dataesr/scientific-paragraphs-categorization huggingface dataset:
When the system begins to lose track of a fish, it draws random lines to areas near the fish, possibly trying to relocate the same animal, but these erroneous data must be visually identified and rejected. When tracking zebrafish, many sources of erroneous data can be avoided by adjusting the settings of the Ctrax program. Ctrax has a Tracking Wizard that allows you to identify whether the fish or the background are darker, the region where Ctrax should track the fish, the background area the fish movements are analyzed within, as well as the high and low contrast thresholds that Ctrax should employ. With the correct features and adjustments, Ctrax can be an extremely useful tool for analyzing the position and behavior of zebrafish. Furthermore, with the ability to use Batch Processing on Ctrax, many videos can be analyzed effectively and efficiently using this program.
Image-J software for manual tracking of zebrafish Image-J  [59]  can be used for tracking fish in short videos with inadequate lighting. Once a zebrafish recording is obtained, the segments where tracking needs to occur should be delineated and their start and end time noted. Once Image-J is opened and the desired video file is opened within the program, the video frames during which tracking will occur must be entered into a dialog box which asks for the entry of the starting and ending frame. Depending on how many frames per second are recorded by the video recording software, the starting frame and ending frame of the tracking period need to be determined. Once the video is loaded and the desired frames set, from the "Plugins" tab at the top of the Image-J screen, Manual Tracking is chosen.
Once Manual Tracking is selected, the "Add Track" option is selected in the dialog box. From here on, a single fish can be tracked by repeatedly clicking at a specific point (e. g. , center of head) of its image in successive frames to ensure precise and consistent tracking. Once the fish has been tracked for all of the designated frames, the "End Track" option on the screen is selected. To track another fish, the same process can be repeated by selecting the "Add Track" option to start and the "End Track" option to terminate tracking. The program assigns each fish with a tracking number, chronologically ordering each fish that is tracked. As tracking is occurring, the coordinates of each fish are automatically recorded in a separate window that opens once tracking begins. It is important to keep this window open until all tracking is completed so that the data obtained is not lost.
Acknowledgments: We thank the  Biomedical Graduation Research Organization at Georgetown University  for administrative and facilities support. Mansi Vohra  and  Erin Wolfe  assisted with animal care and  Arya Sivadhanam  with the assembly of an initial version of the apparatus as part of their student research mentorships. Josephine Millard  provided post-review feedback on a part of the manuscript. We are especially grateful to the two reviewers for their suggestions on improving the manuscript. We additionally acknowledge the  Georgetown Zebrafish Shared Resource , which is partially supported by  NIH/NCI  grant  P30-CA051008.
In this work, we propose an original approach to the thin-layer identification of secondary metabolites (terpenes) based on the acquisition of multicomponent images integrating terpenes to be identified. Its principle consists initially of segmentation by region of each component of the image based on the attribute tuples or colors of each region of the digital image. Then we proceeded to the calculations of region parameters such as standard deviation, entropy, average pixel color, eccentricity from an algorithm on the matlab software. These values allowed us to build a database. Finally, we built an algorithm for identifying secondary metabolites (terpenes) on the basis of these data. The relevance of our method of identifying or recognizing terpenes has been demonstrated compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two terpenes having the same frontal ratio.
It is based on image acquisition, sampling and quantization of images using Dirac's two-dimensional comb. Parameters such as the average pixel color on each channel, color entropy on each channel, standard deviation, equivalent distance, etc. different regions are calculated. The values obtained are stored and constitute a database. From this database, an algorithm is developed and executed on Matlab version 2016 software  [6] , thus enabling the identification to be carried out automatically and quickly. For the understanding of this work, the document is organized as follows: in Section 1, we present photochemical screening and image acquisition integrating terpenes, scanning, filtering these images and calculating parameters, then in Section 2 we present the results of the different parameters calculated on the channels, finally in Section 3, a theoretical and experimental validation of our • UV lamp (366 nm, 254 nm).
Parameter Values For a stain on the thin layer, we identify the corresponding terpene by the traditional method using developers and for this stain parameters are calculated constituting a database and corresponding to this terpene. The parameter values for the terpenes are given in Tables  1 2 3 4. In   The results allow us to conclude that if the isolation is done closer to the center of the spots, the values of the properties are practically identical for the different fractions. Thus the spots incorporating the terpenes will have the same parameter values if we isolate the spots closer to the center of it.
Development of the Identification Program For the different fractions, the parameters have the same values, so this allows us to develop the identification program below (Figure  9 ). We verified the presence of terpenes, in particular linalool, in the hexane fraction of the leaves of Solenostemon monostachyus. The manual thin-layer identification protocol being the same, we segmented the different spots on the thin layer (Figure  10 ). We then calculated the various parameters including color entropy (Entropy), standard deviation (std) and average pixel color (MeanIntensity). We obtained the results shown in Table  4. These values correspond to terpenes in the database. The distance used to recognize objects in the image is the Euclidean distance, defined for two vectors ( ) 1 , , n X x x =  and ( ) 1 , , n Y y y =  de n  by the relation  [14] : ( ) ( ) 2 1 , n i i i d X Y x y = = - ∑ (6) which has values in  ,  or  and are called respectively real, rational or discrete distances.
The normalized value of the Euclidean distance is given by the relation  [14] :  ( ) , n i i i i i x y d x y x y =   - =   +   ∑ (7) allowing decisions to be made from the coordinates of vectors in a database. The values of the average color of the pixels, the standard deviation of each component, the entropy per color component of the four fractions of the compounds are given in Table  5. Table  5  confirms the presence of pinenes, limonenes, linalool, terpenes, in the leaves of Solenostemon monostachyus. For example we have the values of the average color (at R = 91. 20; at G = 114. 5; at B = 213. 21), of standard deviation (s R = 1. 58; s G = 3. 52; s B = 2. 62), of entropy (H R = 2. 51; H G = 2. 14; H B = 2. 47) which correspond to the values of the parameters of Linalool. Values (at R = 74.
Discussion The automated identification of thin-layer terpenes that we performed on digital images allowed us to identify a few molecules present in a hexane, chloroform, ethyl acetate and n-butanol extract. However, values of the average color (at R = 87. 01; at G = 41. 05; at B = 54. 01) do not match the values for terpenes in the database. This could be explained on the one hand by the noise due to stray lights during the acquisition of the images, on the other hand to the limit of our database which does not contain enough molecules, it will therefore be necessary to enrich this database to take into account several molecules (terpenes).
Figures 6-8 illustrate the segmentations of the different spots of the images used. Finally, we have created a database with the values of the parameters of each task (region). The spots corresponding to terpenes previously are identified by the manual identification technique based on the developers. We segmented each spot of the images in Figure 5, we then calculated the average color, standard deviation, color entropy using the algorithms encoded in the Matlab 2016 version run under Microsoft Windows 10 pro on a PC based on Intel i3.
Figures 6 - 6 Figures 6-8 illustrate the segmentations of the different spots of the images used. Finally, we have created a database with the values of the parameters of each task (region). The spots corresponding to terpenes previously are identified by the manual identification technique based on the developers. We segmented each spot of the images in Figure 5, we then calculated the average color, standard deviation, color entropy using the algorithms encoded in the Matlab 2016 version run under Microsoft Windows 10 pro on a PC based on Intel i3.
The nominal maximum pressure at the 100% amplitude level corresponds to 7 MPa (Imasonic data sheet). For acoustic pressure measurements, we used a calibrated 0. 2-mm PVDF needle hydrophone system (Precision Acoustics, Dorchester, UK) together with a 3-D positioning table in de-gassed, de-ionized water at room temperature. First, we validated the linearity of the FUS system within the pressure range expected for BBBo and measured the maximum pressure and its location for output levels ranging from 10%À30%, where 30% corresponds to a 2. 1-MPa peak positive pressure. In addition to hydrophone measurements, we developed an annular array simulation model based on FIELD II  (Jensen and Svendsen 1992; Jensen 1996)  and MATLAB (The Math-Works, Inc. , Natick, MA, USA). FIELD II is based on a linear calculation model in homogenous media, which is a valid assumption for the pressures required for BBBo.
Defined sound fields for BBB opening in rats S. GRUDZENSKI et al. as skull pressure weakening measured with three and eight elements  (O'Reilly et al. 2011a (O'Reilly et al. , 2011b)). We analyzed ICH in brain slides at the location of the BBBo according to MR images in the treated location and corresponding location of the untreated hemisphere. Slides were stained every 116 mm, with the total number of slides depending on the extent of tissue damage and location of application. Grades of ICH were determined and count microscopically and classified into grades 1 (single erythrocytes), 2 (small aggregates of erythrocytes) and 3 (large aggregates of erythrocytes) as described before  (Reuter et al. 2014). Detailed data concerning animals, number of elements, pressure and number of bleeds post-FUS are summarized in Table  3.
hemi), as well as the control counterpart in the contralateral hemisphere (contral. hemi), was defined as the region of interest (ROI), and the mean signal and standard deviation (SD) were measured within each ROI using ImageJ software (National Institute of Health, Bethesda, MD, USA). The resulting T1-signal difference between the two hemispheres was then calculated using the formula Tl À signal difference ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SD À ROI contralat: hemi mean À ROI contralat: hemi 2 þ SD À ROI sonic: hemi mean À ROI sonic:hemi 2 s.
MICROSCOPIC IMAGING, DATA SETS AND STATISTICS A Leica DM 4500 B fluorescence microscope and Leica IM50 Image Manager Software were used (Leica, Wetzlar, Germany) for bright-field and image acquisition. The data set supporting the conclusions of this article is included within the article. For statistical analysis, we used SPSS Software for Windows (IBM, Armonk, NY, USA). For calculation of the correlations of T1weighted signal rise with time after ultrasound application (Fig. 2b ), we applied the Spearman r-test. For experiments involving FUS, we used the MannÀWhitney U-test to compare T1-weighted signal between groups treated at pressures of 0. 4 and 0. 6 MPa (Fig. 3b ). We also used the Spearman r-test to calculate the correlations between the number of ICHs and the acoustic pressure applied (Fig.
Histological analysis of ICH after FUS application In the three rats sonicated with eight elements (animals 5, 6 and 7, representative images in Fig. 4a ) and acoustic pressures relevant for safe BBBo, namely, 0. 6 and 0. 4 MPa, minor bleeds always occurred after 0. 6 MPa in the affected hemisphere, whereas there was only one animal receiving 0. 4 MPa with two single bleeds of grade 1. However, we also detected five bleeds in the corresponding area in the control hemisphere (data not shown). We performed a thorough histological analysis with respect to the number of ICHs graded as 1, 2 and 3 for all sonicated rats and applied pressures (0. 4, 0. 6, 0. 9 and  a. u. = arbitrary units; FUS = focused ultrasound; MRI = magnetic resonance imaging. T1-weighted signal results obtained from measurement of the T1-weighted signal pre-and post-FUS in the treated and untreated hemispheres.
Acknowledgments-The authors sincerely thank  Mrs. Andrea Braun-Kornmu ¨ller ,  Mrs. Anna Roedl  and  Mr. Felix H€  orner for technical support in MRI measurement and animal care and treatment. The authors also thank  Mr. Mario Saribok  for technical support in FUS application,  Mr. Patrick Kiesewetter  for technical support in histological preparation and  Mr. Ju ¨rgen Jenne  for technical consulting on FUS application. This is an  EU Joint ProgrammeÀNeurodegenerative Disease Research (JPND)  project. The project is supported through the following funding organization under the aegis of JPND (www. jpnd. eu): BMBF/Germany  ("SNOWBALL"  FKZ 01ED1617A ). This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No 643417.
Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.
Main Modern microscopy produces massive image datasets that enable detailed multi-scale analysis and can combine several modalities, for example in correlative microscopy. Visualizing, exploring and sharing such data are significant challenges both during the execution of a research project, and after publication to enable open access and re-use 1. To this end we have developed MoBIE, a Fiji 2 plugin for MultiModal Big Image Data Sharing and Exploration. It supports visualization of multi-scale data of heterogeneous dimensionality (i. e. combined 2D, 3D or 4D data), several-TB-size image data, as well as the exploration of image segmentations and corresponding measurements and annotations. MoBIE uses next-generation image file formats, such as OME-Zarr 3 or N5 4 , that enable accessing multi-scale data on local storage or via the cloud, permitting the transparent sharing and publication of data without the need to run a web-service.
In addition, MoBIE allows users to easily configure and share fully reproducible complex views of their data. MoBIE is implemented using existing tools wherever possible, in particular BigDataViewer 5 (BDV) and the ImgLib2 6 and N5-java 4 libraries. MoBIE has enabled integration of multiple modalities and open access for data from different domains of the Life Sciences. Fig. 1  shows examples from developmental biology 7 (panel a), correlative microscopy (panel d) and high-throughput screening microscopy 8 (panel b) and Suppl. Fig. 1  shows further examples. A video demonstrating MoBIE's main functionality is available at https://youtu. be/oXOXkWyIIOk and its full documentation at https://mobie. github. io/. MoBIE extends BDV's functionality to efficiently stream data from remote data storage, conveniently manage an arbitrary number of images and adds a menu to select and control sources, views (see below) and navigation.
objects in the image, the corresponding table row and the point in the scatter plot are linked, so that one can select and navigate all data representations in synchronization. We have also implemented an annotation mode that can be used to add table columns with user observations. The full MoBIE state can be captured as a view, which can be shared with collaborators or be used to create interactive figure panels; panels a, b and d of Fig. 1  are created with MoBIE and available as interactive views. See Suppl. Note 1 for a detailed description of all MoBIE features. To access data with MoBIE, it must be organized according to the MoBIE project specification, see also Suppl. Note 1. A project consists of datasets that contain all data that can be loaded in a single MoBIE instance: image data, stored as OME-Zarr or N5 files, tabular data stored as tab separated value (tsv) files and a JSON file specifying the dataset layout.
The project can be stored either locally or on object storage, metadata and tables can also be hosted on github for version control. The Fiji plugin can access projects from these configurations, which enables using MoBIE to access and share data throughout the full life-cycle of a project: when the data is only accessible to a single researcher or institution, accessible to a research collaboration or published and freely accessible, see Fig. 1b. MoBIE projects can be created either using the Fiji plugin, providing a convenient graphical user interface, or a python library, providing full flexibility and support for large data. Overall, MoBIE enables scientists to seamlessly access, explore and share their massive microscopy data through all stages of a project in a convenient Fiji plugin.
Furthermore, most are stand alone tools and not directly integrated into one of the commonly used bioimage analysis platforms (see below), making their integration with well established workflows for common analysis tasks challenging. Many biologists use a bioimage analysis platform for their data visualization and analysis needs. Popular platforms include Fiji 7 and Icy 8 , which are Java based, as well as napari  9  , which is Python based. Among those tools, Fiji stood out for our purposes as it is very widely used, and is integrated with ImgLib2 10 , a general purpose n-dimensional image processing Java library that was designed for big image data and offers performant lazy evaluation and caching implementations. ImgLib2 also provides the primitives needed for n-dimensional image registration, which are used in the BigWarp  11  and BigStitcher 12 plugins.
MoBIE Plugin usage MoBIE can be installed in Fiji by enabling the "MoBIE" update site. It is then available via "Plugins->MoBIE" (or the plugin search), where several commands for opening and creating projects are implemented. The most common way to open a project is via "Plugins->MoBIE->Open->Open MoBIE Project…" and entering the project's address, for example https://github. com/mobie/platybrowser-project. Upon loading, MoBIE will display a predefined default view. Other views can be selected from the main MoBIE menu. Under "Plugins->MoBIE->Open->Advanced" three commands that offer specifying advanced options for loading projects can be found, including "Open MoBIE Project with S3 credentials…", which allows users to enter the credentials to open projects that contain data stored on non-public S3 storage.
This is for example useful to combine multiple acquisitions of the same object such that the most informative (e. g. highest resolution) version is visible, occluding less informative (e. g. lower resolution) images of the same object (see Fig. 1d ). Such alpha-blending is also useful to check image registrations as by moving the opacity slider some images (against which one may have registered) become more or less visible. For efficient navigation in large spatially distributed image datasets, MoBIE provides a focus ("F") button that centers the current view onto the respective image(s). There are more ways to navigate between images as described in the below paragraphs on tables and scatter plots. The "V" checkbox supports volume rendering of images and segmentation objects (s. b. ) using the Fiji 3D Viewer  15.
Segmentation image data visualization In addition to the visualization of intensity images, an important feature of MoBIE is to provide comprehensive functionality for the exploration of segmentation images. Technically, these images are represented as label mask images (2D or 3D and time), where pixel values encode object indices. The default visualization is a lookup table where different pixel values are assigned random but well discernible colors  16. A common issue with such a visualization is that neighboring objects may by chance have very similar colors that are hard to distinguish. MoBIE thus provides both a context menu item ("Change Random Coloring Seed") and a keyboard shortcut {CTRL + L} to shuffle the random colors. The label rendering can be further configured via the context menu item "Configure Label Rendering".
Tables (Annotations) A core feature of MoBIE is an interactive table display, which is critical for the efficient exploration of large image datasets. Table rows either correspond to image segments (s. a. ) or regions (s. b. ). The coloring and selection of image segments or regions are synched with the image display in BDV. Also navigation is synched as clicking on a table row will center the image display on the respective segment or region; vice versa, selecting an image segment or region in the image display will move the corresponding table row to the top of the current table display. Tables can be sorted by clicking on the column headers. Tables have a menu with items: "Table ", "Select", "Color" and "Annotate". The "Table" menu provides methods to save the table or load additional columns that will be merged into the current table.
Views An important core feature of MoBIE is an expressive JSON schema for specifying complex views on many images, see "Project structure, storage and metadata" for details. Views can save the full viewer configuration. They can be saved via the context menu and loaded again by selection of the view name from a drop-down menu in the main MoBIE menu. Each MoBIE dataset can specify an arbitrary number of views that are available by default and additional views can be loaded from JSON files via the context menu. The view features enable seamless configuration of any MoBIE configuration by arranging it in MoBIE and then saving the view. These saved views can then be shared with other MoBIE users, for example to point out locations in the data or full analysis results that underpin a research result.