dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature Extraction One hundred radiomic features were extracted from the 3D MR images using the Python library PyRadiomics 2. 1. 2 (34)  in which the feature definitions are consistent with the Image Biomarker Standardization Initiative (IBSI)  (35). The only exception is that PyRadiomics and IBSI use different definitions of the Kurtosis firstorder feature, where Kurtosis is calculated using -3 and +3 in the IBSI and PyRadiomics referentials respectively. For first order features, an intensity shifting of 300 (equal to three standard deviations) was applied to ensure that the majority of the voxel intensities were positive before feature extraction. An absolute discretization with a fixed bin size equal to 37 was chosen  (36, 37). This leads to a bin number of 32 considering the mean of the intensity intervals computed for all volumes of interest of patients of the training set (min intensity: 575, max intensity: 2069, mean intensity range: 1190).
Model Building The establishment of the classification model was based on the scikit-learn library version 0. 23. 2  (38)  and included two steps applied to the training set: (1) selection of the ML classifier and feature scaling method and 2) optimization of the hyperparameters. In step 1), a nested cross-validation was used given the moderately-sized dataset and 144 ML models combining nine feature scaling methods (No Scaler, MaxAbsScaler, MinMaxScaler, Normalizer, PowerTransformer-Yeo-Johnson, QuantileTransformer-normal, QuantileTransformer-uniform, RobustScaler, StandardScaler) and 16 classifiers (AdaBoostClassifier, BaggingClassifier, BernoulliNB, DecisionTreeClassifier, Extra TreeClassifier, ExtraTreesClassifier, GaussianNB, Gradient BoostingClassifier, KNeighborsClassifier, LinearSVC, Logistic Regression, MLPClassifier, QuadraticDiscriminantAnalysis, RandomForestClassifier, RidgeClassifier, SGDClassifier) were compared.
ACKNOWLEDGMENTS We would like to thank  Armelle Lesaunier, MD ;  Violeta Fridjoi, MD ;  Philippe Beyssen, MD ;  Juliette Fayard, MD ;  Corentin Provost, MD-MSc ;  Marjorie Latrasse, MD ;  Ceĺine Corcy, MD ,  Joseph Benzakoun, MD-MSc ;  Wagih Ben Hassen, MD-MSc ,  Greǵoire Boulouis, MD-MSc ,  Emmanuèle Lechapt-Zalcman, MD-PhD  and  Jean-Francois Meder, MD-PhD ; for their precious help and advice in this study. Figure 1 has been designed using resources from Flaticon. com.
Linking enzyme activity to primer production We selected NBI as a target because of its many applications, including molecular diagnostics  30, 31  and molecular technologies  2, 33. NBI is currently used in its natural form, although many endonucleases have been engineered for better performance in vitro  34. To build the autonomous selection circuit, we used the PEN DNA Toolbox, a molecular programming language based on dynamically redirecting the activity of a DNA polymerase between short templates using endogenous oligonucleotides  35, 36. Here, the goal was to connect the sitespecific NBI nickase activity to the generation of nbi-targeting PCR primers. The molecular network thus performs a number of tasks: it first senses NBI activity, then amplifies and reshapes the molecular signal, and finally converts it to primer production yield (Fig.
Acknowledgements This research was funded by the  European Research Council  ( ERC-CoG-2014 ProFF ID: 647275 ), PhD fellowships from  French Ministry of Higher Education and Research (CDSN ENS Lyon  and Paris-Saclay resp. ) to A. D. -M. and R. S. and postdoctoral fellowship from  European Union 's  Horizon 2020 research and innovation program  ( Marie Skłodowska-Curie  No  845976 ) to R. E. The microfluidics was realized at the  Pierre-Gilles de Gennes Institute (IPGG)  with the support of the  Equipex/Idex ANR program  ( Equipex   ANR: 10-EQPX-0034 ). We thank  T. Fujii , his laboratory and the  Institute of Industrial Science (IIS) of the University of Tokyo  for hosting the first steps of the project,  V. Taly  for the sample of EA-surfactant, R. Hsu  and  A. Kazuaki  for preliminary work,  D.
Statistical analysis Principal Component Analysis (PCA) was applied on the XRF core-scanning data. MS data, reflecting minerogenic material, as well as the ratio of incoherent to coherent (inc/coh) reflecting organic matter (see  Davies et al. , 2015) , were incorporated in the data set for PCA. Data were standardized prior to analysis using the equation z = (x -µ)/σ in which µ is the mean and σ the standard deviation. 160 PCA was also applied separately to the plant macrofossil concentration data and diatom counts. Prior to analysis, the values were log-transformed (log (x + 1)). The PCA Axis 1 (PC1) sample scores of each data set are plotted against age. PCA was performed using the PAST program  (Hammer et al. , 2001). To better constrain the onset or start point of the change in our data set, we applied a probabilistic model to the log-165 transformed MS data (see  Erhardt, T.
This is consistent with the change from peat to lacustrine deposits in the lake record indicating the formation of the lake (Fig. 1c ) at 2753 (+95/-207, 2σ) cal yr BP, bringing lacustrine diatom species onto the peat surface by temporary flooding and/or wind induced water spray. A short delay in the onset and a more gradual change in the biological proxies in comparison with the minerogenic proxy data can be expected due to a slower response time of ecosystems to changing conditions  (Birks et al. , 2000). 255 In summary, the Morne Rouge 5000 yearlong proxy record points to a regime-shift to wetter and windier conditions occurring shortly after 2800 cal yr BP. Additional evidence for a shift in the SH atmospheric circulation pattern can be found in Antarctic ice core data. The timing of the changes in the peat core coincides with the most pronounced minimum in deuterium excess (d) in the EDC ice core for the complete Holocene (Masson-Delmotte et al.
, 2011)  as well as in the Southern Annular Mode (SAM) in the South Atlantic and Indian Ocean basins during the austral summer (Fig. 3a, b ). In consequence, the westerly wind belts shift equatorward  (Thompson and   3f ). These two patterns hence reflect the dynamical short term (ERA-20C) and radiative long term (model) SST response to the 11-yr cycle and a grand solar maximum, respectively. However, both datasets indicate a decrease in SST in the area around the Crozet archipelago during periods of low solar activity (Fig. 3e, f ) consistent with the deuterium excess (d) minimum during the Homeric minimum in the EDC ice core record (Fig. 2f ). Although less well-335 dated our findings in the Indian Ocean are supported by evidence found in a marine proxy record located off the western coast from southern South America suggesting a persistent link between low (high) solar activity and an equator-ward (pole-ward) shift of the SH westerly wind belt during the last 3000 years  (Varma et al.
Acknowledgements This research was made possible thanks to the logistic and financial support of the  French Polar Institute  ( Programme IPEV   136 ) and the  CNRS  ( Zone atelier de recherches sur l'environnement antarctique et subantarctique ). We 355 acknowledge  Ryszard Ochyra  ( Institute of Botany, Polish Academy of Sciences, Krakow, Poland ) for helping out with some bryophyte determinations. We thank  Bryan Lougheed  ( Uppsala University, Sweden ),  Pieter Vroon ,  Frank Peeters  and  Sander Veraverbeke  ( Vrije Universiteit Amsterdam, The Netherlands ) for additional analysis and interesting discussion. Rick Hennekam  and the late  Rineke Gieles  ( NIOZ, The Netherlands ) are thanked for the XRFanalysis. Funding for NVdP, AM, FA and JS was provided by the  Swedish Research Council  and through a  VR Linnaeus grant  to  Lund University (LUCCI).
As a result, the wealth of microarray data allowed multiple secondary analyses. There are several gene coexpression databases and public tools for A. thaliana including ACT  (Jen et al. , 2006; Manfield et al. , 2006) , Atted-II  (Obayashi et al. , 2018) , AraNet v2  (Lee et al. , 2015) , EXPath 2. 0  (Chien et al. , 2015) , PLANEX  (Yim et al. , 2013) , Genevestigator  (Hruz et al. , 2008) , SeedNet  (Bassel et al. , 2011) , FlowerNet  (Pearce et al. , 2015) , AtGGM2014  (Ma et al. , 2015) , and GEM2Net  (Zaag et al. , 2015) , the latter four employing coexpression networks in their approach. We introduce a new version of the ACT website, originally developed over 15 years ago, bringing the tool up to date with the latest discoveries in microarray analysis and A. thaliana gene-related data.
The enriched terms of all three aspects of Gene Ontology are indeed related to ribosome and translation process, with very low false-discovery-rate (FDR)-adjusted p values, ranging from $10 À176 to $10 À156. KEGG pathway analysis similarly suggested a ribosomal role and Pfam analysis showed an enrichment of ribosomal protein families. Terms related to ''cotyledon'' and ''embryo structure'' emerged as overrepresented plant anatomy terms in plant ontology, while a term combining the last two terms (''plant embryo cotyledonary stage'') appeared as overrepresented in plant structure developmental stage analysis. Transcription factor enrichment analysis using both AtRegNet and Plant Cistrome Database revealed two transcription factors, AT1G72740 (TRB5) and TRB2, which belong to the homeodomain-like/winged-helix DNA-binding family of proteins.
ACT's strength lies in simplicity and focus, specifically catering for molecular biologists, producing easy-tounderstand biologically relevant outputs, avoiding user information overload which characterized the original version and other coexpression tools. Furthermore, the overrepresentation of adjusted p values are presented in commonly understood numeric format (e. g. 1. 0•10 À15 ) instead of the scientific numeric format (e. g. 1. 0E-15), while results with p values>0. 05 are omitted to prevent the inclusion of nonstatistically significant terms. The enrichment summary tables are easy to produce inside ACT, provide various descriptions for each term with links redirecting to relevant external databases and can be easily copied through any web browser. Most tools, including ACT, perform Gene Ontology enrichment analysis.
This specific kind of enrichment analysis is unique to ACT which uses data of confirmed transcription factors directly targeting A. thaliana genes from both AtRegNet and DAP-seq experimentbased Plant Cistrome database. Moreover, the gene list of each ACT sub tree is provided in an easy-touse format for downstream analysis. Links to STRING for creating PPI networks and ThaleMine for gene list analysis which includes bibliography enrichment, are provided through ACT. SeedNet and GEM2Net differ in their approach, as they categorize genes through their gene expression, both positively and negatively, based on seed germination and biotic/abiotic stress, respectively. Although SeedNet provides a comprehensible coexpression network visualization while including both correlated and anticorrelated genes, it offers no enrichment analysis.
Limitations of the study The main limitation of ACT is inherited by the transcriptomic technology it is based on: Microarrays are unable to study the expression of genes for which no probe is available on the surface of the chip. In addition, cross-hybridization may distort the estimation of the correlation between members of the same family of genes and other genes, especially when default CDF is used. These issues are overcome by the use of RNA-seq which steadily replaces microarrays. Publicly available RNA-seq data for A. thaliana have exceeded that of microarray data, both in terms of quantity and quality. However, although RNA-seq has higher sensitivity, its output is highly comparable with that of microarrays, especially in average expression levels  (Chen et al. , 2017). Additionally, RNA-seq-based and microarray-based GCNs have been shown to produce similar correlation values  (Malatras et al.
Affymetrix provides array quality metrics for each sample as well as general guidelines for the value thresholds, for example the percentage difference of present genes between samples of the same study should be no higher than 10% and 3 0 to 5 0 ratio of GAPDH and b-actin should not be higher than 1. 25 and 3, respectively. As an additional quality control within series, Normalized Unscaled Standard Error (NUSE) and Relative Log Expression (RLE) multi-array metrics were used. NUSE boxplots should be centered at 1 with the low-quality samples centered above 1. 1. RLE boxplots should be centered at near 0 and have similar spread with low-quality samples having an absolute spread higher than 0. 2. Low-quality samples were identified primarily based on the output of RLE and NUSE. In the final step, whole plant or mutant samples were identified by examining the meta-data information of each sample and were manually removed.
Since our main aim was the study of the global (i. e. tissue-independent) coexpression landscape of Arabidopsis thaliana, tissue bias had to be minimised by choosing the most representative samples of the entire dataset. Thus, the tree of 6933 sample-leaves, was programmatically pruned in an iterative procedure using an in-house algorithm trimming close leaves, where in each iteration the leaf with the shortest distance to its first common node was trimmed, leaving 3500 leaves which represent the most distinct samples. The gene expression values of the 3500 samples were used for the calculation of pairwise gene correlations as r-values and a gene distance matrix using the same d = 1 -r formula. Finally, the 21,273 gene-leaves coexpression tree was created, using UPGMA based on the distance matrix.
Web tool implementation The web server is hosted on a Linux Ubuntu 18. 04, 16-core, 64 GB memory system. A web-based user interface was created using HTML5 and CSS along with the Bootstrap library and certain JavaScript functions, such as the gene name and probe set ID auto-completion of the search field. All ACT scripts performing tasks such as the database connection, tree visualisation and enrichment analysis are written in PHP and run on an HTTPS protocol-verified Apache 2. 4. 29 web server. An Arabidopsis thaliana gene is selected by the user, deemed the ''driver'' gene, and a gene coexpression subtree with 5 ancestral nodes is produced, based on the location of that driver gene on the gene coexpression tree. A scale bar, referring to r-values, is also displayed at the top of the subtree.
ACKNOWLEDGMENTS We acknowledge support of this work by the project '' ELIXIR-GR: Managing and Analysing Life Sciences Data '' (MIS:  5002780 ) which is implemented under the Action '' Reinforcement of the Research and Innovation Infrastructure '', funded by the  Operational Programme ''Competitiveness, Entrepreneurship and Innovation'' (NSRF 2014 ( NSRF -2020 ) ) and co-financed by Greece and the  European Union  ( European Regional Development Fund).
Deposited data Arabidopsis thaliana microarray samples -ArrayExpress  (Kolesnikov et al. , 2015)  https://www. ebi. ac. uk/arrayexpress/ Arabidopsis thaliana microarray samples -GEO  (Barrett et al. , 2013)  https://www. ncbi. nlm. nih. gov/geo/ Arabidopsis thaliana microarray samples -NASCArrays (Craigon et al. , 2004) http://bar. utoronto. ca/NASCArrays/index. php Full list of microarray samples used for ACT This paper https://data. mendeley. com/datasets/hgvk669v89/ Software and algorithms Single channel array normalisation (SCAN) (Piccolo et al. , 2012) https://www. bioconductor. org/packages/release/ bioc/html/SCAN. UPC. html Brainarray Custom CDF version 23 (Dai et al. , 2005) http://mbni. org/customcdf/23. 0. 0/ensg. download/ pd. ath1121501. at. ensg_23. 0. 0. tar. gz Simpleaffy (Miller, 2018) https://web.
METHOD DETAILS Expression data collection and processing For coexpression analysis, all microarray data should be comparable to each other. Therefore, they need to originate from the same organism and the same type of chip and to be normalised with the same algorithm and the same parameters. ArrayExpress  (Kolesnikov et al. , 2015) , GEO  (Barrett et al. , 2013)  and NASCArrays  (Craigon et al. , 2004 ) public repositories were searched for Arabidopsis thaliana microarray experiments of all chip platforms. It was discovered that the most popular microarray chip in use is the Affymetrix Arabidopsis ATH1 Genome Array [GEO:GPL198; ArrayExpress:A-AFFY-2] representing more than 50% of the total microarray data volume. Arabidopsis thaliana ATH1 raw microarray data (CEL files) and their respective MIAME  (Brazma et al.
, 2001)  meta-data were programmatically downloaded from the aforementioned public repositories. After duplicate and corrupt sample removal, using an in-house PHP script, our dataset consisted of 19,887 unique microarray samples from 1390 studies. A suitable normalization algorithm was selected for this single channel microarray chip: The samples were normalized with the Single Channel Array Normalisation (SCAN) algorithm  (Piccolo et al. , 2012)  using Brainarray Custom Chip Description File (version 23)  (Dai et al. , 2005). A MySQL relational database was designed to store all required data: gene expression values and metadata of each sample, as well as Arabidopsis thaliana gene description terms. Gene names and brief descriptions were downloaded from Thalemine  (Krishnakumar et al. , 2015) , gene ontologies from Gene Ontology (Gene Ontology Consortium, 2021), plant ontologies from Planteome  (Cooper et al.
, 2018) , biological pathways from KEGG Pathways  (Kanehisa and Goto, 2000) , AraCyc  (Schlapfer et al. , 2017)  and WikiPathways  (Martens et al. , 2021) , transcription factor gene targets from AtRegNet  (Yilmaz et al. , 2011)  and Plant Cistrome Database  (O'Malley et al. , 2016)  and protein domains from Pfam  (Mistry et al. , 2021). Most of those data were programmatically downloaded, exploiting BioMart  (Kinsella et al. , 2011)  XML-based data retrieval system, in the majority of the cases.
Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2), is an ongoing global pandemic with high morbidity and mortality. 1 Patients with preexisting conditions have increased morbidity and mortality when infected with COVID-19. 1  There is no published data on the outcomes of patients with severe valvular heart disease (VHD) and COVID-19. Our aim was to assess the clinical outcomes of severe VHD patients infected with COVID-19 from sites participating in a large multicenter collaboration. The COVID-19 Valve Disease (CVD) Registry is an international multicenter collaboration founded by the Institute of Valvular Research (Redmond, WA, United States) in March 2020, in response to the urgent public health crisis of COVID-19. Patients with severe VHD and either confirmed COVID-19 by real-time polymerase chain reaction (RT-PCR) test or strongly suspected COVID-19 by clear clinical presentation (typical symptoms after direct contact with an identified patient) and/or imaging were included.
Patients were included between February 2020 and May 2020 from 26 centers in 9 countries (Spain, Italy, United States, France, Brazil, Denmark, Poland, United Kingdom and Israel). The study was also centrally approved by an institutional review board of Hebrew University. Anonymized data were collected in a dedicated electronic case report form after local institutional review board approval. Due to the observational nature of the study informed consent was waived. The primary endpoint of the present analysis is all-cause mortality from the start of the hospitalization to 30 days. Student's t test or oneway analysis of variance (ANOVA) were used to compare means of normally distributed continuous data between groups. The Mann-Whitney U-test and the Kruskal-Wallis one-way ANOVA were used to compare distributions of nonnormally distributed continuous data.
χ 2 and Fisher's exact tests were used to compare proportions of categorical variables. Time-to-first event rates were estimated using the Kaplan-Meier method and compared with the log-rank test. A two-tailed p-value < 0. 05 was considered statistically significant. Statistical analyses were performed with SPSS 23 (IBM Corporation, Armonk, NY, USA). A total of 136 patients with severe VHD were included, among whom a COVID-19 RT-PCR test was positive in 123 (90. 4%). All but one patient (99. 3%) were hospitalized. Only nine patients (6. 6%) did not have any COVID-19 symptoms (i. e. fever, tachycardia, altered mental status, anosmia, cough, fatigue, shortness of breath, diarrhea or nausea). Severe AS was the most frequent type of VHD (54. 4%), followed by severe mitral regurgitation (MR; 20.
Data collection and outcome measures The screening centre (ADECI 35) was responsible for recording exclusions, inviting the target population, and reminding them to enroll in the study if they did not participate after an initial letter. The centre also prospectively recorded all data regarding participation, FOBT results, and if positive, colonoscopy compliance and findings. Lesions found during colonoscopy and the corresponding treatment were prospectively recorded in the database, and these data were submitted by the gastroenterologists. Since the implementation of CRC screening in 2003, all pathology laboratories in the district and adjacent districts were required to send the details (first name, last name, date of birth) of all patients 50 years of age and older diagnosed with CRC to the screening centre, allowing the screening centre to cross-reference all CRCs diagnosed in patients with a negative test in 'Ille-et-Vilaine'.
In the present study, the In-CRC rate was quite similar in the two gFOBT-based rounds (44. 0%, 44. 6%), i. e. , approximately 4-fold higher than in the FIT-based round (10. 5%). In our gFOBT-based screening programme that started in 2003, the In-CRC rate was 33% in the first round and 40% in the second round (data not shown). Very similar data were recorded thereafter, highlighting the role that the switch from gFOBT to FIT plays in the dramatic decrease reported here. Because the sensitivity of the screening test is an inverse function of the interval cancer rate, the age-and sex-adjusted sensitivity estimates in this study were 54. 5% and 55. 4% for gFOBT and 88. 6% for FIT. In two recent metaanalyses, FIT sensitivity estimates for CRC detection ranged from 69% to 80% and 77% to 94% depending on the positivity threshold.
Sixth, in searching risk factors for In-CRC among negatives, we were unable to examine the burden of participant factors such as smoking, diabetes, hypertension and obesity, which were previously identified in a meta-analysis as increasing the risk of false-negative FIT, because dietary or lifestyle patterns of participants in our study were not recorded. 19  conclude, this population-based study demonstrated a dramatic decrease in the cumulative incidence rate of In-CRCs after switching from gFOBT to FIT within an established mature CRC screening programme. Our study demonstrates the higher sensitivity of FIT compared to gFOBT regardless of age, sex, screening rank (except first screening), season for screening and cancer location. These data provide an additional incentive for countries still using gFOBT to switch to FIT.
The walls were modelled without limitation conditions. Maxwell equations can be used to represent the relationship between the magnetic field H, the temperature of the magnetocaloric material and its entropy, Ec. 2. ( 𝜕𝑆(𝑇, 𝐻) 𝜕𝐻 ) = ( 𝜕𝑀(𝑇, 𝐻) 𝜕𝑇 ) (2) For isothermal conditions, Ec. 2 can be integrated and obtained in the form of the equation below: ∆𝑆 (𝑇, ∆𝐻) = ( 𝜕𝑀(𝑇, 𝐻) 𝜕𝑇 )𝑑𝐻 (3) Table  1. Properties of the material used in the simulation of COMSOL Multiphysics software. 3.