dataesr/scientific-paragraphs-categorization huggingface dataset:
The relationships between plants and their environment including soil microbes can affect this quantification bringing in new challenging parameters into the equation. Data may be acquired in a range of experimental conditions including laboratories, greenhouse, and field or natural experimental site within forests. Data from the latter can be used for biodiversity studies as well where the scope, measurement means, and objectives can be shifted for that purpose. This Research Topic presents a series of articles with an insight into recent advances in plant phenomics. There are 12 research articles and one opinion paper covering the heterogeneity and complexity  (Watt et al. , 2020)  of this rapidly developing scientific domain. The majority of the articles address problems associated with data acquisition and analysis using innovative computational methods, and a few discuss artificial intelligence.
This encompasses biologist-friendly data standards such as MIAPPE  (Papoutsoglou et al. , 2020) , ontologies designed to help biologists describe their datasets  (Shrestha et al. , 2012) , or to support data scientists engaged in data integration  (Cooper et al. , 2017). Technical standards such as the Breeding API  (Selby et al. , 2019)  and databases implementing all of these components such as GnpIS  (Pommier et al. , 2019)  and PHIS  (Neveu et al. , 2018)  have been well-received by the community. In this Topic, MaizeDIG explores the deep integration of phenomic and genomic data using BioDIG (Biological Database of Images and Genomes) web-based software. The only Opinion paper (Sadras) in this topic gives interesting thoughts on the outcome of the 5th International Plant Phenotyping Symposium (IPPS) 2018, and especially the relationship between theory and our ability to understand and make efficient use of the existing data.
The rest of this paper is organized as follows: in Section 2 we introduce the used dataset and we explain the implementation and features of the proposed Graphical User Interface. Then we describe the Basic ACS and the modifications we introduced to make it work in prostate images. Finally, we present details about the Gradient Vector Flow (GVF) model and its implementation into our tool. In Section 3, we include a single experimental case study to demonstrate the advantages of the proposed approach. In Section 4 we discuss the results in the light of the clinical perspective, and in Section 5 we present the conclusions as well as future works.
Database and ROIs References A database with prostate MRI, based on clinical data with tumor and healthy cases was used. The examinations used in our study contained three-dimensional T2-weighted fast spin-echo (TR/TE/ETL: 3600 ms/143 ms/109, slice thickness: 1. 25 mm) images acquired with sub-millimetric pixel resolution in an oblique axial plane. Each study comprises a set of 64 slice images. This database was used in previous works  [11]  and it has been facilitated for testing the software. The ground truth annotations were performed using the ProstateAnalyzer tool  [11] , that allows the drawing of an annotation in a given MRI. For this task, experts with more than 10 years of experience on prostate imaging were asked to provide the annotations using the tool. All the dataset and ground truth data are provided from the medical imaging department of the University Hospital of Dijon (France).
Implementation of the Graphical User Interface for Prostate Segmentation In this section, the code organization and the user interface of the program will be explained. The main window of the program is shown in Figure  3. All the code has been developed in Python, using the modules PyQt for the user interface and PyDicom to open the files included in the testing dataset. The code has been organized in modules that do not interact with each other. The integration of all of them has been made in the Main Window class. The read information will be stored locally on RAM, and after the file or files has/have been opened, a signal will be emitted, and the information can be requested as many times as the user wants. The data available to the user includes the images and the patient data fields.
• Image segmentation. This module stores all the Python code related to the segmentation procedure. The main class in this module is Segmentation-Hub, which is in charge of selecting the right class for the object that will produce the segmentation, according to the method chosen by the user. The selection of the segmentation method can be done in Section 2 of Figure  3. By implementing the methods in this manner, the actual implementation of the segmentation task is completely transparent for other classes that make use of this module. To select certain segmentation method, only a label that defines the method must be provided. This module also contains two other files with useful functions: 1. A specific MatLab module which implements some functions that are found in MatLab but not in Python, and their names are the same as in this program.
2. The other file corresponds to a class that finds the ellipse parameters that better fits to a set of points. This code is used for the shape prior of the snake algorithm (Basic ACS) which will be explained in Section 2. 3. • Image widget. This module is in charge of showing the image, as well as the initial and the resulting contours from the segmentation. The same module is used for both, left and right images. It also captures the clicks from the user to generate either the circular or the manual initial contour. Both types of initialization will be explained later. The image widget is represented in Sections 3 and 4 of Figure  3. • Model 3D widget. It shows a 3D representation of the output segmentation, based on the Python module Matplotlib. This process is relatively simple as it only replicates the given contour vertically.
If the spatial resolution is given in the input DICOM file, the scale of the axis will be given in millimeters. It can be accessed through the button Show 3D Model located in Section 5 of Figure  3. • Snake init widget. This widget is in charge of handling all the functionalities of the Snake initialization group, placed at the top of the central control panel of the application. Each button will request the main window to ask the user to enter the initial contour in the left image. Once the user has finished, the gathered contours are returned back to this module. Afterwards, the corresponding masks for those contours can be generated for the snake algorithm (the mask consists of an image with values of 1 in the interior of the contour, and zeros outside of it). The interface for this module is represented in Section 6 of Figure  3.
• User data widget. It is implemented as an efficient and easy to use manner to handle all the user information as an object, and to show it. This class stores all the information related to the patient or the study, retrieved from the DICOM file. It also contains the corresponding widgets that are shown below the left image area, where the gathered information is displayed (see Section 7 of Figure  3 ). A block diagram of the proposed tool components is depicted on Figure  4. Finally, regarding the user interface showed in Figure  3 , this program has four well-defined sections: • Left panel. In this area (highlighted in blue), the loaded images are shown, and the user can draw the initial contours of the snake of the different areas of the prostate. Each prostate zone is surrounded with a different color to make it easily distinguishable (see Figure  3 ) • Right panel.
• Bottom area. The patient information retrieved from the DICOM file is shown (highlighted in blue). Moreover, information related to the spatial resolution and thickness of the gathered slice are included (Section 7 of Figure  3 ) • Central area. This is the control panel area (highlighted in green). All the functionalities and possible configurations of the program are located in this area. It is represented in the Sections 1, 2, 5 and 6 of Figure  3. To the best of our knowledge, tools as the proposed in this work, which integrate a Graphical User Interface and semi-automated segmentation algorithms for image annotation do not exist (at least not for prostate MR images). Therefore, a preliminary effort to develop a GUI that includes several active contour model implementations is very important and has driven the development of this work.
On the other hand, from the implementation point of view, this program represents an excellent tool to deploy new snake-based algorithms: given that the initial contour and the original image are available in the Segmentation-Hub module, new algorithms can be created using Python, and added as an standalone file to the Image Segmentation folder. This procedure will add another option to the GUI, and the new algorithm's performance and accuracy can be compared with the already existing techniques. This code-base is now available as a GitHub repository to the community  [15]. In order to demonstrate how different segmentation methods can be integrated into the proposed tool, we have implemented various versions of an Active Counter Model within the Image Segmentation module of our tool. These methods have been included not only for demonstration purposes, but also to serve as a baseline for future developments in prostate MRI segmentation research (people can improve them, or develop new methods based on them).
∇φ |∇φ| |∇φ| (20) The parameter γ regularizes the step size, φ is the level-set function, κ = div ∇φ |∇φ| is the curvature, v is the Gradient Vector Flow vector field, and the parameter α regulates the balance between the two forces. The right hand factor of (  20 ) is added directly to Equation (  15 ), giving Equation (  21 ): ∂φ ∂t = λ 1 (M 2 -M 1 ) -∇P ∇φ ||∇φ|| -Pκ + β ακ -v. ∇φ |∇φ| |∇φ| δ(φ(x, y)) (21) This new segmentation algorithm has been included in the list of options to segment the different prostate zones. After having implemented the Basic ACS in Python, the extension with a GVF component is trivial: all the components of this new force are already available in the code except for the vector field v. Since this vector field is fixed for a given image, a new class has been created that solves the optimization problem for the GVF.
This new piece of code is executed before the main loop of the segmentation algorithm. Since there are no rule for setting both α and γ, we arbitrarily decided to set them to 1. In order to adjust the contribution of this new force, the β factor has been introduced. For the following experiments, this factor has been also set to 1. In the coming sections, the graphical user interface result of the integration of all the modules will be showed. Then, the described algorithms will be tested to show their performance.
There are no constraints about the type of contours to use. All the contours can be manual, all of them can be circles or they can be a combination of them. The following area of the control panel is used for triggering the segmentation. This section is depicted in Figure  9. After an image has been loaded, at least one initial contour has been entered, then the segmentation process will start after pressing the Run button. The segmentation method will be triggered as many times as initial contours have been entered, and they will be executed sequentially. During this process, the user can cancel the segmentation at any moment, pressing the Cancel button. Once the algorithm had finished, the results will be shown in the right image. Additionally, the color code of the contours is the same for both, left and right images.
An example of this 3D representation of a result is shown in Figure  10. Regarding to file management tasks provided to this tool, users can open a DICOM formatted file or a folder that contains several DICOM formatted files. When the Open f ile button is pressed, a pop up window will appear, and it will only allow the user to select a file, not folders. If a file is not in DICOM format, then the program will reject it, and it will not do further actions. As it can be seen in the graphical user interface, there are three types of algorithms: Basic ACS, Simplified ACS and GVF ACS. The first and the second algorithms are based on  [12] , but the second one does not follow the mathematical approach described in this previous work. On the other hand, Basic ACS implements exactly what the paper states, as explained in Section 2.
3. Both algorithms have been included so that there is an example in the code of how to add a new algorithm to this code-base. Additionally, the Simplified ACS performs a faster than Basic ACS but with less accuracy. Nonetheless, in order to produce improvements to the original paper, the Basic ACS was required. Finally, considering that this application is focused on DICOM format and it can be used for radiologists and/or medical experts, it is important to maintain the private information of the patient. For this reason, an anonymization module is integrated in this tool. If the user wants to anonymize the opened file or files, it should click into the Anonymize button. If there is a file/files opened, a pop up window will be shown where the user must specify the destination folder of the anonymized files.
These features, as well as the capability to integrate new segmentation models seamlessly make it very appealing for research and teaching purposes in clinical settings. In the former case, it can help in speeding up the creation of datasets for machine learning applications. However, in our experience, doctors are reluctant to use fully automated annotation tools and prefer to use interactive semi-automated tools that enable them to verify and contrast the results with other specialists. In order to test the performance of the proposed tool, we performed one experiment to assess the quality of the segmentation algorithm implemented in this work, which will be detailed in the following section.
Moreover, the results on the TZ area are good for all the algorithms considered in this study. Finally, the results for the tumor area indicate that the best segmentation is obtained using GVF ACS methods. Finally, in order to assess the performance of the various studied segmentation methods, a time analysis has been performed. As in Table  1 , the analysis has been divided by zones and by algorithm. Then, the execution time of the segmentation algorithm has been assessed for each zone, using the same initialization as in Figure  11. For this test, a computer with a microprocessor i7-8650U @ 1. 90 GHz, 16 gb of RAM and running Linux 4. 15. 0-64 has been used. The execution times are depicted in Table  2. All the measurements has been done in milliseconds. Both Simplified ACS and Basic ACS run at maximum 200 iterations, and the GVF ACS runs at maximum 80 iterations.
Both Tables  1  and 2  confirm what it has been said about the Simplified ACS algorithm. It is the least accurate, but it runs faster than the other ones. On the other hand, even though the GVF ACS runs less iterations than the Basic ACS, their running time are similar. This is due to the GVF ACS algorithm computes the same code, plus the additional ones in order to get the Gradient Vector Flow contribution. Nonetheless, for this work, this execution time could have been reduced if the equations related to the shape prior and the smoothness would have been removed. Only the weights have been set to zero for those priors. This code has not been erased so that other developers can prove other parameters values, and try to find other combination of weights to make this program work with either, other image modalities, or other organs to be studied.
Moreover users can share elements from their personal perspective to the shared one: the effect is that the element is added to the shared perspective; if the element is a relation, and the connected nodes do not belong to the shared view, they are added to it as well. Finally, a user can import elements from the shared perspective into her personal one: the effect is that the element is added to her personal perspective (if the element is a relation, the import is extended to the connected nodes). The Perspec-Map 2. 0 tool implementing this interaction model is an Angular-based application, that makes significant use of the HTML5 Canvas technology to represent the multi-perspective map, and is backed by a lightweight PHP server coupled with a MySQL database. Its users are grouped in "projects", each of them containing a number of maps (called "map works").
This phase of exchange and contamination can of course serve as a preliminary work toward the creation of a shared perspective. In most collaborative applications this type of collaboration is not taken into account, because the purpose of the applications themselves is the production of artifacts. Interestingly enough, an example of a process-oriented collaborative environment can be found in the field of computer programming. The popular distributed version control system GIT (Chacon and Straub, 2014) supports various types of collaboration; among these, the possibility for different people to extend and expand a set of programs or libraries in various directions, comparing their code and picking interesting elements from each other's work. While GIT also supports the merge of different expansions in a single shared project, it does not enforce it nor focuses exclusively on this aspect.
Methods. We used the high dynamic range C 18 O (2-1) and N 2 H + (1-0) datasets to produce centroid velocity maps and probe the rotational motions in the sample of 12 envelopes from scales ∼50 to ∼5000 au. Results. We identify differential rotation motions at scales 1600 au in 11 out of the 12 protostellar envelopes of our sample by measuring the velocity gradient along the equatorial axis, which we fit with a power-law model v ∝ r α. This suggests that coherent motions dominate the kinematics in the inner protostellar envelopes. The radial distributions of specific angular momentum in the CALYPSO sample suggest the following two distinct regimes within protostellar envelopes: the specific angular momentum decreases as j ∝ r 1. 6±0. 2 down to ∼1600 au and then tends to become relatively constant around ∼6 × 10 -4 km s -1 pc down to ∼50 au.
The CALYPSO survey The Continuum And Lines in Young ProtoStellar Objects (CA-LYPSO 1 ) IRAM Large Program is a survey of 16 nearby Class 0 protostars (d<450 pc), carried out with the IRAM Plateau de Bure interferometer (PdBI) and IRAM 30-meter telescope (30m) at wavelengths of 1. 29, 1. 37, and 3. 18 mm. The CALYPSO sources are among the youngest known solar-type Class 0 objects  (André et al. 2000)  with envelope masses of M env ∼1. 5 M and internal luminosities of L bol ∼0. 1-30 L  (Maury et al. 2019). The CALYPSO program allows us to study in detail the Class 0 envelope chemistry  (Maury et al. 2014; Anderl et al. 2016; De Simone et al. 2017; Belloche et al. 2020) , disk properties  (Maret et al. 2014; Maury et al. 2019; Maret et al. 2020 ) and protostellar jets  (Codella et al. 2014a; Santangelo et al.
2015; Podio et al. 2016; Lefèvre et al. 2017; Anderl et al. subm. ; Podio et al. in prep. ). One of the main goals of this large observing program is to understand how the circumstellar envelope is accreted onto the central protostellar object during the Class 0 phase, and ultimately tackle the angular momentum problem of star formation. This paper presents an analysis of envelope kinematics, for the 12 sources from the CALYPSO sample located at d ≤350 pc (see Table  1 ) and discuss our results on the properties of the angular momentum in Class 0 protostellar envelopes. We adopt the dust continuum peak at 1. 3 mm (225 GHz) determined from the PdBI datasets by  Maury et al. (2019)  as origin of the coordinate offsets of the protostellar envelopes (see Table  1 ). We report for each source in Table  1  the outflow axis considered as the rotation axis and estimated by  Podio & CALYPSO (in prep.
Observations and dataset reduction To probe the dense gas in our sample of protostellar envelopes, we use high spectral resolution observations of the emission of two molecular lines, C 18 O (2-1) at 219. 560 GHz and N 2 H + (1-0) at 93. 171 GHz. In this section, we describe the dataset 2  properties exploited to characterize the kinematics of the envelopes at radii between r ∼50 and 5000 au from the central object.
Observations with the IRAM Plateau de Bure Interferometer Observations of the 12 protostellar envelopes considered here were carried out with the IRAM Plateau de Bure  Interferometer (PdBI)  between September 2010 and March 2013. We used the 6-antenna array in two configurations (A and C), providing baselines ranging from 16 to 760 m, to carry out observations of the dust continuum emission and a dozen molecular lines, using three spectral setups (around 94 GHz, 219 GHz, and 231 GHz). Gain and flux were calibrated using CLIC which is part of the GILDAS 3  software. The details of CALYPSO observations and the calibration carried out are presented in  Maury et al. (2019). The phase self-calibration corrections derived from the continuum emission gain curves, described in  Maury et al. (2019) , were also applied to the line visibility dataset (for all sources in the restricted sample studied here except the faintest sources IRAM04191, L1521F, GF9-2, and L1448-2A).
In all cases, deconvolution was carried out using the Hogbom algorithm in the MAPPING program of the GILDAS software. 3. 2. Short-spacing observations from the IRAM 30-meter telescope The short-spacing observations were obtained at the IRAM 30meter telescope (30m) between November 2011 and November 2014. Details of the observations for each source are reported in Table  A. 1. We observed the C 18 O (2-1) and N 2 H + (1-0) lines using the heterodyne Eight MIxer Receiver (EMIR) in two atmospheric windows: E230 band at 1. 3 mm and E090 band at 3 mm  (Carter et al. 2012). The Fast Fourier Transform Spectrometer (FTS) and the VErsatile SPectrometer Array (VESPA) were connected to the EMIR receiver in both cases. The FTS200 backend provided a large bandwidth (4 GHz) with a spectral resolution of 200 kHz (0.
27 km s -1 ) for the C 18 O line, while VESPA provided high spectral resolution observations (20 kHz channel or 0. 063 km s -1 ) of the N 2 H + line. We used the on-the-fly spectral line mapping, with the telescope beam moving at a constant angular velocity to sample regularly the region of interest (1 × 1 coverage for the C 18 O emission and 2 × 2 coverage for the N 2 H + emission). The mean atmospheric opacity at 225 GHz was τ 225 ∼ 0. 2 during the observations at 1. 3 mm and τ 225 ∼ 0. 5 during the observations at 3 mm. The mean values of atmospheric opacity are reported for each source in Table  A. 1. The telescope pointing was checked every 2-3 h on quasars close to the CA-LYPSO sources, and the telescope focus was corrected every 4-5 h using the planets available in the sky. The single-dish dataset were reduced using the MIRA and CLASS programs of the GILDAS software following the standard steps: flagging of incorrect channels, temperature calibration, baseline subtraction, and gridding of individual spectra to produce regularly-sampled maps.
Combination of the PdBI and 30m data The IRAM PdBI observations are mostly sensitive to compact emission from the inner envelope. Inversely, single-dish dataset contains information at envelope scales (r ∼ 5 -40 ) but its angular resolution does not allow us to characterize the inner envelope emission at scales smaller than the beamwidth. To constrain the kinematics at all relevant scales of the envelope, one has to build high angular resolution dataset which recovers all emission of protostellar envelopes. We merged the PdBI and the 30m datasets (hereafter PdBI+30m) for each tracer using the pseudo-visibility method 4  : we generated pseudo-visibilities from the Fourier transformed 30m image data, which are then merged to the PdBI dataset in the MAPPING program of the GILDAS software. This process degrades the angular resolution of the PdBI dataset but recovers a large fraction of the extended emission.