dataesr/scientific-paragraphs-categorization huggingface dataset:
The second was related to Claude Laurenco, a senior chemist learning how to teach others to use complex software. Having never previously shown his Resyn Assistant tool, which he had developed over the years, outside his laboratory, he had to learn how to communicate its functionalities to other participants and disseminate its operation and value at a distance. In experiences from 2005 to 2007, the power of technology then being put into action suggested what today appears normal for people working and learning at a distance. At the time, however, it was not yet widely exploited in its complete spectrum of capacities to the extent true within EnCOrE. We were convinced of the importance of our progress and began to propose to several agencies to advance the work. Unfortunately, none understood the interest of working on the "collective brain"  [4, 6]  put into action at a distance, and the projects proposed were not considered research in either chemistry or informatics (computer science or artificial intelligence) that merited long-term support.
Input TreeJ is compatible with a large spectrum of labeled images obtained by any region segmentation software. The interface can be launched on any 2D or 3D label image from 8 to 32 bits. The labeling can be discontinuous (i. e. , missing labels), as frequently encountered as a result of post-processing operations applied to segmented images. The only assumption on the input image is that the segmentation labels are strictly positive; if present, the label 0 should be the background of the segmented structure (limitation explained in the 'Tree file' paragraph below).
Tree file TreeJ allows to save and load a reconstructed tree in an home made format called treeV (file extension. treeV). A treeV file is an ASCII file, containing two arrays: one encoding the tree and the other encoding the annotations of each cell. For an embryo containing n cells, the initial structure (i. e. , without lineage) of the treeV will be: array index 0 1 2. n-2 n-1 cell label 1 2 3. n-1 n mother cell label 0 0 0. 0 0 cell tag T 1 T 2 T 3. T n-1 T n where the two gray lines are the treeV data and T i is the tag of the i-th cell. The tree(s) is stored as a list with as many elements as the highest label number, typically corresponding to the number of input cells (n in the table above) added to the number of reconstructed mother cells (Fig. 1D ). By matching the indexes of the list and the labels of the cell, the tree(s) structure can be encoded in only one array.
Each position in the array corresponds to a cell. The value stored in the array at any position gives the label of the corresponding mother cell. For the root(s) of the constructed tree(s), the value 0 is assigned to indicate the absence of mother cell. This implies that TreeJ implicitly assumes that no cell is labeled 0 in the input segmentation. Otherwise, mismatch assignment would result. The annotations are saved in the treeV file as an array of character strings indexed by cell labels. By default, the annotation of each cell is an empty string. TreeV files can be loaded by TreeJ to continue, edit or visualize a previous lineage reconstruction. To transfer the lineage in another software, Newick format is also available as saving format.
Results: TreeJ functionalities Experimentalist's observations are sometimes untraceable by an algorithm and need to be saved and structured to be analyzed and shared. In plant epithelia, the lineage D Saving the tree built in C using the treeV format. The treeV format contains 2 arrays (shown in gray) storing tree structure and annotations, respectively. In this example, the second array is empty, because the tree is not annotated is one of these data that can be intuitively deduced from the cell pattern by the biologist. Tree data structures are available to represent and exploit such observations. TreeJ provides the interface between these structures and the experimentalist to generate, correct, annotate and visualize cell lineages.
Reconstructed dynamics of Arabidopsis thaliana embryo development Using TreeJ over several embryos, fixed at different stages of their development, allows to reconstruct an approximation of the embryogenesis dynamics  [19]. We annotated the internal and protodermal mother cells of 36 embryos between 33 and 257 cells. Using a python script, annotations from TreeJ were used in combination with the segmented images to compute the distributions of cell volumes (Fig. 4A ) and the volume of every tissue per embryo (Fig. 4B ). Ordering embryos by their numbers of cells allowed us to reconstruct a temporal evolution of these quantities. A globally decreasing trend in the average cellular volume in the two domains was observed (Fig. 4A ). This tendency is consistent with the relatively faster pace of cell proliferation compared to cell growth during early embryogenesis  [14].
5C ). Here, we took advantage of the possibility in TreeJ to store into a single representation an arbitrary number of disjoint trees (Fig. 5D ). Note that, in this application, cells were linked and incorporated in an arbitrary order in the trees without considering their actual lineage relationships, since the trees were used as a way to group cells by position and by tissue. Using the tag-based image creation functionality of TreeJ, a separated image was generated for each tree to extract measurements (Fig. 5E ). The mean cell volume per group (position+layer) was calculated from cell volume measurements obtained with the Analyze Regions 3D plugin of the MorphoLibJ package  [16]  (Fig. 6A ). The statistical analysis showed a similar average cell volume increase for the epidermis and for the cortex layer (Kolmogorov-Smirnov (KS) test, P > 0.
05 ). The endodermis contained smaller cells (KS-test; P < 0. 05 from Position 3) due to asymmetric formative division giving rise to endodermis and cortical cells  [27]. These observations are consistent with a recently reported 3D analysis  [24]. The epidermis is composed of two types of cells: the trichoblasts (giving root hairs) and the atrichoblasts  [28, 29]. Previous 2D descriptions showed a differential shape between these two cell types  [30]. As trichoblasts are wider and atrichoblasts are more elongated, we used TreeJ to evaluate whether their 3D volumes differed at the root tip. We discriminated the two cell types by their cell spatial position relatively to cortex cells and manually grouped cells from each type with TreeJ. As above, the cells linked and tagged at each position could easily be separated in two distinct images and the two cell types could be analyzed separately, a process we automatized over the five longitudinal positions with a python script.
However, to perform new measurements or analyses, the reconstructed trees can be extracted and analyzed with, for example, dedicated python scripts. TreeJ can be used on any segmented tissue, be it animal or plant. Future developments encompass the automation of pairing  [31]  or annotation  [32]  based on previous entries with machine-learning schemes. AI models could be trained with reference data sets created from TreeJ outputs. Adding a pretrained model in an ImageJ/Fiji plugin is now within reach  [33] [34] [35].
Availability of data and materials Most of the plant materials and the raw data of the figures used in this study are available on the Github web page at https://github. com/L-EL/TreeJ/. The plugin is also available at http://imagej. net/plugins/treej. Version v1. 0. 0 can be found here  [36]. Project name: TreeJ. Project home page: https://github. com/L-EL/TreeJ. Archived version: 10. 5281/zenodo. 10041643. Operating system(s): Platform independent. Programming language: Java. Other requirements: java 1. 8 (ImageJ2/Fiji 1. 51n or higher) and itextpdf plugin. License: GNU GPL.
Along with providing an estimation of the impact from lightning and biomass burning on the UTLS chemical composition using the LMDZ-OR-INCA model, the present study consists of going further into the development and the application of the methodology presented in  Cohen et al. (2021) , following three major improvements. First, the daily resolution of the current simulation allows a more accurate separation between UT and LS. Second, the anthropogenic emissions have a monthly resolution, thus allowing a better comparison than in the previous study. Third, the comparison now involves O 3 , CO but also H 2 O measurements on decadal timescales, as well as NO y measurements. The latter are substantially less frequent, so we merged the IAGOS-MOZAIC and the IAGOS-CARIBIC data sets in order to compensate this lack of data as much as possible.
In Sect. 2, we describe the IAGOS data set, the LMDZ-OR-INCA model, the simulation setup and the method used to process the data and to assess the simulation. In Sect. 3, we apply the methodology to the assessment of a bi-decadal simulation from the LMDZ-OR-INCA CCM. We finally discuss the contribution of lightning and biomass burning on the modelled chemical fields. The last two steps treat the extratropical and tropical latitudes separately, in order to account for the differences in the seasons definitions and in the mean tropopause altitude.
Summary and conclusions This study consists of the assessment of a long-term simulation from the LMDZ-OR-INCA chemistry-climate model (CCM) with daily resolved outputs in the upper troposphere-lower stratosphere (UTLS). More precisely, we evaluate ozone, carbon monoxide (CO), reactive nitrogen (NO y ) and water vapour climatologies based on all the cruise IAGOS data set including the IAGOS-CARIBIC data, respectively during the periods  Dec. 1994 -Nov. 2017 , Dec. 2001 -Nov. 2017 , Dec. 1999 -Nov. 2017 and Dec. 1994 -Nov. 2017. In order to allow a direct comparison between the simulation output and the high-resolution IAGOS data sets, we use the Interpol-IAGOS software that projects the IAGOS data onto the model grid  (Cohen et al. , 2021). As a first step, we extend this tool to daily model outputs.
The subsequent IAGOS product (IAGOS-DM) is generated by interpolating the IAGOS data onto the model grid, then deriving weighted monthly averages on each grid cell. Similar to IAGOS-DM, the product based on the simulation output (INCA-M) is also made of monthly averages across the sampled daily gridpoints only. As a second step, we compare the annual and seasonal climatologies derived from these two products. The assessment in the mid-latitudes is made separately in the upper troposphere (UT) and the lower stratosphere (LS) using the model potential vorticity (PV), but also in the UTLS like in a single layer, as an option for the models that do not sort out the potential vorticity. In the tropics, the assessment only accounts for upper-tropospheric air masses because of the higher tropopause altitude.
Code and data availability. The IAGOS data (IAGOS, 2022) are available at the IAGOS data portal (https://doi. org/10. 25326/20) and more precisely, the time series data are found at https://doi. org/10. 25326/06  (Boulanger et al. , 2018). The Interpol-IAGOS software is available at https://doi. org/10. 25326/81  (Cohen et al. , 2020).
relied on the same principle. Hence, their approaches are complementary. MOZAIC started with a fleet of five equipped aircraft measuring ozone and water vapour since August 1994. CO measurements started in December 2001 and NO y measurement were operational on one aircraft between April 2001 and May 2005. On the other hand, CARIBIC samples a wide variety of atmospheric species since 1997, including the ones measured by MOZAIC, from one single aircraft. Since the merge of the two programs in 2008, their respective databases are referred as IAGOS-Core and IAGOS-CARIBIC. In the present study, we consider them as a single database called IAGOS hereafter, approach validated by Blot et al. (2021) for ozone and CO. The period we are analysing spreads from Aug. 1994 until Dec. 2017.
3. 1 1 Horizontal distributionsOzone, CO, NO y and water vapour yearly distributions in the UTLS, UT and LS are shown in Figs. 1-4 respectively, and their corresponding seasonal averages are available in Supplementary Material. Showing the results both with and without the separation is relevant because it can provide a better understanding for some biases visible in the UT or the LS. More generally, it is also relevant as a demonstration of the use of the Interpol-IAGOS software for both the simulations with and without an available potential vorticity field. Concerning the non-separated UTLS layer, it has to be noted that the vertical distribution of the IAGOS sampling relatively to the tropopause level varies geographically, resulting from both tropopause and cruise altitude variations.
, 2022). Since their magnitudes are close to their respective FGE, the discrepancies mentioned for water vapour in the LS, ozone and CO display the same sign at most locations. The features concerning CO and NO y are representative of each season, except summertime NO y which shows a very low correlation. Mostly representative of summer too, the model also shows more difficulties in simulating the NO y tropospheric features, especially in the 35-45 • N band where high values are seen in the simulation only (Fig. A3). A comparison (not shown) with a climatology of observed lightning flash rates from the LIS-OTD database (Cecil et al. , 2014)  showed difficulties from the LMDZ-OR-INCA model to reproduce the lightning geographical distribution, with an important underestimation above marine grid cells and an overestimation above lands.
Purpose. -The purpose of this study was to evaluate the performance of ChatGPT, a cuttingedge artificial intelligence (AI) language model developed by OpenAI, in successfully completing the French language version of the European Board of Ophthalmology (EBO) examination and to assess its potential role in medical education and knowledge assessment. Methods. -ChatGPT, based on the GPT-4 architecture, was exposed to a series of EBO examination questions in French, covering various aspects of ophthalmology. The AI's performance was evaluated by comparing its responses with the correct answers provided by ophthalmology experts. Additionally, the study assessed the time taken by ChatGPT to answer each question as a measure of efficiency.
Results. -ChatGPT achieved a 91% success rate on the EBO examination, demonstrating a high level of competency in ophthalmology knowledge and application. The AI provided correct answers across all question categories, indicating a strong understanding of basic sciences, clinical knowledge, and clinical management. The AI model also answered the questions rapidly, taking only a fraction of the time needed by human test-takers. Conclusion. -ChatGPT's performance on the French language version of the EBO examination demonstrates its potential to be a valuable tool in medical education and knowledge assessment. Further research is needed to explore optimal ways to implement AI language models in medical education and to address the associated ethical and practical concerns. © 2023 L'Auteur(s).
Introduction Artificial intelligence (AI) has increasingly shown promise in various fields, including the medical sector. In recent years, AI has been employed to aid physicians in diagnostics, treatment planning, and research  [1]. One of the most remarkable AI technologies is ChatGPT, an advanced language model developed by OpenAI  [2]. Upon its release in November 2022, the AI chatbot, ChatGPT, quickly became a hot topic, particularly for its use in the context of education. It rapidly demonstrated its ability to answer exam-style questions  [3]  and provide explanations, raising questions about its potential role in education and assessment  [4]. Recently, it has been used for writing scientific articles  [4]  without being detected by human experts  [5]. Thus, it is essential to ascertain the accuracy with which ChatGPT can answer questions on medical examinations.
Each test has 5 questions to answer. These questions are grouped into 19 modules (Fig. 1 ). In total, 6785 questions were processed by ChatGPT. We adapted the examination format for ChatGPT by providing it with questions from the French examination, ensuring that the AI would not have prior knowledge of the specific questions. An independent operator collected the responses of ChatGPT and scored them against the correct answers provided by the ophthalmology experts. No approval from the institutional review board or informed consent was required for this study. ChatGPT chooses answer A which is closest to the correct answer but informs the user that there is a more suitable answer. Translation in English: Which suture do you think is best suited to repairing a penetrating corneal wound? A -6-0 virgin silk; B -7-0 nylon monofilament; C -0-0 nylon monofilament; D -Vicryl 6-0; E -Vicryl 10-0.
Results ChatGPT correctly answered 6188 out of 6785 questions. ChatGPT achieved a 91. 2% success rate on the French language EBO examination, demonstrating a high level of competency in ophthalmology knowledge and application. The AI excelled across all question categories of the examination, indicating a strong understanding of basic sciences, clinical knowledge, and clinical management. The AI model was also able to answer the questions rapidly, taking only a fraction of the time needed by human being (on the order of one second).
Discussion ChatGPT's ability to succeed on a written examination, as demonstrated by the 91% success rate on the French EBO examination, highlights the potential of artificial intelligence (AI) in medicine. As AI technology continues to advance, its application has expanded into various domains, including medical diagnosis, treatment planning, and medical education  [1, 7]. ChatGPT, based on the GPT-4 architecture, was trained on an extensive dataset, including medical texts and journals, up until September 2021  [2]. This extensive training will enable ChatGPT to process and understand complex medical information, leading to a high level of competency on written examinations such as that of the EBO. Previous studies have also shown the efficacy of AI-powered tools in medical applications, such as dermatologist-level classification of skin cancer  [8] , detection of diabetic retinopathy on retinal fundus photographs  [9]  or, more recently, detection of cataract  [10, 11]  and corneal edema  [12]  on OCT scans.
Collaboration between human expertise and AI-driven tools can lead to improved patient outcomes and more efficient healthcare delivery  [17]. The question of the role of AI in education and knowledge assessment is critical. AI models such as ChatGPT might be used as learning assistants, providing personalized feedback and additional explanations to enhance the learning process. ChatGPT might also be used in educational assessments to identify students' knowledge gaps and provide targeted recommendations for improvement. In addition, AI-powered testing platforms might be developed to provide adaptive examinations that adjust the level of difficulty in real time based on a student's performance. In conclusion, ChatGPT's successful performance on the French language version of the EBO examination demonstrates the potential for AI to positively impact medical education and practice.
Design and Implementation SMPT is open-source, under the GNU GPL v3. 0 licence, and is freely available on GitHub (https://github. com/nicolasAmat/SMPT). The repository also provides examples of nets, formulas, and scripts to experiment with the tool. SMPT is a Python project of about 4 000 lines of code, and is fully typed using the static type checker mypy. The code is heavily documented (4 500 lines) and we provide many tracing and debugging options that can help understand its inner workings. The project is packaged in libraries, and provides abstract classes to help with future extensions. We describe each library and explain how they can be extended. The ptio library defines the main data-structures of the model checker, for Petri nets (pt. py), reachability formulas (formula. py), and reduction equations (system.
Commands, Basic Usage and Installation SMPT requires Python version 3. 7 or higher. The easiest method for experimenting with the tool is to directly run the smpt module as a script, using a command such as python3 -m smpt. Our repository includes a script to simplify the installation of the tool and all its dependencies. It is also possible to find disk images with a running installation in the MCC website and in artifacts archived on Zenodo  [4, 5]. As usual, option --help returns an abridged description of all the available options. We list some of them below, grouped by usage. Input formats. We accept Petri nets described using the Petri Net Markup Language (PNML)  [23]  and can also support colored Petri nets (using option --colored) by using and external unfolder  [18]. For methods that rely on polyhedral reductions, it is possible to automatically compute the reduction (--auto-reduce) or to provide a pre-computed version (with option --reduced-net <path>).
It is also possible to save a copy of the reduced net with the option --save-reduced-net <path>. Verification methods. We support the verification of three predefined classes of safety properties: deadlock detection (--deadlock), which is self-descriptive; quasi-liveness (--quasi-liveness t), to check if it is possible to fire transition t; and reachability (--reachability p), to check if there is a reachable marking where place p is marked (it has at least one token). It is also possible to check the reachability of several places, at once, by passing a comma-separated list of names, --reachability p1,. ,pn; and similarly for liveness. Finally, SMPT supports properties expressed using the MCC property language  [28] , an XML format encoding the class of formulas described in Sect. 2. Several properties can be checked at once.
Output format. Results are printed in the text format required by the MCC, which is of the form FORMULA <id> (TRUE/FALSE). There are also options to output more information: --debug to print the SMT-LIB input/output code exchanged with the SMT solver; --show-techniques, to return the methods that successfully computed a verdict; --show-time, to print the execution time per property; --show-reduction-ratio, to get the reduction ratio; --show-model, to print the counterexample if it exists; --check-proof, to check verdict certificates (when we have one); --export-proof, to export verdict certificates (inductive invariants, traces leading to counterexamples, etc. ). Tweaking options. We provide a set of options to control the behaviour of our verification jobs scheduler. We can add a timeout, globally (--global-timeout <int>) or per property (--timeout <int>).
Comparison with Other Tools We report on some results obtained by SMPT, ITS-Tools  [32, 33] , and Tapaal  [19]  during the 2022 edition of the MCC  [26]. We created a public repository  [1]  containing the scripts used to generate the statistics and oracles for the 2022 edition of the Model Checking Contest for the Reachability category. SMPT provides a default competition mode that implements a basic strategy that should be effective in the conditions of the MCC. Basically, we start by running the Random Walk and State Equation methods in parallel with a timeout of 120 s, on all formulas, in order to catch easy counterexamples and invariants as quickly as possible. Then we run more demanding methods: BMC, k-induction, PDR, etc. The rationale is that queries used in the reachability competition are randomly generated and usually exhibit a bias towards "counterexamples" (CEX), meaning false AG properties or true EF ones.