup60.
In the new dataset, S89 phosphorylation peaked during the meiotic divisions (Figure  5A , red line), consistent with Cdc5 mediating its modification during a wildtype meiosis. Given the well-defined pattern of S89 phosphorylation, we reasoned that Cdc5responsive phosphorylation sites would likely have a similar temporal profile to S89. This led to the identification of eight additional Nup60 phosphorylation sites exhibiting similar upregulation during meiosis (Figure  5A ). These sites were located in an Nterminal cluster (T112, S118, S171, S162) and a C-terminal cluster (S371, S374, S394, S395). Many of the phosphosites (T112, S118, S171, S371, S374) exhibited additional signatures of Cdc5-dependence, with low phosphorylation during prophase I arrest (ndt80Δ) and high phosphorylation during metaphase I arrest (cdc20-mn).
Downstream analysis, plotting, and statistics were conducted using R 4. 1. 1 (R Core Team, 2020). Information on the specific statistical test used for each experiment is provided in the corresponding figure legends. The full summary statistics and statistical test output for each experiment is provided in Table  S5 , and all source code used for analysis is provided at https://github. com/jmvarberg/King_et_al_2022. For all experiments, asterisks represent the following p-values: *, p < 0. 05; **, p < 0. 01; ***, p < 0. 001; ****, p < 0. 0001. To visualize nucleoporin detachment in S. pombe, line profiles were measured In FIJI using a line width of 5 pixels on a single middle image of nuclei at the indicated timepoints relative to SPB separation (visualized using SpPpc89-mCherry). Individual line profiles were normalized (min-max) and plotted in GraphPad Prism (v.
MS data acquisition and analysis Sample preparation: 100 mL of meiotic yeast culture OD 600 = 3. 5 in SPM* (2% KAc) was collected per time point and supplemented with 100% Trichloroacetic acid (TCA) to a final concentration of 6. 25%. Samples were washed twice with cold acetone, pelleted and snap frozen in liquid nitrogen. Proteins were extracted in batches of 12 samples by 5 minutes bead beating using 400 µl of 425-600 µm glass beads in a cell Disruptor Genie and 400 µl of lysis buffer (8 M urea, 100 mM ammonium bicarbonate, 5 mM EDTA, pH = 8). The samples were then centrifuged at 16,000 x g for 10 min and the supernatant collected. The extraction procedure was repeated a total of 5 times using 400 uL fresh lysis buffer each time. Total amount of protein was measured with bicinchoninic acid (BCA) assay (Pierce) and 3 mg of total protein was further used per sample.
0. 9992) and further to mgf using MzXML2Search (TPP 4. 7). The converted files were searched with Comet (2014. 02 rev. 0) and Mascot (version 2. 5. 1) using the yeast SGD database (release 13. 01. 2015) appended with the SK1 (for a total of containing 12,043 proteins including one protein entry for the concatenated sequence of the iRT peptides and as many decoy protein entries generated by pseudo-reversing the tryptic peptide sequences) or the W303 entries (for a total of containing 12,071 proteins including one protein entry for the concatenated sequence of the iRT peptides and as many decoy protein entries generated by pseudo-reversing the tryptic peptide sequences) for data acquired for the SK1 or W303 strain, respectively. The search parameters were as follows: +/-25ppm tolerance for MS1 and MS2, fixed cysteine carbamidomethylation, either variable methionine oxidation (for the non-enriched datasets) or variable methionine oxidation and variable serine/threonine/tyrosine phosphorylation (for the phospho-enriched datasets), semi-tryptic and 2 missed cleavages allowed.
The comet and mascot search results were further processed using peptideProphet  (Keller et al. , 2002)  and aggregated using iProphet  (Shteynberg et al. , 2011; TPP v4. 7 rev 0). The search results were filtered for an iProphet cutoff of 1% false discovery rate. The pep. xml file was further processed with LuciPhor for determining the confidence in the phosphorylation site localization. Two consensus spectral libraries were generated using spectrast  (Lam et al. , 2008)  depending on the confidence in phosphorylation-site localization and the two assay libraries thereof were exported using the spectrast2tsv. py script  (Schubert et al. , 2015)  with the following parameters: 5 highest intensity fragments (of charge 1+ or 2+) per peptide, within the mass range 350-2000 m/z, allowing fragments with -79.
All the precursor intensities were then summed to a protein intensity value for the non-enriched datasets. The raw dataset consisted of 14,710 phosphopeptides with single and multiphosphorylated sites that were annotated to the yeast proteomes  W303 and SK1 (17,387 proteins; Engel et al. , 2014). W303 is identical to the proteome release S288C_reference_genome_R64-2-1_20150113. tgz available at http://sgdarchive. yeastgenome. org/sequence/S288C_reference/genome_releases/. The median intensities of phosphosites mapping to the same amino acid and position per protein were merged to generate 7,552 unique phosphopeptides. Additionally, multiphosphorylated peptides were split into single phosphosites and they were retained for further analysis if they were not part of the set of mono-phosphorylated peptides.
The log 2 ratios in Figure  4B -C comparing Cdc5 WT vs Cdc5 KD were calculated using the mean values across biological replicates and time points (2, 3, 4 and 5 h after Cdc5 induction). The reported p-values were corrected for false discovery rate (FDR) and tested against an alpha value of 0. 05. All statistical methods were implemented using R v3. 5. 1 (R Core Team, 2020) and the limma package v3. 38. 3  (Ritchie et al. , 2015). A few considerations are important to correctly interpret this dataset. (1) Cdc5-mediated phosphorylation of a peptide may not necessarily lead to an increase in its abundance. Protein phosphorylation can be linked to the co-occurrence of other PTMs or to protein degradation, which could lead to a decrease in the ability of our approach to detect the phosphorylated peptide.
Acknowledgements: We thank  Cyrus Ruediger ,  Tina Sing ,  Eric Sawyer ,  Kate Morse ,  Anthony Harris ,  Amanda Su ,  Andrea Higdon ,  Emily Powers ,  Helen Vander Wende ,  Gloria Brar ,  Maia Reyes , and  Benjamin Styler  for comments on this manuscript, and  Jasper Rine ,  Rebecca Heald ,  James Olzmann ,  Jeremy Thorner , and the entire  Brar-Ünal Lab  for valuable discussions. This work was supported by funds from  NIH  ( R01AG071801  and  DP2AG055946-01 ) and  Astera Institute  to EÜ; a  National Science Foundation Graduate Research Fellowship  ( DGE 1752814 ) and a  National Institutes of Health Traineeship  ( T32 GM007232 ) to GAK;  ETH Zürich , the  Swiss National Science Foundation  ( 155823  and  176108 ), the  Max Perutz Labs , and the  University of Vienna  to RW and JM; a Ruth.
Data availability All the reagents used in this study are available upon request. All of the ImageJ macros and R scripts used for image analysis, plotting and statistics are available at http://www. stowers. org/research/publications/libpb-1689 and at https://github. com/jmvarberg/King_et_al_2022. All raw MS data files and search results thereof have been deposited to the ProteomeXchange consortium via the PRIDE (Perez-Riverol et al. , 2019) partner repository with the dataset identifier PXD033245.
Moreover, we adapt the popular Caliński-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with kmeans. A special attention is given to the relevant but very challenging problem of inferring alternative supertrees. The use of the Euclidean property of the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for analyzing large evolutionary datasets. We apply the new method to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and genetically related betacoronaviruses.
Tree clustering problem The traditional k-means algorithm (MacQueen 1967) partitions a given dataset into K (K > 1) disjoint clusters according to its objective function based on a specific distance (e. g. Euclidean or Minkowski distance) and the selected cluster validity index (e. g. Caliński-Harabasz, Silhouette or Dann index). Most of the traditional cluster validity indices take into consideration both intragroup and intergroup cluster evaluations. However, we cannot use the standard objective functions or cluster validity indices when clustering trees. Here, we discuss the main modifications that should be introduced into the conventional k-means algorithm in order to adapt it to tree clustering. In case of tree clustering, the objective function of the method can be defined as follows:  = = = K k N i ki k k T C RF OF 1 1 ) , ( , (1) where K is the number of tree clusters, Nk is the number of trees in cluster k, ) , ( ki k T C RF is the RF distance between tree i of cluster k, denoted Tki, and the majority-rule consensus tree (any other type of consensus trees could be considered here) of cluster k, denoted k C.