dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset included only diseased plants. Considering separately healthy (h) and diseased (d) pixels, we analyzed values of the mean and standard deviation of F 0 , F m parameters for eight consecutive days (Table  2 ). As it was said before (Section 2. 3), F v /F m shows a significant decrease with aggravation of the disease symptoms. Thus, we calculated the differences between parameter values for healthy and diseased plant pixels as follows: D m F 0 = m (d) F 0 -m (h) F 0 m (h) F 0 , D s F 0 = s (d) F 0 -s (h) F 0 s (h) F 0 , D m Fm = m (h) F m -m (d) F m m (h) F m , D s Fm = s (h) F m -s (d) F m s (h) F m (1) The found relationships among healthy and diseased pixels (1) allowed estimating the ranges for D i ∈[min{D i }, max{D i }], where i∈{m F 0 , s F 0 , m F m , s F m } (Table  3 ).
Time Healthy Pixels Diseased Pixels To analyze disease symptoms growth, we calculated severity, S , and the plant size, P : m (h) F 0 s (h) F 0 m (h) F m s (h) F m m (d) F 0 s (d) F 0 m (d) F m s (d S = d H * W * 100 %, (3) P = p H * W * 100 %, ( 4 ) where d is the number of diseased pixels, p is the number of plant pixels, H and W are the image's height and width, respectively. To create a dataset for model testing, we used Real-Fluo-Diseased dataset. We divided every original image with four plants into four resized 128 x 128 images that were finally added to the dataset. Thus, we obtained 432 fluorescent images of diseased Arabidopsis thaliana plants.
Synthetic-Fluo-Diseased dataset We used plant labels of CVPPP dataset to produce binary masks of leaves for every Arabidopsis image as it is shown in Figure  1. In CVPPP labels, every leaf was determined by its consistent color. So we used getcolor() function from Python Imaging Library (1. 1. 7 version) to derive the list of colors used to depict leaves separately. Then for every color from the list, we selected pixels belonging to the same leaf and created its binary mask. The resulting leaf masks of size 128 x 128 pixels were then used to simulate fluorescence images of Arabidopsis plants. For every leaf, we performed the following steps: 1. We produced two binary masks: a lesions mask using a speckle algorithm  (Goodman, 2007)  and a healthy leaf mask. 2. We produced a noisy field, x (h) , with randomly sampled parameters (m F 0 , s F 0 ) from Table  1 , learned earlier from the Real-Fluo-Healthy dataset: x h ð Þ = 1 - N m F 0 , s 2 F 0 À Á N m F m , s 2 F m , (5) where N (m i , s 2 i ) is a Gaussian noise realization.
Iterations over the leaves with steps 1 -5 are described in the text. Repeating these five steps, we obtained a synthetic fluorescent diseased plant and an automatic annotation of disease lesions (Figure  1 ). To augment the number of images in the synthetic dataset, we produced n synthetic fluorescent examples for every GT label from CVPPP dataset. The resulting Synthetic-Fluo-Diseased dataset included only diseased Arabidopsis thaliana plants.
All activation functions in the convolutional layers were rectified linear units, ReLU  (He et al. , 2015). The last layer before the prediction was a softmax activation with three classes. Images and labels from all datasets were resized to 128 x 128 pixels. Using ground truth (GT) labels, we created three-channel labels. To reinforce the learning of the lesion class, which was highly unbalanced, we replaced the encoder with an InceptionV3 backbone pre-trained on ImageNet  (Yakubovskiy, 2019). The decoder was not changed from the original description  (Ronneberger et al. , 2015). We empirically found that the best performances were obtained when all skipped connections were kept, in accordance with the intrinsic multiscale nature of plants  (Rousseau et al. , 2015b).
Model training In addition to the data augmentation techniques used to simulate fluorescent images from CVPPP RGB dataset (Section 2. 7), we applied a standard data augmentation strategy to reduce overfitting and improve model generalization. For this data augmentation, we used Albumentations library  (Buslaev et al. , 2020). While the data augmentation strategies of Section 2. 7 focused on contrast and noise distribution, here we generated geometrical transformations such as horizontal flip, vertical flip, and random rotation at 90 degrees and applied them to a 0. 7 shuffled training dataset. The values of three transformations were all randomly selected from their range. Moreover, when an image was transformed, its annotation image was transformed similarly. In addition, since the transformation parameters were randomly selected, it was necessary to generate a random number.
Our training procedure consisted of splitting the data into 80% and 20% training and cross-validation respectively. We shuffled the dataset examples at the beginning of each epoch and used a batch size of 16 examples. We also implemented batch normalization before each activation. The hardware used for training the model was a GPU server equipped with an Intel Core i9-9900K CPU and an NVIDIA TITAN V GPU. We implemented our model with a high-level neural network API called Keras  (Chollet et al. , 2015)  with the 241 Tensorflow  (Abadi et al. , 2015)  backbend running on the Ubuntu 18. 03 operating system.
Model testing To verify the model's performance, we used a Test dataset including Real-Fluo-Diseased images (Section 2. 6). To assess the quality of pixel-wise segmentation, we computed (1) True Positives (TP ): the number of diseased pixels present in both prediction and GT mask; (2) False Positives (FP ): the number of diseased pixels present in prediction but absent in GT mask; (3) False Negatives (FN ): the number of diseased pixels absent in prediction but present in GT mask. Knowing these numbers, we can estimate Recall: R = TP TP + FN , ( 8 ) that describes the fraction of correctly classified diseased pixels compared to the total number of diseased pixels in GT mask. Moreover, Precision value: P = TP TP + FP , ( 9 ) gives us the fraction of correctly classified diseased pixels among all predicted diseased pixels.
Model training strategies For each experiment, we changed the dataset composition (synthetic or empirical images) for learning, fine-tuning, or testing. Then, we applied a standard scheme of sim2real transfer: A Train: empirical. Test: empirical. It was a reference to see what the model could learn from a small empirical dataset of fluorescent plant images. At this stage, the analysis of model errors allowed us to choose the preprocessing strategy for forming the Test dataset from Real-Fluo-Diseased images. A Train: empirical. Test: empirical pre-processed. It was a reference to see how much model precision we gained using the chosen pre-processing strategy. B Train: synthetic. Test: empirical pre-processed. At this stage, we performed the model training on Synthetic-Fluo-Diseased dataset and tested the model on the pre-processed Real-Fluo-Diseased.
This training strategy validated the quality and relevance of the created Synthetic-Fluo-Diseased dataset. Furthermore, the error analysis of the model allowed us to establish the criteria for sim2real transfer strategy described below. C Train: synthetic + n pre-processed empirical examples. Test: n pre-processed empirical examples. Here, we used the first sim2real transfer strategy. It consisted of injection of some empirical images from Real-Fluo-Diseased for which the model failed to correctly segment disease lesion into the Synthetic-Fluo-Diseased dataset and training the model from scratch on this mixed dataset. Then, we tested the model on the other Real-Fluo-Diseased images not used for the training. D Train: synthetic. Fine-tune: n pre-processed empirical Test: n pre-processed empirical examples.
We applied the second sim2real transfer strategy to make the model recognize better the contrast between diseased and healthy pixels. This strategy consists of the model pre-training on the Synthetic-Fluo-Diseased dataset and further fine-tuning the model classifier layer on some empirical examples. Further, we tested the model on the not-seen examples from the Real-Fluo-Diseased dataset. Note that strategies (A-D) used only images of diseased plants.
Pre-processing strategy for a Test dataset The model training and testing on Real-Fluo-Diseased dataset (Section 2. 11, strategy A) revealed that the model failed to segment disease lesions correctly if lesions looked like scattered corrupted pixels and severity was less than 0. 24%. The majority of such images were from the first post-inoculation day, when disease severity varied from 0. 012% to 0. 24%, with a mean of 0. 055%. Several studies showed that in pre-symptomatic detection of plant diseases, differences between healthy and diseased plant tissues become statistically significant from the second day after inoculation  (Boureau et al. , 2002; Grishina et al. , 2021). Thus, we proposed to pre-process the dataset in two steps: 1) we applied morphological operations to eliminate corrupted pixels and to uniform lesions if they included small holes; 2) we eliminated images with S≤0.
Principle statistics of Real-Fluo-Healthy dataset, Real-Fluo-Diseased and Synthetic-Fluo-Diseased datasets The resulting mean value and standard deviation of the both chlorophyll fluorescence parameters F 0 and F m for Real-Fluo-Healthy dataset are given in Table  1. The order of magnitude of the mean value and standard deviation of F 0 and F m remained in the same range in our experiment. Figure  3A  shows a Real-Fluo-Healthy image example of the maximum quantum yield F v /F m and its values distribution. As shown in Figure  3B , Real-Fluo-Diseased dataset has disease symptoms of extremely small size. The mean disease severity of the dataset equals 0. 42%, and maximum severity -4. 2% (Figure  4A ). The plants of Real-Fluo-Diseased dataset have the mean size of 39. 5%, some images have minimum plant size of 1.
Note that this conclusion about the relationship between F v /F m for healthy and diseased pixels is consistent with previous studies  (Maxwell and Johnson, 2000; Matous et al. , 2006; Baker, 2008; Rousseau et al. , 2013; Pavicic et al. , 2021). The obtained ranges for D i ∈R values (1), where i∈{m F 0 , s F 0 , m F m , s F m } , are summarized in Table  3:  As can be seen from the Figures  3B, C , that distributions of mean and standard deviations of F v /F m for considered images from Synthetic-Fluo-Diseased and Real-Fluo-Diseased differ. It is not the only difference between synthetic and empirical datasets. Figure  4  shows that plants on the images from Synthetic-Fluo-Diseased are smaller than in Real-Fluo-Diseased dataset, while the disease severity is higher. The small plant size came from the CVPPP dataset used to simulate plant shapes for the synthetic images.
We kept such a difference in plant size to make the trained model more general and to be able to recognize disease lesions even on tiny plants. If we compare the values of F v /F m over images in both datasets (Figure  5 ), we will see that Synthetic-Fluo-Diseased and Real-Fluo-Diseased datasets differ a lot. Figure  5A  shows that in Real-Fluo-Diseased datasets, F v /F m mean values of healthy and diseased pixels does not overlap while in Synthetic-Fluo-Diseased their do overlap. The values of standard deviations of F v /F m is higher for Synthetic-Fluo-Diseased in comparison with Real-Fluo-Diseased.
Segmentation scores for the dataset augmentation strategies Table  4  shows the model performance results tested under conditions of the defined earlier training strategies. The model training/testing on empirical fluorescent images showed that the model could distinguish lesion pixels from healthy ones. Moreover, the model's error analysis showed that the model had difficulties segmenting both the very tiny disease lesions, with overall severity of less than 0. 24%, and the lesions with corrupted pixels. Thus, we developed a two-stage pre-processing strategy for the Test dataset described in Section 3. 1. The second line in the Table  4  shows the model performance 100% gain for the pre-processed Test dataset. Thus, we validated the preprocessing strategy and applied it to training strategies (B-D).
The presence of 73 empirical fluorescent images in the Synthetic dataset slightly improved the F1-score. Figure  6  illustrates the best model result of lesion segmentation on Real-Fluo-Diseased images. It means that our criteria for sim2real transfer were chosen right. However, the number of empirical images should be increased to give a more pronounced gain of the model precision in lesion segmentation. The results of the model fine-tuning are close to the results of the model training from scratch. Thus, both sim2real transfer strategies can be applied to improve the model's precision.
Discussion In this paper, we proposed a novel methodology to generate fluorescent images of diseased Arabidopsis plants with an automated lesion annotation. Using the U-Net neural network, we demonstrated that created synthetic dataset could be successfully used for disease segmentation on empiric fluorescent plant images at the early disease stages. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Our method of generating fluorescent synthetic images is an essential contribution to the availability of various datasets in digital plant phenotyping, which serve to verify models' robustness and generalization. Despite some synthetic and empirical set otherness, the U-Net model, trained on the synthetic data, showed 0.
793% recall and 0. 723% average precision for disease segmentation on the empirical fluorescent dataset. One could try to improve this segmentation score by using other neural network architectures, for example, an improved DeepLab v3+  (Yuan et al. , 2022). Also important to note that we did not use domain adaptation between the empirical and synthetic datasets. Such compensation for the possible discrepancy between the two domains could be investigated in the latent space of the segmentation algorithm. It would constitute a viable way to increase model performance. We demonstrated that the extremely tiny size of disease lesions on the first post-inoculation day was the main difficulty for precise lesion segmentation. The low model performance could indicate that the F v /F m ratio is not a good indicator of diseases symptoms at such an early infection stage.
, 2018). The use of a synthetic dataset was investigated here with fluorescence imaging. Other imaging modalities are commonly used to monitor plant-pathogen interactions. This includes passive or active thermography, multispectral reflectance imaging in various wavelength ranges, and coherent speckle imaging  (Mohammad-Razdari et al. , 2022). It would be interesting to revisit and extend the approach of this paper to The best result of lesion segmentation on pre-processed Real-Fluo-Diseased with the model trained on the Synthetic-Fluo-Diseased dataset. Precision = 0. 982, Recall = 1. 000, F1-score = 0. 925. these plant imaging modalities. The physics of each of them differs from fluorescence imaging. Therefore the image production model would have to be adapted. The rest of the statistical methodology proposed in this article to train neural networks on synthetic datasets would remain unchanged.
Prenatal ultrasound is a radiological examination that allows optimal follow-up of pregnancies. However, its implementation remains limited in poor countries due to a lack of equipment and trained health workers, such as in Burkina Faso. The aim of this work is to set up an ultrasound tele-expertise system. To achieve this objective, we mobilized human, material and IT resources. The design of the tele-expertise application was based on a generic open source software called "MedShakeEHR" that we have adapted to our context. The application runs in a network on a Linux system. It enables ultrasound data exchange and sharing with a remote expert for interpretation using the DICOM protocol. This device thus offers the possibility to pregnant women to carry out their prenatal ultrasound locally.
04. 5 LTS). It concerns:  MedShakeEHR, free medical software that we used for the design of the teleexpertise application  [2].  Orthanc, standalone DICOM server, for ultrasound data management  [3]. We have made changes to the software to adapt it to our project. All actors have been trained to use it. On the organisational side, the midwife at the rural medical center is responsible for handling the ultrasound system for the examination of the woman and forwarding the ultrasound data to a physician expert for interpretation. This expert is then responsible for forwarding to the staff of the rural health center a written report, attached to the ultrasound images that are given to the patient by appointment.
Application design and implementation We used MedShakeEHR-base and MedShakeEHR-modGynObs modules of the MedShakeEHR software, in which we made modifications to adapt them to our project. The following changes were made:  implementation of a unique patient identification algorithm from the person identification profile (strict traits, extended traits, complementary traits)  [4, 5] ;  integration of a database of localities in the country (village, sector,…);  integration of a reference system for the country's professions;  integration of a ICD-10 database for pathological diagnosis. Fields on the base form that didn't meet our needs have been removed. The finalized application was installed on the CFRTM (Medical Technology Research and Training Center) server with remote access. All actors (assistants, midwives, specialists and supervisors) were trained in the use of tele-expertise application by their level of intervention.
Discussion and conclusions One of the major reasons for the low completion of ultrasound for pregnancy follow-up is the lack of availability of this examination in rural areas, which is essential for the smooth running of pregnancy. The main advantage of the ultrasound tele-expertise system we have set up is the accessibility of this examination, which is a guarantee of the motivation of rural populations. It is also a source of motivation for the staff of rural health centres who feel more involved in monitoring pregnant women. Beyond this work objective, the scheme will eventually allow the creation of a database for monitoring pregnant women, which is necessary for the production of reliable statistics and research. The unique identification system implemented could open avenues for its extension to the HIS of our country.
RNA extraction and sequencing RNA extraction from adipose tissue samples were carried out according to the protocol proposed in the RNeasy Lipid Tissue Mini Kit (Qiagen UK). The sequencing library was prepared by random fragmentation of the cDNA, followed by the 5 'and 3' ligation adapters. Adapter-ligated fragments were amplified by PCR and visualized on agarose gels. Double stranded next generation sequencing RNAseq was commercially performed by MAcrogen (Seoul, Korea) on Illumina HiSeq4000 by triplicate in each group. All RNAseq data are available at GEO database under accession number GSE215903.
Docking analysis We previously characterized the composition of BB extract by UPLC-ESI/qTOF/MS. We found that Delphinidin 3glucoside, Petunidin 3-glucoside, and Malvidin 3-glucoside, are the three main anthocyanins in the extract  (28). For this reason, we considered relevant to test the potential binding interactions between identified transcription factors, their regulatory cell signaling proteins, and anthocyanin metabolites by molecular docking using the SwissDock docking analysis tool 7. Protein 3D structures were searched and downloaded in pdb. format from UniProt Data Bank 8. Chemical structures of metabolites were downloaded from PubChem database  9  and converted to MOL. 2 format.
Statistical analyses All data were expressed as mean ± standard deviation. The differences among samples were analyzed using Two-Way Repeated Measures ANOVA and Tukey's test. The value of p < 0. 05 was considered as statistically significant. Statistical analysis and figuring drawing were carried out using GraphPad Prism 9. 0 (GraphPad Software Inc. , San Diego, CA, USA). We performed a power analysis on the number of animals using the F test: Fixed effects ANOVA-one way with the free software G * Power V. 2. 5. Pie chart of the number of differentially expressed mRNAs, miRNAs, and lncRNAs in diabetic rats treated with anthocyanin-rich black beans extract.
Pathway enrichment analysis from up and downregulated differentially expressed genes in (A) BB group (diabetic rats treated with Anthocyanin-rich black beans extract). (B) DB group (diabetic rats without treatment). Our next step was to use STRING database to explore the potential protein-protein interactions of genes identified as differentially expressed by black bean extract intake. The analysis revealed a network of interactions between identified proteins as presented in Figure  5A , as well as genes that form nodes in the network. The next step was to select the genes with the highest number of interactions with other genes and which potentially play an important role in multi-genomic effects. The number of interactions reached 12 for UBB (Ubiquitin B), or 11 for MST1R (macrophage stimulating 1 receptor) and RRAS2 (RAS Related 2), or proteins like INS1 (Insulin-1 precursor) or INPPL1 (Inositol Polyphosphate Phosphatase Like 1) with 5 or more interactions (Figure  5B ).
Transcription factors potentially involved in the nutrigenomic effect of BB extract Our next objective was to identify transcriptional regulators involved in the observed changes of genes, that is, transcription factors which could have their activity altered by black bean extract and affect the expression of identified significantly modulated genes. To this end, we used the database TRANSFAC and JASPAR using the Enrichr platform. Among the top ten transcription factors identified are GATA2, POU2AF1, IRF3, GATA1, NR2F2 or PPARA (Table  1 ). It could be suggested that circulating polyphenol metabolites generated after BB extract intake could interact with transcription factors and/or cell signaling proteins regulating their activity. With the aim to test this hypothesis, we searched the capacity of major metabolites of black bean to interact and bind to these proteins using a 3D docking online server.
miRNA -Identification of their targets and functional analyses Our gene expression analysis also allowed us to surmise that BB can also lead to alterations in the expression of not only protein coding RNAs but also non-coding RNAs,  Using existing database, we searched for target genes of the identified miRNAs, and nearly 500 target genes were identified. These target genes and identified miRNAs form a network of interactions as presented in the Figure 7A. To identify potential cellular functions affected by these miRNAs, we cross-examined the Mienturnet database to reveal over-represented pathways from both KEGG (Figure  7B ) and Reactome (Figure  7C ) databases, that is pathways associated with each of the miRNA recognized as differentially expressed by black bean extract. Among the pathways identified are PI3K-signaling pathway, Ras signaling pathway, type 1 diabetes mellitus, Insulin receptor substrate 1 related pathway, and regulation of insulin-like growth factor.
Correlation with diseases Together with identification of cellular mechanisms affected by different types of RNAs, we also aimed to identify diseases associated with identified differentially expressed genes. We used the Enrichr database OMIM disease tool that interconnects differentially expressed genes with diseases, revealing their possible role in prevention or development of these disorders. We observed that our genes differentially expressed between the BB and DB groups are significantly associated with metabolic disease, nutrition disorder, cardiovascular disease, and immune system disease (Table  2 ).