dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2 Foetal Bovine muscle tissue features. ProteINSIDE correctly identified 120 muscular proteins present at all the five foetal ages  [3, 5]  and provided a fast overview of biological information available in UniProt and NCBI databases. As for data from AT, the relevance of ProteINSIDE results was checked. We have compared enriched GO terms recovered by ProteINSIDE to those previously published  [3]. We reported here enriched GO terms related to biological processes for proteins from cluster 8 (18 proteins over-abundant at 60 dpc  [3, 5] ; S2 Table ) and cluster 1 (14 proteins over-abundant at 180 dpc  [3, 5] ; S2 Table ). In agreement with previous GO annotations  [3, 5] , enriched GO terms recovered by ProteINSIDE were related to the negative regulation of apoptotic process, DNA binding, and skeletal muscle thin filament assembly for cluster 8, and to glycolytic process and the regulation of translational initiation for cluster 1.
The synthesis of ADIPOQ (originally thought to be limited to adipocytes) by muscular cells remains controversial because of the very low mRNA abundance in L6 myotubes when compared to white AT  [58]  and, its immunohistochemical location in vascular endothelium and adipocytes of the gastrocnemius muscle  [59]. ProteINSIDE revealed 171 interactions between 76 proteins of the dataset ( was identified as a central protein in a network of protein related to muscle hypertrophy and functioning in growing bovine  [60]. Moreover, HSPB1 was proposed to regulate the total number of fibres in Bovine foetuses (B. Picard, unpublished data). Present results suggest that HSPB1 may also contribute to myogenesis that is sustained by a very recent study reporting a repressed formation of myotubes by siRNA inhibition of HSPB1 expression in bovine myogenic cell, while expression of HSPB1 enhanced the expression of desmin and accelerated formation of myotubes  [61].
2. 3 An union analysis of adipose and muscular data to identify pathways involved in the growth of AT and muscle and their cross-talk. There is striking evidence for developmental and functional links between muscle and AT: the successive waves of growth of muscle and AT suggest a priority for muscle growth, and the comparison between lean and fat bovine breeds suggest that an increased muscular development is concomitant with a decrease in AT mass  [2]. In order to increase our knowledge on pathways involved in the growth of both AT and muscle and in the cross-talk between them, we have compared the adipose and muscular datasets. We have identified 46 proteins present both in adipose and muscle tissues that were subjected to a custom analysis with the following settings: GO annotation without IEA codes, prediction of secreted proteins, PPi research on 9 widely used databases (BioGrid, IntAct, MINT, MatrixDB, STRING, Reactome, InnateDB-IMEx, UniProt, and I2D-IMEx), and an extended network of PPi with proteins outside of the dataset in Human.
The extended network linked 1063 proteins (41 from the dataset) by 1400 interactions. By using high values of betweenness and closeness centralities, we proposed 35 proteins (18 from dataset;  Fig 8)  as being central in the extended network. Among them, 6 proteins (external of the dataset) were highly central: VCAM1, GABARAPL1, GABARAPL2, GABARAP, MAP1LC3A, and MAP1LC3B. The presence of these proteins or their mRNA in adipose and muscular cells was reported for VCAM1  [63, 64] , GABARAPL1  [65, 66] , GABARAPL2  [64, 67] , GABARAP  [64, 68] , MAP1LC3A  [64, 69] , and MAP1LC3B  [64, 70]. VCAM1 also named CD106 is a cell surface marker of mesenchymal stem cells found in AT  [71]  and muscle  [72]. The presence of VCAM1 in foetal bovine tissue remains to be shown but should indicate that mesenchymal stem cells contribute to the post-embryonic growth of AT and muscle as already proposed  [2].
Conclusions Using a list of genes or proteins, ProteINSIDE proceeds to a fast overview of biological information for ID of the uploaded dataset, a functional annotation according to GO, a prediction of secreted proteins, and a simple and interactive view of known and experimentally proven PPi. The similar or better reliability of results produced by ProteINSIDE was demonstrated by a bench test involving others available resources. Moreover, we have verified the biological relevance of the results by comparison with our previous analyses. Lastly, we have used ProteIN-SIDE to propose new hypotheses of research that should help to better understand the growth of AT and muscle in bovine. Among the new hypotheses of research that deserved to be investigated, we have focused on an uptake of adiponectin by the foetal muscle and the role of autophagy on the ontogenesis of AT and muscle.
To go further, ProteINSIDE can easily be upgraded to broaden the range of species (as other farm species: e. g. pig, chicken. ) by an update of the database and the workflow. Extended PPi network that includes proteins identified both in foetal AT and muscle. ProteINSIDE built a network between the 46 proteins identified in AT and muscle, and proteins outside of the dataset and know to interact with them in Human species. We filtered and sorted the network using high values of betweenness (an average of 10000) and closeness centralities (an average of 0,318). We have identified 6 proteins that were highly central in the dataset (linked with the maximum of proteins and pathways), 12 proteins were moderately central (engaged in the maximum of pathways but not necessary with many proteins), and 17 proteins were more weakly central (less linked with the maximum of proteins and less engaged in pathways, but central on the network).
The main component of the Global Sea Level Observing System (GLOSS) is the GLOSS Core Network (GCN) of 290 sea level stations. The present definition of the GCN (the definition is modified every few years) is called GLOSS10. In 2012 a new GLOSS implementation plan was developed to update technical standards for GLOSS tide gauge stations, as well as describing the basic terms and obligations for Member States participating in GLOSS. The British Oceanographic Data Centre (BODC) and the Permanent Service for Mean Sea Level (PSMSL) run the GLOSS Delayed Mode Data Centre jointly. It is responsible for assembling, quality controlling and distributing the "final" version of GLOSS sea level data sets and their supporting metadata, as well as carrying out data archaeology on historical analogue sea level records, preserving them in digital form.
WHAT IS GLOSS? The Global Sea Level Observing System (GLOSS) is an international programme conducted under the auspices of the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) of the World Meteorological Organisation (WMO) and the Intergovernmental Oceanographic Commission (IOC). GLOSS was established in 1985 by an IOC Task Team of interested scientists led by Dr David Pugh from the Proudman Oceanographic Laboratory (now the National Oceanography Centre) and Prof. Klaus Wyrtki from the University of Hawai'i. The aim was to provide support and coordination for international and regional sea level science and to improve the quantity and quality of Mean Sea Level (MSL) data supplied to the Permanent Service for Mean Sea Level (PSMSL) (Fig. 1 ). Sea level data are vital to scientists studying major ocean currents and global climate change, to engineers designing coastal installations, to those engaged in operational oceanography (e.
THE GLOSS IMPLEMENTATION PLAN The first GLOSS Implementation Plan (GIP), published in 1990, established the GLOSS Core Network (GCN) of approx. 300 tide gauges distributed around the world, as well as technical standards for GLOSS tide gauge stations and the basic terms and obligations for Member States participating in GLOSS  (Intergovernmental Oceanographic Commission, 1990). The definition of the GCN is modified every few years, and the present definition, GLOSS10, contains 290 sea level stations (Fig. 2 ). The Core Network is designed to provide an approximately evenly-distributed sampling of global sea level variations and several stations were replaced in the present definition to make the network more robust. New data recording and transmission technologies, e. g. tide gauges that measure via radar, may mean that stations can now be set up in locations that had previously been considered impractical to monitor.
A new GLOSS implementation plan was published in 2012 (Intergovernmental Oceanographic Commission, 2012) and called for two significant upgrades to the sea level stations in the GCN. These are: that all GCN stations should report data in near real-time and that all stations should have continuous global navigation satellite system (GNSS) measurements in the vicinity of the tide gauge benchmark (TGBM). The benefit of converting all GCN stations to reporting in near real-time include improved data quality and quantity as problems can be identified and addressed in a timely manner, supporting tsunami and hazard monitoring, and the ability to assimilate tide gauge data into operational models for storm surge and coastal inundation. GNSS measurements of vertical land movement will aid efforts into researching global and regional sea level changes and will support satellite altimetry calibration.
Rates of regional sea level rise expected from ocean thermal expansion and ice melting are altered by vertical land movement, which can impact on sea level rise on the coast. A further new requirement of the implementation plan was that the various GLOSS data centres work towards providing a unified access to the various data streams using relational database tools and cross-referencing techniques to give the feel of a centralised GLOSS web server for all data and metadata sets. There are five data streams taking GLOSS tide gauge data and associated parameters from the network's stations to users. Real-time and near real-time data are collected for the purpose of tsunami monitoring systems and storm surge forecasting, as well as for monitoring the status of the gauge itself. Data are sent to the IOC Sea Level Station Monitoring Facility, operated by the Flanders Marine Institute (VLIZ) on behalf of GLOSS.
These data are raw, with no quality control performed. Fast delivery data are data that are transmitted to the University of Hawai'i Sea Level Center (UHSLC), normally within one month of collection. These data are mainly used for the calibration of satellite altimeter data and generation of blended products. The data are filtered down to hourly values, and are quality controlled by the UHSLC. Hourly (or sub-hourly) values are supplied to the Delayed Mode Data Centre in the September of the year following collection. Monthly and annual MSL data are sent to the PSMSL, usually by email. Occasionally, the PSMSL will write to tide gauge operators, requesting this MSL data. The data are quality controlled and then made available. The Système d'Observation du Niveau des Eaux Littorales (SONEL) observing service, which is supported by the University of La Rochelle and the French CNRS/INSU institute, provides information about the status of GNSS stations at or near to the GCN stations through a web-based monitoring facility.
The data should be supported by metadata, such as instrumentation information, site location and benchmark information. For delayed mode data, it is assumed that the data originators will have already performed a certain level of quality control. The data collated by the delayed mode data centre are high frequency, with most gauges producing data with a time interval of between 5 and 15 minutes. The dataset also contains other parameters where they have been recorded at tide gauge stations, such as water temperature, air temperature and barometric pressure. The data are quality controlled to common GLOSS delayed mode data standards, to which BODC contributed  (Intergovernmental Oceanographic Commission, 2014). The data are converted into a common standard format, QXF, a platform-independent binary format (a subset of NetCDF;  Rew & Davis, 1990 ) so that they can be stored and distributed more efficiently.
Parameter codes are assigned from a controlled vocabulary, as described by  Leadbetter et al. (2013)  and consistent flags, units and absent data values are assigned. After transfer, the data are screened using BODC's in-house software, Edserplo. The primary quality control of sea level is based on the inspection of both recorded data and meteorological residuals; inspection of residuals is especially useful for detecting instrumental faults such as timing errors, datum shifts and spikes. The GLOSS website (http://www. gloss-sealevel. org/) was set up in 2006 to provide access to data and metadata for the GCN stations, to act as a repository for reports and to disseminate information about the GLOSS programme. The metadata for all GCN stations is accessed via the GLOSS Station Handbook. The Handbook, maintained by BODC, contains information about the gauges, their surroundings, and the tide gauge operators.
GLOSS SEA LEVEL DATA ARCHEOLOGY In 2001, GLOSS and the International Oceanographic Data and Information Exchange (IODE) programme initiated a data archaeology project that aimed to rescue sea level information that existed only in analogue form (charts, paper tape, etc. ) and convert it to a digital format. A letter and questionnaire was circulated to GLOSS members, requesting that they assess their holdings of potentially rescuable historical tide gauge data. BODC has been involved in sea level data archaeology through GLOSS, including distributing the questionnaires on analogue data holdings, contributing to conference posters and presentations, and providing advice to the GLOSS Group of Experts. In 2006, the World Climate Research Programme (WCRP) convened a workshop "Understanding Sea-Level Rise and Variability" to identify the uncertainties associated with past and future sea level rise and variability, and determine the research and observational activities needed for narrowing these uncertainties.
A summary statement was issued following the workshop and a volume of reviews of each area of sea level science was published  (Church et al. , 2010). The workshop provided several recommendations to GLOSS, which included: "Pursue data archaeology and complete the GLOSS network of tide gauges, each with high-frequency sampling and real-time data availability, to enable an assessment of the incidence of extreme flooding events. " At the twelfth session of the GLOSS Group of Experts in 2011 the group acknowledged that a large amount of historical data remained in paper form and noted that there had been recent findings in non-oceanographic facilities such as the United States National Archives and Records Administration and the archives of the French territorial divisions. A questionnaire was developed and sent to the GLOSS national contacts, the International Hydrographic Organization national agencies, and was also circulated in the Atmospheric Circulation Reconstructions over the Earth (ACRE) community.
Patrick Caldwell, manager for the Joint Archive for Sea Level based at the University of Hawai'i Sea Level Center, published the findings at the UNESCO "The Memory of the World in the Digital Age: Digitization and Preservation" conference and remarked that "a vast amount of historical tide gauge measurements exist in non-electronic form"  (Caldwell, 2012). In 2012, BODC won a grant to digitise historic paper charts (Fig. 3 ) and scan ledgers (Fig. 4 ) stretching back to 1853 and created over 160 site-years of digitised chart data from 22 sites and approx. 500 years of scans from 14 UK sites. The analogue records, held in the BODC archive, are now available to the wider community. This work has been funded through the JISC Content programme 2011-2013. The GLOSS website lists various other sea level data archaeology projects around the world and BODC will be taking the lead on a progress report to the Thirteenth Session of the GLOSS Group of Experts identifying automated techniques for the digitisation of archived mareograms.
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor subregions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously.
Each of these modalities provides different types of biological information, and therefore poses somewhat different information processing tasks. Because of its high clinical relevance and its challenging nature, the problem of computational brain tumor segmentation has attracted considerable attention during the past 20 years, resulting in a wealth of different algorithms for automated, semi-automated, and interactive segmentation of tumor structures (see  [6]  and  [7]  for good reviews). Virtually all of these methods, however, were validated on relatively small private datasets with varying metrics for performance quantification, making objective comparisons between methods highly challenging. Exacerbating this problem is the fact that different combinations of imaging modalities are often used in validation studies, and that there is no consistency in the tumor sub-compartments that are considered.
As a consequence, it remains difficult to judge which image segmentation strategies may be worthwhile to pursue in clinical practice and research; what exactly the performance is of the best computer algorithms available today; and how well current automated algorithms perform in comparison with groups of human expert raters. In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we organized in 2012 and 2013 a Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) challenge in conjunction with the international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). For this purpose, we prepared and made available a unique dataset of MR scans of low-and high-grade glioma patients with repeat manual tumor delineations by several human experts, as well as realistically generated synthetic brain tumor datasets for which the ground truth segmentation is known.
Each of 20 different tumor segmentation algorithms was optimized by their respective developers on a subset of this particular dataset, and subsequently run on the remaining images to test performance against the (hidden) manual delineations by the expert raters. In this paper we report the set-up and the results of this BRATS benchmark effort. We also describe the BRATS reference dataset and online validation tools, which we make publicly available as an ongoing benchmarking resource for future community efforts. The paper is organized as follows. We briefly review the current state-of-the-art in automated tumor segmentation, and survey benchmark efforts in other biomedical image interpretation tasks, in Section II. We then describe the BRATS set-up and data, the manual annotation of tumor structures, and the evaluation process in Section III.
Image Processing Benchmarks Benchmarks that compare how well different learning algorithms perform in specific tasks have gained a prominent role in the machine learning community. In recent years, the idea of benchmarking has also gained popularity in the field of medical image analysis. Such benchmarks, sometimes referred to as "challenges," all share the common characteristic that different groups optimize their own methods on a training dataset provided by the organizers, and then apply them in a structured way to a common, independent test dataset. This situation is different from many published comparisons, where one group applies different techniques to a dataset of their choice, which hampers a fair assessment as this group may not be equally knowledgeable about each method and invest more effort in optimizing some algorithms than others (see  [59] ).
Once benchmarks have been established, their test dataset often becomes a new standard in the field on how to evaluate future progress in the specific image processing task being tested. The annotation and evaluation protocols also may remain the same even when new data are added (to overcome the risk of over-fitting this one particular dataset that may take place after a while), or when related benchmarks are initiated. A key component in benchmarking is an online tool for automatically evaluating segmentations submitted by individual groups  [60] , as this allows the labels of the test set never to be made public. This helps ensure that any reported results are not influenced by unintentional overtraining of the method being tested, and that they are therefore truly representative of the method's segmentation performance in practice.
The training data for the benchmark was identical to the real training data of the 2012 benchmark. No synthetic cases were evaluated in 2013, and therefore no synthetic training data was provided. The participating groups were asked to also submit results for the 2012 test dataset (with the updated labels) as well as to 10 new test datasets to the online system about four weeks before the event in Nagoya as part of an "off-site" leaderboard evaluation. The "on-site challenge" at the MICCAI 2013 workshop proceeded in a similar fashion to the 2012 edition: the participating teams were provided with 10 high-grade cases, which were previously unseen test images not included in the 2012 challenge, and were given a 12 h time period to upload their results for evaluation. Out of the ten groups participating in 2013 (Table  II ), seven groups submitted their results during the on-site challenge; the remaining three submitted their results shortly afterwards (Buendia, Guo, Taylor).
C. Image Datasets Clinical Image Data-The clinical image data consists of 65 multi-contrast MR scans from glioma patients, out of which 14 have been acquired from low-grade (histological diagnosis: astrocytomas or oligoastrocytomas) and 51 from high-grade (anaplastic astrocytomas and glioblastoma multiforme tumors) glioma patients. The images represent a mix of pre-and post-therapy brain scans, with two volumes showing resections. They were acquired at four different centers-Bern University, Debrecen University, Heidelberg University, and Massachusetts General Hospital-over the course of several years, using MR scanners from different vendors and with different field strengths (1. 5T and 3T) and implementations of the imaging sequences (e. g. , 2D or 3D). The image datasets used in the study all share the following four MRI contrasts (Fig.
No attempt was made to put the individual patients in a common reference space. All images were skull stripped  [75]  to guarantee anomymization of the patients. Synthetic Image Data-The synthetic data of the BRATS 2012 challenge consisted of simulated images for 35 high-grade and 30 low-grade gliomas that exhibit comparable tissue contrast properties and segmentation challenges as the clinical dataset (Fig. 2 , last row). The same image modalities as for the real data were simulated, with similar 1 mm 3 resolution. The images were generated using the TumorSim software, 4 a cross-platform simulation tool that combines physical and statistical models to generate synthetic ground truth and synthesized MR images with tumor and edema  [76]. It models infiltrating edema adjacent to tumors, local distortion of healthy tissue, and central contrast enhancement using the tumor growth model of Clatz et al.