dataesr/scientific-paragraphs-categorization huggingface dataset:
The present paper is thus considered as a natural sequel to the work presented in  [18]. For performing the numerical simulations in this work we have used our in-house, high-performance, modern software: MMICADO; within its framework, we have first proposed a robust time scheme, where we first explicitly calculate the advection/convection terms. Within the framework of Finite Volume space discretization on 2D Cartesian meshes, the divergence terms in these advection/convection equations are computed by employing a second order accurate, multidimensional MUSCL  [19]  slope reconstruction strategy along with the MLP-UB gradient limiting method. A 1D analysis is also presented to highlight the choice made for scalar reconstruction in their primitive form (instead of their conservative form) by using the most and the least compressive form of MUSCL+MLP-UB approach.
Introduction Regulation of gene expression is essential for a cell to fulfill its basic functions, and its dysregulation can lead to serious failures at the cellular, tissular and organism levels. Transcription levels are not only tightly regulated, but for many genes it has now been 1 demonstrated that their transcripts accumulate in specific regions in the cell, thereby producing intricate localization patterns. Such subcellular targeting of mRNAs is thought to play an important role for the spatial control of gene expression; in which improper RNA trafficking is linked to an increasing number of diseases  (1, 2). However, the function and mechanisms of RNA localization are not fully understood and we lack a view of this process at the transcriptomic scale. RNA abundance and localization can be studied at a large scale by image-based assays, where individual mRNA molecules are visualized by single molecule Fluorescence in situ hybridization  (smFISH).
2 Here, we introduce a Python-based version of our widely adopted software package FISH-quant  (12)  for the analysis of smFISH images. Contrary to FISH-quant v1 in Matlab version, we address and improve on each of the specifications mentioned above. The switch to Python allows us to develop a flexible, free and fully open-source software. FISH-quant v2 enjoys a better integration to other open source tools and frameworks, from data analysis to web-based user interaction. Last but not least, FISH-quant v2 facilitates the use of machine learning or deep learning algorithms with the import of dedicated packages, such as scikit-learn(13) or tensorflow 13. We also improve the scalability and the modularity of the package: the software has now been applied to several High Content Screening projects 14-16.
Big-FISH: the core analysis package The repository Big-FISH contains the entire Python code used for the actual analysis. It is organized in several subpackages performing dedicated steps: -I/O operations, images preprocessing and results postprocessing (bigfish. stack) -mRNA spot detection (bigfish. detection) -nucleus and cell segmentation (bigfish. segmentation) -feature computation and coordinates analysis (bigfish. classification) -Plotting of results (bigfish. plot) and -Application of deep learning algorithms for segmentation (bigfish. deep_learning). Dependencies are limited to standard Python scientific libraries: scientific computing (numpy  (19) , scipy  (20) ), data wrangling (pandas  (21) ), image analysis (scikit-image  (22) ), visualization (matplotlib  (23) ) and machine learning (scikit-learn  (13) , tensorflow  (24) ).
Example datasets Two datasets were used for the development and validation of FISH-quant. First, from a screen studying local translation, consisting of 526 fields of view (Dapi and smFISH channels) from 57 separate experiments (27 different mRNAs under different experimental conditions)  (16). 3D images with a z-spacing of 0. 3 μm were acquired on two different systems: (i) a Zeiss AxioimagerZ1 wide-field microscope equipped with a motorized stage, a camera scMOS ZYLA 4. 2 MP, using 63x and 100x oil objectives, (ii) Nikon Ti fluorescence microscope equipped with ORCA-Flash 4. 0 digital camera (HAMAMATSU). Second, from a screen focusing on local translation of centrosomal mRNAs. The data-set consisted of 3678 fields of view (Dapi, smFISH, CellMask and GFP channels) from 218 experiments  (15).
Results The analysis of smFISH images aims at counting and localizing individual RNAs with respect to single cells and other cellular landmarks. It typically encompasses a number of steps:  (1)  segmenting cells and the relevant cellular compartments such as nuclei (depending on the focus of the study and the markers employed), (2) detecting individual RNA molecules appearing as diffraction-limited spots, (3) assignment of spots to cells and (4) analysis of expression levels and RNA localization patterns  (4, 12, (25) (26) (27) , potentially in combination with other phenotypic features  (15, 28). We designed FISH-quant v2 to match this workflow in a flexible and efficient way. This version is entirely open-source and hosted on GitHub under the FISH-quant organization (Figure  1 , https://github.
com/FISH-quant). It is organized in several resources with dedicated repositories and documentation. First, a Python package (Big-FISH) providing the core code for performing computation and analysis. Second, detailed interactive examples with test data for each analysis step implemented in Jupyter notebooks. These examples can be run directly on Binder  (29) , a free and reproducible Jupyter notebook service, without local installation. Third, ImJoy plugins  (18)  provide a graphical user-interface for the most commonly used workflows, and an interactive tutorial that can also run directly without local installation. Lastly, code from future projects either using or further improving FISH-quant will be also hosted here, creating a valuable, centralized resource for the community. A landing page (https://fish-quant.
Python core analysis package We choose Python for the implementation of the core analysis package for several reasons: it allows the development of a free and fully open-source software, it provides established libraries for data and image analysis and is the language of choice for deep-learning implementations. Lastly, it can be interfaced with other tools and frameworks, from data analysis to web design, for instance with ImJoy  (18)  to provide interactive tools for user interaction and data inspection. Our Python package includes several independent subpackages fitting the described workflow (see Material and Methods for more details): preprocessing, segmentation, detection, and analysis. We designed each subpackages with clearly defined input and output data formats, which will be automatically checked.
This then allows using each of these packages independently in a modular fashion. Users can thus create a customized analysis workflow, starting with pre-processing of images to statistical results. This modular design also permits to easily integrate external methods, for instance, a new segmentation method can be combined with our spot detection algorithm. Here, we will only provide an overview of these submodules (Figure  2 ). For a more detailed description of algorithms and methods, we refer to the detailed documentation (https://big-fish. readthedocs. io/en/stable/) and the dedicated tutorials (https://github. com/fish-quant/big-fish-examples). These tutorials can be run directly in the browser with provided test data, and thus allow new users to immediately test these tools. The described methods were developed and validated with the data from two large-screen smFISH studies (15, 16) (see Material and Methods).
5 The detection module implements the methods required to detect spots in 2D or 3D images (Figure  2 ). While initially designed to detect individual mRNAs, the same methods can also be used to detect other spot-like structures  (15) , such as centrosomes, P-bodies, etc. Strong local accumulation of RNAs, e. g. at sites of local translation  (16) , can lead to an underdetection. For such cases, we provide tools to decompose these dense regions and estimate the number of spots based on our earlier work (see Supplementary Note 1)  (27). An important aspect of the detection subpackage is the ability to detect spots without setting any pixel intensity threshold (see Supplementary Note 1). This parameter can be automatically inferred from the image. Such automatization eliminates human intervention and allows scaling to large datasets, such that the subpackage can process thousands of images.
6 To provide simple access for users with no computational background, we implemented several plugins with graphical user interfaces for our computational platform ImJoy  (18). These plugins provide the most commonly used analysis workflow, as we determined from the usage of the Matlab version of FISH-quant, and will thus be suited for a large number of user cases (Figure  3 ). First, a plugin to perform deep-learning-based segmentation. This is currently built on top of CellPose  (11) , but thanks to our modular design, this can be easily exchanged if more performant methods are available in the future. Second, detection of both isolated and clustered RNA. Detection results can be conveniently inspected with Kaibu image viewer plugin in ImJoy and different detection settings interactively investigated.
Batch processing of entire folders is also possible. Lastly, detection results can be assigned to segmented cells and nuclei. We provide an interactive demo version of this plugin that can run directly in the browser without any local installation (https://fish-quant. github. io/fq-interactive-docs/#/fq-imjoy). Using ImJoy provides several advantages beyond simply providing a user interface. Due to its distributed design that separates GUI from computation plugins, it natively allows user-friendly remote computing which allows access to massive data storage and powerful computation resources including GPUs. ImJoy's is a browser-based app where the user-interface plugin is implemented with JavaScript/CSS/HTML. ImJoy then transparently calls the computation functions in the BigFISH package running on a Python plugin engine (e.
g. Jupyter server) to perform the actual smFISH analysis task (Figure  3 ). While this plugin can run on the local workstation, it can be located in a computational cluster even in the cloud or seamlessly switching between them. As illustrated by the demo version, where the engine is running on Binder  (29). Once the plugin engine is installed on the remote resource, the end-user can connect with ImJoy and will be confronted with the same interface, independently on where the analysis is actually performed. Interestingly, this front-end interface can also be opened with mobile devices, providing easy access. ImJoy plugins implemented in JavaScript can not only provide modern and reactive user-interfaces, but also profit from the extensive JavaScript data visualization libraries to build interactive data-inspection tools.
Such interactivity is becoming increasingly important, especially when large and complex data-sets are analyzed where static plots are too limited. As a case example, we provide an interactive t-SNE plot for the data shown in Figure  2  7 (https://fish-quant. github. io/fq-interactive-docs/#/rnaloc-tsne). This plugin can be run without local installation, and enables the user to explore and interact with these complex data.
Discussion Here, we present FISH-quant v2, a Python-based software for the analysis of smFISH images. It is built around a core-analysis package, implemented following rigorous software development guidelines, with detailed interactive documentation and tutorials. This package consists of several interchangeable modules permitting the construction of highly flexible workflows for specific analysis needs. For standard workflows, we also provide user interfaces in ImJoy accessible to biologists without programming skills, which can be used locally or scaled to larger remote computational resources. As demonstrated in two recently published studies  (15, 16) , FISH-quant can be used for large screening data sets thanks to its scalability. Spot detection, segmentation, feature engineering and pattern recognition can be performed over thousands of cells without fine-tuning parameters for every image.
We designed FISH-quant v2 based on the successful previous implementation in Matlab  (12)  integrating new features and user feedback we obtained from several projects over several years. The entire core package is written in Python since this allowed us to address the above-mentioned requirements for a smFISH analysis tool. We use established scientific libraries (see Methods), and kept these dependencies to a minimum facilitating installation, maintenance and the integration with other analysis frameworks. These libraries are developed, validated and maintained by a large scientific community, ensuring long-term support and availability. We further use strict version control, guaranteeing reproducibility. Lastly, all dependencies, as well as FISH-quant, are open-source, thus can be used free of charge, both on local and remote computational infrastructures, and thus analysis can easily be scaled to larger data volumes.
The organization of the analysis modules in the core package matches key steps in smFISH image analysis, but with a special focus on flexibility. All modules (Preprocessing, RNA detection, segmentation as well as data inspection and analysis) can be run independently or replaced by external code, by respecting a strict data format. This allows FISH-quant to be adapted to the respective analysis needs, and build custom workflows. 8 While this flexibility is important, many users require a standard workflow and don't have programming experience. For these cases, we provide ImJoy plugins with a convenient user interface running in the browser  (18). These interfaces are built with modern web libraries and are thus intuitive, and no experience in Python is required to analyze data. Lastly, these ImJoy plugins can be readily extended by more experienced users to further adapt them to their needs.
Participants A pilot sample of 26 patients seeking treatment for GD was obtained for the present study. Data from participants were collected from December 2018 to May 2019 by means of a purposive sample recruitment procedure. Consecutive patients who began cognitive-behavioral treatment for GD were offered the opportunity to participate in the study, and all participants who agreed were selected and provided that they met the inclusion and exclusion criteria. Due to the observational nature of this pilot study, self-selection bias and lack of randomization were not deemed significant as the results at this stage were not to be compared to a control group. In order to participate, the following exclusion criteria were considered: (a) the presence of a psychiatric or neurological disorder such as schizophrenia or other psychotic disorders that might impact game performance, (b) an intellectual disability, and (c) active pharmacological therapy that might interfere with game performance.
FUNDING This research was supported by grants from the  CERCA Programme/ Generalitat de Catalunya  for institutional support. It was additionally supported by grants from the  Ministerio de Economía y Competitividad  ( PSI2015-68701-R ), and from the  Ministerio de Ciencia, Innovación y Universidades  (grant  RTI2018-101837-B-100 ). The research was funded by the  Delegación del Gobierno para el Plan Nacional sobre Drogas  ( 2017I067  and  2019I47 ),  Instituto de Salud Carlos III (ISCIII ;  PI17/01167 ) and co-funded by  FEDER funds/European Regional Development Fund (ERDF) , a way to build Europe. CIBERObn and CIBERSAM are both initiatives of  ISCIII. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. TM-M and ML-M are supported by a predoctoral Grant of the  Ministerio de Educación, Cultura y Deporte  ( FPU16/02087  and  FPU15/02911 ).
This paper investigates the potential of a Wasserstein generative adversarial network to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is proposed for the generator and trained on a synthetic climate database, computed using a simple three dimensional climate model: PLASIM. The generator transforms a "latent space", defined by a 64-dimensional Gaussian distribution, into spatially defined anomalies on the same output grid as PLASIM. The analysis of the statistics in the leading empirical orthogonal functions shows that the generator is able to reproduce many aspects of the multivariate distribution of the synthetic climate. Moreover, generated states reproduce the leading geostrophic balance present in the atmosphere.
Gulrajani et al. (2017)  trained a WGAN on the ImageNet database  (Russakovsky et al. , 2015) , which contains over 14 million images with 1000 classes, and suc-Published by Copernicus Publications on behalf of the European Geosciences Union & the American Geophysical Union. cessfully learned to produce new realistic images. Several techniques developed for computer vision with GANs seem promising for domains in the geosciences. Notable examples of usage to date include  Yeh et al. (2017)  to do inpainting, where the objective is to recover a full image from an incomplete one,  Ledig et al. (2017)  to do super resolution, or  Isola et al. (2017)  to do image-to-image translation, where an image is generated from another one, e. g. , translate an image that contains a horse into one with a zebra.
g. , the solar annual cycle. While χ should stand for continuous multivariate fields, we consider its discretization in a finite grid so that χ ∈ X with X = R n , where n denotes the dimension. Equation (1) describes a chaotic system. The climate is the set of states of the system along its time evolution. It is characterized by a distribution or a probability measure, denoted p clim. Obtaining a complete description of p clim is intractable due to the complexity of natural weather dynamics and because a climate database, p data , is limited by numerical resources and is only a proxy for this distribution. For instance, in the present study, the true weather dynamics M are replaced by the PLASIM model that has been time-integrated over 100 years of 6 h forecasts. Accounting for the spinup, the first 10 years of simulation are ignored.
Thus, the climate p clim is approximated from the resulting climate database of 90 years, p data. The synthetic dataset is presented in detail in Sect. 3. 1. Thus, p data lives in the n-dimensional space X, but it is non-zero only on an m-manifold M (where m n) that can be fractal. The objective is to learn a mapping g : Z -→ X (2) from Z = R m , the so-called latent space, to X. Moreover, g must transform a Gaussian N (0, I m ) to p data ⊂ M. The main advantage of such a formulation is to have a function g that maps a low-dimensional continuous space Z to M. This property could be useful in the domain of the geosciences, notably in the climate sciences, where a highdimensional space is ruled by important physical constraints and parameters. Here the generator is a good candidate for learning the physical constraints that make a climate state realistic without the need to run a complete simulation.
e. , for any (x 1 , x 2 ) ∈ R n , |f (x 1 ) -f (x 2 )| ≤ ||x 1 -x 2 ||, || • || being the Euclidian norm of R n. For any 1 -Lipshitzian function f the computation of Eq. (  5 ) is simple: the first expectation can be approximated by E x∼p clim f (x) ≈ E x∼p data f (x) , (6) where the right-hand side is computed as the empirical mean over the climate database p data that approximates p clim in the weak sense Eq. (  6 ). The second expectation can be computed from the equality E x∼p θ f (x) = E z∼N (0;I m ) f (g θ (z)) , (7) where the expectation of the right-hand side can be approximated by the empirical mean computed from an ensemble of samples of z which are easy to sample due to the Gaussianity. However, there is no simple way to characterize the set of 1 -Lipshitzian functions, which limits the search for an optimal function in Eq.
Critic network The critic network input has the shape of a sample from the dataset X ∈ R nlat×nlon×nfield. Its output must be a real number because it is an approximation of the Wasserstein distance between the distribution of the batch of images from the dataset and the one from the generator that is being processed. The architecture ends with a dense layer of one neuron with linear activation. The core of the structure is taken from the residual network and can be seen in Fig. 1. After the custom padding layers mentioned previously, the critic architecture is a classical residual network, starting with a convolution with 7 × 7 kernels, followed by a maximum pooling layer to reduce the image size and a succession of convolutional and identity blocks (Figs. 2  and 3 ). At each strided convolutional block, s = 2 in Fig.
Generator architecture The input of the generator network (see Fig. 4 ) is an mdimensional vector containing noise drawn from the normal distribution N m (0, I m ) for the numerical experiment m = 64. The output of the generator has the shape of a sample of the dataset X ∈ R nlat×nlon×nfield. The input is passed through a fully connected layer of output shape (8, 16, 128) and fed to residual blocks. The rest of its architecture is also a residual network with a succession of modified convolutional blocks (relative to the one in the critic network). Modifications of the convolutional block are the following. 1. An upsampling layer is added to increase the image size for some convolutional blocks. 2. Wrap and nearest padding layers are added in, respectively, the west-east and north-south directions.
Consequently, a quality check using metrics adapted to the domain on which the GAN is applied is still necessary. Moreover, at the end of the training, a first experiment was conducted to see whether the generations are present in the dataset. The histogram of the Euclidian distance divided by the number of pixels in one sample between one generation and all of the dataset can be seen in Fig. 7. Here, one can see that the minimum is around 0. 8, which shows that the generated image is not inside the dataset. This experiment shows that the generator is able to generate samples without reproducing the dataset. It should be noted that in the WGAN https://doi. org/10. 5194/npg-28-347-2021 Nonlin. Processes Geophys. , 28, 347-370, 2021 framework, the generator never directly sees a sample from the dataset.
There are no stopping criteria for the training, and it was stopped after 35 000 iterations in the interest of computational cost. It should be highlighted that the performance of generative networks and especially GANs is difficult to evaluate. In the deep-learning literature, the quality of the images generated is assessed using a reference image dataset such as ImageNet  (Russakovsky et al. , 2015)  and computing the inception score (IS) or the Fréchet inception distance (FID). Both use the inception network trained on ImageNet: the IS measures the quality and diversity of the images by classifying them and measuring the entropy of the classification, while the FID computes a distance between the features extracted by the inception network and is more robust to GANmode collapse. Because our study does not apply to the ImageNet dataset, it is necessary to compute our own metrics.