dataesr/scientific-paragraphs-categorization huggingface dataset:
The values of the parameters can be set by right-clicking on the parameters. The red font indicates inconsistencies in the configuration. The parameters displayed in bold are the most critical for analysis optimization. When available, pop-ups giving additional information on parameters are automatically displayed on mouse over. CRITICAL All the configuration tests (see the Configuration Test tabs) can be performed in two different modes, a "simplified" mode for inexperienced users, where only the most important parameters can be changed and non technical help is provided, and an "advanced" mode where all the parameters can be accessed and more technical descriptions of the algorithms are provided. The mode can be changed in the Test Mode sub-panel of the Test Controls panel. Dataset menu choose New and enter the name of the dataset.
(iii) First, set the channels by right-clicking on Detection Channel in the configuration tab and choose Add Element. (iv) Right-click on the newly added channel and set a name. CRITICAL STEP depending on the format of the input dataset, the user might need to indicate the keyword allowing finding the image files corresponding to the appropriate channel; refer to the help of the Channel Keyword parameter. (v) Repeat steps (iii) and (iv) for each channel of the input dataset. CRITICAL STEP The number of channels set in the configuration must match the number of channels of the input data otherwise the images won't be imported. In the Configuration tab, unfold the Pre-Processing for all Positions node, then the first node that corresponds to the first position (called first position node below), then the Pre-processing node of that position.
using any ImageJ selection tool (such as a rectangle selection or freehand selection) or by clicking on an object. Objects can be added or removed from the current selection by pressing shift. (iv) To select and visualize all tracks click on the corresponding button in the Editing panel of the Data browsing tab or use the associated shortcut. CRITICAL STEP To select only one track, use the shortcut indicated in the Shortcut table Toggle display object/track and click on one object of the track. All the selections of the current dataset are automatically saved in the database and will appear in the list of the Selections panel. (ii).
Acknowledgements We acknowledge  Johan Elf  ( Uppsala University ),  Stephan Uphoff  ( Oxford University ),  Hanna Salman  ( Pittsburgh University ) as well as anonymous reviewer 4 for kindly providing datasets from their labs that allowed us to test the applicability of our software. Funding: This work was funded by the  Agence Nationale de Recherche  (grant  ANR-14-CE09-0015-01  to ME) and by the city of Paris (program Emergences 2018 to ME). Author Contribution: All authors wrote the article and contributed to tests and documentation, JO developed the software, LR and JO analyzed software performances, LR generated dataset 1, ME generated datasets 2 and 3. Competing financial and non-financial interests: The authors declare that they have no competing financial and non-financial interests.
Download and install FIJI 64-bit version (imagej. net/Fiji/Downloads). 2. Choose Help > Update… 3. Perform any needed updates by clicking on Apply changes 4. Restart Fiji 5. Choose Help > Update again. Repeat steps 3-4 until the message "Your Fiji is up to date!" is displayed. 6. Re-run the updater, click Advanced Mode then click Manage update sites. Tick the checkboxes next to "BACMMAN" and "ImageScience". 7. Click Close and Apply Changes, when prompted restart FIJI. 8. From the plugin menu run the BACMMAN>BACteria in Mother Machine ANalyzer command. and Maven 3 and Java 8 or further. Run the following bash commands (replace <VERSION> by the actual version): Installation as a stand-alone application: TIMING 2-5 min CRITICAL Requires Git Installation under FIJI: TIMING 10-15 min 1. 1. git clone https://github.
github. com/jeanollion/bacmman/wiki/How-to-Adapt- Select one position in the Position list. From the Tasks list select Pre-Processing and from the Run menu choose Run Selected Tasks. TROUBLESHOOTING CRITICAL STEP In this step and (v)-(viii) we detail the different processing steps one by one. All these steps can be run at once by selecting Preprocessing, Segment and Track, Measurements, Extract Measurements in the Tasks list, all the objects in the Objects list and all the positions in the Positions list. See Batch processing box for processing in command line mode. CRITICAL STEP Pre-processed images are stored in the Output folder of the dataset folder, they can be visualized by right-clicking on the position in thePositions list and by choosing Open PreProcessed Images. CRITICAL STEP Before performing pre-processing, you can test it by running it on a smaller subset of data.
CRITICAL STEP When importing a new image format, always check that it was correctly imported by opening it and checking the dimensions of the image (CTRL + SHIFT + P). In particular if T and Z dimension are swapped, this can be fixed by setting the parameter named "Swap T & Z dimensions" of the import method and re-importing the images through the command Import/re-link images. Configuration) 1B. Analysis of a mother-machine time-lapse experiment with (i) Create a dataset and import images. Follow step 1A(i) fluorescent cells and intracellular spots TIMING 2-5 min for (i)-(iii) ; (ii) From the Dataset menu choose New dataset from template, select the Visualize 15-20 min for (iv)-(viii) ; 10-15 min for (iX-Xiii). the results configuration file of dataset 3 (see EQUIPMENT) and enter "dataset3" as name following steps 2A(i)-(v).
TROUBLESHOOTING CRITICAL: A screencast for this procedure is available on BACMMAN's wiki of the dataset. CRITICAL STEP: Segmentation and tracking results are automatically stored (github. com/jeanollion/bacmman/wik/Example-Datasets) (iii) From the in files located in the Output folder of the dataset. You can test a processing (i) pipeline by running it on a smaller subset of data, see step 1D(xxii). Create a dataset and import images. Follow step 1A(i) (vi) (ii) Repeat step (v), selecting "Bacteria" instead of "Microchannels". Visualize the From the Dataset menu choose New dataset from template, select the results following steps 2A(i)-(v), and if necessary edit segmentation and configuration file of dataset 2 (see EQUIPMENT) and enter "dataset2" as name tracking of the dataset. errors as explained in procedure 1C.
(iii) TROUBLESHOOTING From the Run menu choose Import/re-link Images and select the folder containing images of dataset 2. Run image processing TROUBLESHOOTING Follow TROUBLESHOOTING Follow TROUBLESHOOTING Follow step 1A(v) selecting the object class Spot instead of Microchannel steps. Follow steps 1A(iv) step 1A(v) step 1A(vi) TROUBLESHOOTING CRITICAL STEP to display the contour of bacteria while visualizing DNA replication errors (fluorescent spots), use the command to create a selection of bacteria, described in step 2B(iv). Follow steps 1A(vii)-(viii) CRITICAL STEP: You will sometimes need/wish to check and manually edit segmentation and tracking results. For this follow the instructions given in 2A and 2C(i). Perform statistical Analysis. Open the analysis script 2 (See EQUIPMENT) and CRITICAL STEP: (ix) (iv) (v) (vi) (vii) (viii) (ix) Perform statistical analysis.
Follow steps 1A(xiii)-(xv) (xiii) From the Data browsing tab, click on Reload Selections button in the Selections panel. 2C. Adaption of an existing configuration to another dataset (xiv) See step 2B(iii)-(iv) to display and navigate within the selection. (xv) See step 2C(i) to edit segmentation and tracking errors. TIMING 5 min for steps i-iii ; 5 min for step iv ; 5 min CRITICAL STEP after editing, re-run steps 1A(vii)-(viii) and (x) to correct the for step v-vi measurements and update the output data file. (xvi) Run block 5 to generate a plot of mother cell length through time (vii)Select all positions from the Positions list, select Measurements from the Tasks list and choose Run Selected Tasks from the Run menu. CRITICAL STEP: To visualize measurements from the GUI, see step 2A(v) (viii) Select all positions from the Positions list, and select Extract Measurements from the Tasks list and choose Run Selected Tasks from the Run menu.
using any ImageJ selection tool (such as a rectangle selection or freehand selection) or by clicking on an object. Objects can be added or removed from the current selection by pressing shift. (iv) To select and visualize all tracks click on the corresponding button in the Editing panel of the Data browsing tab or use the associated shortcut. CRITICAL STEP To select only one track, use the shortcut indicated in the Shortcut table Toggle display object/track and click on one object of the track. All the selections of the current dataset are automatically saved in the database and will appear in the list of the Selections panel. (ii) configure the parameters using intermediate images, refer to the help of each parameter displayed in the help panel. Depending on the module, additional data may be displayed on right-click menu on the resulting kymograph (refer to (xi) Right-click on the newly created Transformation node, set a transformation (i.
Images of the current position Measurements and choose Add Element. the Shortcut > Navigation / Display menu. (i) (xxiv) Create a new dataset. Follow step 1A(i) before and after the transformation, as well as images or graphs corresponding Right-click on the Measurement, choose a module and configure it. Processing Tracker:BacteriaClosedMicrochannelTrackerLocalCorrections > Segmentation Pipeline: SegmentAndTrack > Algorithm:BacterialFluo and set the value of the parameter Foreground Selection Method:EDGE FUSION > Background Edge Fusion Threshold to 4 by right-clicking on the current value. CRITICAL STEP Refer to step 1D(xxii) to test segmentation in order to determine the value of the parameter for a different dataset. CRITICAL STEP Always save the configuration changes (menu Dataset > Save Configuration changes) before processing.
Follow step 1A(vi) to re-run segmentation and tracking of bacteria, and 2A(i) visualize the new segmentation and tracking results. 2D. Creating and configuring a new dataset TIMING 5 min for i-vii ; 10-(vi) 60 min for xiii-xv ; 10-60 min for xvi-xxii ;10 min for xxiii-xxv. (ii) to intermediate steps will automatically open. (xxv) Repeat steps (xxi) and (xxii) as many times as needed. From the (ix) By default, voxel calibration is read from the image metadata. You can check Perform Segmentation and Tracking for each object class sequentially 2) Follow option 2A to visualize segmentation and tracking results; follow option 2B to use selections in order to visualize or analyse only a part of the dataset; follow option 2C in order (i) From the Segmentation & Tracking Results panel of the Data Browsing tab, click on the position #0: all segmented microchannels will be listed.
CRITICAL STEP: that the images are correctly calibrated by opening the images by right-clicking following step 1A(v) panel. CRITICAL If users want to analyse a time-lapse dataset that differs from those described in on the first position node, choosing Open Input Images and checking the (xxii) To test a processing step (segmentation, tracking, pre-filtering or post-filtering), CRITICAL STEP only the objects that have already been segmented can be 1A and 1B, they can either adapt the configuration files provided here for datasets 1 and 2 or calibration in the image properties (CTRL + SHIFT + P). If calibration is from the Configuration Test tab, select Processing as Step in the Test Controls displayed. (iii) Configuration Test tab, select Processing asStep and Bacteria as Object Class. In the lower part of the tab, select create a configuration file de novo.
Defining de novo pre-processing and processing pipeline requires image analysis skills. CRITICAL In the Configuration and Configuration Test tabs, all the parameters are hierarchically organized and displayed as a tree, in which sub-parameters can be accessed by unfolding the parameter. (vi)Second, set the import image method by right-clicking on Import Method. You should choose here one of three different available methods depending on your dataset: one file containing all images, one file per channel and per position or one file per channel, position and frame. For the two last methods additional configuration is needed, refer to the help of the Import Method Parameter. (vii) Follow step 1A(iii) to import images CRITICAL STEP When importing a new image format, always check that it was correctly imported by opening it and checking the dimensions of the image (CTRL + SHIFT + P).
(xviii) Set the detection channel associated to the object class by right-clicking on Detection Channel in the node of the object class that you just created. (xix) If necessary set the Parent and the Segmentation Parent. Some object classes can be located within others on the images, for instance bacteria are inside microchannels and fluorescent spots are inside bacteria. Image processing takes this into account by segmenting and tracking a given object within an object of another class (called respectively Segmentation Parent and Parent) For instance, in dataset 2, the Parent class of Bacteria and Spots is Microchannels, the Segmentation parent of Bacteria is Microchannels and the segmentation parent of Spots is Bacteria. panel and select the object class in the Test Controls panel; right-click on the operation to be tested and choose a Test option.
To add a selection to an active group, right-click on a selection and enable Active selection group. Shortcuts for each active selection group are listed in the menu Shortcuts > Selections. The selection will be automatically displayed. This command can also be performed on several microchannels at the same time, or on a whole position. (vi)To export selections in a tabular format that can be imported in R or Pandas, select the appropriate selection(s) in the Selections panel and run the Extract Selections command from the Run menu and choose a folder. 2C. Editing segmentation and tracking TIMING 5-15 min for step 1 ; 1-2 min for steps ii-vii CRITICAL : A screencast for this procedure is available on BACMMAN's wiki (github. com/jeanollion/bacmman/wiki/Example-Datasets) CRITICAL: all editing commands and associated shortcuts are listed in the shortcuts list from the help menu.
They can be split again if necessary using the Create Track command. CRITICAL STEP Measurements are not automatically updated after editing segmentation or lineage, they should be performed and extracted again on each position where editing has been done. (ii) Example of manual editing on dataset 2. Open the dataset2 and go to the data browsing tab (iii) Open the kymograph of the first microchannel track (see steps 2A(i)-(iv)): between frames 173 and 183, the two first bacteria are merged by error. (iv).
Bypass of UV lesions by translesion synthesis To validate our system, we constructed 3 integration vectors, containing no lesion, a TT-CPD lesion (cyclobutane pyrimidine dimer) and a TT(6-4) lesion (thymine-thymine pyrimidine(6-4)pyrimidone photoproduct). In order to focus on lesion tolerance mechanisms, we inactivated the repair mechanisms in our parental strain (namely nucleotide excision repair: rad14, and photolyase: phr1), as well as the mismatch repair system (msh2), to avoid the repair of the strand marker. Tolerance events are calculated as the ratio of colonies resulting from the integration of the damaged vector versus the lesion-free one. We integrated the constructs containing a single CPD or TT(6-4) lesion as well as a construct with no lesion. The data express the level of tolerance events (either TLS or DA) relative to the non-damaged construct.
FUNDING Agence Nationale de la Recherche (ANR) Grant [GenoBlock ANR-14-CE09-0010-01]; Fondation ARC pour la recherche sur le cancer; K. M. was supported by a fellowship from Fondation ARC. Funding for open access charge: Agence Nationale de la Recherche [GenoBlock ANR-14-CE09-0010-01]. Conflict of interest statement. None declared.
Single-Crystal X-Ray Diffraction Analysis X-ray crystallographic analyses of the crystals were carried out on a Bruker Kappa Apex II CCD diffractometer using phi and omega scans of narrow (0. 5 • ) frames with Mo Kα radiation (λ = 0. 71073 Å) and a graphite monochromator (Table  1 ). The structures were solved by direct methods in the SHELX-97 software  [17]  and refined by the full-matrix least-squares method against all F 2 in anisotropic approximation by means of the SHELXL-2014/7 software suite  [18]. The hydrogen atom positions were calculated via the riding model. Absorption corrections were applied by the empirical multiscan method in the SADABS software  [19]. The obtained crystal structures were analyzed for short contacts between nonbonded atoms in PLATON  [20, 21]  and MERCURY  [22].
CCDC 1830809 and 1830810 contain the crystallographic data for 1a and 1b, respectively. These data can be obtained free of charge via http://www. ccdc. cam. ac. uk/cgi-bin/catreq. cgi, or from the Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: (+44) 1223 336 033; or e-mail: deposit@ccdc. cam. ac. uk. -of-fit on F 2 1. 01 1. 00 Final R indices I > 2σ(I) R 1 = 0. 0481, wR 2 = 0. 1338 R 1 = 0. 0448, wR 2 = 0. 0863 Final R indices (all data) R 1 = 0. 0584, wR 2 = 0. 1461 R 1 = 0. 0978, wR 2 = 0. 1049 Largest diff. peak / hole, e•Å -3 0. 22/-0. 25 0. 24/-0. 22.
Magnetic Measurements Magnetic susceptibility data (at 2-300 K) on polycrystalline samples were determined using an MPMS-XL SQUID magnetometer of Quantum Design (San Diego, CA, USA) in a 0. 1 T applied magnetic field. The magnetization isotherm was recorded between 0 and 5 T. All data were corrected for a contribution of the sample holder and diamagnetism of the samples estimated via Pascal's constants  [23]. Crystals 2019, 9, 219 5 of 12.
Identification of YWHAZ Variants in Human Patients Through a genome sequencing research study, we identified a heterozygous genetic variant in YWHAZ (c. 689C > G, p. S230W) in a male with a clinical diagnosis of CFC syndrome (proband 1, Figure  1A , Supplementary Text 1). He has short stature, global developmental delay, bilateral proptosis and ptosis, high forehead, pulmonic stenosis, hyperkeratosis, and seizures. Previous clinical testing included a negative RASopathy panel, but trio genome sequencing revealed a de novo serine to tryptophan change in YWHAZ at amino acid (aa) position 230, a location close to the C-terminal end and two residues away from a known casein kinase phosphorylation site at T232 (Supplementary Table  1 ). This missense variant is extremely rare and is absent from the gnomAD population database  (Lek et al.
This missense variant is also absent from the gnomAD population database  (Lek et al. , 2016)  and has multiple lines of computational evidence predicting a deleterious effect (SIFT score of 0; GERP score of 5. 64; CADDv1. 4 score of 32). Like S230W, the specific functional impact of this sequence variant has not been examined. Three additional YWHAZ variants were also identified in three individuals with distinct phenotypes (Probands 3-5, Figures  1C-E , Supplementary Table  1 , Supplementary Text 1). While one of these probands harbors a missense variant (S145L, proband 3, Figure  1C ), two others have premature termination (E14X, proband 4, Figure  1D ) and frameshift (S230Yfs * 44, proband 5, Figure  1E ) variants, respectively. Each of these probands shows developmental delay or intellectual disability, but none has cardiac or skin abnormalities (Supplementary Table  1 , Supplementary Text 1).
Western blot using anti-GFP antibody was then performed to examine the Craf-GFP protein that was pulled down by the anti-HA antibody. As shown in Figure  6A , the amount of Craf-GFP co-precipitated with HA-YWHAZ(S230W) was substantially higher than that pulled down by HA-YWHAZ, demonstrating that the S230W variant binds more Craf than wild type YWHAZ. Similarly, we performed co-IP with HA-YWHAZ/S230W and Braf-Flag and demonstrated that HA-YWHAZ(S230W) has an increased interaction with Braf-Flag than HA-YWHAZ (Figure  6B ). These data thus reveal that the S230W variant differs from YWHAZ in its ability to bind to Craf and Braf.
Variants were then annotated with SnpEff 4. 3, dbNSFP 2. 9. 3, gnomAD, ClinVar, HGMD, Variome Great Middle East and an internal database. Coverage for these samples was 93% at a 20x depth threshold. For probands 3, 4 and 5, exome sequencing was outsourced to GenomeScan (Leiden, The Netherlands). In brief, exomes were captured using the Agilent SureSelectXT Human all Exon v5 library kit (Agilent, Santa Clara, United States) accompanied by Illumina paired end sequencing on the Hiseq2500 platform (Illumina, San Diego, United States), generating 2 × 150 bp paired end reads with at least 80 × median coverage. Data analysis was performed at the LDGA using the in-house sequence analysis pipeline "Modular GATK-Based Variant Calling Pipeline" (MAGPIE) (LUMC Sequencing Analysis Support Core, LUMC) based on read alignment using Burrows-Wheeler Alignment (BWA-MEM) and variant calling using the Genome Analysis Toolkit (GATK).
Imaging and Embryo Quantification For stereo imaging of embryonic phenotypes and in situ hybridization, Nikon AZ100 microscope was used. The body length of the embryos was measured as a straight line from the head to the tip of the tail, which would reflect the effects of both the length and the curvature. NIH ImageJ software was used in body length measurement, and the scatter plot was made using GraphPad Prism 8 software. Student's t-test was used to determine statistical significance of the changes in body length of different samples in pairwise comparisons. For confocal imaging of Craf-GFP localization, Olympus Fluoview 2000 upright confocal microscope was used. Most of the images were taken using a 20× (NA0. 95) lens. Maximum intensity projections of Z-stack images were used for the figures.
Analysis of bacterial cell PALM movies includes two basic steps. The first is the fitting of a shape to the bacterial cell outline, also known as cell segmentation. A number of software packages are available for this task, including Oufti  [70]  and its predecessor MicrobeTracker  [71]. Fluorescence images, either of a cytoplasmic marker or of the cell wall or membrane, can be used for cell segmentation; more commonly in PALM experiments, however, a transmitted light brightfield image is used instead. The second step is the detection and tracking of single molecules and multi-copy foci. Again, there are a number of freelyavailable software packages implementing different detection and tracking algorithms, including the MATLAB-based package u-track  [72, 73]. A common approach is to fit fluorescent spots to a 2D Gaussian approximation of the point spread function.
Conclusion Structure-selective endonucleases are an important class of enzymes in all aspects of DNA metabolism. Determining their inherent substrate preference and cleavage patterns provides important information to establish the in vivo function of these enzymes. Here, we describe two assay formats using fluorescently-labeled DNA substrates paired with gel electrophoresis or FRET analysis that allow kinetic and endpoint analysis of structure-selective endonuclease independent of using radioactivity and compatible with high-throughput screening. Graph shows the slope / reaction rate for assay controls. Kinetic readout (Excitation 535 nm / Emission 665 nm) for 8 minutes at linear phase was measured per assay plate and data was calculated as slope from a time series of 12 data points representing the rate of reaction.
ACKNOWLEDGMENTS HLK was supported by  NIH  grant  R01-CA146940. JB acknowledges support from  NIH  grant  R21-GM-128022-01. PC was supported by the  Taiwan Ministry of Science and Technology  grant  MOST 105-2314-B-002-073-MY4. W-DH was supported by  NIH  grants  R01-GM58015 ,  R01-CA92276 ,  DOD  grant  W81XWH-14-1-0435  and  T32-GM099608  (SM). BVH was supported by  NIH  grants  R01-ES019566  (BVH) and  T32-GM088119  (ECB). ANK was supported by grants to ANK from the Europe-.