dataesr/scientific-paragraphs-categorization huggingface dataset:
It is common practice to assess power before embarking on association studies  [34, 35] , but no tool is currently available for assessing power for detecting AI and differences in AI between conditions. To address this need, we present here the package BayesASE_power. It consists of tools to enable the user to simulate RNA-seq read counts under a previously published Bayesian model of AI  [7, 20]  with any number of replicates, reads, and AI. The results are aggregated across multiple simulated datasets to estimate Type I error and power. We demonstrate how to use BayesASE_ power to plan experiments to achieve the desired power in detecting AI within a condition and/or interactions of AI between conditions.
Methods The model used for detection of AI in any condition and for comparing levels of AI between any two conditions has been described earlier and implemented in the package BayesASE  [7, 22]. We give here the basic definitions and refer the reader to Additional file 1 for further details. One important parameter in determining AI is the probability r of a read aligning to allele g1 (g2) given that it came from that allele, that we define as r i,g1 ( r i,g2 ). Low values of these probabilities correspond to a high degree of ambiguously mapped reads, which occurs when there is little sequence divergence between the two alleles. Reads that do not map ambiguously are termed allele specific reads or informative reads. AI in condition i is measured by the parameter θ i rep- resenting the proportion of reads originating from the allele g1, which that can be written as follows: θ i = E(x i,k /r i,g1 ) E(x i,k /r i,g1 + y i,k /r i,g2 ) = 1/α i α i + 1/α i When θ i is close to 0, we have one extreme case of AI with almost all the reads originating from g2.
Simulated deviations of AI from the null are moderate, generally between 0. 1 and 0. 3, with a maximum of 0. 5. Scenarios that vary the number of allele specific reads, number of replicates, and AI in the different cases were simulated (Additional file 2). The model used for the detection of AI in any condition and for comparing levels of AI between any two conditions has been described earlier and implemented in the package BayesASE (https:// github. com/ McInt yre-Lab/ Bayes ASE)  [7, 22].
Availability of data and materials This study was performed using programs written in Python and R that are available using the MIT license as the package BayesASE_power: https:// github. com/ McInt yre-Lab/ Bayes ASE_ power. The package requires the installation of BayesASE available on PyPI: https:// pypi. org/ proje ct/ Bayes ASE/. All the additional files are available at https:// osf. io/ sw3r2/, with the following https:// doi. org/ 10. 17605/ OSF. IO/ SW3R2.
The SMA is a joint project between the SAO and ASIAA and is funded by the Smithsonian Institution and the Academia Sinica. The JCMT is operated by the East Asian Observatory on behalf of the NAOJ, ASIAA, and KASI, as well as the Ministry of Finance of China, Chinese Academy of Sciences, and the National Key R&D Program (No. 2017YFA0402700) of China. Additional funding support for the JCMT is provided by the Science and Technologies Facility Council (UK) and participating universities in the UK and Canada. The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica, y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA), with financial support from the Consejo Nacional de Ciencia y Tecnología and the National Science Foundation. The IRAM 30-m telescope on Pico Veleta, Spain is operated by IRAM and supported by CNRS (Centre National de la Recherche Scientifique, France), MPG (Max-Planck-Gesellschaft, Germany) and IGN (Instituto Geográfico Nacional, Spain).
The SMT is operated by the Arizona Radio Observatory, a part of the Steward Observatory of the University of Arizona, with financial support of operations from the State of Arizona and financial support for instrumentation development from the NSF. The SPT is supported by the National Science Foundation through grant PLR-1248097. Partial support is also provided by the NSF Physics Frontier Center grant PHY-1125897 to the Kavli Institute of Cosmological Physics at the University of Chicago, the Kavli Foundation and the Gordon and Betty Moore Foundation grant GBMF 947. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. The EHTC has received generous donations of FPGA chips from Xilinx Inc. , under the Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER).
The EHT project is grateful to T4Science and Microsemi for their assistance with Hydrogen Masers. This research has made use of NASA's Astrophysics Data System. We gratefully acknowledge the support provided by the extended staff of the ALMA, both from the inception of the ALMA Phasing Project through the observational campaigns of 2017 and 2018. We would like to thank A. Deller and W. Brisken for EHT-specific support with the use of DiFX. We acknowledge the significance that Maunakea, where the SMA and JCMT EHT stations are located, has for the indigenous Hawaiian people. The grants listed above collectively fund the Event Horizon Telescope project. 1 Methods.
1 GHz and 229. 1 GHz, respectively. Unless stated otherwise, results are derived using the combined low+high band data. Quarter-wave plates at each site except ALMA were used to observe circularly polarized light. The data were correlated with the DiFX software  36, 37. The PolConvert 38 software was used to convert the phased ALMA data  34  from a linear polarization basis to a circular basis after correlation, based on solutions from the calibration of the connected-element ALMA data  39.
Data reduction pipelines The autocorrelation normalization, feed angle rotation, fringe fitting, bandpass calibration, and a priori correction of atmospheric phase turbulence  40  were performed independently by the CASA-based rPICARD  9, 41  and EHT-HOPS  10, 42  pipelines. DiFX produces FITS-IDI and Mark4 data. rPICARD uses the FITS-IDI product and converts it into the Measurement Set format. EHT-HOPS uses the Mark4 data. Both software packages convert the calibrated data into the UVFITS format for further processing. rPICARD performs an upstream correction for the feed rotation angle and uses station-based global fringe fitting based on an unpolarized point source model to correct for phases, delays, and rates consistently for the right circular polarization and left circular polarization signal paths  43.
Imaging In this section, we describe how we obtained our image model from the observational data. In a first step, we have established a blind consensus between different imaging methods. Then, we have fine-tuned the parameters of one method, eht-imaging  46, 47  , for the rPICARD and EHT-HOPS data to obtain final images for the analysis of the Cen A jet structure. The highest resolution images of this southern source prior to this work were obtained within the TANAMI program 31 at 8 and 22 GHz with a maximum resolution of 400 µas, showing an extremely collimated structure with multiple distinct radio knot emission regions  3. In a previous single-baseline non-imaging study of Cen A, a bright compact core was detected at 215 GHz 48.
Final imaging method With the imaging challenge, we have established that different methods converge towards the same robust source structure (Supplementary Fig. 5 ), independent of shared human bias. Further imaging analysis of the rPICARD & EHT-HOPS science release data was pursued with the final M 87* eht-imaging script  49  , which is based on application of a regularized maximum likelihood method that includes a maximum-entropy term. Using a secondmoment-based pre-calibration, LMT gains were stabilized with respect to the better constrained SMT amplitudes  57. As Cen A is sufficiently compact within the EHT beam, the short LMT -SMT baseline measures a Gaussian-like source structure. We have performed an initial self-calibration to a Gaussian with size Θ maj × Θ min at a position angle Θ PA and with a total flux of S G.
In fact, the ∼ 150 m JCMT-SMA baseline sees a flux density of about 5 Jy and at 2 km, ALMA-APEX recovers only ∼ 4 Jy. For M 87*, the EHT measured Z 0 ≈ S 0 /2  49. The numerical values of the final imaging parameters are given in the supplementary material (Table  1 ). Optimal parameters were chosen based on an empirical minimization of χ 2 D , median station gains A (sc) from self-calibration, and patches of spurious flux in the image. Additionally, we took the similarity of image reconstructions from the rPICARD and EHT-HOPS data for a given set of parameters into account to avoid over-fitting to data peculiarities that result from assumptions made during the data calibration. A variety of images that can be reconstructed with various combinations of the free imaging parameters can be shared upon reasonable requests.
We have chosen for an eht-imaging reconstruction of the rPICARD data for our final image, as this imaging method and data set have been studied most extensively. Our images are shown in units of brightness temperature T [K], which is related to a flux density S in Jy through the observing wavelength λ, Boltzmann constant k B , and angular resolution element Ω as T = λ 2 (2k B Ω) -1 S. Fundamental data properties and fits of the final image model to the data are shown in Supplementary Fig. 7. In Supplementary Fig. 8 , we show the measured amplitudes projected along and perpendicular to the jet PA. Along the jet axis, amplitudes fall off quickly at long projected baseline lengths, indicating the absence of substructures along the jet. Perpendicular to the jet, 'bouncing' amplitudes out to large projected baseline lengths occur, due to the strong intensity gradients across the transverse jet profile.
Synthetic data imaging tests We have used the SYMBA software  60  to perform imaging studies based on simulated observations. Given an input source model M, SYMBA follows the entire EHT signal path to predict which source structure I would eventually be reconstructed. Thereby, we can assess how close our image reconstruction comes to the ground truth structure M of a fabricated observed source. SYMBA simulates the parameters of the Earth's atmosphere with the ATM module  61  to add sky noise, signal attenuation, and phase turbulence. Next, gain, leakage, pointing, and focus errors plus thermal noise are introduced for each telescope in the array based on known telescope properties  62. Afterwards, the simulated corrupted data are calibrated by rPICARD in the same way as observational data.
The u, v coverage and SEFD sensitivities are taken from the 2017 Cen A EHT observation track. The simulated calibrated data are then imaged with the same final eht-imaging script used to image the observational data in this work. To assess the robustness of secondary features in our image reconstruction, we have performed three synthetic data tests (Supplementary Fig. 4 ). Firstly, a control study to demonstrate that the output reconstruction from SYMBA correctly matches the input model M final. Then, we have removed the counterjet and emission features at large distance to the apex z from M final to verify that these do not spuriously appear in our simulated observation. Furthermore, we have explored the upper limit on the brightness T sp of potential emission from the jet spine by adding a weak emission component in the central jet region to M final.
Data availability The ALMA raw visibility data can be retrieved from the ALMA data portal under the project code 2016. 1. 01198. V. The calibrated Stokes I VLBI visibility data of Centaurus A can be obtained from a DOI listed under https: //eventhorizontelescope. org/for-astronomers/data with the code 2021-D03-01. Image FITS files and scripts to reproduce the plots are available from the corresponding author upon reasonable request.
Code availability Antenna gains that enter the SEFDs were computed with https://bitbucket. org/M_Janssen/ eht-flux-calibration. The SEFDs were applied with the https://github. com/sao-eht/eat code, which also contains the EHT-HOPS pipeline. rPICARD is hosted on https://bitbucket. org/M_Janssen/picard. Configuration and run files, which make use of self-contained Docker images are at https://bitbucket. org/M_Janssen/casaeht. This work is based on the 'ER6' data production scripts, for which the 30e6ca14fb50275013c668285a3b476f9bc85436 91da63236db34f3a31b5309b18ac159128f28a35 image was used. The eht-imaging software is hosted on https://github. com/achael/eht-imaging. SYMBA is at https://bitbucket. org/M_Janssen/symba. The docker image used here is tagged as dec65699ccc0acdc6e6ba8f218d6724537fc613a and can be found on https://hub.
Acknowledgments The authors of the present paper thank the following organizations and programs: the  Academy of Finland  (projects  274477 ,  284495 ,  312496 ,  315721 ); the  Agencia Nacional de Investigación y Desarrollo (ANID) ,  Chile  via  NCN19 058  (TITANs) and Fondecyt 3190878; the  Alexander von Humboldt Stiftung ; an Alfred P. Sloan Research Fellowship; Allegro, the  European ALMA Regional Centre node in the Netherlands , the  NL astronomy research network NOVA  and the astronomy institutes of the  University of Amsterdam, Leiden University and Radboud University ; the  Black Hole Initiative at Harvard University , through a grant ( 60477 ) from the  John Templeton Foundation ; the  China Scholarship Council ;  Consejo Nacional de Ciencia y Tecnología  ( CONACYT, Mexico , projects  U0004-246083 ,  U0004-259839 ,  F0003-272050 ,  M0037-279006 ,  F0003-281692 ,  104497 ,  275201 ,  263356 ,  57265507 ); the  Delaney Family via the Delaney Family John A.
We further thank the  Leverhulme Trust Early Career Research Fellowship ; the  Max-Planck-Gesellschaft (MPG) ; the  Max Planck Partner Group of the MPG  and the CAS; the  MEXT/JSPS KAKENHI  (grants  18KK0090 ,  JP18K13594 ,  JP18K03656 ,  JP18H03721 ,  18K03709 ,  18H01245 ,  JP19H01943 , 25120007); the  Malaysian Fundamental Research Grant Scheme  ( FRGS) FRGS/1/2019/STG02/UM/02/6 ; the  MIT International Science and Technology Initiatives (MISTI) Funds ; the  Ministry of Science and Technology (MOST) of Taiwan  ( 105-2112-M-001-025-MY3 ,  106-2112-M-001-011 ,  106-2119-M-001-027 ,  107-2119-M-001-017 ,  107-2119-M-001-020 ,  107-2119-M-110-005 ,  108-2112-M-001-048 , and  109-2124-M-001-005 ); the  National Aeronautics and Space Administration  ( NASA, Fermi Guest Investigator  grant  80NSSC20K1567 ,  NASA   Astrophysics Theory Program  grant  80NSSC20K0527 ,  NASA  grant  NNX17AL82G , and  Hubble Fellowship  grant  HST-HF2-51431.
043. 513 ) and  Spinoza Prize SPI   78-409 ; the  New Scientific Frontiers  with  Precision Radio Interferometry Fellowship  awarded by the  South African Radio Astronomy Observatory (SARAO) , which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Innovation (DSI) of South Africa ; the  South African Research Chairs Initiative of the Department of Science and Innovation and National Research Foundation ; the  Onsala Space Observatory (OSO)  national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the  Swedish Research Council  under grant  2017-00648 ) the  Perimeter Institute for Theoretical Physics  (research at  Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science and Economic Development  and by the  Province of Ontario  through the  Ministry of Research, Innovation and Science) ; the  Spanish Ministerio de Economía y Competitividad  (grants  PGC2018-098915-B-C21 ,  AYA2016-80889-P ,  PID2019-108995GB-C21 ); the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ); the  Toray Science Foundation ; the  Consejería de Economía, Conocimiento ,  Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  US Department of Energy (USDOE)  through the  Los Alamos National Laboratory  (operated by  Triad National Security, LLC , for the  National Nuclear Security Administration of the USDOE  (Contract  89233218CNA000001 ); the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No 730562  RadioNet ;  ALMA North America Development Fund ; the  Academia Sinica ; Chandra  TM6-17006X  and  DD7-18089X ; the  GenT Program (Generalitat Valenciana)  Project  CIDEGENT/2018/021.
Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of "big data" in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows.
Introduction Workflows are the keystone of bioimage analysis,  1, 2  and the NEUBIAS community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows  3  and associated resources. In this paper, we aim to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. A bioimage analysis workflow is defined as a set of computational tools assembled in a specific order to process bioimages and estimate some parameters relevant to the biological system under study. To classify these computational tools, in the NEUBIAS community, we have defined these terms: workflows, components and collections  1, 4  as follows.
A workflow is built as a sequence of components coming from one or multiple software packages. It takes an image as input and outputs processed images, numerical values and/or annotations (e. g. biological objects outlines). A component is the software implementation of an image processing or analysis algorithm. We call collection the software package that gathers components and can be in the form of a generalist software platform such as ImageJ and Fiji,  5  Icy,  6  CellProfiler:  7  specialized platforms, such as analyzing a specific modality of microscopy e. g. super resolution image data;  8  or computationally optimized libraries such as ImgLib2  9  or ITK. 10  Most of the time, standalone components cannot solve complex bioimage analysis problems on their ownthat is why they need to be carefully assembled.
Finding workflows or components for a specific biological problem or task The first challenge in the creation of a workflow is to avoid duplicating the effort and being able to easily find and customize a workflow that has been used for a similar biological problem. Today, browsing the documentation of bioimage analysis tools, or asking a specific question in a generic forum, such as the newly created Image. sc forum,  12  will help guide the biologist or microscopist to existing tools. We believe that while this can be a good starting point it may not be sufficient. The NEUBIAS training courses  13, 14  and the NEUBIAS Academy (see  15  in this Gateway) are two of the educational resources that can also help finding and adapting existing workflows. Exposing tools and workflows in a knowledge database has also been identified as very useful by the community.
tools,  18, 19  which has gathered more than 20000 software tools in the full range of life science applications, or for more specific application topics, for example Quantitative Plant, which focusses on tools for the analysis of image data of plants  20  or BioImage. io for pre trained AI deep learning models. By feeding the description of a workflow in the knowledge database BIII (following the recommendation provided), and thanks to workflow/tools interoperability standards, these workflows can be found by other bioimage analysts or automatically discovered and consumed by other registries, such as bio. tools, to reach a broader community.
Comparing workflows Once a candidate workflow has been found, the natural question is then if it is the best solution for the particular task one wants to solve. Table  2  shows three examples of resources comparing workflows. BIAFLOWS  21  is an open-source web platform to reproducibly deploy and publicly benchmark image analysis workflows with a strong focus on microscopy bioimages. The database stores scientific datasets, metadata, and versioned image analysis workflows with parameters optimized for the corresponding datasets. The workflows can be run remotely. The results (e. g. object annotations) from different workflows (or from runs with different parameter values) can be visualized remotely as an overlay on the original images. When the images hold reference annotations, the results are automatically benchmarked by commonly adopted benchmark metrics targeting one of the nine currently supported problem classes.
The benchmark metrics of each workflow run can be browsed per image or as overall statistics over whole datasets. BIAFLOWS brings an automated mechanism leveraging DockerHub to encapsulate, version and make the workflows and their complete execution environment available upon every new release. Overall, BIAFLOWS enables integration and web-based evaluation of heterogeneous workflows originally written for diverse languages and libraries. The Grand Challenge is a website cataloguing a set of challenges, focusing mostly on medical imaging. These challenges are usually hosted by a conference such as IEEE ISBI and run as an annual edition with specific reporting  22, 23  and they gather and evaluate competing workflows to solve a common bioimage analysis task. In the microscopy imaging communities, a particular effort has gone towards nuclei segmentation with the goal of developing a universal nuclei segmenter that works across different imaging modalities, as for instance with the Kaggle Data Science Bowl of 2018, providing a considerable amount of annotated data.
Towards reproducibility and interoperability in bioimage analysis The current paradigm for bioimage analysts is to create workflows using a single platform or application, aka collection, for example Fiji,  25  CellProfiler,  7  or Icy. 6  allowing the possibility to script a workflow calling their components with simplified programming language, these platforms offer ways to share and document the workflows for other users. Besides script creation, there are also options to create sharable elements with no programming skills, as detailed in. 26 This only requires the deployment of the software package to be run. This reliance on graphical user interfaces favors the development of components built for a single collection. While this has stimulated the gathering of active communities around these collections, the coexistence of many multifunctional collections that are developed independently is not ideal for cloud deployment and FAIR principles.