dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Pubmed Cochrane databases were searched up to December 2019 for relevant articles for systematic research. Articles were screened by reviewing their abstracts for the following criteria. 1) Topics: AI and lung nodules management, AI and respiratory medicine, AI and pathology, AI and thoracic surgery. 2) Published in English and in a peer-reviewed journal. Articles of interest that had been cited by the articles identified in the initial search were also reviewed. The Official Journal of the European Union was used as the main source for the legislative aspects of AI.
Machine learning Deep learning "The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it"  [3]  A subcategory of artificial intelligence where computers learn automatically from data accumulation A subcategory of machine learning where the system is able to recognise patterns independently and make predictions based on large quantities of information malignant. To build the algorithm for their AI, they used clinical and radiological data from 165 consecutive patients who had had surgery in their centre. They then tested the algorithm on 100 consecutive patients who underwent surgery for a suspicious lung nodule, without a pre-operative diagnosis.
Natural language processing A sub field of artificial intelligence dealing with the way to program computers to process and analyse large amounts of natural language data. Challenges are set by speech recognition, natural language understanding and natural language generation. It employs a sequence of mathematical processes and comparisons to identify the user's input, possibly correcting certain errors or applying synonyms, in order to identify all the information necessary to understand the needs of the user. by the model) was 15. 2%. Deep Learning sparked interest for a broader use of AI, especially in radiology  [14]. This technique was used by NAM et al. [15]  to detect malignant pulmonary nodules on chest radiographs. The deep learning-based algorithm outperformed physicians in radiograph classification and nodule detection performance.
To do so, they used a NLST dataset consisting of 42 290 CT cases from 14 851 patients, 578 of whom developed biopsy-confirmed cancer within the 1 year follow-up period; 70% of the data was used to train the algorithm and 15% was used as a cross-validation set. The final 15% was used to test the algorithm against radiologists. When prior CT imaging was not available, the model outperformed the radiologists with absolute reductions of 11% in false positives and 5% in false negatives. When prior CT imaging was available, the model performance was on par with the radiologists. This could lead to future approaches to lung cancer screening as well as to support assisted-or second-read workflows. Because detecting nodules or masses in chest radiographs or CT by radiologists can be a hard task when numerous examinations have to be interpreted, AI can serve as a diagnostic aid for suspicious nodules.
[21]  and TORRINGTON et al. [22] ; Model 3: using all variables except for the ones in the risk scores of GOLDMAN et al. [21]  and TORRINGTON et al. [22]. A fourth model was programmed with all variables to estimate major post-operative complications. All four models were tested on 28 patients. Models 1 and 3 were able to correctly classify all 28 test cases whereas model 2 made six errors in classification from the 28 test cases. Model 4 estimated major post-operative complications correctly in all 28 test cases. Other medical specialists with whom thoracic surgeons collaborate regularly have taken an interest in AI and are aware of its potential to change daily practice. YU et al. [26]  published an article in which AI software distinguished between primary lung adenocarcinoma and squamous cell carcinoma using quantitative histopathology features extracted from 2186 haematoxylin and eosin whole-slide pathology images from the Cancer Genome Atlas.
TOPALOVIC et al. [28]  showed that AI outperformed pulmonologists in the interpretation of pulmonary function tests (PFT); 120 pulmonologists from 16 hospitals in five European countries participated in this multicentre noninterventional study. Pattern recognition of PFTs by pulmonologists matched the guidelines in 74. 4 ± 5. 9% of cases and the pulmonologists made a correct diagnoses in 44. 6 ± 8. 7% of cases, whereas AI perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases. The AI-based software's performance was superior compared with the performance of pulmonologists. AI commands a certain level of fascination in the medical world because of the potential of such technology to improve clinical practice. To gain broader acceptance, the challenge for AI is to answer complex clinical questions taking into account, for example, patients' comorbidities.
Limits of AI AI shows promising results in many fields of medicine and has the potential to drastically change daily practice. However, we must be critical of the various applications proposed for it. New software must be thoroughly evaluated, preferably in randomised controlled, trials to highlight its benefits. A programme developed in one institution may not be directly applicable to other institutions. The way in which the algorithm is developed will effectively tailor the model to reflect the clinical experience of that institution. This is both an advantage and a disadvantage to potential users  [11]. The quality of the answers by the AI largely depends on the quality and the amount of data available. The generalisability of AI algorithms across subgroups is critically dependent on factors such as representativeness of included populations, missing data and outliers.
In 2019, the National Academy of Medicine in the USA published a reference document on the responsible development, implementation and maintenance of AI in the clinical enterprise  [46]. The International Medical Device Regulators Forum [47], whose members include Australia, Brazil, Canada, China, EU, Japan, Russia, Singapore and the USA, is a group of medical device regulators working toward harmonising medical device regulation. They have also defined software using AI as a medical device. In the past couple of years, the US Food and Drug Administration approved a number of algorithms using AI for clinical use; for example, in April 2018, IDx-DR, an AI algorithm designed for screening diabetic retinopathy patients using retinal images prior to referring patients to ophthalmologists  [48].
Conscious of the potential of AI, the French government has taken a set of measures necessary to promote and broaden its implementation. Medicines Agency, and also national competent authorities from EU state members, can conduct conformity assessments. Because of its ability to learn from its mistakes, AI software is different to other medical devices. This means that specific checklists are necessary to evaluate their added value for patients. CNEDiMTS issued eight criteria for a public consultation on evaluation of AI software (table  2 ), from 20 November 2019 to 15 January 2020  [56]. The results are not yet available. The study by SHADEMAN et al. [32]  compared the metrics of intestine anastomosis by an autonomous robot to accomplish sutures of an intestine compared to with the metrics for humans.
Acknowledgements Y. W. is supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC). T. M. is supported in part by project  Y6Y2581B11  supported by 2016  National Postdoctoral Program for Innovative Talents. G. C. and S. V. acknowledge partial support from the  China-France LIA FCPPL  and the  Labex Lyon Institute of the Origins -LIO.
During the Miocene, extensive carbonate deposition thrived over wide latitudinal ranges in Southeast Asia despite perturbations of the global climate and thermohaline circulation that affected the Asian continent. nevertheless, the mechanisms of its emergence, adaptability in siliciclastic-dominated margins and demise, especially in southern South china Sea (ScS), are largely speculative and remains enigmatic along with a scarcity of constraints on paleoclimatic and palaeoceanographic conditions. Here we show, through newly acquired high-resolution geophysical data and accurate stratigraphic records based on strontium isotopic dating, the evolution of these platforms from ~15. 5-9. 5 Ma is initially tied to tectonics and eustasy, and ultimately, after ~9. 5 Ma, to changes in the global climate patterns and consequent palaeoceanographic conditions.
The exact paleogeography of these Paleogene-Neogene deltas, however, is only partly known because of the limited amount of data available  29, 30  and because tectonic movements could have overprinted the depositional geometries. Reef distribution in Southeast Asia is presumed to have declined at the end of the Early Miocene owing to increased continentality, augmented oceanic ventilation and increased seasonal runoff, among others  27. Although the deterioration was critical in the southern realms of the South China Sea, the Middle-Late Miocene Luconia platforms continued their expansion over a spatial extent 31 of ~4. 5 ×10 4 km 2 (Fig. 3A ). However, because of a severe deficit in the availability of continuous data records of carbonates from this region, the theoretical underpinning of the mechanisms of evolution and demise of the platforms are largely speculative and prevails as a conundrum along with a dearth of tight constraints of paleoclimatic and palaeoceanographic conditions.
Our data produces evidences for ocean current fluctuations and we demonstrate that one of the most critical factors governing the Late Miocene demise of the buildups was indispensably organized and controlled by the intensification of the Asian summer monsoon winds. Auxiliary to these findings, this work highlights the relevance and the imperative role of palaeoceanography in creating favourable niches and environments for reefal development that can be applicable to other Cenozoic carbonate platforms in Southeast Asia and other parts of the World. the emergence and adaptability of Middle Miocene carbonate systems of southern South china Sea. A detailed analysis of our seismic profiles and strontium isotope dating from the southern South China Sea reveals that the carbonates initiated at ~15.
Also shown, in shades of grey, are the modelled Miocene paleogeographic limits adapted from  29. The paleogeography was constrained through sedimentological and stratigraphic evidences contained within clastic deposits from deep cores and onshore outcrops. Solid black bounding box shows the location and extent of the dataset used in the study (refer to methods for details of the geophysical dataset). (B) Top carbonate seismic horizon map of a chronologically-accordant carbonate platform that was substantially studied herein. Red circles identify locations of drilled wells that penetrated various sequences of the platform would have been optimal for biogenic ecosystems to flourish. As seen in our results, colonization does not appear to exist in most of the interplatform seaways (see Supplementary Fig.
(D,E) Spectral decomposition maps for two (2) time slices, i. e. , 15. 5 Ma and ~13. 9 Ma. Note the individual reefal buildups in "D" followed by coalescing and progradation of the eastern end of the platform in a predominantly ESE and ENE direction in "E". Base map in (A) is a SRTM (Shuttle Radar Topographic Mission) Digital Elevation Model (DEM) of 30 m (1-arc second) spatial resolution (SRTM data courtesy of the U. S. Geological Survey, https://lta. cr. usgs. gov/SRTM1Arc) and gridded bathymetric data is from the General Bathymetric Chart of the Oceans (GEBCO) (15 Arc-Second models courtesy of the International Hydrographic Organization [IHO] and the Intergovernmental Oceanographic Commission [IOC] of UNESCO, https://www. gebco. net/data_and_products/gridded_ bathymetry_data/). The map was created using Geographic Information Systems (GIS) software ESRI ArcGIS version 10.
3 (http://www. esri. com/software/arcgis/arcgis-for-desktop). Base maps and images in (B-E) were visualized, interpreted and constructed with Eliis PaleoScan software version 2018. 1. 0 (http://www. eliis. fr/ products/paleoscan%E2%84%A2-software). Figure labels were added using Adobe Illustrator version CS5. 1 (http://www. adobe. com/products/illustrator. html). (Fig. 6A ) along with dissolution features (Fig. 7B ) that could be related to local tectonic events  40, 41  promoting sporadic exposure. It is interesting to note that the sediment accumulation rates (Fig. 6A ) calculated for the platform demonstrates a relatively stable trend of slow growth and devoid of sharp peaks before the start of the next seismic sequence. A dramatic spike in accumulation rate is seen from ~8. 8 Ma that terminates with the SB4 at ~8.
These imposing orogens could have guised as obstructions that sheltered the southern South China Sea, and as a result, considerably altered wind, oceanographic, morphodynamic and depositional patterns. As mentioned previously, our results based on seismic reflections, core data and supplemented by precise isotopic chronology of Luconia carbonate platforms displays a good correlation with regional tectonic events and global sea level evolution in the Middle Miocene; however, in the Late Miocene, a strong shift towards planetary climate system as the primary controlling factor is evidently observable. The evolutionary pattern of the platform, which initiated at ~15. 5 Ma, bears close resemblance to the growth cycles of carbonate edifices in the Maldives 7 since the Middle Miocene, in terms of timing of aggradation/ progradation, intermittent drowning and platform backstepping (Fig.
Methods Geophysical data and interpretation. The interpretation of carbonate sequences and related morpho-structures was supported by a combination of high-resolution 3D seismic profiles calibrated with well and core data. A world-class seismic dataset covering an area of 420 km 2 with a bin grid of 12. 5 m x 12. 5 m and sampling rate of 4 ms was fully integrated in our regional study. The dataset that includes 1016 inlines and 1357 crosslines with a 25 m step were interpreted using Eliis PaleoScan software. The data are characterized by normal SEG polarity, where an increase of acoustic impedance is shown in positive amplitude, and a dominant frequency of 30 Hz within the platform. Applying an average velocity range of 1965-2547 ms -1 , the maximum vertical resolution was identified as 16-22 m.
The latest chronological dataset that was produced herein involves the measurement of Sr isotope constitution in a VG 354 thermal ionization mass spectrometer at the CSIRO radiogenic isotope facility at North Ryde, Australia. The 87 Sr/ 86 Sr ratios were denominated as the average of 54 measurements of ion intensities and normalized to 86 Sr/ 88 Sr = 0. 1194 using an exponential correction law. During the course of measurements, strontium standard reference NBS987 resulted in 87 Sr/ 86 Sr values of 0. 710235 (2σD error deviation, n = 54). Numerical ages were derived from the look-up table of  56, 57  , and from the curves of  58, 59. The earlier study contains an extensive dataset of 87 Sr/ 86 Sr values by  59  and the isotope analysis was conducted on 137 samples. All 87 Sr/ 86 Sr data were adjusted to a certified NBS987 value of 0.
710248. Both 87 Sr/ 86 Sr ratios and age calculations correlate very closely. The obtained data were then integrated with palaeontological records based on the identification of larger foraminifera. Sediment accumulation rate. Accumulation rates were computed in meters per million years (m. Ma -1 ) as this ratio is equivalent to the Bubnoff unit  60. The accumulation rates were estimated from age-depth plots by drawing best-fit lines over successive depth intervals from seismic data. The age-depth relationship was established based on biostratigraphic and strontium isotopic ages from core data over averaged thicknesses for each seismic unit. Compaction adjustment has not been accounted for, due to the absence of core data within the siliciclastic overburden, thus, they represent minimal values.
It must be noted that the rates are from the vertical dimension, which indicates that they represent only a part of the clinothem accumulation and lateral progradation rates is estimated to be >100 times greater than vertical accumulation  61. core description and sedimentary facies. To characterize the evolution of the carbonate platform, a detailed description of five (  5 ) wells (Fig. 3B, C ) that includes 378 m of core was performed (please refer to  39  ) in terms of lithological changes using 10% HCl, component composition and their abundance, grain size, texture using Dunham classification 62 , diagenetic overprints, visual porosity and pore types. Sedimentological dataset was used for identification of litho-facies types.
A phylogenetic tree was built from alignment of overlapping rbcL sequences (161 nt length barcode) to assess the strains' diversity (Fig. S1 ). Congruent phylogenetic trees and lineage affiliations were also obtained for longer rbcL sequence alignments, built over 729 nt for strains 05, 010, 019, 018A, 018B, 018C, and 346 nt for strains 06 and 022 (data not shown). Both clades P12 and P14 were putative sister species-level entities (OTU>99%), related at genus level (>97% sequence similarity threshold) to a clade P3 previously reported in aquarium-grown Pocillopora acuta colonies  (Massé et al. , 2018; ATPD and Océanopolis aquariums) , and also detected in Pacific reef-collected Pocillopora sp. (from Gambier Archipelago,  Massé, pers. obs. ). Indeed, sequence divergence between P12 and P3, and between P14 and P3 was ~2%.
Fast-growing branching corals are known to harbor less abundant and diverse Ostreobium than slow-growing massive corals, with patchy spatial distribution along the branch growth axis  (Godinot et al. , 2012; Massé et al. , 2018; Marcelino et al. , 2017). Culture bias likely allowed only a fraction of endolithic microbes to be isolated and potential multiple isolation of the same Ostreobium lineage due to filament fragmentation, with one dominant (P1) and one minor (P12/P14) lineage detected per branch. These reasons explain the uneven size of datasets generated in this study, largest for strains affiliated to the P1 lineage (7 strains), and reduced to 2 strains for the P12/P14 lineage. Isolation and characterization of more strains from P12/P14 would be necessary to further test metabolic differences between lineages.
Habitat-driven changes in morpho-functional traits (pigments and fatty acids) Pigments of Ostreobium algae have so far been mainly studied in complex natural microboring communities, i. e. green bands of dead or live corals, using spectrophotometry and Jeffrey and Humphrey equations for chlorophylls, or HPLC technique  (Fork & Larkum, 1989; Fine & Loya 2002; Fine et al. , 2006; Tribollet et al. , 2006; Sangsawang et al. , 2017)  with a few pioneer studies focusing on individual strains of Ostreobium sp. (called O. quekettii Bornet & Flahaut 1889) using HPLC methods  (Jeffrey, 1968; Schlichter et al. , 1997; Koehne et al. , 1999). For the first time, we provide pigment datasets (HPLC profiles) for several Ostreobium strains of a referenced genotype (rbcL P1 lineage) and show differences in endolithic versus free-living forms, supporting adaptation to habitat-driven lower light intensities in the dense coral biomineral versus seawater.
Data analysis Multivariate analysis of pigment compositions was performed using PRIMER 5 software. A triangular similarity matrix was created using Bray-Curtis similarity coefficient, followed by non-metric multidimensional scaling (nMDS) to spatially visualize samples of endolithic, free-living Ostreobium strains and control skeletons with similar composition. Similarity and dissimilarity percentages obtained by SIMPER analysis allowed to determine which pigments (or RT of peaks in case of non-identified pigment) drove the observed differences between datasets (i. e. endolithic versus free-living Ostreobium versus control skeletons). Principal component analysis (PCA) was performed on % level of fatty acids using R software (version 3. 6. 2), to reveal spatial variability among strains, genetic lineage, and growth forms of Ostreobium, and identify FAs that explain most the variance in our datasets.
For δ 13 C and δ 15 N stable isotope values, parametric Student-test were performed with R software (version 3. 6. 2) to determine if the genetic lineage (P1 versus P12/P14) influenced δ 13 C and δ 15 N values for both Ostreobium growth form. Then, an analysis of variances (ANOVA) was performed on δ 13 C and δ 15 N datasets, with post hoc Student-Newman-Keuls (SNK) tests, highlighted differences between Ostreobium growth forms and with their habitat (coral skeleton substrate). Significance threshold were set to p-value <0. 05. S3 , along with polyunsaturated and saturated fatty acid values per growth form and lineage. S5.
Acknowledgments This work was supported by  UPMC IPV  (' Interface Pour le Vivant') doctoral grant  to AM, MNHN grants  ATM 2017-2018  'ECTOSYMBIOCORAL' and Aviv 2017 'Photosymbioses marines' to IDC. Additional funding support came from ISPL-LOCEAN lab  UMR7189  to AT and MCAM lab  UMR7245  to IDC. We thank the director and staff of the  Aquarium Tropical, Palais de la Porte Dorée (Paris, France)  for providing Pocillopora acuta colonies from which Ostreobium strains were isolated. We thank  Denis Fiorillo  at the  Service de Spectrométrie de Masse Isotopique of the Muséum national d'Histoire naturelle (SSMIM, MNHN Paris, France ) for help with organic and carbonate C and N stable isotope analyses. We also thank  Pr. Karim Benzerara  (IMPMC lab  UMR7590 ) for discussions, and the editor and five anonymous reviewers who helped improving the manuscript.
Subjects The data were collected from January 2014 to September 2015 at Massachusetts General Hospital and Brigham and Women's Hospital in fifteen patients with long-standing pharmacoresistant epilepsy. The patients gave informed consent, and the research protocol was approved by the local Institutional Review Board (Partners IRB). Intracranial EEG (iEEG) recordings were made over the course of clinical monitoring for spontaneous seizures. Participants were implanted with multi-lead depth electrodes (i. e. stereotactic-EEG, sEEG) or subdural grid electrode arrays (i. e. Electrocorticography, ECoG) to confirm the hypothesized seizure focus and locate epileptogenic tissue in relation to essential cortex, thus directing surgical treatment. The decision to implant, the electrode targets and the duration of implantation were made on clinical grounds without reference to this research.
From this dataset, we studied nine patients (age range: 17-59 years; 4 females; Supplementary Table  1 ) who satisfied the following inclusion and exclusion criteria. Inclusion criteria included a post-learning sleep session (between the end of the learning and start of the retest sessions) lasting 2 h maximum and containing at least 1 epoch of NREM (N2 or N3) sleep. The exclusion criteria included the occurrence of identified electroclinical or electrographic seizures 2 h before and after the experiment (including sleep periods). These criteria were selected to obtain a reasonable degree of uniformity across participants and avoid potential confounds. Participants were all right-handed, with intelligence in the normal range.