dataesr/scientific-paragraphs-categorization huggingface dataset:
Script evaluation In order to evaluate the performance of the SeptoSympto script, we performed an analysis on two datasets obtained independently of the one used to create the models: dataset 2 containing 40 leaves and dataset 6 containing 55 leaves (Table  1 ). Dataset 2 was evaluated by the SeptoSympto script and by two independent experts. The correlation between experts was 74% for necrosis and 95% for pycnidia. The evaluations of these two experts were compared with the outputs of SeptoSympto (Fig. 3 ). We obtained high correlations for necrosis (94% correlation for expert 1 and 75% for expert 2) and for pycnidia (83% correlation for expert 1 and 80% for expert 2) between the manual evaluations and the script outputs. The correlations between SeptoSympto and the experts are of the same order of magnitude as those obtained between the experts, which was expected since deep learning models are trained on data annotated by another independent expert (expert 3).
To compare the different tools available for STB phenotyping, we submitted dataset 6 to the tool developed by  Steward & McDonald 2014 and Stewart et al. 2016 [19, 20] , to SeptoSympto script and to a visual evaluation (Fig. 4 ). Between the two tools, a correlation of 55% is obtained for the number of pycnidia and 59% for the necrosis area. However, the correlations between SeptoSympto and expert 1 is higher (76% for necrosis and 94% for pycnidia) compared to the ones between ImageJ and expert 1 (45% for necrosis and 57% for pycnidia). It should be noted that expert 1 is independent of the expert 3 who developed the tools. Therefore, this indicates that SeptoSympto is a more accurate tool to quantify septoria symptoms than the previous one  [19].
Script transfer A major problem with phenotyping based on machine learning techniques is over tting. Indeed, it is possible that the model is too speci c to the training set. The analyses presented above show that our script does not exhibit any over tting since it detects necrosis and pycnidia very well on datasets that were not used for training the models. However, dataset 2 was obtained under the same conditions (same varieties, strain, growing conditions and scanner) as the training set. In order to develop a tool that everyone can use, we tested whether SeptoSympto performed as well on datasets from different conditions (species, varieties, strains, growing conditions & scanner) as the dataset used to create the models. We rst evaluated dataset 3 obtained with the same varieties, strain and scanner but the plants were grown under different growing conditions.
The correlations between SeptoSympto outputs and expert assessments were 95% for expert 1 and 90% for expert 2 for necrosis, and 58% for expert 1 and 81% for expert 2 for pycnidia (Additional le 2). The correlation between the experts was 95% for necrosis and 80% for pycnidia. These results are similar to those obtained with dataset 2. Hence, SeptoSympto seems to be adapted to datasets from different growing conditions. With dataset 4, we tested whether SeptoSympto was still performing as well on leaves of another species: durum wheat inoculated with another strain but grown under the same conditions as dataset 3 and scanned with the same scanner. The correlations between SeptoSympto results and those of the expert manual scoring are still high (Fig. 5A ). Indeed, a correlation of 96% for expert 1 and 92% for expert 2 regarding necrosis (with a correlation of 88% between the experts), and a correlation of 69% for expert 1 and 71% for expert 2 regarding pycnidia (with a correlation of 89% between the experts) were scored.
Thus, SeptoSympto seems to perform well on datasets from bread and durum wheat species, and from STB symptoms caused by different strains of Z. tritici. To go further, we tested whether the script was sensitive to image capture from different laboratories. Dataset 5 is composed of 115 leaves from three varieties already present in datasets 1, 2 and 3, inoculated with the same strain as these three datasets, but grown under different growing conditions and taken with a different scanner. The correlations obtained between SeptoSympto and expert 1 results are 83% for necrosis and 81% for pycnidia (Fig. 5B ). Dataset 6 is composed of 55 leaves from the same variety inoculated with different isolates, for which images were obtained with a different scanner but at the same resolution. The correlations between SeptoSympto and expert 1 do not decrease (correlation of 76% for necrosis and 94% for pycnidia) (Fig.
Discussion With the emergence of arti cial intelligence, high-throughput phenotyping is growing exponentially, both on visible spectrum images and on other spectral ranges, as it allows the acquisition of high-quality phenotyping data very quickly, easily, and reproducibly. However, the phenotyping of STB symptoms in wheat is still very much done visually, which is laborious, inaccurate, time-consuming and requires expertise  [38]. We have therefore developed an image analysis script using deep-learning techniques and allowing high-throughput phenotyping of necrosis and pycnidia caused by STB with pre-trained convolutional neural networks. As annotation is longer and pycnidium detection is less e cient in conventional machine learning, we used here deep learning architectures to facilitate annotation and enable more accurate quanti cation of STB symptoms from scanned wheat leaves, notably for pycnidium detection.
This developed script uses two models trained on small datasets: one model trained with the U-net architecture for necrosis detection by semantic segmentation and another one trained with the YOLO v5 architecture that distinguishes pycnidia by object detection. This script was evaluated on 6 datasets, containing images obtained with 3 different scanners at 1200 dpi, 38 varieties of bread wheat or durum wheat inoculated with different Z. tritici strains and grown under different growing conditions. To maximize the performance of our models, sources of variability in image capture were minimized with the same image resolution, xed image backgrounds and no leaf curvature. In addition to source variability, methodological and dataset issues can also be encountered when using deep learning methods  [28].
The training datasets used for learning necrosis and pycnidium detection models are small, so that the models can be easily recreated. When the training dataset is small, it reveals the effectiveness of the detection method used  [39]. Therefore, the architectures used were carefully chosen to best t our problems. Also, to facilitate the adaptation and transferability of this script, no augmentation was performed on the data but a kind of cross-validation was done on several datasets with expert notations in order to avoid a common problem with deep learning models: over tting  [39]. Here, model metrics are low, especially for pycnidia, but correlations between script outputs and expert ratings are high and do not decrease when changing datasets, even for datasets completely different from the training and validation datasets, which shows the performance of our models as well as the absence of over tting.
Although the time required to annotate the images (1 hour for 10 leaves) and to learn the model (1 to 3 hours depending on the number of leaves) is long, it remains quite low for deep learning models since we use small amounts of annotated data for the model learning. With the image analysis tools, there is also an additional image acquisition time compared to the visual evaluation, as leaves have to be collected, pasted and scanned (60' for 80 leaves). However, the execution time of the script to cut the images and detect necrosis and pycnidia (1'45'' for 10 leaves) is almost similar to a visual evaluation (2'22" for 10 leaves). With a visual evaluation, we obtain quantitative data stored in a table. This requires a little storage space, but it is no longer possible to have access to the raw data (the leaf observation).
Here, the raters have at least 10 years of expertise on STB. As the disease is di cult to phenotype, due to the wide variety of necrosis that can be observed and the small size of pycnidia, non-experts can also accurately study the severity of STB with SeptoSympto tool. This tool is intended to be easily usable by all and transferable to all, in order to standardize interlaboratory evaluation of STB symptoms. The key issues for the deployment of new high throughput phenotyping methods are their transferability between laboratories and their acceptance by a majority of researchers. The SeptoSympto tool was developed with the aim of being e cient on a maximum of different datasets scanned at 1200 dpi but also to be easily adaptable to other datasets. We have therefore developed a tool that is as frugal as possible  [41] , in order to build the most accurate models possible using the least amount of resources (small amount of data needed to train the models, and no need to use large computing resources), which allows for easier adaptability.
Indeed, SeptoSympto is an end-to-end script coded in Python using deep learning models. It runs quickly and offers several options to choose the input and output les. In addition, we have shown that this script performs well compared to visual evaluation on six different datasets containing leaves all inoculated with Z. tritici only. The methods used to develop the script were chosen to be not only the most powerful but also the most frugal and easy to use. Models are learned on small datasets and can be trained on computers with different processors and graphic cards to facilitate learning new models. All this allows to easily annotate images and train necrosis and pycnidium detection models. Depending on the applications (plot management, rate phenotyping or epidemiological survey), tools based on image analysis to detect plant diseases can be developed at various detection scales: at the level of the leaf, the plant, or cover in order to quantify, identify or detect early the disease.
Regarding SeptoSympto, it is a leaf phenotyping tool allowing the accurate quanti cation of STB severity for necrosis and pycnidia. Even if the models obtained for SeptoSympto allow precise detection and script transferability, we could create an open-access database with annotated data to train the models on larger datasets with leaves scanned by many laboratories to allow the use of a single script at the international level which is as robust as possible. In addition, a next step could be to adapt this script to the detection of STB symptoms observed in the eld, which are very different from the symptoms observed under control conditions and where other diseases could be observed on the same leaves. This adaptation of the script to the eld data would be easily achieved by using a portable scanner and training new models.
Conclusion The SeptoSympto tool is a script coded in Python that uses deep learning models to detect STB symptoms. The models used have been trained on a small amount of data to facilitate the transferability of the tool and with the U-net and YOLOv5 architectures to quantify necrosis and pycnidia respectively. This script was created in order to develop an easily adaptable and common tool between laboratories to assess STB symptoms. The tool effectively allows accurate and rapid quanti cation of necrosis and pycnidia on durum and bread wheat leaves inoculated with different strains, grown in different conditions and scanned with different scanners. Declarations Figures Figure 1 Overall work ow of model learning and SeptoSympto script. (A) The images of wheat leaves inoculated with Zymoseptoria tritici were annotated on Robo ow in two stages: the rst stage using segmentation for necrosis and the second stage using object detection for pycnidia.
Necrosis annotated images were exported and a model was trained with the Tensor ow library in Python using the U-net architecture. Pycnidia annotated images were exported and a model was trained with Pytorch library in Python using the YOLOv5 architecture. The scripts for model training are available on https://github. com/maximereder/septo-sympto. (B) To run the image analysis, a le must contain different folders and the script SeptoSympto available on https://github. com/maximereder/septo-sympto. The images are stored in a le called "images_input". A le in csv format containing information for each image and the name of each image in the rst column can be added in the folder called "csv_input". The models used for necrosis and pycnidia detection are stored in "models" le. Using the input data, the script will rst detect leaves according to color range and minimal surface, cut and resize each leaf.
High precision indicates minimal false positive detection. High recall indicates maximum detection of true positives. F1 is the harmonic mean of recall and precision. (A) Four models (N0, N1, N2 & N3) for necrosis detection were trained on training and validation datasets of different sizes, using a segmentation based deep learning architecture called U-net. (B) Three models (P0, P1 & P2) for pycnidia detection were trained on training and validation datasets of different sizes, using an object detection based deep learning architecture called YOLOv5.
( A) Comparisons between the assessments of two experts and SeptoSympto for the measurements of necrosis and pycnidia on 50 durum wheat leaves scanned with the Epson Perfection V370 Photo scanner were calculated using Spearman correlations. Dataset 4 was obtained under different growing conditions, a different Z. tritici strain and 18 different varieties from the dataset used for the model trainings. (B) Comparisons between the assessments of two experts and SeptoSympto for the measurements of necrosis and pycnidia on 115 bread wheat leaves scanned with the Epson Perfection V750 Pro scanner were calculated using Spearman correlations. Dataset 5 was obtained under different growing conditions, with the same Z. tritici strain and 3 same varieties from the dataset used for the model trainings.
Identification of paradoxical septal motion Conventional visual (qualitative) identification of PSM was performed independently by two investigators with an advanced level in critical care echocardiography  [14]. PSM was graded as follows: 0 in the presence of a normal septal motion; 1 if a transient end-systolic septal flattening was observed; and 2 if end-systolic septal flattening was sustained or if the septal bulging was inversed (i. e. , directed toward LV cavity) at end-systole. In case of discrepancy, a third investigator with expertise in critical care echocardiography determined the presence of PSM or not, and its grade if applicable  [14]. Inter-observer variability of PSM diagnosis was assessed. The same two-dimensional digital loops were used for LV radial strain analysis. All strain measurements were performed independently by another trained operator using the QLAB 13 software (Philips, the Netherlands).
Data engineering We first confirmed that LV radial strain curves were altered in certain segments when a PSM was present In the presence of a normal septal motion, left ventricular segmental strain curves exhibited a uniform pattern consistent with a homogeneous regional contraction, and the peak of radial strain occurred before the aortic valve closure (mid left panel). In contrast, patients with acute cor pulmonale presented with abnormal left ventricular strain curve patterns (lower panels, white arrows). A change in the pattern of the mid-antero-septal (yellow) and/or mid-infero-septal (red) strain curves was observed: the peak was delayed (i. e. , time-to-peak increased) and occurred typically after aortic valve closure. In addition, the amplitude of the delayed peak appeared related to the severity of the paradoxical septal motion when semi-quantitatively assessed using two-dimensional imaging (septal flattening vs inversed septal bulging, lower panels).
We then developed an R package to automatically extract the data generated with QLAB13 and converted them to a tidy data frame (https:// gitlab. com/ antoi nehum eau/ qlabr aw2re ctang ulard ata). To allow interindividual comparisons, each measured time period was expressed as a percentage of the total length of cardiac cycle, and strain values were normalized by the maximal value measured during the cardiac cycle (Fig. 2 ). In each patient, the following parameters were calculated in each LV segment from strain analysis: (i) The "time-to-peak" defined as the time lag required to reach the maximal value of strain, which was normalized by the length of cardiac cycle and expressed as a percentage. (ii) The "partial area under segmental strain curves" which was calculated as the area under each LV segmental strain curve between 33 and 66% of the cardiac cycle length (focused time period selected on graphical examination of the strain curves where most alterations of strain pattern occurred) to standardize the measurement.
Prognostic value assessment To assess the prognostic value of PSM grade using LV radial strain, we conducted an exploratory analysis in a nested cohort of patients assessed with multiple TEE. Patients with only one TEE were excluded from this Fig. 2  Summary of the process for data engineering. A schematic representation of the quantitative parameters calculated from left ventricular segmental strain curves is provided in the lower left panel. Both the radial displacement and time have been normalized by the maximal displacement and the duration of cardiac cycle, respectively secondary analysis. We stratified our analysis on the severity of RV dilatation and the grade of PSM, when using two-dimensional imaging solely or in conjunction with LV radial strain. Moderate RV dilatation was defined conventionally as a RV/LV end-diastolic area ≥ 0.
Statistical analysis Given that no hypothesis on the expected difference in metrics can be made a priori, sample size calculation was not performed. Missing data were not replaced. Continuous data are reported as medians and interquartile ranges and were compared using Kruskall-Wallis rank-sum test and adjusted with Benjamini Hochberg method for multiple comparison. When p-value was ≤ 0. 05, a paired comparison was performed between groups and p-value was adjusted with a Benjamini Hochberg test to consider the multiplicity of tests. Categorical data are reported as counts and percentages and were compared using Pearson's Chi-square test. We determined inter-observer variability using Fleiss' Kappa and its 95% confidence interval (CI) using bootstrapping. Inter-observer variability for the diagnosis of RV dilatation was also assessed by intraclass correlation coefficient.
We assessed the diagnostic capability of PSM to predict associated RV dilatation and calculated the sensibility and the specificity of this approach. For the survival analysis in the nested longitudinal cohort, variables identified as confounding factors were selected for multivariate analysis (SAPS II, past medical history of cardiomyopathy and age). A multivariate Cox model analysis accounting for new-onset of PSM as a time-dependent variable was used to identify parameters associated with ICU mortality using conventional two-dimensional assessment alone and in association with LV radial strain  [15]. A p-value ≤ 0. 05 was considered significant. All data were generated using R software (4. 2. 1). R packages used are provided in the Supplementary materials (Additional file 1).
In fetal alcohol spectrum disorders (FASD), brain growth deficiency is a hallmark of subjects both with fetal alcohol syndrome (FAS) and with non-syndromic FASD (NS-FASD, i. e. , those without specific diagnostic features). However, although the cerebellum was suggested to be more severely undersized than the rest of the brain, it has not yet been given a specific place in the FASD diagnostic criteria where neuroanatomical features still count for little if anything in diagnostic specificity. We applied a combination of cerebellar segmentation tools on a 1. 5 T 3DT1 brain MRI dataset from a monocentric population of 89 FASD (52 FAS, 37 NS-FASD) and 126 typically developing controls (6-20 years old), providing 8 volumes: cerebellum, vermis and 3 lobes (anterior, posterior, inferior), plus total brain volume.
| Ethics statement This study was conducted in accordance with the principles of the Declaration of Helsinki. Subjects' data were studied in accordance with French regulation (MR-004, declaration of conformity n 2059980v0), following approval by the Paris-Saclay research ethics committee (CER-Paris-Saclay-2020-094). Controls' data were used within the framework of the ethical authorizations of the primary studies (Gene and autism, Inserm C07-33, 08-029 and 11-008).
Then we merged the mask of the vermis with the CERES segmentation in each subject space. We extended the lobules' "cortical" labels in the deep cerebellum by applying a Voronoi front propagation algorithm available in the Morphologist software suite (BrainVISA) (https://brainvisa. info). We finally combined lobules to obtain sub-volumes, according to Schmahmann's anatomical description  (Schmahmann et al. , 1999)  and merged the right and left sides, providing eight volumes and sub-volumes: the whole cerebellum, whole vermis and their subdivisions into anterior, posterior, and inferior lobes (hereinafter referred to as anterior, posterior and inferior hemisphere or vermis). All steps were followed by a visual quality check and manual corrections of obvious errors of segmentation (Figure  1 ).
| Dealing with confounding covariates For the sake of robustness of the fit of the reference typical scaling laws, we gathered a population of 126 controls from three different acquisition sites (sequence and imager), a third only being site-and sequence-matched to the FASD group. As expected  (Takao, Hayashi, & Ohtomo, 2011) , there was a site bias (see control group data, supplementary material Table  A. 1) that we corrected beforehand using a batch effect correcting method  (Fortin et al. , 2017). ComBat has proven to be a method superior to other correction methods, especially for imaging data and when using logistic regression  (Fortin et al. , 2018; Horng et al. , 2022). However, to test the robustness of our results (potential loss of information, information bias), we performed a full post-hoc analysis on the raw data set (without correcting for site or sex effect), that showed very similar results in terms of gradient and reached the same conclusions on the classifier potential (supplementary mate- rial Figure A.
, 2013). Another limitation related to the retrospective construct of this study is that MRI data were acquired in the context of clinical care, on a 1. 5 T imager, which may be seen as not optimum for brain imaging. This is a grounded concern for a structure such as the cerebellum even if it should be pointed out that 60% of participants shared the same site and sequence, and that all sequences were high-quality millimetric IR-prep ultra-fast gradient echo sequences. One could also question the fact that inferior parts of the cerebellum may have suffered from a mild signal drop-off, not visible in quality-check steps, that could have tempered with our ability to reveal a group difference in this area. The last important methodological limitation, which is often found in human studies on NDD, is the choice of the control population.