dataesr/scientific-paragraphs-categorization huggingface dataset:
Note: The working concentration of individual probe sets are: jim (400 nM), chinmo (800 nM), brC (1 mM), nvy (2 mM), mamo (2 mM) and oli (400 nM). Sequences are available at https://doi. org/10. 5281/zenodo. 7674721. These concentrations were established by performing a dilution series for each probe set in order to choose the best one. Cross-reactions between probes may occur (see troubleshooting). 6. Washes -Day 2 a. Wash the samples for 40 min at 37 C in smiFISH wash buffer with rocking. b. Perform 3 additional washes of 15 min at room temperature with smiFISH wash buffer with rocking. c. Wash 10 min in PBST at room temperature with rocking. Note: Store samples for 2 days minimum in pyrex plates at 4 C in Vectashield (H1200) with Dapi to label the nucleus. CRITICAL: 2 days is necessary to allow Dapi to penetrate the tissue.
Note: The software deconvolves by default with the slider on 1, which corresponds to a fairly conservative deconvolution without risk of artifacts. d. Regularization Parameter: 0. 05. Note: This number correspond to the degree of denoising of the image during deconvolution. e. Smoothing: Very high. Note: smoothing option before deconvolution. f. Contrast enhancement, Cut Off and deblurring are disabled. Note: After this process, one new confocal stack is created with the lightning correction. Note: Other methods of deconvolution can be used at this step. However, we found the Lightning extremely powerful compared to other methods. CRITICAL: LAS_X_4. 5. 0 or higher version of the Leica software must be used. Older versions cannot correctly process the large data sets which are created. 10. Save the processed confocal stack.
Timing: 4 h 2] [13]  The Limeseg plugin uses a particle-based active contour method allowing 3D object segmentation on objects labelled on their outline from image stacks, for example confocal stacks containing cells labeled on their membrane (Figure  4 ). The Limeseg plugin does not require a powerful PC. We could run it on a Windows 10, Intel Core i5-6600 CPU 3. 31GHz, 16Go RAM. 11. Identify the cells of interest with the Cell Counter plugin of the ImageJ/Fiji software  [11] [12] [13]  (Figure  5B ). Note: the GFP and DAPI signal are used to find the center of the cell. At this step, the coordinates of each point are managed in the measurement tab.
Note: Once the segmentation is performed on multiple cells or on individual cells, a results table will be created. This table contains the coordinates and the volumes of segmented cells. ii. Save also the. ply files: Plugin\limeseg\show GUI. In the 'write' box of the 'show GUI' window write where to save the. ply file and click SaveStateToXmlPly. Note: A folder containing a. ply file and a 3D file of the cell is created and saved for each cell. These files will be used in MATLAB to quantify mRNA in each cell. CRITICAL: As written above, sometimes several rounds of segmentation are needed to segment all the cells of a lineage A. In this case, compile all the folders containing the. ply file in the same folder. Before compiling all the. ply folders, it is essential that each folder is manually renamed as followed: 'Developmental time_ gene name _ sample name _localisation_cell_number of the cell' Note: For example, in our study, we wrote: LL3_jim_VNC01_T2R_cell_1.
Note: In our example the path is: C:\Count_mRNA_Urdy\LL3_Jim\VNC01\T2R\Limeseg. CRITICAL: Rename each folder containing the. ply files before putting them in the Limeseg folder. If you don't rename them at this step you might overwrite a folder since they can have the same name. Note: Renaming manually the folders can be -time-consuming. Optionally you can use our Python script: https://doi. org/10. 5281/zenodo. 7456891. Before using it download and install Python 3: https://www. python. org/downloads/release/python-3111/. The Script will rename the folders containing the. ply file and pool them in a single Limeseg folder.
MATLAB Timing: 4 h MATLAB (Figure  5 ) is used to quantify mRNA in each Lin A/15 Cell. Two types of files are imported in MATLAB: 1/ the. ply files containing the segmented cells and their coordinates and 2/ the composite stack with the smiFISH signal processed with the Lightning. These stacks are cropped around each cell using the point clouds generated by individual cell segmentation with LimeSeg. The mRNA spots are detected in 3D, in the mRNA channel of these cropped stacks, using the method described by Raj et al. 14  In short, the spots are identified computationally by running a MATLAB image processing script that runs the raw data through a filter, a Laplacian of a Gaussian, a mathematical operator commonly used to enhance spots of the correct size and shape while removing the varying background.
EXPECTED OUTCOMES The expected outcome is the precise quantification of single molecules of mRNA at the single cell level in 3D. The MATLAB script generates two types of files reflecting the number of mRNA in each segmented cell. The first file (Figure  8 ) is a spreadsheet containing the following: Cell centroid in X, Y Z, Cell surface, Cell volume and the number of mRNA in the crop or in the segmented cell. The second file (Figure  5L ) is a 3D representation of the segmented cells where the border of each cell is color-coded according to the mRNA expression level and each mRNA spot is represented by spheres that are visible by transparency through the cells.
illustrates the parameters we used. 20. Define the parameters in lines 77, 78, 82, 83 and 84. a. Define the voxel size of your confocal stack in lines 77 and 78. b. Define the Gaussian parameters to filter images and find particles in line 82 to 84. Chose the parameters that give the best visual detection of mRNA spots. Note: Our datasets are as follow: width of the Gaussian filter = 5 (unit: number of pixels, in our case 1 pixel = 50nm); variance of the Gaussian filter = 0. 25; threshold (filter to remove the background) = 0. 11 for jim, 0. 08 for chinmo, 0. 15 for mamo, 0. 09 for br, 0. 29 for nvy and 0. 15 for oli. The detected spots in each cell have a normal distribution of diameters with a mean of 0. 25 +/-0. 05 micrometers and their maximum intensities displayed a unimodal distribution, arguing that the detected spots are mostly individual molecules.
ACKNOWLEDGMENTS This work was funded by the  Atip-Avenir Program  and the  Equipex+Spatial Cell-ID  ( ANR-21-ESRE-0016 ). We acknowledge the contribution of  SFR Biosciences (Arthro-tool facility ,  UAR3444/CNRS ,  US8/INSERM ,  ENS de Lyon, UCBL)  and the IGFL microscopy platform.
Data and code availability The codes generated during this study are available at zenodo. Remap source code: https://doi. org/10. 5281/zenodo. 7456891 smiFISH analysis source code: https://doi. org/10. 5281/zenodo. 7674721 List of smiFISH probes : https://doi. org/10. 5281/zenodo. 7674721 All data generated and analyzed using this pipeline are available in this study. 1.
The group with non-fluent agrammatism did not differ from the fluent group concerning gender, educational level, aphasia etiology and aphasia severity (as assessed by means of the Token Test and Word Comprehension), while the two groups significantly differed in age. Results are reported in Table  1. A total of 21 participants (70%) suffered an ischemic stroke, seven (23. 3%) haemorrhagic, while one (3. 3%) underwent low-grade glioma resection; the last participant (3. 3%) had a fronto-parietal lesion secondary to an infective pathology. Lesion site was assessed for 25 out of the 30 included participants by computed tomographic (CT) or magnetic resonance imaging (MRI) scan and plotted manually through the open-source rendering software MRIcroGL (https://www. nitrc. org/projects/ mricrogl/).
Each image was than reconstructed on a standard template (MNI) using SPM12 software, which runs in MATLAB (https://www. fil. ion. ucl. ac. uk/spm/software/ spm12/). Scan images were not available for five people. Participants were divided into two groups considering the presence/absence of agrammatism. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Local Ethics Committee (Milano Area B: Resolution 719_2019, ID 11111). Each participant gave informed written consent prior to the study.
Statistical analysis Statistical analyses were performed using the statistical programming environment R (R Development Core Team, 2019). Descriptive data were calculated as mean and SD or absolute number and frequency. In order to identify significant differences between the two groups, the χ 2 test and the Student's t-test were adopted for categorical variables and for continuous variables respectively. The threshold for statistical significance was set at the conventional level α = 0. 05. For contrasts involving the type factor, we considered a corrected level of α = 0. 0056, considering that type had 10 levels, thus implying nine contrasts. Linear mixed-effect models were performed as the main statistical procedure  (Baayen et al. , 2008)  using the R package lme4  (Bates et al. , 2015).
g. , the right wrist of HRP-4C, which has an irregular cylinder-like shape. Other algorithms have also been experimented on, for example genetic algorithms  [25]  or iterative algorithms  [26] , but they appear to have the same limitations. For more complex surfaces, several approaches specific to certain shapes have been proposed. For example, the approach of X. Liu et al. is adapted to surfaces of revolution  [27] , while the one of S. Wang et al. targets high-speed train heads  [28]. More recently, some methods have been developed to fit a mesh of any shape to a parametric surface, such as deep neural networks that learned the surface-fitting operation from a large dataset of meshes  [29] [30] [31]. These methods-especially  [29] -show good results for meshes of any shape, but the obtained surface is hard to adjust to the needs of the user.
Tactile Information Framework A software framework has been developed to handle the information from the tactile sensor to be able to use it in our robot controllers. This tactile information framework is designed as a package for the Robot Operating System (ROS), melodic version  [33]. It acquires the data from the sensor using Bluetooth Serial Port Profile (SPP), then applies different computations to obtain the forces applied to the normal to the cells, and finally sends them as a control input to our robot controller based on mc_rtc (version 1. 14), an interface for simulation and robot control systems  [34]. An overview of the whole process with the tactile information framework and the control framework is given in Figure  2. The tactile information framework acquires array-like data that contain a pressure intensity encoded in 8 bits (from 0 to 255) for each cell (k, l).
Control Framework The control framework used is mc_rtc  [34] , which provides a bridge with ROS for the communication with the tactile information framework. It handles full-body control of several humanoid and non-humanoid robots, including HRP-4C. It embeds several control tasks based on quadratic programming (QP), including position control of end-effectors, posture control, admittance control and other control tasks. We created a mc_rtc package for tactile-based control that interfaces with the tactile information framework, implemented in C++. It obtains the TC messages from the frame-work and uses the calibration model of the tactile sensor to compute the actual forces applied to the wrist of the robot in its own frame. The calibration model is composed of two parts, as shown in Figure  2:  • The intensity/force relation, which is the relation between the intensity obtained with the acquisition software and the actual force applied.
• The planes along v direction P v cross on axis L and are separated with a constant angle α = π m. ( 11 ) By construction, they are orthogonal to the planes P u. To evaluate the precision of the fitting algorithm, we computed the distance between the obtained B-spline and the original right wrist mesh. Table  2  shows this difference with varying parameters n and m, in addition to the time required to generate the B-splines. Knot vectors are automatically generated as uniform knot vectors, as explained in Section 3, and smoothing factor is set to 0. 01 for all the splines. The distances were computed using Meshlab  [48]  software (version 2022. 02). B-splines were generated on a standard laptop PC with 12-core Intel Core i7-10750H 2. 60GHz CPU. As we can see, the obtained B-spline is close to the original mesh, with a mean distance of 0.
Validation of Experimental Method Before going through the validation of the spatial calibration method, we validate with simulation the theoretical analysis developed in Section 3. 5. In particular, we test the geometric method used to compute the applied contact from FTS data using equivalent forces. We recall that a force/couple system with perpendicular force and couple can be reduced to an equivalent system with a force and no couple, by moving the resultant force on a line perpendicular to the system's coplanar plane until the couple is null  [49]. To verify our method with respect to this definition, we simulate 10,000 randomly generated force/couple systems with perpendicular force and couple with the Python script given in Algorithm S1. For each generated system, we compare the application point of the equivalent force vector obtained with the definition and the one obtained with our geometric method (see Equation (  9 )).
Experimental Data The touch experiment presented in Section 3. 5 was performed with 19 distinct touches of the tactile sensor at different points. Among them, 11 resulted in non-null mesh intersections from the motion capture system and non-null force/mesh intersections with the equivalent forces. The computations of the contact positions and normals are performed in real time, as they require only the few computations presented in Section 3. 4. 4. Figure  6  shows the direction and intersection results obtained for these 11 touches and the corresponding results obtained with the calibration method. The exact values are given in Table  S1. The B-spline surface used for comparison has been generated with parameters n = 10 and m = 10. In the given results, the applied forces are defined with their origin and directions.
Validation of the Normals to the Cells Firstly, we compared the normals to the cells given by the calibration method and those obtained from the FTS. Figure  7  shows the absolute values of the difference between the vector directions for x, y and z axes, computed directly from the data of Table  S1. We observed that the mean error on the z axis that almost coincides with the main axis L after transformation to the experiment frame is high for each touch, with a mean of 40. 6 %. We made the hypothesis that the error is caused by an abnormal value of the force along z given by the FTS. A possible explanation of this phenomenon is that the FTS we use has a lower resolution on the z axis than on the x and y axes: for the configuration, we used (SI-1400-60), and the force sensing resolution was 7-12 N for the z axis and 1-3 N on x and y  [50].
This lower precision, combined with the high sensing range of the sensor, could lead to unstable results if the force applied is too low, as was the case when the FTS was touched with the wand (applied forces were between 1 and 16 N along the z axis, with a mean of 3. 6 N). Less probable, this error might result from a wrong force calibration of the FTS along z. The use of a second sensing method-in our case the motion capture-aims to verify the coherence between the results obtained and to validate, or not, the hypothesis made on the FTS. This will be discussed in the next section. The last plot of Figure  7  shows the error between the vector directions but that which is limited to x and y coordinates, to correct the data after this hypothesis. As we can see, the errors are much lower if we do not consider the z axis.
5 mm. Thus, the error obtained for the contact positions is 1. 7 mm compared to a perfectly accurate system (we recall that we assumed an ideal z axis so that this error is computed only from x and y positions). At the end, this experiment proved that the positions and directions of the contacts are well estimated with the generated B-spline surface, with a precision of 3 % for the directions and 1. 7 mm for the positions, if we consider the FTS data as a reference. This precision is lower than the precision of calibration methods developed for non-flexible tactile sensors  [23]  (2 mm for the 3D positions) but was achieved with a flexible tactile sensor. This result could be improved but is fair enough for the targeted use cases, as the tactile sensor is usually touched by a human hand and thus in an area larger than a single cell.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/s23094569/s1. Figure  S1 : Output curves of the flexible tactile sensor. Table  S1 : Positions and directions obtained during the experiment from the force/torque sensor (FTS), the motion capture system and the calibration method. Figure  S2 : Photographs of the elements of the experimental setup: (1) Support including the force/torque sensor, wrist and tactile skin. (2) Wand. (3) Motion capture cameras. Algorithm S1: Python code to verify the geometric computation of equivalent forces through simulation, in which the geometric expression is compared to the one obtained with the definition of equivalent force on 10,000 randomly generated force/couple systems. Figure  S3 : Visualization of the sorted absolute direction errors obtained with Algorithm S1.
The Bidirectional Encoder Representations from Transformers (BERT) architecture o ers a cutting-edge approach to Natural Language Processing. It involves two steps: ) pre-training a language model to extract contextualized features and ) fine-tuning for specific downstream tasks. Although pre-trained language models (PLMs) have been successful in various text-mining applications, challenges remain, particularly in areas with limited labeled data such as plant health hazard detection from individuals' observations. To address this challenge, we propose to combine GAN-BERT, a model that extends the fine-tuning process with unlabeled data through a Generative Adversarial Network (GAN), with ChouBERT, a domain-specific PLM. Our results show that GAN-BERT outperforms traditional fine-tuning in multiple text classification tasks.
, 2020)  model and further pre-trains it on a plant health domain corpus in French to improve the generalizability of plant health hazards detection on Twitter. Some potential applications of ChouBERT are as follows: • The annotation and indexing of the key elements of plant healthrelated events in the text, including named entity recognition, entity linking, and relation extraction. • Topic modeling for detecting emerging issues from a collection of texts. • Natural language inference for finding precursors of pest attacks. In this article, we explored the combination of GAN-BERT  (Croce et al. , 2020)  and further pre-training with ChouBERT. We present a discussion on the results and perspectives of this combination on the text classification task for plant health hazard detection.
GAN-BERT applications Generative adversarial network-bidirectional encoder representations from transformers (GAN-BERT) has been assessed on different datasets with different PLMs. The original authors of GAN-BERT have applied it to English sentence-level classification tasks, including topic classification, question classification (QC), sentiment analysis, and natural language inference (NLI) with the original BERT model  (Devlin et al. , 2019; Croce et al. , 2020). Later, MT-GAN-BERT  (Breazzano et al. , 2021)  extends GAN-BERT to a multi-task learning (MTL) architecture to solve simultaneously several related sentence-level classification tasks reducing overfitting. MT-GAN-BERT is assessed with English and Italian datasets, using BERT and UmBERTo , respectively, for sentence embedding generation.
However, the effective zone of such scenarios remains to be studied. The numerous applications of GAN-BERT witness its capacity for fine-tuning PLM on sentence-level classification tasks in a low resource setting. However, none of the works presented in this section have studied the correlation between the labeled/unlabeled data ratio and the performance of GAN-BERT or the impact of using domain-specific PLMs. The lack of specifications for these hyperparameters makes the GAN-BERT setting a black box to newcomers and could lead to expensive grid search experiments for optimization  (Bergstra and Bengio, 2012). Furthermore, the granularities of different classification problems are not comparable. Therefore, it is unfair to compare the performances of GAN-BERT plus the PLMs pre-trained in different languages or domains over these tasks.
In this study, we address these shortcomings by applying the GAN-BERT settings to CamemBERT  (Martin et al. , 2020) , ChouBERT-16, and ChouBERT-32 and probing the different losses over varying labeled and unlabeled data sizes to give more insights into when and how to train GAN-BERT for domain-specific document classification. https://github. com/Kungbib/swedish-bert-models. Method. Data Data annotation by domain experts is expensive and timeconsuming. Therefore, the main challenge of detecting natural hazards from textual contents on social media is to identify unseen risks with low resources for training. We reuse the labeled tweets produced by ChouBERT  (Jiang et al. , 2022) , tweets about corn borer, barley yellow dwarf virus (BYDV) and corvids for training and validation, and tweets about unseen and polysemous terms such as "taupin" (wireworm in English) for testing the generalizability of the classifier.
Since the binary cross entropy loss adopted by the discriminator of GAN-BERT favors the majority class when data are unbalanced, for the different training experiments, we sampled ChouBERT's training data to 16, 32, 64, 128, 256, and 512 subsets, each subset having equal number of observations and nonobservations. We used the same validation data and test sets for all the experiments. In the validation set, there were 79 observations and 213 non-observations; in the test set, there were 58 observations and 447 non-observations. Among the data collected by ChouBERT, there is not only a small set of labeled tweets but also many unlabeled tweets. For the unsupervised learning, we have 12,308 unlabeled tweets containing common insect pest names (other than those in the labeled data) in France.
Text classification with a pre-trained language model Following the study of  Jiang et al. (2022) , the ChouBERT models are further-pre-trained CamemBERT-base models over French Plant Health Bulletins and Tweets and the ChouBERT pre-trained for 16 epochs (denoted as ChouBERT-16) and for 32 epochs (denoted as ChouBERT-32) are the most efficient in finding observations about plant health issues. Thus, in this study, we combine GAN-BERT settings with CamemBERT, ChouBERT-16, and ChouBERT-32. To make our state-of-the-art model, we fine-tune CamemBERT, ChouBERT-16, and ChouBERT-32 for the sequence classification task over the same training/validation/test sets by adding a linear regression layer a to the final hidden state h of the [CLS] token to predict the probability of a label o: p(o|h) = softmax(W a h), (7) Where W a is the parameter matrix of this linear classifier.