dataesr/scientific-paragraphs-categorization huggingface dataset:
Since one of the objectives of the contest is to find the planetary component in the image, we had to refine our reconstruction to discard spurious components associated to systematics, noise, or to the secondary lobes of the synthesised beam. To refine the SQUEEZE solution, for the ten different images, we filter the three principal morphological components by masking all the other emission/noise in the field-of-view. With these masked maps, we then used BSMEM for a second iteration to find the best image. BSMEM uses a gradientdescent method to perform the minimisation process for the reconstruction. For this software, the regularisation uses mainly Gull-Skilling entropy and the hyper parameter value is selected automatically for the user. We performed 60 iterations for the reconstruction.
submission from J. Young The BSMEM (BiSpectrum Maximum Entropy Method) software was first written in 1992 to demonstrate image reconstruction from optical aperture synthesis data. It has been extensively enhanced and tested since then. BSMEM was adapted during 2015-2016 to work with the "OImaging" graphical user interface developed at JMMC. The 2018 contest entry was produced with v2. 1. 1 of BSMEM ‡ The BSMEM algorithm applies a fully Bayesian approach to the inverse problem of finding the most probable image given the evidence, making use of the Maximum Entropy approach to maximize the posterior probability of an image. The algorithm uses a trust region method with non-linear conjugate gradient steps to minimise the sum of the log(likelihoodχ 2 of the data given the image and a regularization term expressed as the Gull-Skilling entropy k I k -M k -I k log(I k /M k ).
The model image M k is usually chosen to be a Gaussian, a uniform disk, or a delta-function centered in the field of view, which conveniently fixes the location of the reconstructed object (the bispectra and powerspectra being invariant to translation). This type of starting model also acts as a support constraint by penalising the presence of flux far from the centre of the image. The oifitslib utility programs were used to merge the CHARA and VLTI contest datasets into a single OIFITS file, and to remove the complex visibility and triple product amplitude data. An initial reconstruction was made from the combined dataset using a simple circular Gaussian prior model. The approximate orientation and axial ratio of the protoplanetary disk were inferred from the initial reconstruction, and a new model image consistent with these parameters was created.
The model image was created in LITpro and comprised a compact circular Gaussian, representing the star, plus a flattened disk encompassing all of the believable circumstellar disk flux in the initial reconstruction (the model image can be found in the second HDU of the submitted FITS file). The submitted reconstruction was made using this second prior image. Given the systematic errors deliberately introduced into the contest dataset, BSMEM's automatic hyperparameter estimation was not used. The optimum regularization weight was chosen using the "L-curve" approach. The final reduced chi-squared of the submitted image was 1. 23. From the submitted image, the position angle (N through E) of the protoplanetary disk is 43 degrees. The supposed planet is 3. 76 mas from the central star, at position angle (N through E) 28.
Quantitative data will be analysed statistically using STATA software V. 15. Qualitative data will be transcribed and thematically analysed using MaxQDA software. Ethics and dissemination The Human Research Ethics Committee of the Canton Vaud (CER-VD) has certified that this research study protocol falls outside of the field of application of the Swiss Federal Act on Research Involving Humans. The publications and recommendations resulting from this study will form the starting point for future improvements to the protection of pregnant women at work and their unborn children. This study started in February 2017 and will continue until January 2020.
Quantitative methodology Sample selection and data collection The questionnaire for companies will be carried out by telephone on a sample of 200 companies distinguished by economic sector and size. The sample will be chosen at random from the Business and Enterprise Register by the Swiss Federal Statistical Office. An appointment will be made with a competent person within the organisation to answer the questionnaire. This telephone interview should be completed within 20 min. The expected success rate for this operation is 25%, which implies that the initial sample will have to comprise 800 companies. The questionnaire for gynaecologist-obstetricians and midwives will be sent out via email to all of Frenchspeaking Switzerland's specialist gynaecologists-obstetricians, to doctors undergoing their specialisation and to members of the Swiss Federation of Midwives (FSSF), whether they work in hospital environments or private practice.
Statistical analyses Data from the questionnaires' responses will be extracted into an Excel spreadsheet format and will be treated using STATA V. 14 software. Statistical analysis of the responses to the questionnaires from healthcare professionals will involve several stages: ► Simple descriptive and correlational statistics on all the items recovered, including associations, will be tested using Fisher's exact test. ► Multivariate and hierarchical cluster analyses. This analysis aims to generate hierarchical clusters (or typologies), from the variables describing healthcare professionals' practices on the one hand and from the variables describing their attitudes vis-à-vis legal provisions on the other. The objective is to identify the typologies of practice and attitudes by grouping together subjects who gave similar responses.
Data Data will be collected from the semistructured qualitative interviews with the principal actors involved in maternity protection in participating companies: (1) workers who have been pregnant at work within the last 5 years; (2) HR managers, employers and heads of department; (3) occupational physicians or occupational safety specialists; (4) other actors in the company who are involved in the implementation of MPL (eg, occupational health nurses, social workers, staff or union representatives). Table  3  resumes the number of interviews involved in each case study.
Thematic analysis of the interviews and focus groups Interviews and focus groups will be respectively audio and video recorded, and their transcripts will be written out verbatim. In transcripts, participants' names will be substituted with a pseudonym, and any personal references which might enable identification of one of the participants will be erased. A thematic analysis will be undertaken to identify all the key categories and themes which emerge from the interviews. Segments of discourse will be coded using MaxQDA software in order to systematically treat passages referring to different themes. 29 Any inter-rater disagreement as to which codes should be applied to which segments will be resolved by consensus among the research team. This activity is, therefore,  Open access a co-construction of knowledge, whose goal is to help knowledge from these different domains emerge using an inductive approach.
EthICs And dIssEMInAtIon Ethical and safety considerations The Human Research Ethics Committee of the Canton Vaud (CER-VD) has certified that this research study protocol falls outside of the field of application of the Swiss Federal Act on Research Involving Humans because it will not collect 'personal health data'. The research team members have made sure that the study respects the following ethical principles: all the personal data gathered will be treated confidentially; questionnaire, interview and focus group participants will be anonymised; written informed consent will be requested from all the interview and focus group participants; data will be securely stored; and the data will only be used for research purposes. Participation in this research study will be voluntary. In the hypothetical case where, during an interview, an employee reveals the existence of a working environment which might be dangerous for her health or that of her colleagues, the research team will suggest that she contacts the Institute for Work and Health.
Bioinformatic analysis and model building Reference genomes used for bioinformatics analyses are U00096. 3 for E. coli MG1655 and NC 003197. 1 for Salmonella LT2. Dedicated scripts for the detection of C>G bubbles and [(YC)N 9→13 ] n motifs in genomic sequences and for the production of sequence descriptors for a given DNA template (or genomic region) were written in language Python 2. 7 (the definitions of the descriptors and Python scripts are provided in Supplementary information). The C>G bubbles were identified using a 78-nt sliding window, as described previously  (13). DNA regions were defined as C>G bubbles if they comply with %C ≥ %G (percentages calculated over the 78-nt window) at all positions and with %C > %G at one position at least. Regions were considered devoid of C>G bubbles if they comply with %C ≤ %G at all positions.
Minimum RNA folding free energies were determined with the Mfold software (52) using a locally installed 3. 2 version and the RNA Quickfold option (standard RNA setting rules: 37 • C; 1 M Na + , 0 M Mg 2+ ; structures: 5% suboptimal, default window size, 100 folding max; no limit on maximum distance between paired bases). The 104 DNA templates tested in vitro were divided into three classes ('Strong', 'Weak' or 'None') according to their capacity to elicit Rho-dependent termination (see legend to Figure  3  for details). The capacity of each sequence descriptor to discriminate between classes was evaluated with ANOVA (Analysis of Variance) and post-hoc Student-Newman-Keuls (P ≤ 0. 05) significance tests using Kaleidagraph 4. 0 software (Synergy). Multivariate analyses, including Principal Component Analysis (PCA), Projection to Latent Structures Discriminant Analysis (PLS-DA), and OPLS-DA were performed with SIMCA 14.
1 software (Umetrics) using a training set composed of the termination responses ('Strong', 'Weak' or 'None') measured with the 104 DNA templates used in this work (Supplementary Table  S1 ). Explanatory variables (descriptors) were centered and auto-scaled to unit variance (UV scaling) using the default settings of SIMCA. The significance of PCA, PLS-DA and OPLS-DA components and the performance of resulting models were tested by jackknife cross validation (CV) with the training set as implemented in SIMCA (1/7th of the data held out and used as test set per CV round). Quality assessment for fit and prediction was based, respectively, on values of R 2 , the fraction of the Sum of Squares (SS) explained by the component (or R 2  cum for the several components of a model), and Q 2 , the fraction of the total variation of 'descriptors' or 'response' (i.
e. dummy Y pred variables in (O)PLS-DA) that can be predicted by a component (Q 2 cum for several components). The reliability and degree of overfitting of the (O)PLS-DA models were assessed, respectively, with CV-ANOVA of the cross-validated predictive residuals  (53)  and permutation plots (100 permutations) as implemented in SIMCA. Receiver Operating Characteristic (ROC) curves were calculated in SIMCA with the full training set of 104 observations and distinct moving threshold parameters for PCA-class (proprietary P Mod XPS probability) and (O)PLS-DA (dummy Y pred response variable) models. For validated models with Q 2 cum < 0. 5, we verified that the quality parameters remain stable after permutation of the rows in the dataset, as recommended  (54). Following guidelines from SIMCA's manufacturer, sequences with a 'probability of model membership' (proprietary P mod XPS + parameter in SIMCA) lower than 0.
e. component weights)  (57, 58). In this way, patterns of similarities among variables and/or observations are more easily identified and can be used to generate predictive models. We first performed PCA with the whole set of 104 templates (i. e. observations) and 111 descriptors (i. e. variables) using the dedicated SIMCA software (see methods). The resulting PCA model contains four significant principal components which are able to separate 'None', 'Weak' and 'Strong' observations in fairly distinct populations even though some population overlap remains (Figure  4B  and Supplementary Figure  S5 ). As expected, PCA separation is most effective along principal component 1 for 'None' versus 'Weak' or 'Strong' populations (Figure  4B  and Supplementary Figure  S5 ), which is in large part due to C>G bubble and G-rich motif descriptors (Figure  4C ).
The significant separation obtained with PCA (Figure  4B ), where specific classes are not defined a priori (unsupervised approach), encouraged us to test several supervised classification approaches suited for prediction  (58). Using the SIMCA software, we generated prediction models based on (i) PCA-class analysis where disjoint (one per class) PCA models are used, (ii) Projections to Latent Structures Discriminant Analysis (PLS-DA) and (iii) Orthogonal PLS-DA (OPLS-DA). The PLS-DA and OPLS-DA methods rely on similar principles to build (O)PLS components representing the best possible compromise between the description of the explanatory variables (here 'descriptors') and the prediction of the response (here, 'None', 'Weak' or 'Strong'). In OPLS-DA, orthogonal components are also produced to isolate the variation in the explanatory variables that is uncorrelated (orthogonal) to the response  (58).
We found that the downstream sequences are more frequently and more strongly associated with the 'Strong' termination class than the upstream sequences (Figure  5D ), which is consistent with 3exonucleolytic trimming of the Rho-dependent transcripts. Taken together, these data strongly suggest that a 'Strong' region status (as determined with the OPLS-DA-111 model) is a good prognostic for in vivo Rho-dependent termination. This status likely reflects a sequence composition that is intrinsically favorable to Rho activity but does not provide information on the potential presence of other regulatory signals, such as intrinsic terminators, within the same region. For instance, 2. 5% of the 'Strong' regions predicted for the MG1655 genome also contain intrinsic terminators listed in regulonDB  (59) , accounting for ∼40% of the terminators in this database (Supplementary Table  S8 ).
4% of the MG1655 or LT2 sequence. We anticipate that additional relevant descriptors, of possibly higher sequence complexity, will be found with a larger training set. Increasing the number of observations would also lend higher statistical power to OPLS-DA modelling of Rho-dependent termination. In this respect, sampling genomic sequences that are not reliably modeled by the current OPLS-DA-111 model (Figure  5B , inset) could prove particularly effective. Using new observations to build a 'naïve' set of data sufficiently large and representative of each termination class to better evaluate the false positive rate of the model could also prove useful. However, a major hindrance to the significant expansion of the training set or assembly of a naïve dataset (or to the deployment of our method in species harboring potentially divergent Rhodependent machineries  (6, 45, 48) ) rests with the relatively high cost and low-throughput of current transcription termination assays.
Indeed, Rho-dependent regulation in the wellconserved 5 UTR of mgtA  (9, 73)  was also not detected with this system  (69). Thus, it may be worth looking deeper into the potential involvement of Rho in btuB regulation. In summary, we have built the largest collection of DNA templates for in vitro characterization of Rho-dependent termination and identified new terminators that could play an important role in the regulation of E. coli, Salmonella, and related species. The resulting dataset was used to develop sequence descriptors and a multivariate OPLS-DA model that fits experimental data (including transcriptomics) reasonably well. This work thus establishes the proof of principle and provides a solid groundwork for computational modelling of Rho-dependent termination (the strategy might even be tested with the NusG-dependent fraction of terminators once a relevant dataset is available for model training).
In particular, we aimed to test a recent hypothesis that a major, more than 300 • C along-strike contrast in subduction zone temperature concluded from the geology of Turkey resulted from a corresponding major change in trench strike (see next section;  van Hinsbergen et al. , 2016). To this end, a simple 3-D thermo-kinematic numerical setup was designed and computed using the finite-element code ELEFANT  (Thieulot, 2014; Lavecchia et al. , 2017). Below, we review selected present-day subduction zones and their geometric characteristics as a basis for our numerical model setting. Then, we summarize the rationale behind the hypothesis of  van Hinsbergen et al. (2016)  relating lateral variations in metamorphic grade recognized in the geology of western and central Turkey to oblique subduction.
Symbol Name Value Units C p specific heat 1250 J kg -1 K -1 ρ volumetric mass density 3300 kg m -3 µ effective viscosity 10 22 Pa s -1 k thermal conductivity 2. 5 W m -1 K -1 the effects of trench geometry and subduction rate on mantle flow and therefore on the temperatures. We used the finite-element code ELEFANT  (Thieulot, 2014; Lavecchia et al. , 2017)  to solve the mass, momentum and energy conservation equations in three dimensions: ∇ • v = 0, (1) -∇P + ∇ • (2µε) = 0, (2) ρ 0 C p ∂T ∂t + v • ∇T = ∇ • (k∇T ), (3) under the Boussinesq approximation with v the velocity, P the dynamic pressure, µ the effective viscosity, ε the strainrate tensor, ρ the volumetric mass density, C p the specific heat, T the absolute temperature, t the time and k the thermal conductivity. All parameter values are given in Table  1.
Acknowledgements. We thank  Luuk Schuurmans  for investigating several aspects of the presented research. Riad Hassani  is warmly thanked for his help with the implementation of the stabilized Q 1 Q 1 finite-element formulation in ELEFANT. Alexis Plunder  and  Douwe J. J. van Hinsbergen  were funded through  ERC starting grant SINK  ( 306810 ). Douwe J. J. van Hinsbergen  acknowledges  NWO Vidi  grant  864. 11. 004. Alexis Plunder thanks Loes van Unnik Hoorn  for tremendous discussions during his postdoc at  Utrecht University  and the preparation of this paper. The perceptually uniform color maps davos and vik were used in this study to prevent visual distortion of the data (http://www. fabiocrameri. ch/visualisation. php). Reviews by  Manuele Faccenda  and an anonymous reviewer were much appreciated and helped to strengthen the paper.
Statistical Analysis Comet assay data were analysed by two-way analysis of variance (ANOVA), and mode of interaction × time was used to compare the effects induced by the chemicals in the analysed mode of interaction. Differences between samples with normal distribution were evaluated. ANOVA was performed using OriginPro 6. 1 software (OriginLab Corporation, Northampton, MA, USA). Dose-response relationships were determined by Pearson's correlation. Differences were regarded as significant at p ≤ 0. 05. The results were presented as means ± standard error of the mean (S. E. M. ).
The results for this sample are currently being written up. For the purpose of the present paper, a data-file was compiled of respondents self-identifying as having normal weight and a little overweight, and from this sample, only those respondents were included in the data analysis who were considered as having normal weight based on a BMI smaller than 25. This cut-off value left n = 447 respondents for data analysis. Note that of these 447 respondents with objectively normal weight based on BMI < 25, 74 (16. 6%) categorized themselves as having a little overweight. Similarly, of the 265 respondents who were objectively overweight as indicated by a BMI > 25, 35 (8. 6%), categorized themselves as a person with normal weight. The final sample consisted of 447 respondents with normal weight (BMI < 25; N = 447; 223 female, 224 male; M age = 56.
All these procedures were performed following conventional methodologies by the histological platform of the GABI Joint Research Unit (INRA, Jouy-en-Josas). Tissues were visualized using a high-capacity digital slide scanner (3DHISTECH Ltd. , Budapest) and Panoramic and Case software (3DHISTECH Ltd. ). For each animal, at least three tissue sections were evaluated to characterize alterations in mucosal architecture, the degree of immune cell infiltration, and Goblet cell depletion (Ameho score: 0-6)  (Ameho et al. , 1997).
In brief, the cells were washed with PBS and incubated for 30 min with 0. 5 µg of purified anti-mouse CD16/CD32 and surface antibodies (anti-CD4, anti-CD3) in PBS with 10% FBS and 1% sodium azide (Sigma-Aldrich). Intracellular staining was performed as follows using the Foxp3 Transcription Factor Staining Buffer Kit (eBioscience) in accordance with manufacturer instructions. Briefly, samples were washed with PBS and incubated for 20 min with a permeabilization/fixation buffer. They were then stained with intracellular antibodies (anti-T-bet-APC and anti-Gata3-PerCP or anti-Foxp3-PE) in permeabilization buffer over a period of 30 min. Samples were subsequently washed in permeabilization buffer, resuspended in PBS, and analyzed using an Accuri C6 cytometer (BD). The data obtained from the cytofluorimetric analysis were processed using CFlow Sampler software (BD).
Statistics Statistical analyses were performed using GraphPad (GraphPad Software, San Diego, CA, United States). Survival curves analyses have been performed by Logrank test (Mantel Cox). For weight curves, a multiple unpaired T-test was performed per day with fewer assumptions corrected for multiple comparison with Holm-Sidak method. Normality and variance analysis were performed using Shapiro-Wilk normality test and one-way ANOVA (Brown-Forsythe test), respectively. For normal samples (Gaussian distribution) with equal variances two-way ANOVA has been performed to compare the effect of the strain and the dose for the first trial and of the sex and the dose for the second trial; multiple comparisons were carried out using Tukey's test. For non-normal samples or/and with unequal variances non-parametric tests have been performed inside the groups (Kruskal-Wallis test); multiple comparisons were carried out using Dunn's test.
ACKNOWLEDGMENTS We would like to thank the a Bridge platform ( UMR 1313 GABI ) and the MIMA2 platform for giving us access to the virtual slide scanner (Pannoramic SCAN,  3DHISTECH ). We wish to thank  UEAR  and the histological platform staff (especially  Abdelhak Boukadiri)  for their technical help; they also engaged in fruitful discussions with us. Finally, we also thank  Aurelie Cotillard  for her advice and help in the statistical analyses.
All samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole MS operated at unit mass resolving power with electron impact ionization and a 50e750 atomic mass unit scan range. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and were curated by visual inspection for quality control using software developed at Metabolon, Inc  [23]. 2. 6. Statistical analysis 2. 6. 1. Gene expression, and blood/plasma biochemical data Statistical analyses were performed using Prism 5. 0. Data from the two conditions were analyzed using two-way ANOVA, using time and diet condition as the grouping variable.