dataesr/scientific-paragraphs-categorization huggingface dataset:
g, h Proteomics analyses of proteins recovered with the Acyl-RAC. g Venn Diagram of proteins identified in the three samples. h Result of the search in the SwissPalm database of proteins identified in at least two proteomic analyses. Complete protein list is provided in Supplementary Dataset 5. NT: not transfected; tot: total cell lysate;ctr: negative control (tris-base); palm: fraction of palmitoylated proteins (hydroxylamine); 2BP: 2-bromopalmitate. ns (not significant) p > 0. 05; *p ≤ 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. Sample size, complete statistical analysis, and p values are provided in tables in supplementary datasets. binding to the PM compensate for the loss of paxillin binding to β3 integrin (β3 VE/YA ). The paxillin LIM domain interaction network includes talinhead domain binding.
h Quantification of the integrins flow in adhesions, expressed as mean displacement over time (µm/ min) per replicate. Statistical analysis is provided in Supplementary Dataset 2. i Octet biosensor analysis of the interaction between GST-tagged LIM domain variants and Ni-NTA sensor functionalized with His-tagged talin-head domain (talin 1-405 aa). The binding response at 320 nM talin-head domain concentration is reported, n = 3 measurements per condition. j Octet biosensor analysis of the interaction between MBP-tagged and biotinylated LIM domains (LIMs) immobilized on streptavidin sensor with His-tagged talin-head (TH) domain (talin 1-405 aa), n = 4 measurements per condition. ns (not significant) p > 0. 05; *p ≤ 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. Sample size, complete statistical analysis, and p values are provided in tables in supplementary datasets.
One hour before imaging, culturing medium was replaced with F12 medium (Sigma-Aldrich), supplemented with FBS, penicillin/streptomycin, and glutamine. Photoactivation was performed on a Nikon A1r confocal laser scanning microscope equipped with a ×60 oil immersion objective and a 37 °C and 5% CO 2 incubation chamber. Laser wavelengths of 488 and 561 nm were used to acquire three pictures at 5 s intervals before photoactivation and 1 frame every 2 s for 3 min after photoactivation. Excitation of photoactivatable GFP molecules was achieved by means of a 405 nm laser (10% power), on a single ROI matching the size of an mCherry-positive focal adhesion. Using Imaris combined with MATLAB scripts, we identified the area of photoactivation, automatically repositioned it in case of lateral shift according to the mCherry signal, and extracted the mean green intensity within the ROI for each time point.
Adhesions of a given cell, were segmented frame by frame, using the neurite function of Metamorph applied on the mCherry paxillin or β3 integrin-derived fluorescence channel. Only in the case of β3 VE/ YA _CN+mCherry_paxN-term_CC and β3 VE/YA _CC+mCherry_CN adhesions were identified in virtue of the citrine fluorescence because of the non-FAs-specific mCherry localization. Thereafter, Matlab was used to correct the possible translational drifts of cells, and their respective adhesion masks, occurring during the time course of the acquisition. Then background subtraction, followed by a nonlocal means filter was used to diminish the contribution of spatially stationary areas, i. e. , mainly outside of adhesions. Dense optical flow was estimated for all the pixels in the frames, using Farnebäck's method  84.
LifeAct_GFP was obtained as previously described  87. The amino acid compositions of the fluorescent proteins can be found in the FPbase database, at the following IDs. Photoactivatable GFP (PA-GFP): FPbase ID 7QYHY (gift from Christoph Ballestrem, Wellcome Trust Center for Cell-Matrix Research, University of Manchester, Manchester, UK)  88  ; mCherry: FPbase ID ZERB6; citrine: FPbase ID VR7EN. SpyTag003/SpyCatcher003 sequences can be found in the Addgene repository  69. DNA sequence analysis was performed for all constructs and mutants to ensure error-free amplification and correct base replacement. Statistics and reproducibility. Datasets of measurements of the fluorescence ratio did not consistently satisfy normal distribution and therefore statistical comparison was performed using the Wilcoxon test based on ranks.
For comparison among more than two groups the Kruskal-Wallis extension was applied, followed by the Dunn's correction. Differences of integrin flows in adhesions among samples were assessed by one-way ANOVA test. The half-lives calculated from FLAP data were subject to one-way ANOVA test, while for half-lives calculated from FRAP experiments the Kruskal-Wallis test was employed. All multiple comparisons were followed by Tukey-Kramer post hoc correction. The nomenclature used in figures is based on the following criteria: ns (not significant) p > 0. 05; *p ≤ 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. For each experiment, sample size, complete statistical analysis, and p values are provided in tables in supplementary datasets, as specified in figure captions. All experiments were repeated at least three times and no repeated measurements were performed.
Acknowledgements We are grateful to  Dr. Sunil Dogga  for introducing us to the Acyl-RAC technique, to  Dr. Patricia Vazquez  for her contribution in the initial phase of this work and to  Monica Julio Barreto  for the technical help. All the members of the  Bioimaging Core Facility of the Center Medical Universitaire  are acknowledged for help in microscopy and image analysis. We thank all the members of the  Proteomics Core Facility  of the  Faculty of Medicine, University of Geneva , for their engagement in the challenging task of processing our samples. This work was supported by the  Swiss National Science Foundation  to BWH, grants  31003A_166384  and  310030_185261  and by  Academy of Finland  to VPH, grant  331946. We also acknowledge  Biocenter Finland  for technical support.
Data availability Microscopy images that support the findings acquired during the current study are available in Yareta with the identifier doi: 10. 26037/yareta:23jacb27ibdm5erlqxcdluzwrm  89. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  90  with the dataset identifier PXD023810 and DOI 10. 6019/PXD023810  91  , while detailed sample handling by the Proteomics Core Facility is available in Yareta. List of figures that have associated raw data: 1b-n, 2a-b, d-h, 3a-i, 4a-h, 5b-e, g-i, 6c-g, 7b-l, 8b-h, 9b-e, g-j, Supplementary 1a-h, 2a-e, 3a-d, 4a, c-f, h-j, 5a-l, 6a-d, 8a-i. Source Data for graphs are available in Supplementary Dataset 6. Any other remaining information is available from the corresponding author upon reasonable request.
Code availability MATLAB codes were developed in-house at the Bioimaging Core Facility, Faculty of Medicine, University of Geneva. The MATLAB code used for automatically reposition the photoactivated ROI in case of lateral shift during the acquisition time is available at https://github. com/Bioimaging/XTBxyt_ShapeTracker. The MATLAB code used to estimate the dense optical flow for all the pixels in the frames, using Farnebäck's method, is available at https://github. com/Bioimaging/Sliding-adhesions/tree/main.
Yet, given the complex underlying processes they imply and the lack of direct observations, they could not always be used as quantitative tuning targets but simply inspected visually It should be noted that not much effort was put in to tuning the representation of the modes of rainfall variability, such as that associated with the Madden-Julian Oscillation. More importantly, the ECS or the evolution of surface temperature (SST or Global mean surface temperature) during the historical period were not considered as targets for the tuning. Only the present-day global temperature is considered as a tuning target. This translates into a piControl climate relatively warm or cold for a respectively small or large ECS. This tuning strategy is detailed below.
The potential role of internal climate variability for the ocean heat uptake also needs to be properly removed  (Gastineau, Mignot, Arzel, & Huck, 2018). Finally, the tuning protocol presented here does not account either for other possible source of inertia in the system, such as that associated with the land surfaces and the melting of the permanent sea ice. Nevertheless, this simple setup provides an easy way to indirectly tune piControl and historical simulations on present-day observations to a large extent. One advantage of its simplicity is that it could be very easily shared among modeling groups in order to define a common present-day protocol for the CMIP DECK (Diagnostic, Evaluation and Characterization of Klima). Third, the tuning strategy is based on the release of a relatively large (15) number of versions of the coupled model, each of which being derived from the previous one after parameter calibration and/or code improvement based on the performance of the previous version.
Data Availability Statement LMDZ, XIOS, NEMO, and ORCHIDEE are released under the terms of the CeCILL license. OASIS -MCT is released under the terms of the Lesser GNU General Public License (LGPL). IPSL -CM6A-LR code is publicly available through svn, with the following command lines: svn co https://forge. ipsl. jussieu. fr/igcmg/ browser/modipsl/branches/publications/IPSLCM6. 1. 11-LR_05012021 modipsl cd modipsl/util;. /model IP-SLCM6. 1. 11-LR. The mod. def file provides information regarding the different revisions used, namely: (1) NEMOGCM branch nemov36STABLE revision 9455; (2) XIOS2 branchs/xios-2. 5 revision 1873; (3) IOIPSL/ src svn tags/v224; (4) LMDZ6 branches/IPSLCM6. 0. 15 rev 3643; (5) tags/ORCHIDEE20/ORCHIDEE revision 6592; (6) OASIS3-MCT 2. 0branch (rev 4775 IPSL server). The login/password combination requested at first use to download the ORCHIDEE component is anonymous/anonymous.
We recommend to refer to the project website: http://forge. ipsl. jussieu. fr/igcmg_doc/wiki/Doc/Config/IPSLCM6 for a proper installation and compilation of the environment. The python code developed for Figure  1  and 3 and 5-7 is available under https://zenodo. org/record/4415818. The preprocessed data sets used for Figures 5-7 and partly Figure 1 are available https://zenodo. org/record/4415805. Figure 1 uses CMIP6 outputs available for all simulations except pdControl and pdControl PM0 which are available here: https://zenodo. org/record/4415805. Figures 8 and 9 were made using the CliMAF Python library (Climate Model Assessment Framework, https://github. com/rigoudyg/climaf).
Conflict of Interest The authors do not declare any competing interests. It benefited from the ANR funded project Convergence (grant ANR-13-MONU-008) and the "Investissements d'avenir" program with the reference ANR-11-IDEX-0004 -17-EURE-0006. The CMIP6 project at IPSL used the HPC resources of TGCC under the allocations 2016-A0030107732, 2017-R0040110492, and 2018-R0040110492 (project gencmip6) provided by GENCI (Grand Équipement National de Calcul Intensif). This study benefited from the ESPRI (Ensemble de Services Pour la Recherche à l'IPSL) computing and data center (https://mesocentre. ipsl. fr) which was supported by CNRS, Sorbonne Université, École Polytechnique and CNES and through national and international grants. Support from the European Commission's Horizon 2020 Framework Program is acknowledged, under Grant Agreement number 641816 for the "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)" project (11/2015-10/2020).
g. , India, Ethiopia, Tanzania, Colombia, and Peru)  (Fernie et al. , 1968). Inventory of coffee field genebanks throughout the world has been undertaken in the past by several authors  (Bettencourt and Konopka, 1988; FAO-WIEW Database (cited in Bramwell et al. , 2017) ;  Eira et al. , 2007; Labouisse et al. , 2008; Dulloo et al. , 2009; and Phiri, 2013) , but the latest inventory reveals a total of more than 21,000 accessions being conserved in field genebanks globally  (Bramel et al. , 2017). CATIE International Coffee Collection (CICC), established in 1949 in Turrialba, Costa Rica, is one of the world's largest collections of C. arabica and a few diploid coffee species, with 1,960 accessions, which includes samples of the historic collecting missions by FAO, ORSTOM, and IPGRI  (Bramel et al.
Information on Each Accession The first step involves gathering and analyzing all management, passport, characterization, and evaluation data for each accession contained in the field genebank that is held on record and identifying any gaps in information on each accession. Often, such information is scattered in different paper and computer files, and it is important that all the information is combined in one database to facilitate analysis. This step also involves carrying out a thorough literature review to gather information about the origins of the accessions and to identify occurrences of the same material in other field genebank, both within and outside the country, to ascertain which accessions may be duplicated elsewhere. In the CICC, the relevant information was extracted from two principal databases: (i) CATIE's catalog database that contains the passport information (Introduction Book), to verify taxonomy information, varietal name, accession number, specific observations on the accession, its origin, and date of introduction; and (ii) the separate CAFE-BASE database, which also has passport information, but also comprises field inventories and evaluation data.
In addition, the results of a 2019 inventory and individual health status assessment of plants of each accession (see below) were held in different files. All the information was brought together in one database to enable a full analysis of the data available for all the accessions of the collection. Step 2: Carry Out a Full Inventory of the Field Collection The next step is to undertake a full inventory of the field collection. This involves the physical counting of the number of trees alive for each accession in the field. For each accession extant in the field, the following information should be recorded.
• Accession number. • Description of the accession. • The precise location of the accession, depending on how the collection is organized in the field (e. g. , section, blocks, lines, etc. ). Step 3: Determine Health Status of Each of Plant in the Collection It is important that the health status of each plant in the accession be assessed. This involves making a physical observation of the plant in the field and classifying it into one of three categories: critical, poor, and healthy (see criteria and categories below). Figure  2  illustrates visually the status of coffee plants that are in three health categories. This step is best done at the same time as step 2 above. Step 4: Update the Field Collection Database, Indexed by Accession Number and Ensuring That All Information Is Contained Within the Same Database In CICC, all information containing the accession level data on field genebank records in the CATIE catalog, CASE-BASE, inventory files, characterization, and evaluation were analyzed by CATIE information technology specialists who compiled and organized all the information into a single Excel document, using the accession number as the common denominator.
This work required reviewing and debugging the information, extracting the data and generating a consolidated field genebank record database, and using unification programming algorithms. In addition, a full bibliographic search for publications containing characterization of coffee from CICC was undertaken from the CATIE's ORTON Library database, as well as from PROMECAFE Network. The extracted information, including 31 titles from ORTON Library and 48 titles from PROMECAFE Network, was compiled in Excel sheets and integrated with the new consolidated field genebank record database. Step 5: Develop Specific Criteria and Categories to Score and Prioritize Each Accession of the Field Genebank To prioritize a field genebank collection for appropriate actions, a set of criteria and categories and a scoring mechanism should be developed.
Composition of the CICC The 2019 inventory of the CICC revealed a total of 1,975 accessions in the field, organized in 10 sections, labeled A to H, Musas and Citricos, in CATIE's campus. However, when compared with records of the passport data and introduction files, there was a disparity among them, summarized as follows: • 172 accessions of 2019 inventory without passport data. • 18 of 2019 inventory without passport data and not found in introduction book. • 111 accessions on records with passport data, but not in the field. • 130 accessions recorded in introduction book, but not in field. • 47 accessions found in the field (2019 inventory) with no accession number (these were hybrid varieties from CATIE's breeding program). After accounting for these disparities, the total number of accessions remaining in the CICC as of December 2019 was 1,895 accessions.
Consequently, there were three sets of accessions for the same material, but with different CICC accession numbers, totaling 155 accessions. Fifty-two accessions were received from Institut de Recherches du Cafe et du Cacao (IRCC), Paris (with IRCC numbers) in 1985 (T. 16689 to T. 16741); 45 accessions were received from Institut de Recherches du Cafe et du Cacao,    (Charrier and Bethaud, 1985). Furthermore, 58 accessions were received as clones introduced and multiplied in vitro from ORSTOM, Montpellier, in 1995 (T. 21259 to T. 21316). Thus, the same ORSTOM mission population (ET code) had been introduced and assigned a different CATIE accession number (T series). For example, accessions T. 16689, T. 17173, and T. 21259 all represented the same ORTSOM mission population ET-01 and were introduced from Paris, Cameroon, and as in vitro material from Montpellier, respectively.
Uniqueness Table  6  shows the number of accessions that belong to the different "uniqueness" categories, within and outside the center of diversity (see Methods section). Unfortunately, we do not have the full passport data (especially the site information) on all the accessions in the collection. This limits the kind of analysis that we can do, and overall, the uniqueness criterion is difficult to apply to all accessions in the CICC. It was only possible to do this analysis for the accessions for which information on locality names was available, which included mostly the historic collection missions of FAO  (Fernie et al. , 1968) , IPGRI, and ORSTOM  (Guillaumet and Hallé, 1978). The most unique accessions from the FAO and ORSTOM missions were those that came from only a single locality within the center of diversity.
The World Coffee Research has also characterized 847 wild accessions using molecular markers  (Klein et al. , 2016). Currently, the characterization data of 7 traits for only 34 accessions are made available and are uploaded on the Genesys portal https://www. genesys-pgr. org/; however, the full passport data set of the complete CATIE coffee collection can be found on the portal. Among the remaining four criteria, the type of genetic resources, that is, whether they were of wild origin, cultivated varieties, or resulting from experimental and breeding activities, was considered as the most important criterion given that it has been demonstrated that wild types are genetically more diverse compared with cultivated arabica varieties  (Lashermes et al. , 1996; Scalabrin et al. , 2020). However, it is important to clarify that among the so-called "wild materials" accessions in CICC, very few were truly from the wild  (Fernie et al.
CONCLUSION The in-depth study of the CICC allowed its management to carry out a full inventory of the field collection in December 2019, including an assessment of the health status of each tree across all surviving accessions, and to reassemble all its field genebank records into one database system, which henceforth will greatly facilitate the monitoring of the coffee field genebank. It allowed the management to reconcile its records on file with what is actually conserved in the field. Furthermore, the study also allowed CATIE to develop a full rationalization plan for its collection. A new site has been identified, and work has started for the multiplication of 1,104 priority accessions, which will be part of the new collection.
ACKNOWLEDGMENTS We wish to thank the management of the  Global Crop Diversity Trust and CATIE  for facilitating this study. Our thanks also go to  Stephane Dussert  and  Thierry Joet of IRD, Stephanie Greene of USDA Fort Collins and Benoit Bertrand of CIRAD and Melanie Bordeaux of Foundation Nica-France  ( La Cumplida, Nicaragua ) for providing information on safety duplicates held by them in their genebanks. We thank  Andreas Ebert ,  Jan Engels ,  Francisco Anzueto  for providing comments on the prioritization methodology developed for the study. Thanks are also due to CATIE field genebank staff for undertaking the field inventory and  Djuneid Dulloo  for making the flowchart diagram in Figure 1.
Datasets We generated 2 different datasets in this work: the first one was used to train and evaluate the segmentation performance of the CNN models, while the second one served as an exploratory use case, to assess root growth under alternative photoperiods and provide an example of the novel temporal phenotypical parameters that can be extracted with ChronoRoot. Note that all these images were obtained with the proposed 3D-printed hardware, and both are available to encourage reproducible research. Importantly, when splitting the training, validation, and test partitions, we were careful not to include images corresponding to the same video on different partitions, to avoid overoptimistic biased evaluations. r Dataset used to train and validate the deep learning mod- els for root segmentation: The dataset used for training consisted of 331 images from 55 videos (on average 6 images from the same plate at different states of growth); 11 of those videos were annotated by an expert biologist.
The dataset used for testing consisted of 55 images from 11 different videos, all annotated by the same expert. The tool used for the manual annotation was ITK-SNAP  [5]. In total, 240 plants distributed over the 66 videos were used for training/testing the methods. r Use case dataset for plant phenotyping under alternative photoperiods: We used 12 videos for each photoperiod, with pictures taken every 15 minutes. We took the first 17 days (1,632 frames), and after processing the videos we proceeded to discard the results from the first 3 days prior to seed germination. We selected 25 plants from each photoperiod to perform the temporal growth analyses.
3D-printed device for temporal image acquisition The ChronoRoot device is an affordable and modular imaging system based on 3D-printed and laser-cut pieces and off-the-Downloaded from https://academic. oup. com/gigascience/article/10/7/giab052/6324285 by guest on 23 August 2021 shelf electronics (Fig. 1. 1 and 1. 2). Each module consists of a Raspberry Pi (v3)-embedded computer controlling 4 fixed-zoom and fixed-focus cameras (RaspiCam v2), and an array of infrared (IR) LED back-light. In between each camera and the corresponding IR array, there is a vertical 12 × 12 cm plate for seedling growth, allowing automatic image acquisition repeatedly along the experiment without any modification or movement of the imaging set-up. The 4-plate module is small (62 × 36 × 20 cm) and can be placed in any growth chamber.