dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical and laboratory data All data were extracted from our computerized medical records (DX Care, Dedalus, France). Demographic characteristics including age and sex were recorded in both groups. For COVID-19 patients, the following data were recorded: time from onset of symptoms (fever, cough, shortness of breath, tiredness, diarrhea) and hospital admission to DE-CTPA; antithrombotic treatment before DE-CTPA; maximal and at DE-CTPA oxygen requirements; need for invasive mechanical ventilation; maximal and at DE-CTPA D-dimer levels; highest value of C-reactive protein (CRP); and duration of hospital admission (for alive patients). For non-COVID-19 patients, the established or suspected type of infectious pneumonia (bacterial, viral, fungal for the main types) was obtained from medical records, taking into account the CT findings, the type of anti-infectious therapy instituted and its outcome when the infection was not microbiologically documented.
Statistical analysis Normality was tested using the Shapiro-Wilk test. Normally distributed values, expressed as mean (± standard deviation), were compared with two-tailed Student's t-tests; non-normally distributed values, expressed as median (interquartile range (IQR)), were compared with Mann-Whitney U-tests. Categorial parameters were compared with χ2 or Fisher's tests when appropriate. Relationships between hypoperfusion extent and ratio to D-dimer and O 2 levels were assessed using Pearson correlation. A p value < 0. 05 was considered significant. All statistical analyses were performed using the XLSTAT v2018. 7 software.
Ninety-six and 87 respectively COVID-19 and non COVID-19 potentially eligible patients were identified by our database search. After application of the exclusion criteria, 29 and 8 respectively COVID-19 and non-COVID-19 cases were excluded from the study, for reasons given in Fig. 1. The remaining 67 COVID-19 and 79 non-COVID-19 patients were included. There were 43 males and 24 females (age 64 [55-76], median [IQR]) in the COVID-19 group and 34 males and 45 females (age 71 [55-82]) in the non-COVID-19 group.
After the testing protocol, other patients were also excluded from analysis if they did not present ischemia during the test, or if they showed discordance between arterial lesion location and pain location description (Fig. 1 ). Participants eligible for inclusion were informed and gave their written consent. All experimental protocols were approved by the local ethics committee (Committee for Protection of Persons Ouest II). The protocol was promoted by the Angers university hospital and registered in Clinicaltrials. gov website under reference: NCT02754804, April 28th, 2016 (https:// clini caltr ials. gov/ ct2/ show/ NCT02 754804).
Before placement of the sEMG probes, skin was shaved and cleaned with alcohol. The probes were placed according to "Surface EMG for Non-Invasive Assessment of Muscles" recommendations  40. sEMG signals were recorded at 1000 Hz and stored by the Zebris software (Zebris EMG measuring system, Germany, 2011, version 1. 16). sEMG signals were band-pass filtered between 15 and 499 Hz with a second-order Butterworth filter and full wave, rectified to create linear sEMG envelopes. sEMG data were normalized to a 100% gait cycle using gait parameters synchronized with sEMG signals. To determine muscle activation level, the Root Mean Square (RMS) was calculated and normalized with respect to the RMS peak values of 10 gait cycles during the pain-free phase, according to the "peak dynamic method"  41. The sEMG process was made with a custom script in Matlab 2016a (MathWorks Inc, Natick, MA, USA).
Statistical analysis. Data are expressed as mean(SD (standard deviation)). All the statistical procedures were performed using Statistica software V 10. 0 (StatSoft, Inc. 2011. STATISTICA-data analysis software system-version 10). Levene's test and Fisher's post-hoc least significant difference (PLSD) analysis were performed to assess variance homogeneity of the series. Prevalence between groups was compared with a Chi-squared test. Differences in anthropometric measurements (ABI, Body Mass Index), gait parameters and EMG signals were studied according to the locations of ischemia (PROXDIS vs DIS), the intensity of pain (pain-free, onset of pain and maximum pain) and between legs, using a three-way ANOVA procedure; α power was indicated for each test. When normality of the distribution was not assessed, a Kruskal-Wallis test was done.
Electromyography. Because sEMG records presented some failures for three patients, sEMG data analysis was performed in nine patients with DIS ischemia and ten with PROXDIS ischemia, only. Between location of ischemia and pain effects, there was no interaction effect for electromyographic parameters (Table  3 ). Influence of location of ischemia. TA showed significant differences between locations for the activation peak (DIS activation peak was 4. 5% higher than PROXDIS) and the activation time (DIS activation time was 19. 7% higher than PROXDIS) (Table  3 ). Impact of ischemic pain intensity. Onset of pain did not induce any change in sEMG parameters compared to the pain-free phase. Between the pain-free and the maximum pain phases, three sEMG parameters were affected (Table  3 ): the TA activation peak in the PROXDIS group only (decreasing by 13.
Patients and ethics Three distinct cohorts were used for the data collection and analyses registred in ClinicalTrial. gov and consent were obtained from parents. Details on IRB and ethical committee are available in supplemental data. Cytokines and IFN score assessment Whole blood was sampled on EDTA tubes and plasma was frozen at -20°C within 4 hours following blood collection. Plasma concentrations of IL-6, TNF-a, IFN-g, IL-10, MCP-1, IL-1ra and CD25s were measured by Simpleplex technology using ELLA instrument (ProteinSimple), following manufacturer's instructions. Plasma IFN-α concentrations were determined by single-molecule array (Simoa) on a HD-1 Analyzer (Quanterix) using a commercial kit for IFN-α2 quantification (Quanterix). Whole blood was collected on PAXgene blood RNA tubes (BD Biosciences) or on EDTA tubes for IFN signature, RNA extraction was performed with the kit maxwell 16 LEV simply RNA blood associated with the Maxwell extractor (Promega) and quantified by absorbance (Nanovue).
The sequencing was then carried out on a NOVAseq 6000 Illumina sequencer using 2x250bp read length protocol. Paired-end sequences were aligned and annotated using MiXCR 3. 0. 13 (21)  Repertoire analysis Analysis were performed in R 4. 0. 3 on the clonotype lists obtained with MiXCR. For each clonotype, read count was recorded. Frequencies for TRBV, TRBJ and clonotypes were calculated based on the total read counts per sample. Chord diagrams were made using the circlize package  (22)  on TRBVBJ frequencies , CDR3 length barplots were made using ggplot2 (23) on clonotype frequencies. Stimulation with SARS-CoV-2 overlapping peptide pools and flow cytometry Briefly, overnight-rested PBMCs were stimulated with SARS-CoV-2 PepTivator pooled peptides (Miltenyi Biotec) at a final concentration of 2 μg ml-1 for 1 h in the presence of 2 μg ml-1 monoclonal antibodies CD28 and CD49d, and then for an additional 5 h with GolgiPlug and GolgiStop (BD Biosciences).
Dead cells were labeled using LIVE/DEAD Fixable eF780 dye from Invitrogen. Surface markers, including APC-conjugated anti-CD3, BUV486-conjugated anti-CD4, PE-Cy7-conjugated anti-CD8, APC-Cy7-conjugated anti-CD14, APC-Cy7-conjugated anti-CD16 and APC-Cy7-conjugated anti-CD19 (BioLegend) were stained. Cells were then washed, fixed with Cytofix/Cytoperm (BD Biosciences) and stained with V450-conjugated anti-IFNγ (eBioscience). Negative controls without peptide stimulation were run for each sample. All samples were acquired on a BD LSRFortessa (BD Biosciences) flow cytometer and analyzed using FlowJo version 10 software.
Considering the importance of identifying image and video distortions, in this paper, we propose a novel approach that performs the multi-distortion identification/classification task by decomposing it into several sub-tasks, where each sub-task is responsible for identifying a single type of distortion. Our proposed method is a deep multi-task learning (MTL) model that efficiently and accurately classifies single and multiple distortions. It consists of a pre-trained CNN shared between all tasks and N separate parallel fully connected (FC) neural networks, where each FC neural network is responsible for identifying a specific type of distortion. The proposed method has been evaluated on natural scene image databases including popular IQA databases. Furthermore, an evaluation on laparoscopic videos database was conducted to show the efficiency and adaptability of our proposed framework to different applications.
In  [19] , Gabor filters are applied to both clean reference and distorted images, then mean squared error (MSE) is calculated between them, the results represent the features used by a quadratic bayes normal classifier (QNBC) to classify the type of distortion present in the distorted image. However, since these last two described works use full-reference IQA metrics, they require the presence of the clean reference image to identify the type of distortion, which may not be practical in most realword applications. Namhyuk Ahn et al. [21]  addressed the problem of distortion classification in an image without a reference image using CNNs architectures. In order to reach convergence quickly, they used pre-trained models on ImageNet dataset  [39]  including VGG 16  [40]  and ResNet-101  [41].
They also created a new Flickr-Distortion dataset to train their model. Mateusz et al. [22]  proposed ensemble learning method composed of two CNNs for distortion classification. The two CNNs architectures are similar in terms of number of layers and layout, but differ in the size and number of parameters used during the training. Both architectures contain three convolutional and max pooling layers followed by two FC layers. Their proposed solution out-Accepted manuscript / Final version performs other SVM-based solutions by more than 10% in terms of accuracy. Bianco et al. in  [23]  proposed to first analyze features extracted from different layers of different deep neural network (DNN) architectures, then evaluate their relevance for distortion classification using clustering. The best features were then used for the recognition of the type of distortion.
Multi-task learning: definition and notations Let us consider N related learning tasks {T i } N i=1 , MTL aims to enhance the learning of a model M of task T i by exploiting the knowledge contained in all or a subset of the N tasks  [16]. In the case of supervised learning, a task T i is associated to a training dataset D i composed of M training samples D i = {z i j , y i j } M j=1 with z i j ∈ R di is the jth training sample of dimension d i and y i j its label. The training dataset of a task T i can be represented by a matrix Z i = {z i 1 ,. , z i M } ∈ R M ×di. We can distinguish homogeneous MTL that, in opposite to heterogeneous MTL, consists in tasks of one type i. e. , classification or regression  [44]. On the other hand, according to the dimension d i of the feature space of input samples i, we can distinguish heterogeneous-feature MTL from homogeneous-feature MTL implying that the input samples have the same dimension d i = d j , ∀i = j.
The shared part is a feature extractor f θ (I) that takes as input an image I and outputs a vector of features x ∈ R F , with F the number of features and θ represents the parameters of the shared CNN. The task-specific heads are classifiers g φi (x) that takes as input the same vector of features x but each identifies a specific type of distortion, where φ i stands for the parameters of the classifier. The feature extractor f θ (I) consists of a pre-trained CNN on ImageNet dataset  [39] , while all classifiers consist of a network of FC layers randomly initialized. Overall, MTL aims to improve the generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. Given that the distortion identification is performed on the same input and CNNs are known to be efficient in extracting the most salient and relevant features, we chose to share the CNN among all tasks, so the features in the shared layers do not need to be recalculated for the.
Input and output processing The image datasets adopted in this study are not large enough to train the deep DenseNet 169 from scratch, hence the choice of using a pre-trained DenseNet 169 on ImageNet. To further compensate the lack of training samples while respecting input dimensions, we chose to perform patch-wise training. In patch-wise training, the CNN is trained on a small patch of the image instead of the whole image. Therefore, given an input image I, a fixed number of patches of size 224 × 224 is extracted over which our MTL model loops. Each patch is treated individually, this means that for each patch k, each classification block g φi (x) outputs a probability pi,k indicating the presence of a specific distortion d i in this patch. An average of all predictions is computed and rounded at the end of each classifier to label the image as follows: pi = 1 K K k=1 pi,k , (5) where K denotes the total number of patches extracted from image I.
In addition, the task-specific part contains five parallel classifiers, because the provided laparoscopic video quality (LVQ) dataset covers five distortion types  [15] , including noise, defocus blur, motion blur, smoke and uneven illumination. The end-to-end MTL model is then composed of a shared pre-trained VGG16 followed by five parallel classifiers each responsible for identifying a single type of distortion as shown in Figure  3. For hardware constraints and more precisely the limits in terms of memory, patch extraction cannot be done on video frames. Instead, frames extraction is performed which allows to use the model as if it was fed with input images and at the same time increase the number of training samples. Since the distortions are applied to all frames of a given reference video when building the dataset  [15] , therefore, frames of the same video are assigned the same labels.
Natural scene image datasets In order to evaluate the proposed method on natural scene images, four publicly available image quality databases are exploited: KonstanzLin artificially distorted image quality database (KADID-10K)  [49] , computational and subjective image quality (CSIQ)  [48] , Tampere image dataset (TID2013)  [13]  and laboratory for image & video multi distortion (LIVEMD)  [37]. KADID-10K contains 81 pristine images of size 512 × 384, each degraded by 25 distortions at five severity levels. CSIQ is composed of 866 images that were obtained from 30 clean original images of size 512 × 512 using six distortion types and four to five severity levels. TID2013 provides 25 reference images of size 512 × 384 and 3000 distorted images at five levels of distortion. Unlike KADID-10K, CSIQ and TID2013 datasets, which are single distortion datasets, LIVEMD is a multiply distorted image dataset, i.
e. , it contains distorted images counting multiple distortion types simultaneously. LIVEMD dataset  [37]  contains 15 reference images from which two subsets of distorted images are created using three distortion types, including blur, JPEG and noise. The first subset is obtained by applying blur followed by JPEG, each at three different severity levels. In the same way, the second subset is obtained by applying blur followed by noise. This results in a total of 450 distorted image. We considered three distortion types that are common in the three databases: (a) JPEG compression (JPEG), (b) white noise contamination (noise) and (c) Gaussian blur (blur). This leads us to use 1215, 450, 375 and 405 images respectively from the KADID-10K, CSIQ, TID2013 and LIVEMD datasets. Table  1  summarises the main features of the considered natural images datasets.
Laparoscopic video dataset Laparoscopic video quality dataset  [15]  consists of a total of 20 reference videos, each of 10 seconds duration. These videos were extracted from Cholec80 dataset  [50]. The selection of videos was made to include maximum possible variations of scene content and temporal information. For scene content, ten different categories were chosen: bleeding, grasping and burning, multiple instruments, irrigation, clipping, stretching away, cutting, stretching forward, organ extraction and burning. Each reference video is distorted by five different kinds of distortion (single or multiple distortions) with four different levels, resulting in a total of 800 videos. The resolution of videos is 512 × 288 with a 16:9 aspect ratio and a frame-rate of 25 frames per second (FPS).
Implementation details For natural scene image datasets, the training is performed with back-propagation using ADAM optimiza-Accepted manuscript / Final version tion  [51, 52]  with a learning rate of 0. 0001, a 10 -6 decay and momentum of 0. 9. A dropout is applied after each FC layer of size 512 with a rate equals to 0. 25. Dropout is a regularization technique used to reduce overfitting in artificial neural networks. It consists of randomly ignoring some nodes of a given layer during training. This method forces each node to assume different degrees of responsibility for the inputs as they are either activated or dropped out randomly during the training process. This has the effect of making the training process noisy, thus avoiding overfitting  [53]. For an end-to-end training, the model is trained iteratively by back-propagation over 60 epochs.
In each epoch, the training set is divided into batches for batchwise optimization. Each mini-batch contains six images (M = 6), each represented by a fixed number of patches (K = 4 for CSIQ and TID2013, K = 6 for KADID-10K and K = 8 for LIVEMD) of size 244 × 224, thus leading to an effective batch size of M × K patches. For laparoscopic video dataset, in each epoch, the training set is divided into batches of videos for batchwise optimization. Each mini-batch contains 10 videos, each represented by ten frames (M = 100), thus leading to the effective batch size of M ×K = 100 frames (K = 1).
Evaluation procedure In order to train and evaluate our proposed MTL model, we randomly split each dataset into two subsets of non overlapping content, 80% for training and 20% for testing. This procedure is repeated 10 times and the median of overall accuracy, precision, recall and F1-score values are reported and used to evaluate the performance of our model on the test set. Precision is defined as the number of true positives (TPs) divided by the number of TPs plus the number of false positives (FPs) as follows : precision = T P T P + F P , (9) in our case, TP is when the model perfectly identifies the presence of a distortion type. While, FP is when the model confuses the presence of a distortion type with another. Recall is defined as the number of TPs divided by the number of TPs plus the number of false negatives (FNs) as follows : recall = T P T P + F N , ( 10 ) in our case, FN is when the model mistakenly predicts the absence of a distortion type.
The default threshold may not represent an optimal interpretation of the predicted probabilities, so they must always be adapted to the problem under consideration, and therefore adjust the threshold value accordingly. Hence, we studied the performance of our model, in all datasets, according to the values of the threshold. Figure  6  shows the accuracy of our classification model as a function of the threshold. The threshold has been varied within a range of [0. 1, 0. 9] with a step of 0. 1. The accuracy is then calculated according to this hard threshold. From this figure, one can notice that for a wide range of probabilities. Our model achieves its best performance for a threshold value between 0. 4 and 0. 5. Therefore, a threshold value of 0. 5 was adopted and the results provided below were obtained based on this threshold value.
To quantify blur distortion, an algorithm designed to measure local perceived sharpness in images relying on both spectral and spatial properties is used  [55]. To quantify JPEG distortion, a no-reference quality measurement algorithm for JPEG compressed images is exploited  [12]. To quantify noise distortion, a technique to estimate the noise level proposed in  [56]  is adopted. The scores obtained from each of these three distortion-specific IQMs are used to form a vector of features that will be introduced in a SVM to perform the classification of distortion. The remaining six methods are deep learning-based approaches. The comparison of our model with state-of-the-art methods is first performed in terms of accuracy, as reported in Table  2. From this table, we can notice that our model outperforms all considered methods in all datasets, this is particularly remarkable for LIVEMD dataset, which is the most challenging.
In addition, BRISQUE provides fairly good results for a handcrafted method, while DIIVINE performance is poor on all datasets. IQM-G method also manages to provide quite good results on the different datasets, which means that the three distortion-specific IQMs chosen as feature extractors are complementary and their combination succeeds to discriminate the distortion type. Each of the handcrafted-based methods provides balanced results on the different datasets, which means that their performance is not strongly tied to the content of any dataset. However, their results generally remain inferior to those achieved by deep learning-based methods. We can also notice that the Kede  [34]  and Golestaneh  [36]  methods achieve the best performance among deep learningbased approaches, but without going beyond the proposed method.
Overall, it can be noted that handcrafted methods have proven that they can be competitive with deep learning-based methods if the extracted features are relevant for distortion identification and adapted to the datasets. The importance of the feature extraction step is also well illustrated through the performance of BRISQUE, DIIVINE and IQM-G methods, since all of them use a SVM classifier but provide different accuracy, because each one extracts different types and number of features. The difficulty with such approaches is choosing features that cover all types of distortion. However, since our method is a data driven approach, the selection of distortion-specific features is done automatically across the different towers, i. e. , the classifiers specializing in a single distortion type.
In addition, we computed the precision and recall for each distortion type separately. Precision for all considered datasets is reported in Tables  3, 4 , 5 and 6, while recall scores are reported in the diagonal of the normalized confusion matrices plotted in Figure  7. From Table  3 , we can notice that our model obtained 100% precision for all the distortions considered. Likewise, most of the deep learning-based methods provide good results, this is due to the fact that KADID-10K is the dataset with the largest number of distorted images, thus allowing to better learn the features of each distortion type. From Table  4 , we can notice that our model delivered 100% precision for JPEG and noise distortions on CSIQ dataset, while obtained 96. 66% for blur distortion. Also, IQM-based metric obtained a precision of 100% for noise distortion on CSIQ dataset, while provided low performance for blur and JPEG distortions.