dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, a unique K profile derived from experimental data by locally weighted scatterplot smoothing (lowess) was applied along all root axes (Supplemental Figure  1B ). The direct determination of k is, at present, experimentally not achievable in species with highly branched and tiny roots such as Arabidopsis, as it would require to measure radial flows in root segments between consecutive branching points (~2mm in the present case)  (Meunier et al. , 2018). We therefore estimated radial conductance values by inverse modeling, using the RSA and outgoing water flow [J v (P)] data determined in the 10 plants described above. Considering that k is constant from the root base to tips, its value was determined in individual plants, as presented in Table  1 , and ranged from 3. 4 to 22. 1 10 -8 m.
Next, we investigated the length of lateral roots with respect to their position along the parental axis. The scattered data indicated a progressive but stochastic growth of laterals on the bearing axis (Figure  3B ). Yet, first order lateral roots showed a more pronounced growth than second order lateral roots at a same distance of their respective parental root tip. We therefore established distinct length laws for the two orders of lateral roots. These observations allowed us to deduce the parameters of a root model that can be used to generate RSAs. Using realistic ranges of nude tip length (between 5 and 35mm) and average internode length (1-2. 75mm), we modeled the stochastic process of lateral root branching by a firstorder Markov chain on growing root axes  (Lucas et al. , 2008). In addition, the length of simulated lateral roots was determined by fitting lateral root length along parental root axes (Figure  3B ).
Sensitivity analysis of root water transport to architecture and hydraulic parameters Next, we applied the HydroRoot model to calculate the sap flow at a driving pressure of 0. 3MPa [J v (0. 3MPa)] in our collection of simulated RSAs using three distinct, yet realistic hydraulic configurations. For this, we selected three contrasting but uniform k values, all derived from cut-andflow experiments, and applied a varying multiplying factor (ax) to the median axial conductivity (K) profile determined in the same approach (see Figure  2C ). Thus, in addition to an intermediate hydraulic configuration (mean k value: 32. 8 10 -8 m. s -1. MPa -1 ; ax=1), we defined a low hydraulic (minimal k : 9. 9 10 -8 m. s -1. MPa -1 ; ax=0. 5) and a high hydraulic (maximal k : 71. 4 10 -8 m. s -1. MPa -1 ; ax=2) setting.
Digitalizing of root architectures Excised root systems were spread out in a Petri dish containing a thin film of water, and gently brushed prior to being digitized at 600-900ppi with a desktop scanner. Images were analyzed with ImageJ software. For a given axis, internodes and lateral roots were traced back with straight or segmented lines, which were then saved in the ROI manager. This procedure was applied for the primary root as well as each lateral root carrying lateral roots, which resulted in a table containing the internodes and lateral roots length for each branched root.
Modeling principles The HydroRoot model was developed in a Python programming language, as a component of the OpenAlea platform  (Pradal et al. , 2008; Pradal et al. , 2015). HydroRoot uses a Multiscale Tree Graph (MTG)  (Godin and Caraglio, 1998)  to represent root hydraulic architecture, which consists of the topology of a root system (branching positions, root lengths, root radii, etc. ) and its hydraulic structure (local radial and axial conductivities). The RSML format  (Lobet et al. , 2015)  is used to import and export the data to/from the HydroRoot model. The model is open source and available through its public repository (https://github. com/openalea/hydroroot). The hydraulic aspects of HydroRoot consisted in two main components: the radial water flow between the bathing solution and the xylem vessels and the axial transport through the xylem vessels.
Statistical Analysis All data sets were obtained in at least four independent biological repeats (plant cultures) with the indicated total number of plants. Pairwise comparisons of model parameters were performed using a Student t test (P < 0. 05). We observed that the absolute lengths of first order lateral roots (i. e. carried by the primary root) Figure legends. were very similar between plants of a same age, despite the variations in length shown by the primary root. Thus, plotting lateral root length as a function of the relative distance to the tip of the bearing axis provides a comprehensive description of lateral root length repartition. This representation was conserved in second order lateral roots, whose length was referred to the longest first order lateral. (A) and (B) show cumulated data from 13 individual primary roots (black) and 9 first order lateral roots (grey).
19); for a discussion see  Rakel et al. (2020). In the model, all rate constants were corrected by ambient temperature (Eq. 20-22). Rates increase with temperature in an exponential manner until an upper temperature boundary is reached and the temperature response is inverted. The model was implemented in Matlab using DEBtool (available online at https://github. com/add-mypet/DEBtool_M). Model parametrization was done using the Movement, feeding and assimilation p OM = p am L 2 X (x K + X) -1 with x K = p am F m -1 and X = μ OM OM Eq. 1 p X = p OM κ x -1 Eq. 2 Jx = μ OM p X Eq. 3 f OM = p X p Am -1 L -2 Eq. 4 B = k b L 2 max(0, M -m 0 ) Eq. 5 D = exp(-b w max(0, M -m 0 )) Eq. 6 f M = B b max -1 L -2 Eq. 7 Reserve dynamics and energy fluxes dE/dt = p Ap C Eq. 8 p A = f OM f M p am L 2 Eq. 9 p C = E * (v/L -r) Eq.
The study of  Bart et al. (2019)  provided data on the growth (measured as weight) and reproduction of A. caliginosa at different food qualities and quantities. Experiments were conducted in meadow sampled Luvisol soil from Versailles with hand sorted individuals from an agricultural field at the research facility (for details on the study site and soil conditions, see  Bart et al. , 2019). The here presented approach uses the growth and reproduction data of worms fed with horse dung at either 1 or 3 g/individual/14 days. Worms were kept either individually or grouped after puberty. The grouped earthworms produced cocoons and their output was also recorded. Three additional experiments with A. caliginosa were conducted in the here presented study to determine burrowing activity in relation to environmental factors: cast production was quantified as function of temperate and burrow rates were measured as function of soil organic matter and moisture contents.
In total, five treatments were experimentally tested: 15% vs. 20%, 25% vs. 20%, 25% vs. 15%, 15% vs. 10% and 20% vs. 10%. Burrowing activities were described as total length burrowed per day and total length burrowed. The experiments were carried out at 20 • C and organic matter content of 4%. To compare the experimental results with the data from the literature, the units of the soil moisture content were unified to water content  [%]. Details on the calculations can be found in the Supplementary Material.
3E ). In the original experiment  (Holmstrup, 2001)  with two different soils (3% and 4% organic matter content), adult worms could grow and reproduce for a 14-day period before being transferred to different intermittent moisture conditions for another 14 days. The change in fresh weight and the reproduction rate under these conditions could be well described by the model (Fig. 3E, F ), under the following assumptions. As in the experiment total fresh weight (including gut content) was determined and the net worm weight was unknown, we estimated the initial structural length (L 0 = 0. 24 cm) based on the growth and reproduction data and calculated the change in fresh weight relative to initial. We additionally estimated the contribution of the gut content the total fresh weight to be 0. 3 g (Fig.
Capsule endoscopy (CE) is a valid alternative to conventional gastrointestinal (GI) endoscopy tools. In CE, annotation tools are crucial in developing large and annotated medical image databases for training deep neural networks (DNN). We provide an overview of the described and in-use various annotation systems available, focusing on the annotation of adenomatous polyp pathology in the GI tract. Some studies present promising results regarding time efficiency by implementing automated labelling features in annotation systems. Thus, data are inadequate regarding the general overview for users, and may also be more specific on which features provided are necessary for polyp annotation.
Introduction For colonic pathology, screening and diagnostic colonoscopy or sigmoidoscopy is the 'gold' standard for investigation, but new tools such as capsule endoscopy (CE) are up-and-coming, and may even be more sensitive for polyp detection  [1, 2]. The field of gastroenterology (including colorectal cancer screening) has, therefore, an urge and interest in developing new and modern pathology screening tools. As CE is already implemented as a standard tool for small bowel investigation, improvements must be made in the colon CE (CCE)  [3] [4] [5]. Overall, CE is a valid alternative to conventional gastrointestinal (GI) endoscopy tools. In the development of CE, data material and credible databases with annotated polyps are required to generate deep learning (DL) algorithms. Unfortunately, there is a lack of large and annotated medical image databases publicly available for training deep neural networks (DNN)  [6] [7] [8].
The highly time-consuming annotation process may explain this. As a short cut, some papers suggest data augmentation of already annotated medical images to increase the sampling number in databases, or even train DNNs from scratch  [6, 8]. In the case of polyp detection, as polyp morphology varies greatly, the need for adequate and broad annotated samples of polyps is crucial for developing DNN in CE. There is, therefore, a limit for the use of argumentation in producing algorithms that pass external validation. The tools used to process polyp annotation have been described with increasing interest in the literature over the past 10-15 years, due to the continuous development in healthcare technology, and CE development. The process of accomplishing beneficial samples of annotated polyps demands precision work and easily operable annotation tools; there are no recent review articles published focusing on a practical and efficient approach.
Annotation Tools After reviewing the literature, the following annotation tools are presented; Computer Vision Annotation Tool (CVAT), Cord Vision (CdV)/Encord, Fast Colonoscopy Annotation Tool (FastCAT), GTCreator, EIR system, and Ratsnake  [9] [10] [11] [12] [13] [14] [15]. ImageLabeler (MatLab) is also included, due to first-hand knowledge of the system within polyp annotation. Finally, the following presented data and information are synthesized in Table  1.
Annotation Tools After reviewing the literature, the following annotation tools are presented; Computer Vision Annotation Tool (CVAT), Cord Vision (CdV)/Encord, Fast Colonoscopy Annotation Tool (FastCAT), GTCreator, EIR system, and Ratsnake  [9] [10] [11] [12] [13] [14] [15]. ImageLabeler (MatLab) is also included, due to first-hand knowledge of the system within polyp annotation. Finally, the following presented data and information are synthesized in Table  1. ↓↓ ↓ ↓ ↓ ↓ NR Workload non-experts - ↓↓ ↑↑ ↓ ↓ - NR Learning ↑ NR ↑ NR NR NR NR 2 Shapes M M NR M M NR M File formats M M S S M NR M Shortcuts M M NR M M NR M 3 Configuration D + W W NR D D NR D Costs Free Contact NR Free Free NR Contact Abbreviations are as follows: -, baseline; M, multiple; S, single; NR, not reported; D, download on local computer; W, web-based configuration.
CVAT is frequently used as a baseline reference in articles with experimental study set-up to present novel annotation software. Some citations even refer to it as state-of-theart annotation software  [10, 13]. Hansen Ulrik Stig et al. compared CVAT with a novel annotation software platform, Cord Vision (CdV) (later renamed as Encord)  [10]. CVAT is remarkably slower in the annotation process compared to CdV, regarding both the manual and automated annotation process provided by CdV. Compared to each other in a 120 min project, CVAT labels 2241 ± 810 frames compared to 10,674 ± 5388 frames labeled by CdV (p = 0. 01), with a respective average labelling speed of 18. 7/min to 121/min. The authors, therefore, suggest that an automated annotation tool can outcompete the already established and reputable manual annotation software, herein CVAT.
CdV/Encord Cord Vision (CdV)/Encord is a novel annotation software platform developed by Encord to automate annotation processes for computer vision  [19, 20]. The company was founded in 2020, and has raised a total of USD 17M over the last few years to develop a good replacement to the already present manual annotation software. It has facilitated over 250 million frames and images, and served customers in various verticals, from sports analysis and satellite imaging to medical imaging. [19] [20] [21]  The software includes models to allow multiple regions of interest (for polyp annotation; abnormalities) to be tracked within the same sequence. Regarding gastroenterology and pathology annotation (hereby polyp annotation), the software is only described in one article co-authored by the developers  [10].
As the interest in AI application for medical investigation of the gastrointestinal tract increased in the last decade, Hansen et al. suggest CdV as a novel software by embedding automated labeling features and model functionality into the annotation process. As noted for CVAT, CdV was compared in a labelling experiment with data from the Hyper-Kvasir dataset, comprised of frames with polyps  [22]. Of the annotated labels by CdV, only 3. 44% ± 2. 71% were hand-drawn (the rest were generated through models or tracking algorithms). Yet, it results in a significant improvement in efficiency and task completion  [10]. The efficiency improvement compared to CVAT results from the different polypoidal morphology, as the trained object-tracking algorithm of CdV could follow the non-linear trajectories of the polyps in question.
The paper suggests CdV as a provider of features that increase polyp annotation efficiency because the software's models and tracking algorithms are sufficiently trained, resulting in high-precision labelling. Note that the result is based on a single comparative study, and the actual positive as polyp annotation remains unclear. The software provides a variety of video formats such as. mp4,. webm, and. mkv. As well as the multi-facilitating software, it provides editor tools in the form of drawing, editing, and renewing annotations; the tool s biggest brag is the ability to perform automated labelling  [20]. Lately, the company launched a new data quality assessment technology that automatically detects errors within annotated training data  [23]. The tool applies to the growing self-supervised learning technique by differentiating the most egregious cases and passing them back to "human eyes for further help".
Krenzer et al. recently published an article presenting a new annotation software, Fast Colonoscopy Annotation Tool (FastCAT)  [13]. The researchers argue for an urgent need for a faster and less people-demanding annotation process. They implement a two-system annotation software with a small expert annotation part and a large non-expert annotation part. FastCAT reduces annotation time spent by the domain expert by increasing the annotation burden for the non-experts, while still maintaining proficient high-quality data. FastCAT is an annotation software that is shown to significantly reduce the workload of expert annotators (with a factor of 20) and improve overall annotation speed  [13]. The reduction in overall annotation speed is due to the faster annotation by the nonexpert, caused by an already marked bounding box in the correct location, or with slight adjustments that need to be annotated by the expert.
GTCreator GTCreator was presented in 2019 by Bernal et al. as a more flexible annotation tool than other existing tools  [9]. It was designed to cover all the possible annotation opportunities within an image, such as object annotation, text annotation, and semantic labeling for classification. Within image annotation, the software provides opportunities to change image scale, mask transparency, brush size, freehand definition, and changes in contrast for better image visibility. The tool also provides pixel-wise editing as an option for slight polygon point replacement. GTCreator also provides structural enhancements in which the annotation session begins by downloading a definition file configuration. This file defines the name of the annotation set, description of the annotation task, and relative paths in which both dataset images and annotation will be stored.
This makes it possible to divide annotation tasks among different annotators. Furthermore, providing an easy way to resume the annotation session may be part of a great reduction in the time-consuming process of starting an annotation session. Therefore, the structural superiority of the tool is a prominent advantage compared with other tools, as time loss in annotation sessions extends the annotation process itself, for example, with structural difficulties. As another feature, GTCreator provides GT revision, allowing novice experts/annotators to mark an image for later inspection by an expert. All annotations can be stored in a CSV file, compatible with the most common software environments. In a comparative study (both quantitative and qualitative), Bernal et al. compare GTCreator with RatSnake, LabelMe, VGG image annotator (VIA), Video Image Annotation Tool (VIAT), and ImageJ  [9].
A qualitative comparison reveals some of the GTCreator's abilities, such as allowing fast and easy navigation through the images to be annotated (in other words, enables the creation of image collection). This feature is also provided by VIAT and ImageJ, and exposes the pre-annotation time as an important factor in the mean total annotation time. The three annotation tools in question are observed to have a big difference in mean total annotation time compared to the latter tools. Another feature provided by GTCreator is the ability to browse the dataset using filters defined according to text metadata values. As consecutive frames tend to be, or at least can be, low in variability, GTCreator allows annotation transfer among images and text metadata. This allows and enhances the structural advantage that GTCreator has, accomplished regarding a significant cut-off in mean total annotation time.
Rapid Image Annotation with Snakes (Ratsnake) Iakovidis et al. presented Ratsnake as an image annotation tool for computer-aided diagnosis  [12]. It is a generic annotation tool that is only described in detail concerning the annotation of kidney biopsy images. However, data on the Ratsnake as an annotation tool in gastroenterology and polyp annotation are available, as it is used in comparative studies  [9]. The software is publicly available and developed in Java. Functions are many, such as the capability to retrieve and store multiple images and annotations from both local and web-based storage. Furthermore, Ratsnake is fully compatible with LabelMe and can be used as an alternative cross-platform software to retrieve and edit image annotations from large collections of LabelMe databases available online  [24, 25].
This advantage regarding polyp annotation is not investigated, but should be noted, as some databases with polyps can be compatible with LabelMe and, thereby, Ratsnake. Ratsnake provides both manual and semi-automatic annotation protocols. The manual annotation protocol allows the user to mark landmarks around a region of interest (ROI) that can later be interconnected with either linear or non-linear interpolation. There is also an opportunity to subdivide images into small square regions where the user can mark a ROI by selecting appropriate grid cells in either (1) select grid cells within a ROI or (2) select grid cells around a ROI. This feature is available with free-hand annotation, and may result in higher quality (sensitivity) output from annotation regarding training DNNs. However, the most notable feature of Ratsnake is the semi-automatic approach based on 'snakes', where the concept enhances efficiency in image annotation.
The user manually marks a ROI, and Ratsnake is, thereby, able to combine the manual and semi-automatic approaches and offer an efficient annotation of similar objects in the following images sequences  [24]. This is a highly relevant feature for polyp annotation, as the same polyp can be detected in consecutive images in a sequence. As for the workflow in Ratsnake, manual annotation provides the first steps, followed by a possibility for the user to copy/paste annotations in relevant ROIs, and, in the end, modify the annotation by a 'snake' (semi-automatic). Ratsnake provides highly relevant features in the polyp annotation field, with functionalities such as manual and semi-automatic annotation, free-hand annotation, data storage, and general compatibility with other annotation systems.
ImageLabeler (MATLAB) MATLAB's app "ImageLabeler" is also assessed based on first-hand knowledge of polyp annotation with the software. Unfortunately, no relevant articles in the literature are found on this software, and the following is a combination of experiences of the reviewers. ImageLabeler provides many features as described above; as the name implies, the software can only annotate images. It is integrated into MATLAB, and the user license can be bought at their official website or obtained through deals with universities or research units. As the user opens the app, ImageLabeler provides a structural and easy-to-use front page, and images can be downloaded from files on the local remote. In our research group, the images are stored as JPG in a drive that it is accessible to multiple users.
Efficiency An overview of the different tools that evaluate efficiency is listed in Table  1. Unfortunately, 'gold' standards or even baseline annotation tools for adequate evaluation are lacking in the literature, and evaluation on efficiency is difficult to assess. However, Encord does stand out because of its well-developed automated labelling feature's significant reduction in time consumption during the annotation process. Also, they claim an increased task completion, and discuss the possibility of higher actual positive polyp annotation as their tool can follow the non-linear trajectories of the polyps. Other annotation tools such as CVAT, Ratsnake, EIR system, and MatLab also provide this feature completely or to some degree, but a comparative study regarding automated labelling and efficiency is missing.
At least the data on Encord are an indication that tools featured with automated labelling are more efficient and sensitive for polyps, but more evidence must be presented on the subject. Some annotation tools improve their efficiency by reducing expert workload by optimizing manual annotation protocols or implementing automated labelling. Reducing expert annotation is based on a cheaper and less demanding process, by releasing experts to do other tasks. For tools aiming for lower workload and higher efficiency, there are, unfortunately, no comparative studies between manual annotation protocols (higher workload burden for non-experts) and automated labelling (lower workload burden for both non-experts and experts). Some interesting aspects of investigating would be the differences in true positive polyps annotated between the two categories and, hence, more efficient annotation.