dataesr/scientific-paragraphs-categorization huggingface dataset:
4 As a result of this search, we selected ten workflow systems to complement the selection of articles from the first search. To complement these two sets of research works, we also searched for literature reviews addressing tools and techniques for distributed biomedical analyses. The terms used during the search were "distributed biomedical tools", "genomic analysis", and "distributed biomedical analyses". We then filtered out works unrelated to scientific collaborations, data analysis, or biomedical collaborations. We ended up with a set of 53 papers. Finally, we have performed a cross-reference analysis from these sets of articles, looking for papers being cited or citing the papers in the sets. We have then filtered out the referencing papers using the same criteria applied to the original set.
The input data for a specific task may be prepared by the previous task. This is, however, not always the case: in general, the edges represent data flows (or better data dependencies) only implicitly. Finally, some workflow description languages provide a graphical user interface (see Galaxy  [52]  and Kepler  [9] ), while others, such as Snakemake  [67]  and Nextflow  [121] , use scripting languages. Table  2  shows the most important workflow systems, classified with respect to the type of graph supported for the definition of the workflows, their user interface, and the specification language used or generated by the tool. The column Workflow graph indicates if the tool supports the definition of workflows using Directed acyclic graphs (DAG) or Directed Cyclic Graphs (DCG). The user interface column indicates how the workflow definition is specified via a textual interface or a graphical one to represent the tasks and the control flow.
Industry standards improve communication among researchers and facilitate exchanging knowledge through platforms such as myExperiment, BioSharing, and WorkflowHub. Table  3  presents the three features thato characterize the support for experiment reproducibility in the SWFMSs. The first column indicates if the system provides some data provenance mechanism. SWFMSs labeled with 'Yes' comply in some way with practices and recommendations, like those proposed by World Wide Web Consortium (W3C)  [86] , or they provide some proprietary provenance strategy. For example, Taverna represents metadata at the dataflow level based on the PROV 6 language defined by W3C. However, the information stored does not provide details of intermediate data during the workflow analysis  [115]. On the other hand, Kepler records all details during the workflow execution, including data evolution and step details.
Provenance features also enable researchers to debug transformations, e. g. , to search for errors. However, there are still many open challenges regarding data provenance; for example, incomplete data provenance records limit the reproducibility of workflows  [64]. The column "WF Exchange" shows the supported formats for exchanging workflow definitions or their components. Pegasus abstractly describes the workflow using a DAX format (Directed Acyclic Graph in XML) up to version 4. 0, and the following versions use a serialization format YAML. Galaxy is based on a format that is not readable and a more recent, experimental one. In Taverna, the workflow and its components are represented using the SCUFL language based on the XML format. Kepler exports the workflow components to the Kepler Archive (KAR) file based on the JAR file format from Java.
Knime exports to format KNWF; it is a KNIME Workflow data based on an XML/ JSON format. Nextflow has its own format that represents the specification of the workflow based on Groovy programming language. Swift, through the Swift language, represents the workflows in a format based on C-like syntax. Triana defines the workflows using files based on XML representing the name, specifications, and parameters. Snakemake uses Snakefiles, Python-based rules including input and output data between them. Wings expresses the workflow components and dependencies in workflow templates using the Resource Description Framework (RDF) and the Semantic Web Rule Language (SWRL). Finally, the table shows if a platform supports annotations in the workflow. Annotations help scientists to understand the design of the experiment.
The second column, Language Binding, enumerates the programming language that is supported by each tool. A system with more language bindings is supposed to provide better interoperability properties. For example, Galaxy provides Python, PHP, Java, and JavaScript libraries, allowing researchers to define their analytical experiments in their preferred language. Knime, on the other hand, supports the inclusion of Python scripts as custom code in the workflow steps defined in their graphical user interface but does not provide language bindings to use the tool from a programming language. Similarly, Taverna and Triana enable customizing Java code through the graphical interface during the workflow design. In contrast,.
[33]. In contrast, Li et al. [74]  claim that such cost minimization strategies are insufficient and propose a two-stage approach, first preallocating datasets to specific data centers during workflow build-time, and then dynamically distributing newly generated datasets at runtime. Other authors have proposed other heuristics to address the placement problem  [24, 43, 136, 148]. However, finding the optimal data placement strategy is an NP-hard problem  [79]. Studies on computation placement are rare: very few approaches address the problem of selecting the best computation facility for a given task, and most rely on cloud environments guaranteeing the availability of (almost) arbitrary computational resources required by collaborative scenarios. Similarly, studies relying on MapReduce and workflow systems lack the functionality necessary for multi-site processing  [41, 98].
In addition, Pegasus uses DAGMan  [117]  and HTCondor  [120]  to model task-based workflows submitted to a pool of resources in HPC clusters. Kepler  [9]  includes prebuilt components (referred to as actors) to model external data sources and grid facilities. Finally, Taverna uses the SCUFL language to explicitly define the data flow between processors to model the passing of data between services associated with bioinformatic atomic tasks. Pegasus and Triana implement the strategy replica location service (RLS)  [143]  that allows access to information about copies in different physical locations to support scalability, reliability, and security during distributed executions. The same approach is used by the Giggle (GIGa-scale Global Location Engine)framework  [25]. The second column of Table  5  shows the classification of BATTs according to their data location and computation strategies.
Taverna, for example, provides simple security mechanisms, such as authentication based on web services. Similarly, Galaxy offers limited capabilities such as libraries to make a secure connection through a web API. If the tool provides a more advanced security mechanism, we mark it with two-plus symbols (++). For example, some BATTs provide explicit mechanisms to secure data during storage, transfer, and processing. For instance, in Kepler, the Security Analysis Package (SAP) provides information security mechanisms such as input validation, data integrity, and remote access validation. The package triggers an alarm when a potential alteration of the information is detected  [65]. However, some researchers have pointed out that the SAP package is not yet part of the current release of Kepler due to its runtime overhead  [104].
Stand-alone solutions on a single machine. Stand-alone applications are used to process small datasets on a single machine. For example, CAFE implements alignment-free sequence analysis on singlemachine architecture. Decades ago, DNA sequences were aligned using algorithms based on dynamic programming, but this strategy turned out to be inefficient over time due to the length of the investigated sequences. More generally, due to a large amount of data to be analyzed in many cases, stand-alone applications are rarely used nowadays. Therefore, implementations based on parallel approaches such as MPI techniques, GPU computing, or MapReduce frameworks are preferred nowadays. Similarly, other biomedical applications have migrated to parallelization techniques, for example, short reads assembly or SNP identification  [57, 88, 108].
Need for fully distributed collaborations This section has identified several research opportunities in data and computation placement, data privacy and security, and performance and scalability. These areas of investigation make sense for fully distributed collaborations only when they are considered together. In the discussion, we explain how to achieve fully distributed collaborations. First, the CAP theorem  [51]  applied in the same logic to distributed systems indicates that it is impossible simultaneously to have the three properties in a distributed system: consistency, availability, and partition tolerance. Therefore, a trade-off has to be enacted between the three properties to achieve a practical system supporting a distributed database in an FDC system. Similarly, the scalability trilemma  [10]  states that highly distributed systems cannot simultaneously combine: decentralization, scalability, and security.
Acknowledgments I would like to express my gratitude to supervisors  Mario Südholt  and  Luis Daniel Benavides  for their support and guidance during my research work. Additionally, to the co-authors Alban Gaignard and  Richard Redon  for their contribution to the paper from their biomedical experience. My doctoral research is collaboratively financed by the  Escuela Colombiana de Ingeniería (Bogotá -Colombia ) and the  IMT Atlantique (Nantes -France). I thank both universities  for the agreement established to fund my doctoral thesis research.
1 ). In this study, the multi-dynamic software ADAMS is used to establish the rigid-flexible coupling model of the support. The operation height of the support is set to 7. 2 m in the simulation model. The base is defined as rigid and bonded to the ground (the lower bottom surface of the base is constrained); the canopy, goaf shield, and the front and rear bars are defined as flexible using Hypermesh. The friction contact mode is adopted for the connection hinge joints to fully consider the dynamic behaviour (the friction coefficient is set as 0. 3). The density, Young's modulus and Poisson's ratio of the structural parts is defined as 7860 kg/m³, 2. 1e 11 Pa and 0. 3, respectively. The front and rear leg system are equivalently replaced using a spring-damper system; the stiffness of the legs are defined in Section 2.
Numerical experiments. In this section, we present a variety of numerical experiments that show the effectiveness and efficiency of the proposed preconditioner. First, we compare it against state-of-the-art algebraic multigrid preconditioners including AGMG  [34, 35] , BoomerAMG  [14] , and GAMG  [1]. Then, we include numerical experiments where the proposed preconditioner is used to solve coarse problems from other multilevel solvers, thus emphasizing the algebraic and robust traits of our method. Except for AGMG which is used through its MATLAB interface, all these experiments are performed using PETSc  [7]. In particular, the proposed preconditioner is a natural extension of the PCHPDDM infrastructure  [24]  which we use to solve the concurrent generalized eigenvalue problems from  (3. 3)  via SLEPc  [20] , and then to define our multilevel preconditioner by selecting the appropriate local eigenmodes depending on the user-specified value of τ.
Altogether, accumulated evidence has shown high heterogeneity within ASD in the participation of functional brain networks and behavioral manifestations, but also, in the longitudinal trajectories at the single subject level. Moreover, in relation to neuroimaging studies, recent work has shown additional sources of heterogeneity due to variations in the diagnostic and inclusion criteria, and differences in the processing neuroimaging pipeline  31, 32. ASD is also a polygenic highly heterogeneous condition, with 1010 different genes being associated with ASD as of July 8th 2022 according to the SFARI gene human-database, see also  33. Of those, 213 have a relevance score of 1, meaning having maximum pathophysiological published evidence in relation to ASD. This high genetic complexity is also another manifestation of the heterogeneity in this condition.
Participants A total of N=2156 subjects from the ABIDE I  48  and ABIDE II  53  repositories were initially considered in this study, of which 1026 were ASD patients and 1130 were typically developing control (TDC) subjects. These data were collected across 35 different scanning Institutions. For each participant both anatomical and functional MRI data were used. Acquisition parameters for each scanning site are found at http://fcon_1000. projects. nitrc. org/indi/abide/. In addition, we also used the cognitive performance and disease severity information from the Autism Diagnostic Observation Schedule-Generic (ADOS-G), Autism Diagnostic Interview-Revised (ADI-R) and the verbal, performance and full Intelligence Quotient (IQ) scores (respectively, VIQ, PIQ and FIQ).
Data quality-assurance We discarded participants having scanning duration shorter than 5 minutes after scrubbing, lacking of full brain coverage, and with an average framewise displacement greater than 0. 3 mm 54. Participants from scanning studies KUL sample 3 and NYU sample 2 were also omitted because they only contained ASD subjects and therefore those cohorts did not provide any TDC. As a consequence, the number of finally considered subjects was 1541 (884 TDC, 657 ASD), corresponding to 33 scanning studies that were further merged into 24 institutions following the guidelines provided in http://fcon_1000. projects. nitrc. org/indi/abide/. The descriptive statistics per institution (number of subjects, ASD cases, mean age, sex distribution) is found in Table  S1.
Neuroimaging pre-processing and functional connectivity matrices A state-of-the-art pre-processing pipeline was adopted using FSL 5. 0. 9, AFNI 16. 0. 01  55 and MATLAB 2020b. We first applied slice-time correction, volume alignment to the average one to correct for head motion artifacts, which was followed by intensity normalization. We next regressed out 24 motion parameters, as well as the average cerebrospinal fluid (CSF) and average white matter signal. A band-pass filter was applied between 0. 01 and 0. 08 Hz, and linear and quadratic trends were removed. All voxels were spatially smoothed with a 6 mm FWHM. FreeSurfer v5. 3. 0 was used for brain segmentation and cortical parcellation. A total of 82 regions were generated, with 68 cortical regions from the Desikan-Killiany Atlas (34 in each hemisphere) and 14 subcortical regions (left/right thalamus, caudate, putamen, pallidum, hippocampus, amygdala, accumbens).
Association between subtypes and transcriptomics We computed the association between pseudo-R 2 maps (more info at Suppl. Info) and brain transcriptomics maps using spatial autoregressive models to reduce the correlation-bias produced by the similar transcriptomic expression in proximal brain regions  45. This analysis was implemented by means of the maximum-likelihood estimator routine (ML_Lag) from the Python Spatial Analysis Library (pysal)  56. As a result, for each brain gene we obtained one t-stat and one p-value, which allowed us to assess the association with the pseudo-R 2 maps while accounting for possible spatial autocorrelations. Among the significant associated genes, we identified as relevant those genes included in the SFARI database (https://gene. sfari. org/) with gene score equal to one.
Gene set enrichment analysis and protein interaction analysis We only considered for the analyses such genes with a p-value FDR corrected < 0. 05 in each subtype. After that, we performed a Gene Set Enrichment Analysis (GSEA) using WebGestalt  57  (http://www. webgestalt. org/) introducing as the input the list of the corrected genes and the t-stat from the association analysis, so the spatial correlation-bias was accounted for. We computed the GSEA for GO biological process  58  and Reactome pathways  59  and we only considered such enriched categories with a p-value FDR corrected < 0. 05. For the protein interaction analysis, we used the tool STRING v11. 5  60  for generating a physical protein-protein interaction network for each subtype, with Experiments and Databases as interaction sources.
05) and present in the SFARI gene human-database with a relevance score of 1. significant enrichment for subtype 1, the one with brain hypoconnectivity. However, for subtype 2, the enrichment of the NEG-genes included GO: Biological processes and Reactome pathways related to glutamate signaling (affecting both AMPA and NMDA receptors) and synapse organization, in relation with the excitationinhibition imbalance occurring during development of brain circuits (Fig. 4A ). We also assessed which NEGgenes participated in each biological process and pathway (Fig. 4B ), finding that genes DLG4, GRIN2B, GRIA2, and SHANK2 were participating in most of them; and in particular, the gene DLG4 did it in all of them. In addition, the DLG4 gene was the one with the highest degree in the protein interaction network.
Second, we have used Combat, a rigorous data harmonization method to eliminate the variability between MRI scans across the 24 institutions, one of the largest sources of variability when combining imaging data from multiple institutions  65. Third, our analysis of brain connectivity was carried out on a large-scale, where each brain region is represented by its connectivity pattern across the entire brain. Therefore, we do not consider a priori any brain region as more dominant or relevant than the others. Fourth, we made use of a consensus clustering approach we have developed  38, 39  , and that has been successfully tested by others  66  , to group subjects in the same subtype if the connectivity vectors are similar across all the regions analyzed, which in our case involved a total of 68 cortical regions and 14 subcortical ones.
Brain Atlas (AHBA) data preprocessing to be used for association with brain connectivity patterns in each subtype. E: ASD subtyping after consensus clustering applied to brain connectivity matrices. Using the results from D and E, we provide a biological characterization of each subtype using regression analysis between gene expression and connectivity patterns, and with a spatial autoregressive process for correlation-bias correction. F: Physical gene interaction network obtained only for the group of FDR-significant genes. G: Biological characterization of the significant associated genes using Gene Set Enrichment Analysis (GSEA). (Left) Histograms of association values between pseudo-R 2 and gene transcription activity (different values correspond to association with different genes). This procedure was repeated using the pseudo-R 2 values for each subtype.
B=C-P Eq. (  6 ), where P is the expected co-assignment matrix, uniform as a consequence of the null ensemble strategy obtained by repeating the permutation of labels 1000 times. Such a modularity matrix B encodes all the information about the interaction between subjects at different levels. As a result, one could now define any distance quantity applied to this matrix for assessing clustering. Instead, we directly fed this B matrix into a generalized Louvain method for community detection (https://github. com/GenLouvain/GenLouvain), yielding an optimal output partition that maximizes the network modularity.
Transcriptomics To build brain transcription maps, we took advantage of the publicly available data in the AHBA  11. The dataset consisted of MRI images, and a total of 58,692 microarray-based transcription profiles of about 20,945 genes sampled from 3,702 different regions across the brains of six humans. To pool all the transcription data into a single brain template, we followed a similar procedure to that employed elsewhere  12  , which includes: (1) Probe reannotation using a re-annotator toolkit 13 ; (2) Removal of probes whose sampling proportion in any of the six brains did not exceed the 70%; (3) Unique probe to gene assignment using the maximum differential stability (DS) criterion 14 ; (4) Removal of the inter-subject differences by pooling together the Z-scores of the transcription values for each gene and brain; (5)   Computation of a single transcription value for each region in the left hemisphere of the Desikan-Killiany atlas by calculating the median of all the values belonging to the given region; (6) Gene filtering considering only brain-specific genes relative to other tissues using the Human Protein Atlas  15, 16  (https://www.
Acknowledgments The authors are really pleased to thank  Amaia Iribar Zabala  for her fantastic work at the initial stage of this project and  Dr. Rafael E. Oliveras-Rentas ,  Prof. Bennett Leventhal ,  Prof. Young Shin Kim ,  Prof. Oswald Quehenberger ,  Prof. Jose Delgado  and  Prof. Timothy Verstynen  for the useful and insightful discussions. AJM is funded by a predoctoral contract from the  Department of Education of the Basque Country  ( PRE-2019-1-0070 ). MTH and JMC are funded by  Ikerbasque: The Basque Foundation for Science. JMC  is funded by the  Department of Economic Development and Infrastructure of the Basque Country (Elkartek Program  Grant  KK-2021-00009 ).
The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced.
CMIX is an international collaborative effort co-led by National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) aimed at intercomparing state-of-the-art cloud masking algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. CMIX was recommended following the first Atmospheric Correction Inter-comparison eXercise (ACIX)  (Doxani et al. , 2018) , and was conducted in conjunction with ACIX-II-Land and ACIX-II-Aqua  (Pahlevan et al. , 2021). The focus of this effort is on open and free imagery acquired by Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and Sentinel-2 MultiSpectral Instrument (MSI) sensors, with corresponding cloud masking algorithms applied. Five existing cloud reference datasets for Landsat 8 and Sentinel-2 are utilized to compare ten cloud masking algorithms.