dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics and Reproducibility GraphPad Prism version 8 software (San Diego, CA) was used for statistical analysis. All experiments were repeated twice independently. Antibody levels are presented as the individual responses and geometric mean +/-standard deviation (stdev). Ordinary oneway ANOVA with Tukey's multiple comparison test were performed to compare the pediatric, adult and negative populations in Figures  1  and 2 , and the early and late samples in Figure  5. For Figure  2j , percentages were calculated by dividing each mean antibody value by the sum of the total antibody responses, and compared using a Chisquare test between the "observed" (pediatric) versus "expected" (adult) distributions. For Figure  6c  and Supplementary figure  3 , a linear mixed effects model was fitted to account for correlated responses for the longitudinal samples dataset.
ConTeXt, with LuaMetaTeXengine (version 2020. 05. 18) developed by Hans Hagen (http://www. pragma-ade. nl) which uses TeX, Metapost and Lua to obtain the 3D clusters of points shown in Figure 3abc, Figure 7c and Supplemental Figure 2. For clarity, only the first 144 COVID-19 pediatric samples of the dataset are represented in the clusters of points, along with n=36 COVID-19 adult samples and n=28 negatives. In the cluster (N, ORF3b, ORF8), the equations of the red lines are: (1) in the plane (N, ORF8) :830*log (N) +0. 3843*ORF8=4801 and -350*log (N) +1. 036*ORF8=790, and (2) in the plane (ORF3b, ORF8): 0. 035*ORF3b+0. 1334*ORF8=409. 284 and 0. 074*ORF3b+0. 0437*ORF8=221. 812. These straight lines allow the most accurate discrimination between negative controls and positive adult populations.
Acknowledgements The authors thank the patients and their families for their participation, and are grateful to the hospital staff, clinicians and nurses, particularly  Karen YS Yui , for sample coordination. We thank  Professor JT Wu ,  Dr Mahen RP Perera  and  Dr Kathy Leung  for providing donor plasma controls. This study was partly supported by the  Theme based Research Grants Scheme  ( T11-712/19 -N),  Health and Medical Research Fund  ( HMRF COVID-190115  and  COVID-190126 ),  National Institutes of Allergy and Infectious Diseases, National Institutes of Health (USA)  (contract  HHSN272201400006C ).
8 Nd 0. 2 CuO 4 sample were obtained by heating the La 1. 8 Nd 0. 2 CuO 3. 5 sample in the air at 400 C. X-ray powder diffraction patterns were obtained using a D8 Bruker Advance diffractometer equipped with a Cu anticathode (CuKa l ¼ 1. 54056 A) at room temperature. The measurement was carried out in the range of 10e140 with a step of 0. 014. The crystal structure of the prepared samples was made using the Rietveld method using the Fullprof  [34]  computer program. The background was estimated by a Legendre function, and the peak shapes were described by a pseudo-Voigt function. The obtained crystal structures were supported by the charge distribution method CHARDI  [35]  by the mains of CHARDI2015 computer program  [36]. In brief, CHARDI calculated the charge of each element in the structure from the interatomic distance and the total deviation of calculated charge from the formal oxidation number for the cations (s cat ) and anions (s ani ).
Temperature-dependent X-ray diffraction (TDXD) was performed using a powdered diffractometer, with CuKa 1 radiation l(Ka 1 ) ¼ 1. 5406 A, selected with an incident beam quartz monochromator combining the INEL curve sensitive detector (CPS 120) and a high-temperature Rigaku attachment. These data were collected in air, with a heating rate of 10 C/h from 450 to about 700 C. Temperature calibration was performed with standard materials within the temperature range involved. For the differential thermal analysis, the "Adamel ATD 67" analyzer was used with a heating rate of 10 C/h in the temperature range [25e950 C]. Electrical measurements were performed using an HP4192 A impedance analyzer in the 13 MHz-5 Hz frequency range and the 200e650 C temperature range with a 10 C heating step. The powder of the "pseudo-S 00 La 1.
Mazza implemented the BVS model in the program Jumpiter  [40]  to simulate the transport pathways of Na þ in the NaSICON compound. The most energetically favorable pathways of Na þ are the points in the crystal structure where BVS is close to the formal oxidation number (i. e þ1 for Na þ ). Adams has developed this model further, relating the "valence units" to an energy scale by expressing the bond valence using a Morse-type interaction energy  [41]. In brief, the pathway approach identifies regions of low site energy E(A): EðAÞ ¼ D 0 " X N i¼1 S A À X i À S minA À X i S minA À X i 2 À N # þ E Coulomb ðA À BÞ (2) where the E Coulomb term describes the electrostatic interactions of A cation with the framework. The simulation of the diffusion of oxygen pathways is carried out using the computer program Softbv  [42].
8 Nd 0. 2 CuO 3. 5 phase. The oxidation of the reduced phase leads to the phase before reduction, but it has been noticed that by heating in air, the "pseudo-S 00 La 1. 8- Nd 0. 2 CuO 3. 5 phase is transformed from 400 C to a topotactically in La 1. 8 Nd 0. 2 CuO 4 , thus covering the T 0 -Nd 2 CuO 4 type structure which crystallizes in the I4/mmm space group. The X-ray diffraction analysis on a diffractometer of the "BrukerD8 Advance" type gives us the diagram of the phase T 0 -La 1. 8 Nd 0. 2- CuO 4 represented in Fig. 2. The refinement of the cell parameters is carried out by the Rietveld method implemented in the Fullprof software. The powder diffractogram was recorded with a counting step of 0. 014 (2q). The space group chosen is I4/ mmm. The agreement obtained between the calculated and experimental crystallographic data is satisfactory with reasonable parameters.
We thank the staff at  RIBF  for providing the beams, the  EU-ROBALL Owners Committee  for the loan of germanium detectors, the  PreSpec Collaboration  for the use of the readout electronics. Part of the WAS3ABi was supported by the  Rare Isotope Science Project  which is funded by  MSIP  and  NRF of Korea. This work was supported by the  Priority Centers Research Program in Korea  ( 2009-0093817 ),  OTKA  contract number  K100835 , the  U. S. DOE, Office of Nuclear Physics  (Contract No. DE-AC02-06CH11357 ),  NRF-2016R1A5A1013277  and  NRF-2013M7A1A1075764 , the  Spanish Ministerio de Economía y Competitividad  under contract  FPA2017-84756-C4-2-P , the  European Commission  through the  Marie Curie Actions call   FP7-PEOPLE-2011-IEF  (Contract No. 300096 ),  German BMBF  under Contract No:  05P12PKFNE ,  JSPS KAKENHI  Grant No.
Outcomes and follow-up Data was captured at baseline and every 6 months (±3 months) when a participant presented to the clinic for a standard of care visit. Case report forms captured age, gender, race, past and current use of immunosuppressive medications, scleroderma-specific antibodies (done locally as part of clinical care), modified Rodnan skin score (mRSS), and any standard of care assessments such as pulmonary function test (PFT), right heart catheterization (RHC), transthoracic echocardiogram (TTE), or chest imaging (chest X-ray or high-resolution computed tomography (HRCT)). Physicians completed a vascular, cardiopulmonary, gastrointestinal, renal, and vascular standardized assessment at the specified time points. Participants also completed the HAQ-DI questionnaire. Pulmonary hypertension (PH) was defined by a mPAP ≥ 25 mmHg on RHC, in accordance with international guidelines  [17].
We explored percentages of mRSS worsening and FVC worsening as previously defined. Worsening was counted among those who had baseline and at least one follow-up measurement. We also explored PH, LVEF ≤45%, scleroderma renal crisis (SRC), and all-cause mortality. For PH, LVEF ≤45%, and renal crisis, we reported counts and percentages in two parts: (1) events before/at baseline and (2) events during follow-up among those who did not have events before/ at baseline. We reported PH, SRC, and mortality for the whole cohort and reported LVEF ≤45% for those who had TTE. All analyses described above were conducted in SAS (version 9. 4). Additionally, we did time-to-event analysis for mRSS worsening, FVC worsening, and allcause mortality, by plotting cumulative event curves. Figures were plotted via R package "survival" and "survminer" (R version 4.
The recruitment rate in the different centers was not recorded in this registry started in 2012 and this is one of the limitations of this study. We were thus not able to specify among the patients seen during the inclusion period and fulfilling the selection criteria which proportion was finally recruited in the PRESS registry. This limits the discussion of a potential selection bias. The limitations of this study also include the limited followup duration as the registry is ongoing and we will learn more about the internal organ involvement and related mortality in longer term follow-up. This absence of long-term data may have led to a low event rate that precludes sub-analysis and specific survival modeling. Longer follow-up duration of this cohort may also help to confirm the importance of antibody status and their relevance in association with molecular signatures for patients' stratification and prediction of skin trajectory or organ involvement  [52].
Acknowledgements The authors would like to thank all participating centers and patients. They would also like to thank  Rachel Wessel  from the  UM scleroderma program  for her administrative support. disclose. JG reports grants from  EICOS Sciences , grants from  Cumberland Pharmaceuticals. RD reports personal fees from  Formation Biologics , personal fees from  Eicos Sciences, Inc , and personal fees from Corbus Pharmaceuticals. EB reports grants from  NIH  ( K23 AR075112 ), grants and personal fees from  Boehringer Ingleheim , grants and personal fees from  Eicos Sciences, Inc , and grants from  Pfizer. VS reports grants and others from Corbus; grants and others from Bayer; grants and others from  CSL Behring ; others from  Forbius , during the conduct of the study; grants and others from  Boehringer Ingelheim ; grants and others from Eisocs; and grants from Reata.
Funding  Suiyuan Huang  and  Dinesh Khanna  are supported by  NIH/NIAMS   K24 AR063120. Dinesh Khanna  is supported by  NIH/NIAMS   R01 AR070470. The PRESS registry was supported in part by  NIH/NIAMS   K24 AR063120. Alain Lescoat  was funded by the  French network of the University Hospitals HUGO (Hôpitaux Universitaire du Grand Ouest)  ( AAP JCM2020 ) and a grant from  Rennes University Hospital  ( CORECT Visiting Grant   2020 ).
Data analysis Antiviral activity was assayed as a percentage of inhibition of SARS-CoV-2, normalized to the quantity of viral genomes at the lowest concentration of CPZ (i. e. 2 μM) for each of the three independent replicates. Percentage cytotoxicity was calculated relative to untreated cells (0% toxicity). Antiviral activity and cytoxicity data were analysed using GraphPad Prism Version 8. 4. 2 for Ma-cOS (GraphPad Software, San Diego, CA, USA; www. graphpad. com ). Non-linear regressions were performed and the half maximal inhibitory concentration (IC 50 ), 90% maximal inhibitory concentration (IC 90 ), half maximal cytotoxic concentration (CC 50 ) and selectivity index (SI; calculated by dividing CC 50 by IC 50 ) were calculated from '[agonist] vs. response -variable slope' curves with constraints to remain above 0% and below 100%.
Conclusions This first in-vitro study of the antiviral activity of CPZ against SARS-CoV-2 in monkey and human cells supports that CPZ, a wellknown drug with antiviral properties and an excellent tolerance profile, could be tested clinically as an alternative to currently used drugs or combinations of drugs for the treatment of COVID-19. This proof of principle for the feasibility of CPZ to treat COVID-19 is a critical step for a future clinical trial (NCT04366739).
Acknowledgements The authors wish to thank the  Centre National de Reference  des virus des infections respiratoires for sharing reagents and protocols,  Olivier Schwartz  and his team for sharing the A549-ACE2 cell line, and the  Fondation Pierre Deniker  for its support. Funding: This study has received funding from  Institut Pasteur  (covid-therap), from the  French Government 's  Investissement d'Avenir programme and as a recognition as a Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (Grant No. ANR-10-LABX-62-IBEID ). ESL acknowledges funding from the  INCEPTION programme (Investissements d'Avenir  Grant  ANR-16-CONV-0 0 05 ). Competing interests : None declared. Ethical approval: Not required.
High throughput quantitative analysis of microscopy images presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. In this paper we introduce a weakly-supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple the more complex auxiliary semantic segmentation task and other associated tasks such as detection or enumeration. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to state-of-the-art architectures when no precisely annotated dataset is available. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate detailed feature maps of the biological structures of interest.
Introduction The development of powerful microscopy techniques that allow to characterize biological structures with subcellular resolution and on large field of views tremendously increased the complexity of quantitative image analysis tasks  [1]. The resulting images exhibit a wide range of structures that need to be identified, counted, precisely located, and segmented. Expert knowledge is commonly required to achieve successful identification and segmentation of the multiple structures of interest in microscopy images  [2, 3]. These tasks can be tedious and time consuming especially for large databanks or for the comparison of multiple biological conditions. It was recently demonstrated that deep convolutional neural networks (CNN) are excellent feature extractors  [4]. They were successfully applied to segmentation (e.
g. whole cells, nuclei, dendritic spines), enumeration (e. g. cell counting), and classification (e. g. state of cell) of structures in microscopy images  [5] [6] [7] [8] [9] [10] [11] [12]. The most common deep learning (DL) approaches applied to microscopy and biomedical images are fully-supervised and require precisely annotated datasets  [9, 11, 12]. Hence, it is often a limiting step in the application of DL for quantitative analysis of biomedical imaging  [3, 13, 14]. To alleviate the annotation process, weakly-supervised DL methods were introduced  [14] [15] [16] [17]. Bounding box 1 (Figure  1c ,d and Supplementary Fig. 3 ). The required time to annotate a single cell is increased by approximately 2 folds when going from points annotations to bounding boxes, and from bounding boxes to precise annotations (Figure  1e ).
We retrieved the first principal component of every pixel using principal component analysis (PCA) decomposition on the feature space to generate a single feature map that was used to solve different sets of specific auxiliary tasks (Figure  2b ,c & Methods). To characterize the performance of MICRA-Net we compared the results obtained on different datasets with three established baselines: i) pretrained U-Net (in the following sections referred to as U-Net)  [9] , ii) Mask R-CNN  [10] , and iii) Ilastik  [29]. These baselines were chosen as they are widely used in the literature and they allow semantic segmentation with none or simple modifications (see Supplementary Note 2 & 3 for Representative examples from the participants may be found in Supplementary Fig. 1 -3 as well as the specific curves per cell line in Supplementary Fig.
4. The inter-participant agreement was calculated using the F1-score as a function of IOU for precise (blue) and BBOX (orange) annotations in a all versus one manner  [27]. The F1-score for points annotation (green) was calculated with a maximal distance of association of 30 pixels. Plotted are the bootstrapped mean (line) and 95% confidence interval (shade, 10 000 repetitions). c-e) Shown are the distribution of median scores from the inter-participant comparison calculated in a all versus one manner. c) Distance between associated point markers. d) Average distance between the precise contours of participants annotations was calculated for precise annotations. e) Average required time per objects on different cell lines for each supervision level. f) Evaluation of the annotation task required to generate the training set for all microscopy datasets used throughout the paper for fully-supervised (FS) and MICRA-Net approaches.
Reported above is the effective number of decisions (number of extracted crops for MICRA-Net and number of edge pixels for fully-supervised learning) and the required time in hours. For MICRA-Net the number of decisions corresponds to the number of extracted crops and the annotation time per crop (assignation of a positive or negative annotation) was on average 2 seconds for all datasets. For fully-supervised learning, the decision and annotation time was evaluated for each dataset separately on a precisely annotated subset of images (see Methods). dataset specific implementation details). This rendered a similar task between the baselines and MICRA-Net.
Multi-class segmentation of synthetic images To validate the classification and segmentation performance of MICRA-Net, we created a synthetic dataset containing N randomly sampled cluttered handwritten digits from the MNIST dataset  [30]  (Modified MNIST dataset, Figure  2c  & Methods). Each image may contain several instances of digits (from 0 to 9), as well as variable levels of noise and signal to mimic slight variations akin to those that may be observed in microscopy images (see Methods). The first step was to classify the digits appearing on each image to validate the representation capability of the network, which is confirmed by the obtained class-wise mean classification testing accuracy of (98. 9 ± 0. 5) % (mean ± std). In addition to the classification task, MICRA-Net generates class-specific segmentation maps of the digits in the modified MNIST dataset.
Using the information embedded in the Grad-CAMs of the hidden layers (L 1-7 ) to precisely locate each digit in the image significantly increased the segmentation performance of the network when compared to the maps obtained from the Grad-CAMs of the last layer only (L 8 ) (Figure  2d , Supplementary Fig. 5c, d  & Supplementary Fig. 6 ). A U-Net  [31]  trained on the same dataset using a fully-and weakly-supervised training scheme was used as a baseline to better evaluate the performance of MICRA-Net. Fully-supervised learning consisted in training with the binary digits contours from MNIST, while weak contours were generated by a dilation of the digits with a square of size {5, 10, 25} pixel as a structuring element (see Supplementary Note 1). Figure  2e  shows that MICRA-Net achieves similar or superior segmentation performance compared to all weakly-supervised training instances of the U-Net and is only outperformed on all measured metrics (F1-score, intersection over union (IOU), and symmetric boundary dice (SBD)) by fully-supervised training (Supplementary Fig.
Class-specific segmentation of super-resolution microscopy images The next question that needed to be addressed was the applicability of our approach for super-resolution microscopy image segmentation, for which precisely annotated datasets are rarely available. The auxiliary task was the semantic segmentation of STimulated Emission Depletion (STED) microscopy images of two  nanostructures of the F-actin cytoskeleton in neurons: 1) a periodical lattice structure (rings) and 2) longitudinal fibers (Figure  3a, b )  [2]. The F-actin nanostructure segmentation task is challenging since the morphology of neurons is highly variable throughout the dataset, and there are many distractors around the structures of interest  [2]. Figure  1f  shows that image-level annotation reduced by more than 19 folds the time required by an Expert to generate the training dataset compared to precise identification of the structure boundaries that would be required for fully-supervised DL approaches.
144 On the main classification task, MICRA-Net achieves an accuracy of 75. 2% and 83. 7% on the testing 145 6 dataset for the F-actin periodical lattice and longitudinal fibers, respectively. This is inline with a mean inter-participant classification accuracy of (80 ± 5) % and (75 ± 7) % for periodical lattice and longitudinal fibers respectively (calculated from 6 participants using a leave-one-out scheme from 50 images), confirming the model capability to handle data of this nature (Supplementary Fig. 8 ). As described in the previous section, an informative feature map was generated from the PCA decomposition of the combined L 1-7     extracted features. Thresholding of this feature map resulted in detailed binary masks that were used to solve the segmentation task. We relied on a precisely annotated dataset consisting of 25 images of each structure (Supplementary Fig.
9 ) to evaluate the performance of all trained models: i) MICRA-Net, ii) multi-participants polygonal bounding box annotations (6 participants on 25 images of each structure: User-Study), iii) U-Net trained with polygonal bounding boxes  [2] , iv) Mask R-CNN trained with polygonal bounding boxes, and v) Ilastik trained using scribbles (see Methods & Supplementary Note 2 for specific details). MICRA-Net achieved equivalent or superior segmentation performance on the precisely annotated dataset in comparison to both the User-Study and all baselines when comparing the common segmentation metrics (Supplementary Fig. 9 -11 & Supplementary Tab. 2, 3). Thus, even if trained with weak image-level annotations, MICRA-Net can extract the necessary structural information to generate detailed segmentation maps for both nanostructures.
A qualitative visual inspection of the segmentation masks suggested that MICRA-Net segmentation produced a finer detailed mask compared to the weakly-supervised baseline segmentation  [2] , especially for fibers, for which it provides detailed segmented contours of single fiber strains (Figure  3c , Fibers). Custom performance metrics that were adapted to the F-actin nanostructures were required to better characterize this observation. For the F-actin periodical lattice, we measured the Fourier Transform (FFT) of the segmented areas for frequencies corresponding to the periodicity of the lattice (180-190 nm  [32] ) (Figure  3b  & Methods). The FFT-metric calculated on the areas segmented with MICRA-Net is not significantly different from the one obtained from the precisely annotated dataset (Figure  3b ).
For all other baselines, evaluation of the FFT-metric on the segmented areas shows a significant difference with the precisely annotated dataset. This suggests a better segmentation of the periodic structure for our approach over weakly-supervised baselines (Supplementary Tab. 3, 4). Similarly, a custom metric based on the pixel intensity distribution of the segmented areas was developed to evaluate the approaches on the fiber segmentation task (see Methods). While no difference was observed for the regions identified with MICRA-Net compared to the regions from the precisely annotated dataset, a significant increase in the proportion of low-intensity pixels (regions between single fibers) was observed for all weakly-supervised baselines (Figure  3d  & Supplementary Tab. 5). This supports a higher accuracy to precisely identify the contours of individual fibers or periodical lattice regions of MICRA-Net over weakly-supervised U-Net segmentation.