dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, to make our epithelial segmentation tool easily accessible to a broad audience, we created a graphical user interface (GUI) and a documentation for it (https://github. com/baigouy/EPySeg). This interface allows for building, training and running convolutional neural networks. It is built in a way that non-experts can rely on the default settings to get a decently trained network and gain knowledge about deep learning, while the advanced users can visually fine-tune parameters to get optimal results. Also, since the majority of scientific computers are not deep learning-ready, making it difficult to train convolutional neural networks, we provide a minimal user interface to run EPySeg on Google Colab, along with online guidelines, to allow for everyone to use it (https://github. com/baigouy/notebooks).
Two of the datasets were acquired on regular Leica or Zeiss confocal microscopes (Leica SP5 and LSM 510), while the third training set was acquired on a spinning disc (Roper) to further increase the variability between images. Importantly, we paid a lot of attention to the quality of the segmentation masks fed to the convolutional neural network and cropped out regions where segmentation quality was poor as well as regions that were not segmented (e. g. cells adjacent to the tissue of interest) in order not to perturb the learning process.
Convolutional neural network building and training Convolutional neural networks rely on the TensorFlow and Keras tools and were generated using the segmentation_models library from (https://github. com/qubvel/segmentation_models). Typically, we used Linknet  13  architectures and varied the encoder layers. We found the vgg16 and seresnext101 encoders  21, 22  , both known to perform well at classification tasks, very efficiently segment epithelia. Networks were trained for 100 to 300 epochs on the complete train set at every epoch. Depending on their memory requirements, networks were trained with a batch size varying between 2 and 64 images and with a tile size ranging from 64 to 256 pixels in width and height. We chose the intersection over union (IoU) also called the Jaccard index for the loss function as it is particularly well suited to evaluate differences between binary images.
iiasa. ac. at/iamc-1. 5c-explorer/ (last access: 22 October 2020) for AR5 and SR1. 5 respectively; both databases are hosted by the IIASA Energy Program. ) For the IPCC's forthcoming Sixth Assessment Report (AR6), it is anticipated that the number of scenarios will be in the several hundreds to a thousand (for example, see the full set of scenarios based on the Shared Socioeconomic Pathway (SSPs) at https://tntcat. iiasa. ac. at/SspDb, last access: 22 October 2020). Both the number of scenarios and the tight timelines of the IPCC assessments render it infeasible to use the world's most comprehensive models to estimate the climate implications of these IAM scenarios.
The second is that the forcing in these scenarios includes periods of increase, sudden decrease due to volcanoes and longer-term stabilisation rather than the simpler changes seen in the idealised experiments. Fitting all three of these regimes is a more difficult challenge than fitting the idealised experiments alone. Only 6 models (Table  2 ) have been able to submit emulation configurations. Furthermore, each RCM is calibrated to a different number of CMIP models, with some modelling teams unable to provide any calibrations at all. The reason is that there is to date no common resource of calibration data from the CMIP6 repositories. The technical challenge of diagnosing, stitching together, creating area-weighted averages and de-drifting a large amount of CMIP6 output data within a short time period has turned out to be a hurdle for many modelling teams.
Conclusions RCMs are used in many applications, particularly where computational constraints prevent other techniques from being used. Due to their importance in climate policy assessments and in carbon budget calculations, as well as their applicability to a wide range of scientific questions, understanding the behaviour and output from RCMs is highly relevant and requires continuous updating with the latest science. Here we have presented the Reduced Complexity Model Intercomparison Project (RCMIP), an effort to facilitate the evaluation and understanding of RCMs in a systematic, standardised and detailed way. We hope this can greatly improve ease of use of, and familiarity with, RCMs. We have performed RCMIP Phase 1, which provides an initial database of experiments conducted with 15 participating models from the RCM community.
This gap is particularly important to fill given the widespread use of RCMs throughout the integrated assessment modelling community and in large-scale climate science assessments. We welcome requests, suggestions and further involvement from throughout the climate modelling research community. With our efforts, we aim to increase understanding of and confidence in RCMs, particularly for their many users at the science-policy interface. Code and data availability. RCMIP input time series and results data along with processing scripts as used in this submission are available from the RCMIP GitLab repository at https://gitlab. com/rcmip/rcmip (last access: 22 October 2020) and archived by Zenodo (https://doi. org/10. 5281/zenodo. 3593569, Nicholls and Gieseke, 2019). The ACC2 model code is available upon request.
The implementation of the AR5IR model used in this study is available in the OpenSCM repository: https://github. com/openscm/ openscm/blob/ar5ir-notebooks/notebooks/ar5ir_rcmip. ipynb (last access: 22 October 2020,  Nicholls, 2020). The model version of ESCIMO used to produce the RCMIP runs can be downloaded from http://www. 2052. info/wp-content/ uploads/2019/12/mo191107%202%20ESCIMO-rcimpfrom% 20mo160911%202100%20ESCIMO. vpm (last access: 22 October 2020,  Randers et al. , 2020). The vpm extension allows you to view, examine and run the model but not save it. The original model with full documentation is available from http://www. 2052. info/escimo/ (last access: 22 October 2020). FaIR is developed on GitHub at https://github. com/ OMS-NetZero/FAIR (last access: 22 October 2020), and v1.
5 used in this study is archived at Zenodo  (Smith et al. , 2019). The GREB model source code used is available, upon request, on Bitbucket: https://bitbucket. org/rcmipgreb/greb-official/ src/official-rcmip/ (last access: 22 October 2020). The last stable versions are available on GitHub at https://github. com/christianstassen/greb-official/releases (last access: 22 October 2020,  Stassen et al. , 2020). The Held et al. two-layer model implementation used in this study is available in the OpenSCM repository: https://github. com/openscm/openscm/blob/ar5ir-notebooks/ notebooks/held_two_layer_rcmip. ipynb. Hector is developed on GitHub at https://github. com/JGCRI/ hector (last access: 22 October 2020). The exact version of Hector used for these simulations can be found at https://github. com/ ashiklom/hector/releases/tag/rcmip-phase-1 (last access: 22 October 2020,  Link et al.
, 2020). The scripts for the RCMIP runs are available at https://github. com/ashiklom/hector-rcmip (last access: 22 October 2020,  Link et al. , 2020). MAGICC's Python wrapper is archived at Zenodo (https://doi. org/10. 5281/zenodo. 1111815, Nicholls et al. , 2019)  and developed on GitHub at https://github. com/openclimatedata/pymagicc/ (last access: 22 October 2020). OSCAR v3 is available on GitHub at https://github. com/tgasser/ OSCAR (lasty access: 22 October 2020, Gasser, 2020). WASP's code for the version used in this study is available from the supplement of  Goodwin (2018) : https://doi. org/ 10. 1029/2018EF000889. See also the WASP website at https:// waspclimatemodel. info/downloads/ (last access: 22 October 2020). The other participating models are not yet available publicly for download or as open source.
Please also refer to their respective model description papers for notes and code availability. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-5175-2020-supplement. Author contributions. ZN and RG conceived the idea for RCMIP. ZN, MM and JL set up the RCMIP website (https://www. rcmip. org/, last access: 22 October 2020), produced the first draft of the protocol and derived the data format. All authors contributed to updating and improving the protocol. ACC2 results were provided by  were provided by ZN. CICERO-SCM results were provided by JF, BS, MS and RBS. EMGC results were provided by LM, AH and RJS. ESCIMO results were provided by UG. FaIR results were provided by CS. GIR results were provided by NL. GREB results were provided by DD, CF, DM and ZX.
Hector results were provided by AS and KD. MAGICC results were provided by MM, JL and ZN. MCE results were provided by JT. OSCAR results were provided by TG and YQ. WASP results were provided by PG. ZN wrote, except for the model descriptions, the first manuscript draft, produced all the figures and led the manuscript writing process with support from RG. All authors contributed to writing and revising the manuscript. Competing interests. The authors declare that they have no conflict of interest. no. CE170100023). Katsumasa Tanaka benefited from state assistance managed by the National Research Agency in France under the "Programme d'Investissements d'Avenir" under the reference "ANR-19-MPGA-0008". Robert Gieseke has been supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (grant no.
Acknowledgements. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modelling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. RCMIP could not go ahead without the outputs of CMIP6 nor without the huge effort which is put in by all the researchers involved in CMIP6 (some of whom are also involved in RCMIP). We also thank the  RCMIP Steering Committee -comprised of Maisa Corradi ,  Piers Forster ,  Jan Fuglestvedt ,  Malte Meinshausen ,  Joeri Rogelj  and  Steven Smith - for their support and guidance throughout Phase 1.
Radioactivity was measured in a TopCount apparatus (Packard). The receptor preparation used during these experiments presented a Bmax of 49 pmol/mg proteins and a Kd of 5. 58 nM for [ 3 H]-rosiglitazone. The compounds were solubilised in pure DMSO and diluted to the appropriate working concentrations (100 mM to 0. 1 nM). For each compound tested, plots of ligand concentration versus DPM of radioligand bound were constructed and apparent Ki values were estimated from nonlinear least-squares fit of the data assuming simple competitive binding. The details of this assay have been reported  18.
Molecular docking studies The crystallographic data for PPARc co-crystallized with tesaglitazar were downloaded from the PDB data bank (http://www. rcsb. org/-PDB code: 1I7I). The studied compounds were built using the atomic partial charges calculated with the Gasteiger-H€ uckel method and their geometry was optimised by the Powell method available in the Maximin2 procedure to a gradient of 0. 001 Kcal/ mol. Å using the Tripos force field  21. UCSF Chimaera 1. 12  22  (https://www. cgl. ucsf. edu/chimaera), a visualisation tool for molecular modelling and structural biology, was used to remove molecules, ions, and water and to minimise the structure of proteins and ligands, using the Gasteiger charges with 1000 steps of minimisation. Both ligands and receptor were then converted to pdbqt format.
AutoDock Vina 23 (http://vina. scripps. edu), an opensource programme for molecular docking, was used to investigate ligand-protein affinity. A grid box was centred (x ¼ 17. 949, y ¼ 19. 682, and z ¼ 15. 341) on the active site of PPARc LBD, to cover the pocket with the main residues of receptor binding site. The best conformation was chosen with the lowest docked energy, based on complete docking search (ten runs). The interactions of PPARc LBD with the ligands, hydrogen bonds, bond lengths, Root Mean Square Difference (RMSD) were analysed using Chimaera software. The docking solutions of tesaglitazar into binding site using AutoDock Vina converged consistently towards a unique binding mode within a 1. 56 Å RMSD in comparison with the co-crystallized tesaglitazar and showed binding energy of À8.
Molecular docking studies Molecular modelling studies were conducted to understand the large shift of binding affinity induced by the replacement of an oxygen atom by a sulphur atom in the 6-benzoyl substituted benzazinone series of compounds while no loss of affinity occurred with the 7-benzoyl derivatives. Whereas compounds 33-36 were synthesised and evaluated as racemic mixtures, only (S)-enantiomers have been analysed in docking studies, since (S)-stereoisomers displayed the best pharmacological profiles described in the literature. Crystal structure of PPARc (PDB id: 1I7I) 37 co-crystallized with tesaglitazar was downloaded from PDB database. The binding poses of 7-benzoyl benzazinones 34 and 36 with the LBDs of PPARc are shown in Figure  2(A). In addition to the classical hydrogen bondings with His323, His449 and Tyr473, it was apparent that the benzoyl moiety and the benzoxazin-3-one or benzothiazin-3-one cores are buried in a binding cavity of PPARc LBD composed of a cluster of hydrophobic residues (Leu255, Ile262, Ile281, Val339, Ile341, Met348 and Leu353).
Automated quantification. To perform a standardized, automated analysis of structural remodeling, JavaCyte was developed for the public domain image analysis platform Fiji (www. fiji. sc), an extended version of ImageJ. Output files with the position and dimensions of all structures recognized by the algorithms are stored as comma separated value (csv) files, compatible with most statistical packages, for further analysis. The code of JavaCyte was made available as an open source at GitHub in the repository stored under: https ://githu b. com/ jwint ersUM /JavaC yte. Fibrosis, myocyte size and myocyte dissociation index, clustering coefficient. A Phansalkar's algorithm for automatic local thresholding was used to delineate WGA-positive pixels (Fig. 3a, b ). The total area of fibrosis was calculated as the fraction of WGA-positive pixels to the total pixel count.
e. the 'healthiest' patients in our study population, by more than two standard deviations. Distribution of CDI was visualized for each image as a heat map (white cells are closely apposed to neighbors while dark red cells are isolated from neighbors, Fig. 3f ). Furthermore, clustering of enhanced inter-myocyte distances was tested in analogy with the local clustering coefficient, as described in graph theory. Myocytes separated by a larger than normal area of endomysial fibrosis are connected in a clique. The clustering coefficient of myocytes in a clique is enhanced. The impact of augmented clustering coefficients in a clique directly corresponds to the size of the clique. The average clustering coefficient of all myocytes in an image was measured using Matlab. Permutation testing was applied to calculate the expected clustering coefficient in the case of a completely random distribution of inter-myocyte distances measured in the image.
Fibroblast density. Vimentin positive objects were selected applying Landini's color threshold  20. Vimentin shows some cross-reactivity to endothelial cells  21  , which was confirmed by overlaying thresholded GS-I4B/ CD31 and vimentin images. For analysis of fibroblast density, we therefore subtracted the thresholded GS-I4B/ CD31 images from the thresholded vimentin images. For each image, the number of fibroblasts per myocyte was calculated as a measure for fibroblast density (Fig. 4 ). Validation and performance measures. Performance measures were obtained by comparing automated results to data obtained through manual segmentation and measurement. In a first stage, object recognition was tested by quantifying the number of true positive (TP), false positive (FP) and false negative (FN) objects in human atrial samples.
Statistics. The open source software environment for statistical computing, 'R' (www. r-proje ct. org), was used for all statistical analysis. For each tissue sample, a minimum of three independent images was obtained, while the operator was blinded to the experimental group or patient group. For analysis of endomysial fibrosis, cardiomyocyte size, CDI and capillary size, the median value per image was calculated. Differences were tested using a mixed-effects model. For analysis of goat samples, the models contained the fixed-effect variables 'sublayer (endocardium/epicardium)' , 'group (Sham/ST-AF/LT-AF/AVB)' and the interaction term 'sublayer*group'. The factor 'goat study number' was included as a random-effect variable. For analysis in human samples the models included the fixed-effect factor 'group (CVRP+/CVRP-)' and the random-effect 'matched pair_ID' to identify matched patients.
Results Workflow. We developed a suite of algorithms for the automated analysis of the previously described features of myocardial tissue structure, JavaCyte, as a plug-in tool for the commonly used image analysis environment Fiji. We recommend that images for analysis are taken at the same magnification (200-400×) and with similar illumination and exposure, preferably using the same microscope and camera for all images. At magnifications below 200×, the resolution of digital images will become inadequate to accurately determine the dimensions of the recognized structures, particularly the thickness of endomysial septae. At magnifications above 400×, fewer structures will be present within an image, increasing possible selection bias of the photographed locations. The program has been successfully tested for jpg, tiff and bmp image formats, but will work in principle for all standard image formats that can be imported into ImageJ.
3E ). For WGA images, the locations where myocyte size and myocyte-to-myocyte distances were measured are indicated. This allows the user to inspect each analyzed image in order to detect potential recognition problems. In addition, numerical data is written to. csv files, with individual measurements of the location and size of the recognized structures. These files can be directly imported for further statistical analysis in e. g. R or SPSS. Inter-myocyte distance and myocyte size in goat models of AF. Structural parameters in the epicardial and endocardial layer from the LA free wall of ST-AF (n = 7), LT-AF (n = 6), AVB (n = 6) and sham-operated (n = 6) goats were studied. In agreement with our earlier quantification in the same model  6  , no significant differences in total ECM content were observed between groups in either of the layers (Fig.
Following validation of JavaCyte in previously characterized goat models, the algorithm was applied to a clinical test case. As a result of multiple underlying comorbidities of unknown, varying durations, atrial tissue structure in patients tends to be more variable and extensive, affording a challenge to test the performance of automated quantification. One of the most important attributes of JavaCyte is standardization and reproducibility of data, independent of the operator. This quality was confirmed by studying inter-observer variation between 2 individual observers who individually took 10 photographs per sample of 10 unique patients. The images were then analyzed using JavaCyte, eliminating interobserver variation in the data analysis phase. In this way, the impact of potential bias in area selection during microscopy and photographing of the tissue was assessed.
Conclusion Here, we provide a flexible and expandable, open source tool that is well-suited for the accurate characterization of myocardial structure including the quantification of fibrous tissue distribution, myocyte hypertrophy, fibroblast density, capillary density and size, and the spatial interrelations between these features. Compared to conventional analysis, our method is highly time-and cost-efficient, and largely eliminates observer bias and inter-observer variability. Limitations. For experimental work described in this manuscript, frozen cardiac tissue samples were used. Although JavaCyte is only tested in frozen sections, we expect the program to work for fixed tissue slides as well, provided the staining protocol is optimized. JavaCyte was tested at magnifications ranging from 200 × to 400x.
Acknowledgements This study was supported by a Grant from the  Netherlands Heart Foundation  ( CVON2014-09 ,  RACE V: Reappraisal of Atrial Fibrillation: Interaction between hyperCoagulability , Electrical remodeling, and  Vascular Destabilisation in the Progression of AF)  and a Grant from the  EU  ( CATCH ME Horizon 2020 ,  EU-H2020-PHC-RIA (633196) ).
Source: Johannes Goudjanou, Premium Hortus To know more, visit: http://premiumhortus. com Open Food Network (OFN) is a collaborative global network of local non-profit organizations that have jointly developed Open Source software and made it available to food hubs worldwide through localized online platforms (like OFN UK, OFN Australia, Open Food France, Katuma, OFN Canada, etc. ). This community's mission is to build a shared digital infrastructure (and shared knowledge) to enable short food chain operators to manage their activities, cooperate more efficiently, and build food sovereignty. Just as consumers and producers join together to create food hubs, in OFN, food hubs come together to create digital infrastructure and share its use.
Participation in food safety regulation (India) A good way to keep producers and consumers informed is to ensure their representation in participatory regulatory revision processes. In India, the Food Safety and Standards Authority of India (FSSAI) invites representatives from various civil society organizations (CSOs) to revise the norms of organic regulation. Through this procedure, regulation drafts were sent and CSOs had multiple discussions with the regulator. Many significant changes were accepted in the process, such as the recognition of Participatory Guarantee Systems (PGS) as a valid form of certification, the harmonization of various compliance mechanisms under a unified regulation for organic food, and the creation of an official logo "Jaivik Bharat". The regulator also launched a portal called "India Organic Database Integrity Portal" for consumers to trace food from the certifiers (both third-party and PGS).
Simple Summary: Pancreatic cancer has a dramatic outcome (survival curve < 6 months) that is the consequence of late diagnosis and the lack of efficient therapy. We investigated the relationship between the 22 mucin gene expression and the patient survival in pancreatic cancer datasets that provide a comprehensive mapping of transcriptomic alterations occurring during carcinogenesis. Using unsupervised hierarchical clustering analysis of mucin gene expression patterns, we identified two major clusters of patients: atypical mucin signature (#1; MUC15, MUC14/EMCN, and MUC18/MCAM) and membrane-bound mucin signature (#2; MUC1,. The signature #2 is associated with shorter overall survival, suggesting that the pattern of membrane-bound mucin expression could be a new prognostic marker for PDAC patients.