dataesr/scientific-paragraphs-categorization huggingface dataset:
MCF-7 MeDIP sequencing reads (DRA000030,  (36) ) and MYC ChIP-seq reads (SRR575112) were downloaded from https://ddbj. nig. ac. jp/resource/sra-submission/ DRA000030 and from https://www. ncbi. nlm. nih. gov/sra?term= SRX188954, respectively. Differential wig files were generated by subtracting the signal in EV cells from the signal in either TET2 CD or TET2 mCD cells after normalization to the number of reads. SCL-exo reads were mapped separately on both strands with Bowtie  (91). The resulting SAM files were processed with a Python script (https://mycore. core-cloud. net/ index. php/s/4gyZ9dLTqgo86dt) to identify 5hmCpGs  (98). Heatmaps were generated online with Cistrome ((41); http://cistrome. org/ ap/root). ChIP-seq and SCL-exo data were normalized to input as follows: for every position of the wig file, a window of 100 bp surrounding that position was considered, and the input signal values in that window were averaged.
(G) Expression fold change in TET2 CD cells compared with empty vector cells of 126 genes associated with lysosome biogenesis and function and engaged by TFEB (ChIP-seq data from HUVECs) within 2 kb of their transcription start sites. (H) Differential H3K4me3 ChIP-seq signal at MYC high (left panel) and MYC low (right panel) transcription start sites of TFEB lysosomal target genes. (I) Correlation heatmap between transcription factors regulating lysosome biogenesis and lysosomal genes in BRCA tumors (normal-like tumors, n = 639). (J) CLN3 and CTSD mRNA levels in TET high tumors (TCGA BRCA dataset, fourth quartile of expression, n = 302) ranked according to MYC expression (n = 151 for MYC high and MYC low ). (K) Representative images of MCF-7 clones treated for 4 or 24 h with 2. 5 μM SRT1720 (scale bars: 20 μm).
Acknowledgements We are grateful to  Romain Gibeaux  for his help with microscopy experiments. We thank  R émy Le Gu ével  ( ImPACcell, Biosit, Rennes ) for his help in setting up cell migration assays, and members of the  GEH (Rennes) and GenomEast Platform (IGBMC, Strasbourg ) sequencing facilities. This work was funded by the  AVIESAN/Plan Cancer and La Ligue Contre le Cancer. The  Genomic Paris Centre facility  was supported by the France G énomique national infrastructure, funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-0009 ).
Data Availability All sequencing data are available at GEO (accession code GSE173344, https://www. ncbi. nlm. nih. gov/geo/). Unnormalized wig files can be visualized at UCSC genome browser: https://genome. ucsc. edu/ s/savner/MCF7_TET2. Flow cytometry data can be accessed at FlowRepository (https://flowrepository. org/) with the reference FR-FCM-Z446, reviewer access: https://flowrepository. org/id/ RvFrDg4TjBSBZutxyIkLijvfWiuMcPcDaAiOmgI0wmqhoVhddiaSEbEbFDGxkFoF.
(F) Expression levels (RNA-seq-normalized read counts, mean ± SEM, n = 3) of transcription factors regulating lysosomal genes in MCF-7 clones. (G) Expression fold change in TET2 CD cells compared with empty vector cells of 126 genes associated with lysosome biogenesis and function and engaged by TFEB (ChIP-seq data from HUVECs) within 2 kb of their transcription start sites. (H) Differential H3K4me3 ChIP-seq signal at MYC high (left panel) and MYC low (right panel) transcription start sites of TFEB lysosomal target genes. (I) Correlation heatmap between transcription factors regulating lysosome biogenesis and lysosomal genes in BRCA tumors (normal-like tumors, n = 639). (J) CLN3 and CTSD mRNA levels in TET high tumors (TCGA BRCA dataset, fourth quartile of expression, n = 302) ranked according to MYC expression (n = 151 for MYC high and MYC low ).
The Doal Study The Doal study was a prospective, observational, cohort study aimed at reporting the use of raw mother's own milk in the study NICUs among mothers intending to breastfeed. It also aimed to investigate if the early administration of raw mother's own milk (before 7 days of life) was associated with breastfeeding continuation  [20]. Mothers of infants hospitalized before 7 days of life and at least during 24 h were eligible, the study was then presented as often as possible, and informed consent was necessary for inclusion. At 7 days of life, the included mothers were interviewed to collect data on factors known to be related to breastfeeding such as socioeconomic status, previous experience of breastfeeding, antenatal breastfeeding information, milk volume monitoring, and breastfeeding support.
Data Collection Breastfeeding outcomes, maternal characteristics (age, parity, smoking status, multiple birth, intention to breastfeed), and neonatal characteristics (gestational age, birth weight, sex, center, length of stay, and outcome at discharge) were extracted from patients' hospital electronic medical files (IntelliSpace Critical Care an Anesthesia version H. 02; Philips N. V. , Koninklijke, The Netherlands). Breastfeeding outcomes were also verified manually using patient records.
The variables tested were NICU, maternal age, smoking, parity, sex, gestational age, birth weight, multiple gestation, length of stay, and outcome at discharge. The model was fitted using a General Estimation Equation method to account for neonates nested within centers. A stratified analysis by center assessed the association between periods and breastfeeding intention. Results were expressed as adjusted odds ratios (OR) and their 95% confidence intervals (95%CI). The breastfeeding intention was compared between study period using chi-square and a stratified analysis by center to control for confounders. Analyses were performed using SAS version 9. 3 software (SAS Institute, Cary, NC, USA).
Population During the two periods, a total of 655/658 admitted neonates were included in the overall analysis, and 422 in the subgroup analysis (Figure  1 ). The maternal and neonatal characteristics of included infants in the two periods were comparable (Table  1 ). The model was fitted using a General Estimation Equation method to account for neonates nested within centers. A stratified analysis by center assessed the association between periods and breastfeeding intention. Results were expressed as adjusted odds ratios (OR) and their 95% confidence intervals (95%CI). The breastfeeding intention was compared between study period using chi-square and a stratified analysis by center to control for confounders. Analyses were performed using SAS version 9. 3 software (SAS Institute, Cary, NC, USA).
The AO assisted imaging mode allows in general for a better assessment of the underlying vascular structure, as small details can be visualized, which aids in the interpretation of the data acquired with the large field of view mode. We want to emphasize here that AO-OCTA, an extension that is not available to any OCT instrument on the market, provides a much better impression of the true extension of retinal vasculature and allows for visualization of the choriocapillaris network. The current system, with a light exposure of ~ 1. 3mW at the cornea, uses only about half the power of state of the art swept-source OCT instruments. This is partly because a conservative approach has been taken to safety calculations for the simultaneous operation of an SLO channel and an OCT channel, and because eye safety standards omit consideration of the constant rapid movement of the OCT and SLO imaging beam foci over the fundus.
Image post processing. The GPU is used for post processing of the acquired OCT data. The workstation contains the NVIDIA Quadro P2000 graphics card for fast data evaluation. Multithread GPU-based post processing of acquired data is used for visualization of the imaging results within a few seconds. OCT angiography is achieved by acquiring four B-scans per position in both large and small fields of view. An algorithm based on amplitude decorrelation of the full spectrum is used that has been previously presented  44, 45. Each pair of successive B-scan amplitudes (A 1 (x,z) and A 2 (x,z)) is registered to each other (using cross correlation) and the decorrelation D(x,z) is calculated between the two successive B-scans as follows: The total intensity I(x,z) at a specific location is calculated via the sum of the two B-scans: In order to remove noise, pixel locations that showed values in the intensity image I(x,z) below a certain intensity threshold are set to zero in the decorrelation image D(x,z).
The transformation parameters for each pair are computed by minimizing a normalized correlation metric, using a stochastic gradient descent optimizer. For each OCT 3D acquisition, two volumes are obtained. If any of the volumes is free of blinks, that volume is used for further processing. Otherwise, the B-scans from the second volume are used to fill in the missing information at the blinks, obtaining a complete 3D dataset. Each B-scan of this resulting volume is spatially aligned to the previous one, allowing only translations (in both the axial and lateral directions). To avoid the over-correction effect of the registration, the computed translations are smoothed, so that only the sudden spikes caused by the motion are corrected. For the OCTA data, the translations are computed following this same approach in the paired OCT data, and then applied to the OCTA.
Acknowledgements This work was supported by  Horizon 2020 , the  European Union 's  Framework Program for Research and Innovation  under grant agreement no. 780989  (MERLIN). The funding organization had no role in the design or conduct of this research. JA, CV and PLA acknowledge financial support from the  Spanish Ministry of Economy and Competitiveness  through the  "Severo Ochoa" program for Centres of Excellence in R&D  ( CEX2019-000910-S ), from  Fundació Privada Cellex ,  Fundació Mir-Puig ,  Generalitat de Catalunya  through the  CERCA program ,  la Caixa" Foundation  ( ID 100010434 ), under agreement  HR18-00313 ,  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 754510  and  Laserlab-Europe   EU-H2020 (871124 ).
Effect of test compounds on intracellular calcium mobilization At the end of the incubation, the medium was removed and cells (LSMCs) were incubated in assay buffer for 30 min in presence of the FLUO-4 NW probe. Then, cells were treated sequentially with the test compounds or their respective solvents, or reference compounds. All treatments were performed by direct injection onto the cells 5 s after beginning the recording, and the fluorescence was recorded for 2 min by videomicroscopy. Real-time imaging of calcium mobilization was videorecorded using an epifluorescence microscope (10× objective) equipped with a Nikon DS-Fi1 camera and NIS-Elements software. Measurements were performed on all images constituting each video (recording over around 120 s; corresponding to approximately 718 images).
For each test condition, the fluorescence intensity was measured in three identically treated wells (n = 3). Raw data were analyzed using GraphPad PRISM® software and Microsoft Excel® software. Four independent experiments were performed, and all conditions (test products, reference compounds, and controls) were tested in triplicate. The first experiment was performed for the pharmacological validation of the model using the reference compounds. The three other experiments were for the evaluation of the test compounds. For each replicate of each condition (n = 1, corresponding to a single video), the evolution of the fluorescence intensity over time was evaluated by plotting the curve of the intensity and measuring the area under the curve (AUC). The baseline was determined 1-2 s after the injection of the test compounds to exclude the early nonspecific fluorescence emission due to compound autofluorescence from the measurements.
Acknowledgments We thank the  Bioalternatives and Pierre Fabre  teams involved in the project for their valuable contributions. In particular, we would like to acknowledge  Julien Oré ,  Lucile Pineau ,  Sarah Collober ,  Fabienne Guyard ,  Sevda Cordier Dirikoc ,  Nathalie Pedretti ,  Nathalie Coussay  and  Valérie Hamon de Almeida  for their expertise and assistance with all aspects of the study.
Objective: : To realize a tool for automatic segmentation and detection of lumbar disc degenerative disease and fractures in MRI images using convolutional neural networks. Material and methods: : We developed, on one hand, a Picture Archiving and Communication System (PACS) with a DICOM viewer (Digital Imaging and Communications in Medicine) to extract training data and implement 2 CNN networks dedicated to the segmentation task and on the other hand, the analysis of simple lumbar pathologies on the other hand. Two hundred forty-four MRI scans T2 weighted sagittal were selected at the university hospital Pasteur 2 of Nice -France. After accomplishing segmentation and classification task for all structures of interest, we trained two neural networks (U-Net++ and Yolov5x) to segment and detect discs and vertebras.
Patients and data This retrospective study was conducted in accordance with the tenets of the Declaration of Helsinki. Written consent was waived because of the retrospective nature of the study. The imaging data sets were provided by lumbar non-contrast MRI examinations of outpatients from the radiology department of Nice University Hospital over the period 2016-2018. The non-contrast imaging protocol is typically used for evaluation of low back pain and/or radiculopathy. We screened studies but we excluded the majority of patients undergoing evaluation for potential spinal trauma, infection, inflammation, tumors, or postsurgical follow-up. These indications are outside the scope of our objective and require case-specific interpretation and/or measurements. Our dataset was composed of 244 MRI acquisition volumes from patients (14 images of 768 × 768 pixels, i.
Preparation of training data All volumes were acquired using an MRI modality and represent spinal structures in sagittal section in T2 STIR weighting. We developed a centralized image archiving system on a server capable of distributing the images over the network to a DICOM viewer (desktop and web version) allowing to read the exams and to perform all the usual reconstructions. Each image in DICOM format contained 16-bit unsigned grayscale format and was exploited as normalized image with a rescale of intensity [-1. 0, 1. 0] for the neural network. A radiologist was required to manually segment and label each structure of interest into one of the classes of Table  1. An example of the structures of interest is given in Fig. 7  and 8. The annotation of the training images was carried out from our viewer, first manually and then semi-automatically.
On the server side, once the networks were pre-trained with the first segmented samples, the segmentation was then performed in a semi-automatic way, i. e. with an AI run applied directly by the archiving server. The server analysed each image twice, once for the vertebras and  -Another pass used YOLOv5x (You Only Look Once v5x) to automatically detect vertebral level, fractures, disc disease and disc herniation. After that, the radiologist could apply manual correction to correct neural networks errors. The final annotated images (ground truth) were then transferred on the archiving server. We were therefore able to correctly classify the pixels of each instance in one of the 64-targeted classes. For the development and validation of the algorithm, various splits of the dataset were used to ensure that the model was tested against cases that it had not seen during training.
Yolov5x (a technique known as transfer learning). Therefore, rather than training a completely blank network, the weights of the lower levels were optimized by a feed-forward approach to recognize structures found in images in general, and by recycling the weights of the higher levels using backpropagation. This method allows for better results and faster learning. We justified our approach based on results obtained on classification tasks where the pre-trained backbone, which extracts low level features from images, can be used on specialized datasets and has shown good results  [39, 40]. For Unet++, we froze the backbone corresponding to the encoder part of the network, which extracts low-level features learned from ImageNet. The decoder and skip-connection layers were all fine-tuned to fit the segmentation task.
Data augmentation To have a good output, high volumes of data are needed for CNN models. Data augmentation is a process consisting inartificially increasing the training dataset by performing a series of transformations to the image, such as rotations, reflections, and elastic deformations  [22]. These transformations are usually selected based on the analysed data and subjective criteria based on researchers' experience  [22]. Data augmentation is also an effective method to reduce the "overfitting" of CNNs caused by limited training samples. This was achieved through a combination of random image translation (max 30% of dimensions), central cropping (max 20% of dimensions), horizontal flipping (0. 5 probability) and rotation (max 45 • ). Moreover, Gaussian noise augmentation was used to mimic low-quality noisy images.
The errors were therefore mainly due to classification errors, in particular on the unbalanced classes in the dataset (and this despite the use of the "focal loss"). Figs. 9 and 10 show the average performance of the YOLOv5x network in terms of Mean Average Precision (mAP) coefficients, which represents the area under the inverse of the precision as a function of the recall, and of the F1-Score estimated in relation to the ground truth annotations.
5% for a precision of 75% for the disc disease detection and obtained a recall of 88% for a precision of 90% for the vertebral fracture. The modest overall accuracy of YOLOv5x neural networks may have several explanations: -A lack of data to train the model, evidence being its tendency to overlearn on the training dataset seen on the loss function curves. -An intrinsic limitation of these neural networks unable to provide very high-quality object detection on generalist datasets (COCO2017 -IMAGENET). It can be noted that our results with the YOLOv5x network are consistent with those obtained on "mainstream" image sets such as COCO2017 (AP95: 0. 55; AP50: 0. 69)  [9]. -Another possible problem may lie in the underlying DICOM datasets, which are obtained on 1. 5 T machines that are often noisy compared to high field MRI.
However, compared to other work on MRI spine  [1, 5] , we believe that the main problem of object misclassification is mainly due to the insufficient volume of available data. Regarding segmentation, we obtained satisfying results, equivalent to the results obtained by other teams  [11, 12, 14, 15, 16, 17, 18]  and in particular with the DeepSpine network  [5] , which obtained 0. 93 DICE index for vertebrae and discs in 2018. We find it difficult to do better with current technologies and limited datasets in size. In general, we found that U-Net++ gave better DICE coefficients when the region was larger and with high contrast. When smaller or very low contrast we used, it was in all cases localized and classified, but somewhat more difficult to segment. Training CNNs usually requires a large amount of data in order to obtain satisfying results in terms of pixel/voxel classification and to perform satisfactory semantic segmentation.
However, as mentioned, it is rarely possible to obtain such large annotated datasets in a clinical setting. Using a strategy based on a derivative of the U-Net++ made it possible to locate the anatomy of interest with good accuracy and an acceptable amount of data in a clinical setting. Concerning real application, the validation test on 40 MRIs showed maximum recall measures above 99% for detecting discs, degenerative disc disease/herniated discs, and for vertebral numbering, presence of vertebral fracture for a low probability threshold p > 0. 20 (which generates false positives and poor positive predictive value). The interest of these techniques could lie in the possibility of sorting the examinations (in the manner of a diagnostic test) to improve the order of reading from the most urgent, to the least urgent, i.
Conclusions In this study, we applied the neural network technique (Artificial Intelligence) to the segmentation of spinal MR images and to the identification of pathologies. We consider that if the objective was reached on the task of segmentation of the discs and vertebrae, there is still progress to be made on the task of detecting pathologies of these discs and vertebrae. It is important to note that despite the lack of data to train the model, our PACS system is now operational and will be used to augment the dataset and be extended to other common diseases. The objective of the level identification task is achievable with yololike networks, but requires more data.
| MATERIAL S AND ME THODS The study formed part of an Erasmus+ project "Oral Potentially The questionnaire was composed in a survey administration software (Google Forms®). The survey was anonymous and did not collect any personal data of participants. Prior to proceeding with the completion of the questionnaire, students were obliged to consent and confirm their understanding of the study aims. The participation in the survey was voluntary. ] [10]  Questions were composed in several iterations until a consensus was reached by all partners. The questionnaire consisted of four parts. In the first part, general data on demographics were collated (sex and country). In part 2, students considered their clinical experience and competence to diagnose OPMDs on a 5-point Likert scale (1 -poor, 5 -excellent and 1 -completely inadequate and 5 -completely adequate).
In part 3, student attitude towards future education strategies on OPMDs, learning modalities and knowledge assessment was assessed by multiple-choice questions. In part 4, student knowledge of risk factors, clinical presentation, malignant transformation and classification of OPMDs were assessed by multiple-choice questions. Individual knowledge scores were calculated as a sum of correct answers from this section obtained by each student. Data were organised on Microsoft Excel® and stored in a secure shared online folder (Google Drive®). "Find and Replace" function was used for data coding to eliminate errors that may have occurred with manual entry. SPSS® version 11 was used for statistical analysis (performed by VB). Kolmogorov-Smirnov test was used to assess the normality of distribution.
Cohort Description We performed an international, multicenter, retrospective, observational study involving 21 institutions. IRB approval was given at each institution. We included patients with UTUC classified as high-risk according to the EAU guidelines  [3]  and treated with RNU between April 1990 and March 2020. The definition of high-risk included having any of the following: multifocal disease, tumor size ≥2 cm, high-grade cytology, high-grade biopsy, local invasion on CT, hydronephrosis, or variant histology. Patients with low-risk UTUC or distant metastasis (cM1) at the time of surgery were excluded. Patient information was collected from the medical records, and all data were anonymized before data sharing.