dataesr/scientific-paragraphs-categorization huggingface dataset:
This decomposition was made at five scales, and only the four last scales were considered to compute coefficients. For each of them, three statistical measures were computed: the standard deviation, the energy, and the entropy. The third category of methods investigated was in regard to deep learning methods and more specifically in regard to CNN, which are known to be well-suited methods for image classification, thanks to robust feature patterns  [5]. Many architectures were used to address ImageNet challenges, and their associated performances were analyzed  [31]. Instead of training this network from scratch, as we have data constraints and also computational constraints, we choose a Domain Adaptation approach for these models. Most of the papers to date have dealt with CNN trained on ImageNet  [7] , as this database contains thousands of classes and more than 14 million images, meaning that the extracted features from these networks can be used in various fields.
As discussed in a previous work  [32] , Inception-V3 architecture pre-trained on ImageNet is thought to be the most relevant for medical applications. As RCM images can have various forms and can contain specifics details compared to other image modalities, this research compares the most well known CNN architectures: VGG-16  [33] , Inception-V3  [34] , ResNet  [35]  and Inception-ResNet  [36] , with accuracies of 0. 71, 0. 76, 0. 78, and 0. 80, respectively, on the ImageNet database  [31]. This method involves the use of Transfer Learning, by removing the last layers devoted to the classification task, in order to obtain a new representation of the image data as features. Furthermore, in order to reduce the number of features provided by the previous step, a global pooling layer on each activation layer was performed.
Conclusions This research investigated the classification of malignant tumors and particularly LM/LMM pathologies at the image-level and at the patient-level. Firstly, at the image-level, an analysis was performed of previous research and proposed methods based on Transfer Learning showing that the Inception-ResNet architecture trained on the ImageNet database is quite relevant for classifying RCM images, with a weighted F 1 score of 0. 82 between benign and malignant labels. Secondly at the patient-level, supervised decision methods were compared to MIL methods based on feature extraction with the Inception-ResNet architecture. On the first hand, the classification of malignancy over patients achieves a weighted F 1 score of 0. 84 with an AUC score of 0. 88 using the supervised method based on a dynamic threshold.
Experimental All 1 H and 13 C{ 1 H} NMR spectra were obtained on either a Bruker Avance III 400 or Avance 600 spectrometer, the latter being equipped with a cryoprobe. All spectra were recorded using CDCl 3 which was dried and degassed over molecular sieves prior to use; 1 H and 13 C{ 1 H} chemical shis are reported relative to SiMe 4. The mass spectra were obtained using either Micromass 70-SE spectrometer using Electron Ionisation (EI) or a Thermo Finnigan MAT900xp spectrometer using Fast Atom Bombardment (FAB). Elemental analysis was carried using Elemental Analyser (CE-440) (Exeter Analytical Inc). PXRD patterns were measured on a Brucker AXS D4 diffractometer using Cu Ka 1 radiation. The diffraction patterns obtained were compared to database standards. For TEM characterisation a 4 mL droplet of nanoparticle suspension (chloroform) was placed on a holey carbon-coated copper TEM grid and allowed to evaporate in air under ambient laboratory conditions for several minutes.
Acknowledgements We thank the  EPSRC  for funding and beamline staff at DUBBLE,  ESRF  for their technical support and the  Netherlands Organisation for Scientic Research (NWO)  for making beam time available on the DUBBLE beamlines at the  ESRF. WB's contribution is based upon work supported by  Oak Ridge National Laboratory , managed by  UT-Battelle, LLC , for the  U. S. Department of Energy.
RNA sequencing and data analysis In order to perform the transcriptomic maps of the S. simiae CCM 7213T, S. capitis SK14 and S. epidermidis RP62A, strains were grown in TSBg at 37 • C and 200 rpm until exponential phase was reached. Total RNAs were extracted as previously described  (30). RNA sequencing and preliminary data analysis was carried out by the Stab Vida company. The RNA-seq reads from the S. simiae CCM 7213T, S. capitis SK14 and S. epidermidis RP62A samples were aligned using the Rockhopper program  (31)  and the complete genome sequences from S. simiae NCTC13838 (NZ LT906460. 1), S. capitis AYP1020 (NZ CP007601. 1) and S. epidermidis RP62A (NC 002976. 3) as references, respectively. The obtained read coverage files (. wig) from the plus and minus strands were converted to BigWig (.
bw) files with the wig2BigWig program. The generated. bw files and the gene annotation files, including the positions of transcriptional terminators (TTs) predicted by TransTerm HP program  (32) , were loaded into a public web server based on Jbrowse  (27)  (http://rnamaps. unavarra. es/). The 3 UTR lengths of the orthologous monocistronic mRNAs were manually annotated and plotted against the lengths of previously described S. aureus 3 UTRs (5).
For the mapping of the 3 UTR of rpiRc from S. epidermidis and S. capitis primers RACE-3XFLAG-A and RACE-RpiRc-sau-B were used. PCR products were run in 2. 5% agarose gels and bands of the expected size were purified and ligated into the pGEM-T Easy vector (Promega). The resulting reactions were used to transform E. coli XL1-Blue cells (Stratagene). Ten isolated white colonies were then analyzed by Sanger sequencing. Transcript boundaries were determined by blastn analysis and nucleotide frequencies representing the 5 and 3 ends, respectively, were registered as percentages (%).
Next, they were ligated into pJET and digested with BamHI/ KpnI and BamHI/EcoRI in the case of 3xF FtnA and 3xF RpiRc, respectively, and inserted into pEW. The p 3xF RpiRc+3 UTR IS256 plasmid was generated by PCR simulating the 3 UTR of rpiRc from the S. aureus strain 2010-60-6511-5 (GenBank: JJCE00000000. 1), which carries an IS256 insertion. Briefly, IS256 was amplified from the S. aureus strain 15981, using primers IS256-fw and IS256-3 UTR-RpiRc-rvs and the 3 UTR fragment was amplified from p 3xF RpiRc, using the RpiRc-CDS-SpeI-fw and 3UTR-RpiRc-42-IS256-rvs primers (Supplementary Table  S3 ). Then, an overlapping PCR with RpiRc-CDS-SpeIfw/IS256-3 UTR-RpiRc-rvs primers was used to generate the 3 UTR carrying the IS256 insertion. The amplified product was ligated into pJET. Natural restriction sites were used to digest p 3xF RpiRc with SpeI/EcoRI, pJET 3xF RpiRc with NspI/EcoRI and pJET-3 UTR+IS256 with SpeI/NspI, followed by a double-fragment ligation into the pEW plasmid in order to recreate an rpiRc gene with the IS256 insertion in its 3 UTR.
The plasmid carrying 3xF RpiRc with the IS1181 insertion in the 3 UTR, p 3xF RpiRc+3 UTR IS1181 , was generated simulating the 3 UTR of rpiRc from the S. aureus strain DAR1183 (GenBank: KK099086. 1) by PCR. This was achieved by amplifying the IS1181 from the S. aureus strain N315 and the 3'UTR from p 3xF RpiRc using primers 3 UTR-RpiRc-IS1181-fw / IS1181-rvs and IS1181-3UTR-RpiRc-fw / pCN-univ-AT, respectively (Supplementary Table  S3 ). An overlapping PCR with the primer pair 3 UTR-RpiRc-IS1181-fw/pCN-univ-rv-AT was used to generate the 3 UTR carrying IS1181, which was then cloned into pJET. Natural restriction sites (SpeI/KasI or SpeI/HindIII) were used to digest p 3xF RpiRc while HindIII/KasI were used to digest pJET-3 UTR+IS1181 with HindIII/KasI. Then, a double-fragment ligation into the pEW plasmid was performed to recreate an rpiRc gene carrying the IS1181 insertion in its 3'UTR.
Identification of elements disrupting the rpiRc mRNA sequence All available S. aureus genome sequences were retrieved from ftp://ftp. ncbi. nlm. nih. gov/genomes/ through the wget command-line utility. The Fasta files were loaded into a local nucleotide database using the Geneious software package. Next, a Blastn algorithm was locally run using the rpiRc mRNA as a query. Genomes presenting pairwise alignment disruption located at the rpiRc 3 UTR were registered. When possible, sequences surrounding the position of alignment disruptions were used to look for insertion sequences in the IS database (https://isfinder. biotoul. fr/) (40). Note that in some cases, due to the assembly protocol, repeated sequences were eliminated from contig ends avoiding the identification of the IS responsible for the alignment disruption.
UTRs are highly variable among staphylococcal species Next-generation RNA sequencing (NGS) technologies allow to accurately determine mRNA boundaries and, therefore, determine the lengths of the untranslated regions (UTRs). To evaluate the evolutionary relationship between coding sequences (CDSs) and their corresponding 3 UTRs, we performed genome-wide comparative analyses. Using high-resolution transcriptome maps of S. aureus as a reference, we analysed the 3 UTR sequence conservation of close phylogenetic members of the genus Staphylococcus (Supplementary Figure  S1 ). In order to simplify the study, we focused on monocistronic mRNAs. This ensured that each analysed CDS was flanked by a 5 and a 3 UTR. First, we generated a query database including the 3 UTR sequences plus the last 200 nucleotides (nt) of each monocistronic CDS (∼590 sequences).
Meanwhile, in most of the other staphylococcal species, the maximum is located within the region comprised between -10/+10 nt from the stop codon (Figure  1B  and Supplementary Figure  S3B ). It is expected that non-coding sequences accumulate more changes throughout the course of evolution than their corresponding CDSs, which cannot significantly change without dramatically affecting protein functionality. In agreement with this, we reasoned that 5 UTRs (excluding the ribosome binding region) should suffer a similar degree of nucleotide variation than the 3 UTRs. Therefore, we performed the same blastn comparison, using a query database that included the 5 UTR sequences plus the first 200 nt of the CDS (Supplementary Table  S5 ). As shown by the scatter plots in Supplementary Figure  S4 , evident variations among the 5 UTRs of orthologous genes existed.
Most of the mRNAs encoding orthologous proteins contain 3 UTRs with different lengths and sequences in Staphylococcaceae The fact that the conservation of several 3 UTRs was almost fully lost after the CDSs indicated that the mRNAs encoding orthologous proteins had different 3 UTRs. To determine and compare the real length of 3 UTRs among closely-related staphylococcal species, we performed RNA sequencing of whole transcripts from the S. simiae, S. epidermidis and S. capitis strains. The obtained transcriptomic maps were loaded into a Jbrowse-based server  (27) , which is publicly available at http://rnamaps. unavarra. es. Then, the lengths of the 3 UTRs from each S. aureus orthologous monocistronic gene were manually annotated by combining the RNA-seq data and the Rho-independent transcriptional terminator predictions (using TransTermHP algorithm)  (32).
We reasoned that alignment disruptions could help us identify repeated regions. Following this idea, we used the whole rpiRc mRNA sequence as the query to perform blastn against all draft and complete S. aureus genomes available on the NCBI database. The results showed that 33 S. aureus genomes presented alignment disruptions within the rpiRc 3 UTR (Supplementary Table  S6 ). Since many of these genomes were not fully closed, some alignment disruptions occurred due to the query sequence aligning to different contigs. By analysing the extremes of the aligned contigs, we identified that 16 out of the 33 genomes contained IS insertions in different locations of the rpiRc 3 UTR sequence. Specifically, we were able to map nine IS1181  (54) , five IS256  (55)  and two ISs of the IS30 transposase family (Figure  5A  and Supplementary Table  S6 ).
coli and B. subtilis, as observed in S. aureus, we performed similar genome-wide 3 UTR conservation analyses. Since the 3end transcript boundaries were recently released for E. coli and B. subtilis  (28, 29) , we generated a query database including the sequences of all mapped 3 UTRs plus the last 200 nt of their corresponding CDSs. Using blastn, we compared the E. coli and B. subtilis databases against representative genomes of species from the Enterobacteriaceae family and the genus Bacillus, respectively. We found a similar outcome to the one observed for the genus Staphylococcus, meaning that 3 UTR sequences were also variable among mRNAs encoding orthologous proteins (Figure  8 , Supplementary Figure  S15 , Supplementary Tables  S7  and S8 ). The bacterial species of the Enterobacteriaceae family showed a higher number of conserved 3 UTRs in comparison with the Bacillus species.
ACKNOWLEDGEMENTS We thank  Dr Jaione Valle ,  Prof. Cristina Solano ,  Prof. I ñigo Lasa ,  Prof. Josep Casades  ús and  Prof. Pascale Romby  for their excellent advice and their critical reading of the manuscript;  Prof. Tomas Maira-Litrán  for the anti-PIA-PNAG antibodies;  B. Garcia  for her helpful assistance with RNA extraction. We also thank the following organizations for providing strains and plasmids: Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA) for providing pCN40 (NR-46131) and pCN47 (Ref. NR-46141) plasmids constructed by  Prof. Emmanuelle Charpentier  and Staphylococcus aureus strains  N315  ( NR-45898 ) and Staphylococcus epidermidis strain RP62A (Ref. ATCC 35984) through  BEI Resources, NIAID ,  NIH  and the  Human Microbiome Project  for providing Staphylococcus capitis strain  SK14 (Ref HM-117 ) also through  BEI Resources, NIAID ,  NIH.
DATA AVAILABILITY The transcriptomic maps of S. simiae CCM 7213T, S. capitis SK14 and S. epidermidis RP62A strains are available at http://rnamaps. unavarra. es/. The transcriptomic data has been also deposited in GEO under accession GSE143600. Other data or material that support the findings of this study can be made available by the corresponding author upon request.
The saturation equation with φ = 1 is the equation used in algebraic VOF solvers such as interFoam, the VOF solver of the open-source CFD code OpenFOAM R. There, the relative velocity v r is used as a compression term to force the fluid-fluid interface to be as sharp as possible  (Rusche, 2002). This compression velocity acts in the direction normal to the interface. In the VOF framework, the normal to the fluid-fluid interface is computed using the gradient of the saturation. Rusche (2002)  proposes a relative velocity oriented in the direction normal to interface with a value based on the maximum magnitude of v: v r = C α max v n lg , (22) where C α is a model parameter used to control the compression of the interface and n lg is mean normal vector. For low values of C α , the interface diffuses.
Numerical implementation The two-phase multi-scale micro-continuum model proposed in this paper is implemented in the open-source CFD platform OpenFOAM R version 7. 0 from https://www. openfoam. org. This code is a C++ library that solves partial differential equations with the finite-volume method. It handles complex structured and unstructured three dimensional grids by default and has demonstrated a good scalability for parallel computing of flow in porous media  (Orgogozo et al. , 2014; Horgue et al. , 2015; Guibert et al. , 2015). One of its features is that it solves the coupled equations using sequential approaches. The present section details the solution algorithm developed in this paper. Particularly close attention is paid to the description of the velocity-pressure coupling.
Study population The Constances study was approved by authorities regulating ethical data collection in France (CCTIRS: ComiteConsultatif pour le Traitement des Informations Relatives à la Sante; CNIL-Commission Nationale Informatique et Liberte) and all participants signed an informed consent. The Constances cohort is a national population-based cohort of randomly recruited participants, including volunteers aged 18-69 years at baseline recruited in 22 selected Health Screening Centers (HSC) from the principal regions of France  [32]. Using a random sampling strategy stratified according to unequal inclusion probabilities, participants were invited to participate, with a response rate of 7. 3%  [32]. At inclusion, the selected subjects are invited to complete questionnaires and to attend a HSC for a comprehensive health examination.
Data The present study used the data collected by questionnaire at baseline and at the first 12 month-follow-up for the 39,487 participants included between 2012 and 2015 (S1 Fig and  S1  File). We excluded subjects with the following characteristics at baseline: 1) participants professionally non active at baseline (n = 14,240), 2) self-declared CTS or chronic hand symptoms at baseline (n = 2,295), 3) pregnant women at follow up (n = 333) and 4) workers with missing data for at least one of the variables studied (n = 4,601). Finally, 18,018 persons (8,733 men and 9,285 women) with complete data have been included in the study.
Interaction between biomechanical wrist exposure and chemical exposure was tested and was not significant (p = 0. 300 in men and p = 0. 205 in women). A sensitivity analysis was performed for a subpopulation restricted to low-grade white-collar workers and blue-collar workers. All analyses were performed using SAS 9. 4 using the logistic procedure. Results are presented as Odds ratio (OR) with their 95% confidence intervals (95% CI). A p value <0. 05 was considered as statistically significant.
The  CONSTANCES Cohort Study  is supported by the  Caisse Nationale d'Assurance Maladie (CNAM) , the  French Ministry of Health , the  Ministry of Research , the Institut national de la santeet de la recherche medicale. CONSTANCES benefits from a grant from the  French National Research Agency  [grant number  ANR-11-INBS-0002 ] and is also partly funded by  MSD ,  AstraZeneca  and  Lundbeck. This work was also supported by the  French National Research Program for Environmental and Occupational Health of Anses  ( EST-2014/1/077 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript.
| Participants Twenty healthy men were recruited to be exposed to 5 days of DI. Two subjects were excluded before BDC-5 for reasons unrelated to protocol. A total of 18 subjects were included in the study and randomly divided at BDC-2 into Control or Cuffs group (9/9 split). Participants were anonymized and designated in data sets by single letters. Subjects B, E, F, I, K, M, O, Q, and S were included in control group while A, C, D, G, H, J, N, P, and R were included in Cuffs group. All subjects were informed about the experimental procedures and gave their written consent. The experimental protocol conformed to the standards set   1. There was no significant difference between groups at baseline (Table  1 ).
| Statistical analysis All data are presented in plots (individual data) or as mean ± SD (group data). The average variation observed in participants after DI compared to before DI is expressed in percentage (%). The normality of each distribution and homogeneity of variance were assessed with the Kolmogorov-Smirnov and Bartlett test, respectively. Statistical significance was checked using a two-way ANOVA for repeated measures. Data were analyzed using GraphPad Prism version 6. 02 for Windows (GraphPad Software, La Jolla, California).
E Y W O R D S bedrest, erythropoiesis, iron misdistribution, muscle atrophy, spaceflight F I G U R E 1 Dry immersion method: the subject is immersed in thermoneutral water covered with an elastic waterproof fabric (Photo MEDES) by the Declaration of Helsinki and was approved by the local Ethic Committee (CPP Est III: October 2, 2018, n° ID RCB 2018-A01470-55) and French Health Authorities (ANSM: August 13, 2018, ClinicalTrials. gov Identifier: NCT03915457). Baseline group characteristics are detailed in Table.
ACKNOWLEDGMENTS The authors thank the volunteers and the staff of MEDES for participation in this protocol at the MEDES space clinic in 2018 and in 2019. This work was supported by the  Centre National d'Etudes Spatiales. The work was also supported by  INRAE  and  Brittany Council  and partially supported by an  AlimH department grant. The authors thank  Stephanie Zindilis  and  Elisabetta Andermarcher  for expert manuscript editing.
K E Y W O R D S bedrest, erythropoiesis, iron misdistribution, muscle atrophy, spaceflight F I G U R E 1 Dry immersion method: the subject is immersed in thermoneutral water covered with an elastic waterproof fabric (Photo MEDES) by the Declaration of Helsinki and was approved by the local Ethic Committee (CPP Est III: October 2, 2018, n° ID RCB 2018-A01470-55) and French Health Authorities (ANSM: August 13, 2018, ClinicalTrials. gov Identifier: NCT03915457). Baseline group characteristics are detailed in Table.
Study design ICARE is a large French case-control study of respiratory cancers  [20]. Briefly, all histologically confirmed primary malignant lung or upper aerodigestive tract incident cancer cases, aged 18-75 years and living in 10 French geographical areas ("départements") were recruited from French cancer registries in 2001-2007. Controls were randomly selected within the general population of the same geographical areas as the cases, using a random digit dialing procedure. Controls recruitment waves were conducted approximately every two months in 2001-2007, Data Protection Act and cannot be shared publicly. The large number of variables allows data to be indirectly identifiable, and making such data freely available is prohibited. Furthermore, an authorization from the CNIL (Commission nationale de l'informatique et des libertes), the French Data Protection Authority, may be required to transfer the data, especially abroad.
Data collection Subjects were face-to-face interviewed by trained interviewers with a detailed standardized questionnaire to collect information on sociodemographic characteristics, lifetime occupational history (all jobs held for at least one month) and lifetime smoking history. For each smoking episode, start and end dates, as well as average smoking intensity (number of cigarettes smoked per day) were reported. For each job, start and end dates, industrial activity (further coded using the French Nomenclature Activities (NAF) 1999) and occupation (further coded using the International Standard Classification of Occupations (ISCO) 1968) were recorded.
We thus used a more flexible normalizing transformation that allows normalizing distributions that are far from normal without relying on strong prior assumptions. More specifically, we used a Isplines function implemented in the lcmm R package  [22, 23]  with a combination of knots that best fitted the data according to Akaike Information Criteria (AIC). For smoking, we used three knots at 0, 20, 100 cig/day. For asbestos, because of the peak of annual intensities at zero (Fig S1b, Supplementary materials), no transformation function allowed the LCMM to converge when we included all subjects who were ever exposed to asbestos. We thus created an a priori class of subjects with a very low cumulative exposure over lifetime, and identified other classes using the LCMM on the remaining subjects.