dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of image stitching software in comparison with commercial solutions Image stitching by our open-source software ISAM-CIP, based on identified features in the scenes, was quite satisfactory. Feature detection is achieved using the freelicense Oriented Fast and Rotated BRIEF (ORB) algorithm. However, the literature describes two more effective methods for this task, with a lower computational cost and a higher rate of feature detection: the Scale Invariant Feature Transform (SIFT) and the Speed Up Robust Features (SURF)  (Rublee et al. 2011). One well-known stitching software based on SIFT is AutoStitch  (Brown and Lowe 2007)  which is robust to scale and orientation variations as well as to different illumination conditions. Its demo version (freely available) allows the user to get a mosaic from three-band images with excellent seamless results.
Conclusions The open-source tools developed in this research are freely available for any researcher and advisers serving smallholder farmers in the analysis of vegetation parameters such as NDVI or cropping area. These tools, of course, have their commercial counterpart which have higher prices, and the work presented here focused not only on reducing those costs but also on giving the scientific and agricultural communities the "know-how" and the skills to improve their capabilities. Open-Software tools developed during this research: Spectra-CIP, ISAM-CIP, and WMIA-CIP can be freely downloaded from our website. We encourage scientists and other users to test the software and provide us with feedback, which will be useful to improve the tools. Open-hardware information for its replication can also be provided by direct communication with the corresponding author.
Available information includes blueprints for 3-D printed-plastic platforms and selection criteria for the camera and optical systems. Also, dataset, i. e. , >500 multispectral images acquired in the Kilosa, Tanzania experiment, can be freely downloaded from  Loayza et al. (2017b). UAV-based technology is a new frontier in the agricultural sector and brings the ability to acquire data with unprecedented precision. A lot of work is, however, required to make the technology more accessible to users to allow them to gather accurate data without incurring higher costs. Training and advocacy are needed to make UAV-based remote sensing a regular tool for gathering agricultural data.
Acknowledgments We thank our colleagues  Elijah Cheruyot  and  Arnold Bett  from the  University of Nairobi  and  Luis Silva  from the  International Potato Center , who provided insight and expertise that greatly assisted the research. We would also like to show our gratitude to  Dr. Corinne Valdivia  ( University of Missouri ) for leading the translational research component of the Project and for establishing the community of practice that made the work possible. The authors are endebted to  Dr. Victor Mares  for his critical comments and contributions for the refinement of the manuscript.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: were 0. 3055 0. 3091 99. 45]. The viewing distance was 60 cm. The experiments were designed using PsychToolbox 3  (Brainard, 1997) , MATLAB 2011b. In all experiments, the stimuli were presented on a uniform gray background (L* = 77. 54, u* = 4. 48, v* = 4. 48) Stimuli We produced 13 sets of colors. The colors were defined by a line between the (approximate) prototypes of: (1) red (L*u*v* = [58. 22 157. 12 55. 52]) and orange  [69. 11 106. 44 74. 51 ], (2) orange and yellow  ([92. 56 14. 80 113. 54] ), (3) yellow and green  ([50. 13 À53. 06 68. 06] ), (4) green and blue  ([39. 92 À18. 78 À136. 33] ), (5) blue and red, (6) red and brown  ([38. 87 61. 93 41. 68] ), (  7 ) brown and yellow, (8) brown and green, (  9 ) gray ([60 0 0]) and red, (10) gray and yellow, (11) gray and green, (12) gray and blue, (  13 ) black ([0 0 0]) and white ([100 0 0]).
Resting-State fMRI Image Acquisition and Preprocessing We acquired 10-minute series of whole-brain resting-state BOLD sensitive images (gradient-echo (GE) echo planar imaging (EPI) sequence, 45 slices, slice thickness = 3mm, FOV 220 3 220mm, A>>P phase encoding direction, TR = 2990ms, TE = 26ms, flip angle = 90 ). Data preprocessing and statistical analysis was performed with the CONN v. 17 functional connectivity toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012) (https://www. nitrc. org/projects/conn). We used standard preprocessing steps including: slice-time correction, realignment, segmentation of structural data, normalization into standard stereotactic MNI space (in RDS, deformations were applied to the whole brain except for the voxels contained in the lesion mask to avoid deformation of the lesioned tissue) and spatial smoothing using a Gaussian kernel of 6 mm FWHM.
Task-fMRI For RDS and each healthy control, first-level analysis was implemented. in SPM12 software (https://www. fil. ion. ucl. ac. uk/spm/ software/spm12/). Data were high-pass filtered and modeled by regressors obtained by convoluting the experimental conditions and the button presses with the canonical SPM hemodynamic response function (a combination of 2 gamma functions, with a rise peaking around 6 s followed by a longer undershoot). We used four contrasts to explore RDS's ventral visual stream architecture. For the domain localizer, we used contrasts of tools versus (faces+houses), faces versus (houses+tools), and houses versus (tools+faces). For the color localizer, we used contrast of (colored images+ colored Mondrians) versus (gray-scale images + gray-scale mondrians) (referred henceforth as color versus grayscale).
ACKNOWLEDGMENTS We are deeply grateful to  Mr. RDS  for his patience and good humor during endless hours of testing. We also thank  Dr. Karynne Moreau  for patient referral and clinical follow-up, as well as  Sami Abboud ,  Alfonso Caramazza ,  Maurizio Corbetta ,  Bradford Mahon ,  Gabriele Miceli ,  Michel Thiebaut de Schotten , and two anonymous reviewers for advice and discussion. The research leading to these results was promoted by the  Inserm  (protocol  C13-41 ), was approved by the  Ethical Committee Ile-de-France I , and has received funding from the  Agence Nationale de la   Recherche program ''Investissements d'avenir''  ( ANR-10-IAIHU-06 ). K. S. K. was funded by the  E ´cole des Neurosciences Paris Île de France. C. W. was supported by the grant ' Cardinal Mechanisms of Perception ' No.
A user of Transplanet can thus choose to model the ionosphere of Venus, Earth, Mars or Jupiter. The ionosphere is one of the key interaction region between the planetary environment and solar radiation and solar wind. It is critical for Planetary Weather activities in terms, for example, of radio signal propagation through atmosphere and radio-occultation experiment. Important assets of the Transplanet service are: to give access to a large set of parameters (density, temperature, velocity, heat flux) self-consistently solved for all the main ionospheric species taking into account realistic inputs, such as the solar flux to allow comparative studies at different planets to provide modeling results easily downloadable from the Transplanet website in Common Data Format (CDF) to allow direct visualization of the results in the Automated Multi-Dataset Analysis (AMDA) webtool  (Jacquey et al.
, 2010)  developed by Centre de donn ees de la Physique des Plasmas (CDPP) through IVOA SAMP protocol to compare simulation results with observations from different spacecraft and even from some ground-based facilities in the terrestrial case (e. g. Incoherent Scatter Radar), whose data are also available in AMDA At the best of our knowledge, the Transplanet service has currently no equivalent elsewhere. Although a large set of ionosphere models (physical and empirical) are also accessible for online run at the Community Coordinated Modeling Center (CCMC) website (https://ccmc. gsfc. nasa. gov), they are only available for the terrestrial case. Section 2 will briefly introduce the main features of the IPIM model and will describe some of the strategic developments made for the planetary-independent version.
Mars Atmosphere. The Neutral atmosphere is taken from the Mars Climate Database (MCD)  (Forget et al. , 1999; Millour et al. , 2015) , which provides density (neutral species considered: CO 2 , O 2 , N 2 , O, H and CO), temperature and velocities (horizontal zonal and meridional components) for different solar conditions. Magnetic field. No magnetic field is presently considered. The former model version TRANSMARS had a mode to solve for the induced magnetic field resulting from the interaction between the solar wind and the Mars upper atmosphere  (Shinagawa and Cravens, 1989). However, this mode put strong constrains on the geometry which made it difficult to account for in the initial version of the unified model. As a result, we preferred not to include such an option, though we are working on it for a future version.
Acknowledgement The original IPIM Model was developed by  P. -L. Blelly  and  A. Marchaudon  at  IRAP (CNRS and Toulouse University ) and is a property of  Centre National de la Recherche Scientifique (CNRS). Simulation results of the Transplanet models have been provided by the Planetary Space Weather Services through their public Online Runs system (htt p://transplanet. irap. omp. eu/). Transplanet is a service developed by  CNRS/IRAP/CDPP  for the Planetary Space Weather Services of the  Europlanet 2020 Research Infrastructure. Europlanet 2020 RI  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  654208. This work is also supported by the  Programme National Soleil Terre (PNST)  from  Institut des Sciences de l'Univers of CNRS (INSU/CNRS)  co-funded by  Centre National d' Etudes Spatiales (CNES).
If they are freely diffusing, they may be supramolecular structures polymerizing upon some signal. Our first imaging protocol falsely led us to think that they were transported towards the nucleus after assembly. However, these movements proved to be artifacts and caused by a phototoxic effect. 1 A second imaging protocol involved the use of TIRF microscopy with a low illumination power, which diminished the phototoxic effects. We filmed the dots for long times and process the acquired structures to analyze their motility. We tracked the dots in Fiji using TrackMate  (Tinevez et al. 2017 ) and, because the dots are well separated, the tracking proved relatively easy. We then analyzed the tracks using MSD analysis, to conclude on their motility with certainty. The MSD analysis is also the subject of this module, and we will then go from Fiji to MATLAB to perform it.
Datasets The data for this module consists in a subset of the data only from the paper. It only features 5 movies over 2 conditions: 5 Ctrl: A couple of dots can be seen wandering in the cells, even if there is not stimulation. They are permanent instead of transient, and probably non-specific. They give a control of how spurious particles would perturb our measurements. 5 IL1: Following IL-1 stimulation. In the study, this was the "easy" case, for the dots were bright and large compared to e. g. the dots we see after TNF stimulation. You can find it on Zenodo: 7 https://doi. org/10. 5281/zenodo. 1341987. The dataset (download size about 800 MB) is organized as follow: The movies themselves are not very pretty. Bright dots can be seen over a cell background caused by the soluble pool of NEMO.
They bleach over time. The temporal resolution is not very high (0. 5 s) and the SNR is not high either since we had to compromise on laser power to avoid cell death. Files are. tif movies, made for ImageJ, with the right spatial and temporal calibration. They are already split cell by cell, and have a ROI that encloses the cell. There also are. xml files from TrackMate and _Tracks. xml files generated from TrackMate, ready to be imported in MATLAB. But we will do the tracking ourselves.
MATLAB 5 We rely on MATLAB for the MSD analysis part, with the Curve Fitting toolbox. 5 You need to know at least a little bit about MATLAB features, like logical indexing and structures. We will not be introducing them here. 5 Because we will install specialized functions and classes in MATLAB, you also need to know at least a little bit about the MATLAB path. 7 https://mathworks. com/help/matlab/matlab_env/what-is-the-matlab-search-path. html 5 We built a special class to perform the analysis that you can download here: 7 https://github. com/tinevez/msdanalyzer/zipball/master @msdanalyzer is a MATLAB class, so you have to put the @msdanalyzer folder in the MATLAB path, but not its content. 2 For instance on my MATLAB installation, I have a folder called /Users/tinevez/Development/Matlab/msdanalyzer that is on the path and that contains the @msdanalyzer folder.
Workflow We will deal separately with single-particle tracking in Fiji using TrackMate, and track motility analysis in MATLAB using @msdanalyzer. The two following sections are largely independent and present different concepts. To perform the MSD analysis, please use the dataset linked above that include them. TrackMate is a Fiji plugin dedicated to tracking. It can do cell-lineaging (and was initially developed for this very purpose, see  Tinevez et al. (2012) ) but also has automated analysis tools to perform single-particle tracking of sub-cellular structures. It ships a user-friendly graphical user interface that allows to inspect tracking results and curate them. The following part describes how to use TrackMate to generate the tracks over one of our movies. An extended documentation for this plugin can be found here: 7 https://imagej.
Step 1: Loading Image Data and Launching TrackMate For the example below, we will use the Cell_02. tif in the NEMO-IL1 folder. You can display a. tif file by performing a simple drag and drop on the Fiji toolbar. This movie does not have many dots, which will simplify getting familiar with the workflow. It is very important that you check the dimensionality of the image at this point of the analysis and correct it if required. To do so, check the image properties in Fiji ( Image Properties. menu item or + P ,. Fig. 4. 1). In our case we have a 2D over time acquisition, so make sure the metadata reports 1 z-slice and 307 frames. Also, TrackMate reports any quantities (space and time) in physical units, so the pixel size. and frame interval must be correct since you will not be able to change them further in the analysis.
Step 4: Particle-Linking Now that we have spots, we want to link them over time and build tracks. The tracks will be what we will analyze in the section dedicated to motion analysis, and we will do it in MATLAB. But for now we need to generate these tracks. In TrackMate, particle-linking happens similarly to the detection step. You are now presented with a. Fig. 4. 6 Filtering panel with two filters on contrast and X position, additionally setting the spot color by contrast value.
Step 6: Export Results We want now to export the track results in a format that can easily be re-imported in MATLAB. The XML file that is generated when you press the Save button in the GUI contains all the information to restore a tracking session: settings, parameter values, path to images, etc. It is probably not well suited to the simple track import we want to perform. Move to the last panel of the TrackMate GUI called Select an action. It offers a selection of miscellaneous actions that do not fit in other panels. In the list, one action called Export tracks to XML will generate the format we want. It is a simple format derived from the one used in the ISBI single-particle tracking challenge (whose results are the subject of  Chenouard et al. (2014) ) and suited for tracks that do not have split nor fusion events.
4 <?xml version="1. 0" encoding="UTF-8"?> <Tracks nTracks="22" spaceUnits="um" frameInterval="0. 5" timeUnits="s" generationDateTime="Wed, 5 Sep 2018 14:11:51" from="TrackMate v3. 8. 0"> <particle nSpots="112"> <detection t="0" x="53. 66873043851335" y="11. 384524705860331" z="0. 0" /> <detection t="1" x="53. 6447201035091" y="11. 417907762121915" z="0. 0" /> <detection t="3" x="53. 565164363562936" y="11. 45756457406076" z="0. 0" />. This is what we will use in MATLAB later. You can see that each track is represented by a particle section, containing several detection items, with t, x, y and z. In our case, z is always 0 since we have 2D time-series. ? Exercise 4. 6 Perform tracking and exports for all the other movies included in the dataset. Then move on to the next section.
Motility Analysis with Mean-Square Displacement Tracking is almost never the last step of an analysis workflow. Tracking tools such as TrackMate produce tracks and their role stops there. But tracks are just an intermediate data structure in the workflow. Their subsequent analysis will produce the numbers upon which we will draw a scientific conclusion. Because this track analysis is specific to the scientific question to be addressed, tracking tools remain generic and seldom include specialized analysis modules. Another toolset is required for track analysis, and in this module we will focus on using MATLAB. The main reason for this choice is that there exist ready-to-use functions to import the XML files we produced in the previous section, which underlies the importance of interoperability.
Step 1: Importing Tracks into MATLAB Close Fiji and launch MATLAB. We want to import the tracks generated above into the MATLAB workspace. Rather than writing our own XML importer, we can use one that was made specifically for TrackMate, and that is distributed with Fiji. It is called importTrackMateTracks and you can find it in the scripts folder of Fiji: To make these scripts usable from MATLAB, open the path editor, and add the scripts folder to the path (. Fig. 4. 11). This can also be achieved using addpath('. /path/to/your/Fiji. app/ scripts'); in the MATLAB prompt. Once this is done, the functions in this folder are visible and can be called from MATLAB. For instance, we can now get the help of the function we want to use in MATLAB: Read the help section of the function and try to find the correct syntax to import the tracks in a desirable way.
4 We need to create an analyzer first, before giving it data. This is done like this: >> ma = msdanalyzer(2, 'um', 'frames') Now ma is an empty msdanalyzer object, set to operate for 2D data (this is the meaning of the '2' as first argument), using µm as spatial units ('um' because MATLAB does not handle UTF8 characters very well) and frames as time units. As stated above, this object is empty, and we have to feed it the tracks with the addAll( ) method. Luckily for us, as you can read in the help of the addAll method, it expects the tracks to be formatted exactly in the shape we have. So we can run directly: >> ma = ma. addAll(tracks) ma = msdanalyzer with properties: TOLERANCE: 12 tracks: 22x1 cell n_dim: 2 space_units: 'um' time_units: 'frames'. Note that for now, it just has the 22 tracks of the first movie we analyzed.
We want to add the tracks coming from the other movies in the same category. For instance, we will later add to the same msdanalyzer object all the tracks coming from all the movies of the NEMO-IL1 folder. But for now, we can use some of the methods of the msdanalyzer to have a nice track overview: >> ma. plotTracks % Plot the tracks. >> ma. labelPlotTracks % Add labels to the axis. >> set(gca, 'YDir', 'reverse') >> set(gca, 'Color', [0. 5 0. 5 0. 5]) >> set(gcf, 'Color', [0. 5 0. 5 0. 5]) In. Fig. 4. 12, the results of these commands are displayed next to the TrackMate results. The track colors happen to be the same, but this is by chance. As a side note, look at the line 3 in the above snippet. When displaying images, the Y axis runs from top to bottom. Fig. 4. 12 TrackMate tracks displayed in MATLAB contrary, if the MSD increases faster than at linear rate, then it must be transported, because Brownian motion could not take it away that fast.
Introduction Plant phenotyping regroups all the observations and measures that can be made on a precisely identified plant material in a characterized environment. This very general definition of phenomics  [1]  includes diverse types of properties and variables measured at different physical  [2]  and temporal scales, ranging from field observation of plant populations to molecular cell characterizations, including for some research community metabolomics or gene expression. The acquisition of these data is conducted in various experimental facilities like greenhouses, fields, phenotyping networks, or natural sites. It can be done using many different devices from hand measurements to high throughput means. The resulting complex and heterogeneous datasets include all the environment and phenotypic variable values at each relevant scale (plant, micro plot,.
) and very importantly the identification of the phenotyped germplasm, i. e. , the plant material being experimented. In addition, there are often relationships between levels, i. e. , physical scales, inside datasets and between different datasets. The resulting rich wealth of data is usually formatted in a very heterogeneous manner and is difficult to integrate automatically. Phenotyping experiments are expensive and are not exactly reproducible since the environmental conditions are difficult if not impossible to completely control. Furthermore, most traits are highly dependent on genotype by environment interactions, which increases again the uniqueness and the value of the data collected to describe environmental conditions and resources available to the plants during their lifecycle.
Hence, being able to reuse phenotyping data to carry out large meta-analysis would allow better deciphering the genetic architecture of traits across environments. It could help the prediction of genotype performances in the context of the climate change adaptations. An example is the use of data series collected over centuries that have demonstrated or supported the modelling of the impact of climate change on crops  [3, 4]. In this context, long-term data management following the Findable, Accessible, Interoperable, and Reusable (FAIR) principles  [5]  is among the main challenges of modern phenomics. There are two answers to this challenge: data standardization and data integration. Several initiatives are developing tools for standardizing phenotyping data description. The Minimal Information About Plant Phenotyping Experiment (MIAPPE, www.
This helps to connect crop phenotyping data to genomic data across species. Besides, through the mapping, CO inherits the ontological structure of TO and can be used for building an ontology optimized for data sharing and integration between crop research communities. Finally, the Breeding API (BrAPI, www. brapi. org) [12]  is building a specification of web services to enable standard data exchange between information systems and tools. All these tools are facilitating data standardization and are now widely adopted by the international plant community  [13] [14] [15]. Data integration relies on datasets and data repositories interoperability and links different datasets together  [16]  in order to avoid data silos. It is achieved by following the Linked Data principles  [17]  and in particular by defining and identifying the key resources, i.