dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Data and sample collection. The present study includes a total of 20 M. bovis isolates from 18 different animals slaughtered at the Soure municipal abattoir, Marajó Island, in North of Brazil. These isolates ensued from the screening of 48 cattle and buffalo, slaughtered for meat production purpose, presenting TB-like lesions, from October 2014 to December 2015. Briefly, 13 isolates were obtained from nine cattle and the remaining nine isolates from buffalos. Different points of origin for these animals were detected within the island: six animals from Chaves, 10 from Soure, one from Santa Cruz do Arari and one from Cachoeira do Arari (Table  1 ). The samples were obtained during official post-mortem routine inspection performed by the veterinarian technical manager of the municipal abattoir and this study was submitted for ethical review by the Animal Use Ethics Committee of the State University of Pará, which issued an opinion waiver certificate in accordance with Brazilian Law 11794 (October 08, 2008).
Clade-specific SNPs detected for the Marajo strain along with affected genes, functional effect and affected gene product. All strain-specific SNPs are listed with positioning relative to the genome of M. tuberculosis H37Rv and M. bovis AF2122/97 with synonymous variants occurring at essential genes highlighted in bold. a Relative to the genome position of M. tuberculosis H37Rv, GenBank Accession NC_000962. 3; b Relative to the genome position of M. bovis AF2122/97, GenBank Accession NC_002945. 4; c Open Reading Frame; d Gene Essentiality according to DeJesus et al. (2017) using saturated Himar1 transposon libraries 22 ; e Non-essential gene by DeJesus et al.
Acknowledgements We are grateful to  Professor Márcio R. T. Nunes  ( Center for Technological Innovation, Evandro Chagas Institute ),  Maria L. Lopes  and  Jacira S. Nascimento  ( Mycobacteriology Group at the Evandro Chagas Institute ) for technical assistance and to  Professor Stefan Niemann , head of the Molecular and Experimental Mycobacteriology Group at the  Research Center Borstel , for the comments and collaboration. This work was supported by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES) ,  Instituto Evandro Chagas/MS/SVS  and Fundação para o desenvolvimento científico e tecnológico em saúde [ PRES-012-FIO-16 ]. M. L. C. is supported by  CAPES -Finance Code 001 , [ 194-88881. 187587/2018-01 ,2018]. Parts of the work were funded by the  German Center for Infection Research (DZIF).
7) 15. 6735 G00021 2015 Abomasum Cattle 6 Chaves 997/0822 1 16287213 (98. 6) 476. 0113 G00022 2015 Lymph nodes Cattle 7 Soure 997/0822 1 15229721 (96. 0) 440. 3858 bovis isolates from cattle and buffalos in the Marajó Island (Pará, Brazil). For each isolate, the year of isolation, city of origin along with classical genotyping data and mapping statistics are shown. a Genomes excluded due to low coverage depth and/or low percentage of mapping reads; b Shared International Type and SB-number according to SITVIT2 and Mycobacterium bovis Spoligotyping Database (MBovis. org); c 24-loci MIRU-VNTR profiles: 1 (225322341363454251322312); 2 (225322341363454251322312); 3 (225322341362454251322312); 4 (225322141363474251322312); and 5 (225322341363474251322312).
On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggests promising research questions. Further, this study illustrates that crowd-sourcing and deep learning complement each other well for the exploration of image datasets.
Recently,  Stevens et al. (2019b)  described a collective cloud classification activity by a team of 13 scientists supported by the International Space Science Institute (ISSI). This ISSI team aimed to identify mesoscale cloud patterns in visible satellite imagery taken over a trade-wind region east of Barbados. Organization, or clustering, of clouds has been shown to have important implications for climate in the case of deep convection  (Tobin et al. , 2012) , which raises the question to what extent this is the case in shallow clouds. The ISSI-team's hand-labeling effort resulted in around 900 subjectively classified images. An initial application of machine learning to these images (by the first author) proved promising but also highlighted the need for more training data in order to obtain robust and interpretable results.
As we will show later, this thinking paid off for the machine learning models we trained. The Zooniverse interface was further configured to serve participants an image randomly drawn from our library of 10 000 images. After being classified by four different users, images were retired, i. e. removed from the image library. In addition, no user was shown the same image twice. With the interface in place, cloud classification days were set up at the Max Planck Institute for Meteorology in Hamburg, Germany on Nov 2nd and at the Laboratoire de Météorologie Dynamique in Paris, France on Nov 29th, 2018. After a brief instruction at the start of the day and a warm-up on the training dataset, 67 participants, most of them researchers, from the two institutes, labeled images for an entire day. The activity yielded roughly 30 000 classified images, i.
4a. At first glance, IoU values of around 0. 2 seem low. In classical computer vision tasks such values would certainly indicate low agreement. However, as mentioned above, this dataset is different from classical object detection tasks in that there are more than two labelers for most images and there are many cases in which one or more participants did not label an image. In fact, the primary reason for the low mean IoU score are zero values, which arise from some users detecting a feature while others did not. Take Fig. 3b  as an example. Here, three of four users agreed to a high degree of accuracy on the location of Flowers but the last user did not submit a label. This results in three "no label"-"label" comparisons and three "label"-"label" comparisons. Even with perfect agreement between the three Flower labelers, the mean IoU would only be 0.
Application of deep learning While the 10 000 images labeled on Zooniverse already provide a useful dataset for further analysis, they only cover a small fraction of the globe for a small fraction of time. Only 0. 6 % of the data available during the selected eleven year period were labeled. In this section, we explore whether deep learning (see Sidebar 1) can help to automate the detection of the four organization patterns and if so what can be learned from it. The pattern recognition task can be framed as one of two machine learning problems: object detection and semantic segmentation. Object detection algorithms draw boxes around features of interest, essentially mirroring what the human labelers were doing. In contrast, segmentation algorithms classify every pixel of the image. Fig. 6  shows examples of these two approaches for images from a validation dataset  that was not used during training (see  Rasp (2020b)  for more, randomly chosen examples).
This suggests that despite the uncertainty in the human dataset, the deep learning algorithms are able to filter out a significant portion of this noise and manage to distill the underlying human consensus. To quantitatively compare the deep learning algorithms against the human labelers, we compute the mean IoU for each human individually as well as for the two algorithms (Fig. 4b ). Both algorithms show a large agreement with the human labels for a random validation dataset. The fact that the scores are higher than the mean inter-human IoU directly reflects the fact that the algorithms tend to produce less noisy predictions. Further analysis shows that the algorithms inherit some biases from the human training labels. The frequency and accuracy of the predicted labels is higher for patterns with a higher inter-human agreement, most notably flowers (Supplemental Fig.
3 ), which could slightly bias the deep learning predictions. The main advantage of deep learning algorithms is that they are very fast at inference, one second per image compared to the 30 seconds a human needed on average, and they are more scalable. This allows us to apply the algorithm to the entire globe (Fig. 7a ; see Supplement for details). A healthy skepticism is warranted when applying machine learning algorithms outside of their training regime  (Rasp et al. , 2018; Scher and Messori, 2019). A visual inspection of the global maps (see  Rasp (2020c)  for more examples), however, suggests that the algorithm's predictions are reasonable and physically interpretable as discussed below. Naturally, over land the predictions have to be assessed with greater care because no land was present in the training dataset.
This could offer clues as to how meso-scale organization, and hence cloudiness, has changed in the past, and may change in the future. This example helps highlight how crowd-sourced and deep-learned data-sets create new ways to study factors influencing shallow clouds and their radiative properties, and hopefully stimulates ideas for adapting the approach to other problems. The growing accessibility of these new research methodologies makes their application all the more attractive. Platforms like Zooniverse make it easy to set up a labeling interface free of charge. Plus, even if we did not do so, it is also possible to make the interface open to the public. Deep learning has also become much more accessible. Easy-to-use Python libraries 4 with pre-trained models for many applications in computer vision as well as accessible online courses 5 make it possible even for non-computer scientists to apply state-of-the art deep learning techniques.
We used a k-means clustering with eight clusters to find similar patterns within our search perimeter. As input to the algorithms we used the climatological means of LTS, FF10 and TCWV for each of the four seasons. The eight clusters explain more than 90% of the variance in the dataset and provide large enough regions to fit 21 • longitude by 14 • latitude boxes reasonably well. Fig. S1  shows the clusters for the four seasons. Our analysis indicates that the meteorological conditions over the Northwestern Atlantic change with season. This is not surprising due to the migration of the ITCZ, but it illustrates that we shouldn't expect to see the same cloud patterns or at least the same distribution throughout the year. The final choice of seasons and regions was made to match the climate of region 1 in DJF (Table  S1 ).
Deep learning models Two deep learning models are used, one for object detection and one for semantic segmentation. For object detection, an algorithm called Retinanet  (Lin et al. , 2017)  is used. Here we used the following implementation in Keras  (Chollet and Others, 2015) : https://github. com/fizyr/keras-retinanet, which uses a Resnet50  (He et al. , 2015)  backbone. The original images had a resolution of 2100 by 1400 pixels. For Retinanet the images were downscaled to 1050 by 700 pixels. This is necessary to fit the batch (batch size = 4) into GPU RAM. For semantic segmentation, we first converted each human classification, i. e. all boxes by one user for an image, to a mask. Sometimes boxes for different patterns overlap. In this case, the mask is chosen to represent the value of the smaller box.
Overall, the amount of overlapping boxes is small, however, so that the resulting error is most likely negligible. To create a segmentation model, we used the fastai Python library v1 7. The network architecture has a U-Net  (Ronneberger et al. , 2015)  structure with a Resnet50 backbone. For the segmentation model the images were downscaled to 700 by 466 pixels (batch size = 6). To create the prediction masks, first a Gaussian filter with a half-width of 10 pixels was applied to smooth the predicted field. Then, for each pixel the highest probability for each of the four patterns was used, if this probability exceeded 30%. This last step counteracts the tendency to predict background, which is by far the most common class in the training set.
Acknowledgements First and foremost, we would like to thank all the participants of the cloud labeling days. Special thanks go to  Ann-Kristin Naumann  and  Julia Windmiller  for initiating this collaboration and to  Katherine Fodor  for suggesting Zooniverse. SR acknowledges funding from the  German Research Foundation  Project  SFB/TRR 165  "Waves to Weather". This paper arises from the activity of an  International Space Science Institute (ISSI) International Team researching "The Role of Shallow Circulations in Organising Convection and Cloudiness in the Tropics". Additional support was provided by the  European Research Council (ERC)  project  EUREC4A (Grant Agreement 694768 ) of the European Union's  Horizon 2020 Research and Innovation Programme  and by the  Max Planck Society. We acknowledge the use of imagery from  NASA Worldview , part of the NASA Earth Observing System Data and Information System (EOSDIS).
7 ). It also shows that the simultaneous variation of both features, or further combined with cell asymmetry, produces skewed and multimodal null distributions of neighbors counts that would be hard to predict given an observed dataset. Indeed, it would require to derive a parametric model of this relationship (if it exists and if it is at all possible) for which parameters could be accurately estimated from one image. On the other hand, by disregarding this existing relationship between cell size and shape, and cell neighbor count, alternative approaches necessarily lead to the construction of an incorrect null distribution in general (Supplementary Fig. 8 ). These methods would work only in the case where cells are organized as a honeycomb grid, a hypothesis rarely met. SET takes into account cell shape, size, and asymmetry to accurately sample such a null distribution without the need to actually derive a parametric model.
46. The samples were treated with anti-cingulin and anti-hair cell antigen to visualize membrane junction and cell identity. The Xenopus epidermis image was acquired from a stage 33 larva. The visualization of membranes and cell identities was made possible by phalloidin labeling of the actin, acetylated alpha tubuli-488 for cillia, andlectin-pna-594 to label mucins in goblet cells and SSCs  47. The image was extracted from a 3D stack using the SME algorithm  48. Cell segmentation. The minimal input to the SET model is an image of segmented cells where each pixel takes as value the integer label that represents all the pixels of the same cell. All 2D cell segmentations presented in this manuscript were performed using a modified version of the "Morphological Segmentation" plugin of the MorphoLibJ package of ImageJ/Fiji 49.
The computation of the last is made very efficient by the cdist function from the scipy Python package (see the code for implementation details). Therefore, when the cells could reproduce correctly the observed image using five parameters (e. g. E18 and adult ependymal cells) we choose this option. In this case the approximate computation time was between 1 h 30 min and 10 h for 1000 SET simulations on 200 cpus Intel Xeon Processor 2400 MHz, depending on the image size. When cell were highly asymetrics such that five parameters did not reproduce properly the observation (e. g. Xenopus), then we used eight parameters and it took between 9 and 16 days to compute about 300 simulations on the same computer configuration. All calculations were submitted in parallel thanks to the IBENS computing cluster.
Note that the code made available offers the possibility to parallelize computation on all CPUs of a single computer. Furthermore, we anticipate that this type of computation would significantly gain to be ported to GPU computing as it can be highly parallelized per cell; however, we have not investigated this possibility. Ethical aspect. The experiments using mice were performed in conformity with French and European Union regulations and the recommendations of the local ethics committee (Comité d'ethique en experimentation animale no. 005). Mice were bred and maintained in the animal facility of IBENS (Agreement 5502 from the French Ministry of Research and Agreement OGM2014 from the Préfecture de Paris-French ministry of interior). The minimal number of animals was used for the project and the procedures implemented ensured their welfare during their lives.
Acknowledgements We thank our colleagues who provided image data:  David Sprinzak ,  Guy Richardson , and  Richard Goodyear  for the chick basilar papilla;  Bellaiche Yohanns  for the Drosophila dorso thorax;  Jean-Christophe Palauqui  for the Arabidopsis thaliana root;  Katia Belcram  for the Arabidopsis thaliana shoot apical meristem; and  Peter Walentek  for the Xenopus epidermis. We thank  Xavier Morin  and  Mary Ann Letellier  for valuable comments on the manuscript and  Felipe Delestro  from the  Bioinformatics platform of IBENS  for the design of the figures. This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54 MEMO LIFE ANR-11-IDEX-0001-02 PSL* Research University.
Data availability The chick image from ref. 46  was provided by David Sprinzak, Guy Richardson, and Richard Goodyear, and was initially from ref. 45  , Copyright 1997 Society for Neuroscience. The Drosophila image from ref. 44  was provided by Yohanns Bellaiche with the permissions of AAAS. The Arabidopsis thaliana root image was provided by Jean-Christophe Palauqui. The Arabidopsis thaliana shoot apical meristem image was provided by Katia Belcram. The Xenopus image was provided by Peter Walentek. Mice ependyma images were produced by Nathalie Spassky. A copy of these image data is made available on the Github page along the code to run the method https://github. com/ biocompibens/cellmodelling.
Subjects Fresh fecal samples from ten male healthy volunteers (mean age of 27. 10 years old) with a BMI of 25. 39 ± 1. 04) (mean ± standard error of the mean (SEM)), were collected and submitted to DNA extraction by three different methods (G'NOME, PROMEGA, and QIAGEN). A second dataset (referred to as the "French cohort") has been used from our published work (at Mondor Hospital, Creteil, France-Prof. I Sobhani) and was sequenced in 2014 at the Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg  [9]. For this work, we used the 50 control sub-group from this cohort for which 16S as well as WMS had been performed.
Bionformatics Analysis The "French cohort" were downloaded from the European Nucleotide Archive (ENA) database (http://www. ebi. ac. uk/ena) under the access number ERP005534. Amplicon and WMS data (paired-end reads) were quality checked using the fastQC software (version 0. 41) and quality-controlled using the Trimmomatic software (version 0. 35, minimum base quality score of 30, using a sliding window of 5bp). For 16S datasets, quality checked paired-end reads were merged using ExpressionAnalysis  [20]. They were also confirmed by using FLASH2 (version 2. 2. 0)  [21]. We selected three representatives algorithms for the 16S rRNA dataset analysis of taxonomical assignation: MALT (v0. 4. 1, using recommended parameters for 16S analysis: "-mif " and "SemiGlobal" alignment) and MEGAN6 (Community edition, v6.
17) for the comparative approach  [22] , QIIME1 for the OTU/clustering approach  [23] , and DADA2  [24]  for the statistical inference of amplicon sequence variants (ASV)-based approach. For each pipeline, we used default parameters and the SILVA rRNA database, version 123  [25]. QIIME1 is now outdated, but this version has been largely used in literature. Consequently, it is worthy to provide the community information concerning its reliability when one wants to refer to previous published results. For the WMS dataset, we used the mOTU-v2 pipeline  [26]. Paired-end reads had already been processed, so no pre-processing was performed here. It is widely accepted that 16S rDNA should not be used below the genus level  [27]. We summarized counts to the genus level with in-house scripts that used the R software (version 3.
6. 1) for 16S rRNA and WMS taxonomical assignations. After summation at the genus level, counts were normalized relative to the total number of reads in the sample. OTU, taxon, AVS, or mOTUs that were above the genus levels but without an official NCBI taxonomy ID (taxid) (see below) were not considered in further comparisons. OTU tables and scripts are made available: https://github. com/dmestivier/Microbiota-16S-WMS.
Genus Name Correction Even when we used the same 16S rRNA database by using the three 16S pipelines, we still noticed a few discrepancies between genus names, some of which were associated with the same NCBI taxid. We also found differences in genus names between the 16S and mOTU-v2 pipelines. Examples include "g_Candidatus Soleaferrea"/"g_Candidatus_Soleaferrea" (space or underscore separator), "g_Chroococcidiopsis"/"g__Chroococcidiopsis_CC1," "g__Lachnoclostridium"/"g_Lachnoclostridium_5," and "g_Coriobacteriaceae_UCG"/"g__Coriobacteriaceae-UCG" (no official NCBI taxonomy with "_CC1/_UCG/. " suffixes). Another example is "g_Escherichia" (MALT)/"g_Escherichia-Shigella" (QIIME1)/"g__Escherichia/Shigella" (DADA2). Notably, MALT also has a "g_Shigella" genus, which was not merged into the "g_Escherichia" genus because it is an official NCBI taxonomy genus name.
Statistical Analysis We used the shiny application for metagenomic analysis server (SHAMAN,  [28] ) for graphical visualizations, comparisons using Principal coordinate analysis, and differential abundance analysis (based on the DESeq2 package)  [29, 30]. Other figures were generated using in-house scripts with R. Heatmaps were generated using the pheatmap package (Version 1. 0. 12), Venn diagrams were produced using the VennDiagram package (Version 1. 6. 20) and intra-class correlation (ICC) analyses were performed using the psych package (Version 1. 9. 12. 31).
16S rRNA Taxonomic Assignments WMS and 16S rRNA datasets from the sub-cohort of 50 control individuals from Henri Mondor hospital (see the French cohort in  [9]  were downloaded from ENA (access number ERP005534). After quality checks, reads were assigned a taxonomy using four pipelines: mOTUs-v2 for the WMS dataset and MALT + MEGAN6 (referred to as MALT), QIIME1, and DADA2 for the 16S rRNA dataset (see Material and Methods). All taxonomic assignments were summarized at the genus level (Table  S1 ). For the 16S rRNA dataset, the average mean number of reads per sample was 365,160 ± 205,946. QIIME1 identified 21,462 OTU and 564 genera (mean reads per sample: 200,951 ± 134,898; ~55. 03% of all reads used for taxonomic assignments). MALT identified 564 taxa and 351 genera (mean reads per sample: 190,252 ± 139,868; ~52.
10% of all reads used for taxonomic assignments). DADA2 identified 3623 amplicon sequence variants (ASV) and 383 genera (mean reads per sample: 217,066 ± 148,294; ~59. 44% of all reads used for taxonomic assignments). Regardless of the 16S pipeline used, 40-50% of reads were filtered during the taxonomic assignments. The average mean number of reads per sample for the WMS dataset was 15,625,002 ± 7,195,800 reads (min: 2,304,390; max: 28,086,026; median: 18,033,238), of which 0. 03% (~433,359 reads/sample) did not lead to a taxonomic assignment. Moreover, 22. 06% of all genera were filtered, many of which were assigned to the "unknown" genera (see Materials and Methods). Overall, 564 genera were merged and identified by at least one out of the four pipelines (Table  S1 ). Only 160 genera (28.