dataesr/scientific-paragraphs-categorization huggingface dataset:
More frequent macrocyclization has certainly contributed to the drift toward the disc edge. Another reason for that trend is that next-generation kinase inhibitors need to escape a crowded IP space while being as selective as possible. Therefore, the design of new chemically diverse structures is highly desirable. The annotated kinome tree provides a visual overview of the kinase space targeted by approved PKIs and PKIs currently in development. Because we focused on PKIs having received an INN name, inhibitors identified by their research code in the clinicaltrials. gov database were not considered and, therefore, do not appear on the kinome tree. The most targeted family is the Tyrosine Kinase group (TK) while the Casein Kinase 1 group (CK1) stays untouched. TKs have been shown to play a critical role in cancer biological pathways  [21].
The PKIDB database can be accessed through a website page available at http://www. icoa. fr/ pkidb. It can be browsed using a spreadsheet-like interface and is monthly updated. We hope that this resource will assist researchers in their quest for novel kinase inhibitors. NB: since the analysis of PKIDB (version December 2017), 2 new kinase inhibitors (capivasertib, and neflamapimod in phase 2 clinical trials) were added to PKIDB, resulting in the database consisting of a total of 182 PKIs.
Materials and Methods Since 1953, when the first INN list for pharmaceutical substances was published by the World Health Assembly  [23] , a cumulative list of around 8800 names provides a unique and universally available designated name to identify each pharmaceutical substance. An important feature of the INN system is that the names of pharmacologically-related substances demonstrate their relationship by using a common "stem". Each stem helps the community in medical and pharmaceutical areas to recognize that the substance belongs to a group of substances having similar pharmacological activity. Here, we have collected the entire set of protein kinase inhibitors with known INN by filtering data retrieved from several sources including the ChEMBL DrugStore  [24]  and clinicaltrials. gov  [25].
Missing structures and additional information were retrieved later using ChemSpyder  [26] , Unichem  [27] , DrugBank  [28] , PDB  [5]  and Guide to Pharmacology  [29]. Then, a manual annotation and curation of structure and data were performed. First, using a blacklist we removed some erroneous INN like "afatanib" or "idelisib" which should be spelled afatinib and idelalisib, respectively. Then, we manually checked chemical structures, FDA approval of new PKIs and added indications. The name of pharmaceutical companies that develop each drug candidate was manually collected. We manually annotated the inhibitor type based on previous work from the Möbitz dataset  [30]  and completed by visual inspection of co-crystal structure when available. The targeted kinase of each molecule was manually collected when no target was provided in DrugBank.
, Montreal, QC, Canada)  [33]. Three compounds (diacetylene, benzene and adamantane) were added to the PKIs dataset as reference shapes to delimit the PMI triangle. For all molecules, an energy minimization was performed in MOE before PMI calculation using the MMFF94x forcefield. Calculator Plugins Marvin 15. 2. 9. 0, 2015, ChemAxon (http://www. chemaxon. com) [13]  were used for additional logP calculations as well molconvert for 2D molecule depiction in the website http://www. icoa. fr/pkidb. Two PCAs were performed in R with the ade4  [34]  and factoextra packages version 1. 7. 6 and 1. 0. 4, respectively. We used a set of 11 physicochemical descriptors (Table  4 ) known to be well suited to describe the chemical diversity in PCA reduction analysis. In the first PCA, we collected 974 approved oral drugs by removing PKIs, salts, and molecules with a phase <4 from the ChEMBL drugstore  [12].
A manual suppression of outliers was done after PCA projection of the 974 molecules in PC1 and PC2. Eighteen outliers were removed, and the final projection contained 956 oral approved drugs and 180 kinase inhibitors. An outlier is a molecule characterized by an extreme value in some variables and therefore results in an excessive contribution to the PCA that biases the projection. The second PCA was conducted after manual annotation of inhibitor type in order to highlight the chemical diversity in inhibitor type. Except for PCA, all figures are made using Python Matplotlib package version 2. 1. 1. Figure  2  uses the backbone of the VEGFR2 receptor (PDB ID 4ASD) as a template and was created in MOE.
Acknowledgments: The authors wish to thank the  Région Centre Val de Loire and Janssen  for financial support. The authors also thank  ChemAxon  for providing an academic license free of charge. F. C. , S. B. and P. B. are supported by  LABEX SynOrg  ( ANR-11-LABX-0029 ). The authors also thank  Laurent Robin  for maintaining the website PKIDB.
01)τ k and τ 0 = 10 -6. For SLP in Φ(x; ν) = f (x) + ν p i=1 max(0, -g i (x)) it was set ν 0 = 5. In Algorithm 1 (reported in the main paper) ρ bad = 0. 10 and ρ good = 0. 75, and the linear subproblems were solved using the Matlab function linprog(Interior Point Algorithm) with default choice parameters. For each test function 25 runs were performed, as required by the CEC'2006 Special Session. The results are reported in Table  2. For each one of the test cases the following statistics are reported: f and k arithmetic mean of the function values and the number of iterations, σ f the standard deviation on function values, min f and max f minimum and maximum function values obtained, time(s) total time in seconds, feas the percentage of runs in which a feasible solution is found. Noticeably, PSOc 3 always finds feasible solutions in all runs, while SLP method is not so robust from this point of view, in 4 out of 13 tests (g07, g08, g09, g10) a feasible solution is found in 75%-90% of the runs and especially in instance g06 a feasible solution is never found.
2016). Noticeably, in many runs the standard deviation is really low. As suggested for example in  (Derrac et al. 2011; García et al. 2009 ) a nonparametric analysis of the results was performed. First, the Wilcoxon test was used to compare the methods performance, based on the mean values found, through the Matlab function signrank. Wilcoxon test is a nonparametric test that is used to determine whether two independent samples were selected from populations having the same distribution, therefore it can be employed to detect significant differences between the behaviour of two algorithms. The null hypothesis is that the difference between two sample means is zero. It emerged that the null hypothesis of equivalence of the two algorithm cannot be rejected at significance level α = 0. 05. Denoting with R + the sum of ranks for the problems in which SLP outperforms PSOc 3 , and R -the sum of ranks for the opposite, R + = 33 and R -= 58 are obtained.
On the other hand SLP method results to be equivalent to all the hybrids, except for MMC-DE-SC-interleaved for which SLP shows an improvement at significance level α = 0. 01 and R + = 77, with R + the sum of ranks for the problems in which SLP outperforms MMC-DE-SC-interleaved. Then a Friedman test was performed to compare PSOc 3 , PSO-3P-SC, MMC-DE, MMC-DE-SC-interleaved and SLP, followed by a post-hoc analysis through the Matlab function multcompare. This function, using the statistics provided by the Friedman test, performs N × N multiple comparisons, where N is the number of algorithms to be tested, and estimates the difference of average ranks (MathWorks 2017;  King and Mody 2010). The result of the test is a matrix of multiple comparison results, returned as an p-by-6 matrix of scalar values, where p is the number of pairs.
(2014). MEIGO is a toolbox, whose Matlab code is available, in which an enhanced scatter search method (ESS), a population-based metaheuristic, is implemented. In the toolbox an option is available to apply a local search to the current population to increase the convergence to optimal solutions. In the experiments this option was not enabled, as it employs first-order information (approximated by finite differences) and therefore a comparison with PSO methods, that uses only function values, would not be fair. To compare the codes they were applied to the problems in Table  1  and the performance profile approach in  Moré and Wild (2009)  was employed. The main idea behind the performance profile approach is briefly outlined here, for a more detailed description see  Dolan and Moré (2002).
Immunological analysis was conducted blind, and clinical data were not available to the immunology staff before the end of the study. NI including catheter-related bloodstream infections, ventilator-associated pneumonia, and sternal wound infections were defined based on the Center of Disease Control (CDC) and National Nosocomial Infections Surveillance criteria  [12]  and were prospectively recorded during the PICU stay or within 30 days after surgery. All parents were informed of the project and written consent was waived. The study was approved by the local ethics committee of the University Hospital of Nantes.
Statistics Statistical analyses were performed using GraphPad Prism software (GraphPad, La Jolla, CA). The Kruskal-Wallis test was used for comparisons of multiple groups (preoperatively, 1, 2, 3, and 4 days after CPB). Dunn's multiple comparisons test was used as a post hoc test for intergroup comparisons. Continuous nonparametric variables were expressed as medians (extremes values). The Mann-Whitney test was used to compare two independent groups on day 4 (infected versus non-infected patients). Significance was defined as p-value less than 0. 05.
Zircon has been the best choice for decades to date and correlate geological events of the Earth's history as it preserves long-lived isotope systems  1, 2. The mineral offers a range of isotopic systematics and trace element distribution to investigate petrochronological information, such as magmatic and crustal differentiation history of granitic plutons and their petrogenesis  3. In many studies, the isotopic signatures of zircon have made promises to detect provenance of detrital sediments and to trace supercontinent reconstructions from Nuna to Pangaea  [4] [5] [6]. Uranium and thorium are readily incorporated into zircon as trace elements, whereas lead isotopes ( 206 Pb,  207  Pb and 208 Pb) in the crystal are produced by radioactive decay from 238 U,  235  U and 232 Th, allowing the age of the crystal to be determined by isotopic dating.
The 3D nanopetrography of the inclusions and fractures in the zircon can be visualized and quantified by segmenting the dataset into different phases and using 3D rendering techniques (Fig. 1f , video in supplementary material). A well-defined euhedral shape of the zircon can be seen outlining the crystal geometry, and transparent rendering of the outline yields the internal nanotextures including distribution of fractures, inclusions and pores. Inclusions (in red in Fig. 1f ) can be broadly divided into 2 categories: elongated, narrow inclusions with prismatic shape corresponding to those with intermediate grayvalue, and shorter roundish inclusions with a relatively low grayvalue, but containing the majority of the bright phase. Mineralogy of these inclusions is discussed in the X-ray diffraction section below.
X-ray absorption and fluorescence Imaging While mapping the zircon internal structure in 3D, holotomography does not provide any chemical information as to which trace elements are responsible for producing the zoning pattern, or to the exact composition of inclusions. On selected virtual 2D slices through the zircon, the trace element distribution can be obtained from X-ray fluorescence tomography (XRF-CT, see e. g. Wildenschild and Sheppard 31 for an overview). A detailed description of the data treatment is left to the analytical methods section, but in short, an XRF-CT scan is performed with zircon in the focus of the X-ray beam, so that only a narrow strip of approximately 50 nm × 50 nm through the zircon is illuminated at a time. Scanning the sample across the beam and rotating between line scans, a sinogram is obtained in the pencil beam CT geometry.
A more detailed discussion of detection limits and quantification of XRF-CT data is given in the analytical methods section. Part of the inclusion appears void with all methods: absorption-, fluorescence-and holotomography. 3b. The zircon core in orange, the trace element rich zone surrounding the core in purple, non-zoned sectors outside the core in red, and oscillatory zoned domains in cyan. Major X-ray fluorescence peaks are indicated, and all spectra are normalized to the number of pixels in the corresponding region.
After this, the imaging camera was replaced by the FReLoN diffraction camera, and a total of 3001 diffraction images were acquired over a 360° rotation with 1. 2 s dwell time per step (0. 6 s exposure + 0. 6 s read-out). Data analysis. Nanotomography. After acquisition, the projection images of the four CT scans acquired of each sample were aligned and phase maps calculated using the same Paganin-like approach as Villanova et al. 33  , using in-house software developed at the ESRF. The phase maps were used as input to tomographic reconstruction using the ESRF PyHST software. After reconstruction, the volumetric data was semi-automatically segmented using various filtering, thresholding and volume growing tools in Avizo software (v. 9. 3, ThermoFisher Scientific, Hillsboro, Oregon, USA) and 3D renderings of the data produced with VGStudioMax software (Volume Graphics GmbH, Heidelberg, Germany).
XRF-CT and attenuation tomography. An inhouse-developed Matlab (Mathworks Inc. , Natick, Massachusetts, USA) program was used to correct the fluorescence spectra for detector dead time, normalize them to the incoming beam intensity, and perform tomographic reconstruction of both XRF and attenuation data with the filtered back-projection (FBP) method. Typically XRF-CT data are analyzed by least-squares fitting the measured spectra to determine the contributions of characteristic X-ray emission from selected elements. This leads to one sinogram per element, which can then be tomographically reconstructed to obtain the spatial distribution of that element in the sample. In this work, an alternative approach was taken, performing tomographic reconstruction on each channel (corresponding to 10 eV) of the fluorescence spectrum individually.
This is the case for the Rb and Sr observed in the inclusion in A1973b (Fig. 5 ), in the sum spectrum especially the Rb is completely masked out by the U L α fluorescence line, and only revealed through isolation of the fluorescence originating from the inclusion. In this work, the ROI imaging toolbox of the PyMCA software  34  was used for analysis and visualization of the spectra after reconstruction. The energy scale was calibrated using the Ar K α , Hf L α and Y K α fluorescence lines present in the sum spectrum of each sample. For the fits presented in Table  1  and Supplementary Figure  S1 , the MCA Advanced fit tool of PyMCA was used: the background was first removed using the 'strip background' method with 25 channel peak width and 4000 iterations. After this, the remaining spectrum was fitted with Gaussian peaks corresponding to the K-fluorescence lines of Ar, Ca, Ti, Cr, Mn, Fe, Kr, Rb, Sr, Y, Zr and Mo, and the L-fluorescence lines of the lanthanides (except Pm), Hf, Tl, Pb, Rn, Th and U, using Poisson weighting of the data and 30 fit iterations.
6f ). The full-field diffraction scan used in the case of the inclusion in A1973b is conceptually simpler: diffraction from both the inclusion and zircon is present in every image. The only image processing used in this case was to apply a top-hat transform (commonly used to isolate small bright objects in an image) to the entire stack of 3001 images, in order to emphasize the contribution of the inclusion, which would otherwise be very weak compared to the zircon peaks. After this, the accumulated diffraction pattern was calculated and azimuthally integrated as in steps 5-6 above. In this work, in-house developed Matlab scripts were used for the FBP reconstruction, calculating the top-hat transforms and thresholding and segmentation of images (steps 1-5 above), PyHST was used for the forward projection (part of step 2 above), and the XRDUA software  37  for calibrating the acquisition geometry, background subtraction and azimuthal integration of the accumulated images (step 6).
Acknowledgements We thank the entire ID16B team at the  ESRF , especially  Rémi Tucoulou ,  Julie Villanova  and  Sylvain Labouré  for assistance in operating the beamline and data analysis. We are grateful to  Marta Majkut ,  ESRF , for discussions regarding the setup and analysis of the diffraction experiments, and to  Hannu Huhma  and  Raimo Lahtinen  of the  Geological Survey of Finland  for providing the zircon samples and useful discussions. As per journal policy, the editor and reviewers' names cannot be disclosed, however, both authors acknowledge their constructive reviews and suggestions.
Both mediation models had a complex two-level structure in which the predictors of the mediator and the mediator were measured at level 2 (parent), whereas the outcome was measured at level 1 (offspring). This type of mediation model has been labelled '2!2!1' in the literature. 23 Random intercept terms at level 2 were included in the models. Cluster-robust standard errors were computed in order to take the correlation among parents within each of the different centres (cluster variable) into account. Due to the complex mediation pattern (see the Supplementary Appendix, available as Supplementary data at IJE online), only controlled direct effects  24  (i. e. the effects of exposures on the outcome that would be observed if the mediator were controlled uniformly at a fixed value) were calculated.
Sensitivity analyses Sensitivity analyses (see the Supplementary Appendix, available as Supplementary data at IJE online) were performed in order to check whether: • the covariates included in the models represent the 'minimal sufficient adjustment set' (i. e. the group of measured covariates that needs to be included in order to eliminate confounding) through a directed acyclic graph (DAG; Supplementary Figure  1 , available as Supplementary data at IJE online),  25  using DAGitty (http://dagitty. net); • the inclusion of one unmeasured confounder in the models  26  changes the estimate of the direct effects of grandmothers' smoking on offspring's asthma, using the Umediation package [https://github. com/SharonLutz/ Umediation] in R3. 4. 1.
At present, public health strategies do not focus on the environment of male adolescents with regard to the health of their future offspring, and to do so would represent a paradigm shift in preventive policies. Our results are consistent with findings from the RHINE study,  16  a questionnaire-based postal follow-up of the ECRHS subjects from the seven Nordic centres listed in Supplementary Table  1 , available as Supplementary data at IJE online. A minority of the parents evaluated in RHINE (11. 5%) also underwent clinical examinations as part of the ECRHS and are included in this report. The present work is based on clinical interview data from these Nordic centres and 19 additional centres (located in other parts of Europe and Australia). One report from the Avon Longitudinal Study of Parents and Children (ALSPAC), showing that body fat increases in the sons of fathers who had started smoking in early puberty, also supports the hypothesis that paternal lifestyle and exposures well before conception may influence the health of their offspring.
However, the ECRHS is not a family-based study. It recruited a representative sample of men (fathers) and women (mothers), but their partners (co-parent of the offspring) did not participate in the study. Moreover, the information regarding grandparents and offspring was parent-reported, rather than directly assessed. This could have generated an information bias across generations and between the parental lines (see the Supplementary Appendix, available as Supplementary data at IJE online). It is also possible that important confounders [e. g. parental socioeconomic status (SES) and offspring's smoking history] were not included in the models. However, as we believe that an early start of smoking is one of the major SES-related exposures responsible for the influence of SES on health, we might expect that adjusting for this would attenuate our smoking-related associations.
Acknowledgements The  ALEC  project leader is  Deborah Jarvis. The manuscript was done with ALEC Workpackage 2 led by  Cecilie Svanes. Other Workpackage  leaders in ALEC are  John Henderson  ( University of Bristol, Bristol, UK), Judith Garcia-Aymerich [ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain], Nicole Probst-Hensch (Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland ) and  Cosetta Minelli  ( Imperial College London, London, UK ). The principal investigators and team members of the ECRHS are reported in the Supplementary Appendix, available as Supplementary data at IJE online. The authors commemorate the late  Professor Roberto de Marco , a passionate scientist and enlightened man who led the  Unit of Epidemiology and Medical Statistics (University of Verona ) until October 2015.
He contributed to the ALEC study with extraordinary commitment and inspiration. funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 633212. The coordination of the European Community  Respiratory Health Survey (ECRHS)  was supported by the  European Commission  (phases 1 and 2) and the  Medical Research Council  (phase 3). Local funding agencies for the ECRHS are reported in the Supplementary Appendix, available as Supplementary data at IJE online.
Database In this retrospective study, we used data from the nationwide Disease Analyzer database (IQVIA). The Disease Analyzer database contains demographic, clinical, and pharmaceutical data anonymously obtained from a nationwide sample of general and specialist practices  [9]. IQVIA regularly assesses the quality of information, and it has been previously found that the database is representative of primary care practices in Germany. The sample of practices is drawn based on the distribution of practices by region, specialty, and physician age groups in Germany. IQVIA updates the statistical plan, which forms the basis for the panel with the above-mentioned criteria, on a yearly basis based on the universe of all practices in Germany  [9]. Moreover, the panel practices transmit patient data to IQVIA (Frankfurt) on a monthly basis.
Statistical analysis The prevalence of VVC, defined as the proportion of women diagnosed with VVC (denominator: all women who visited the gynecological practices), was analyzed. The association between VVC diagnosis and the predefined variables was studied using a multivariate logistic regression model. Finally, the percentages of women diagnosed with VVC receiving therapy were also analyzed. P-value less than 0. 05 was considered significant. All the analysis were carried out using SAS 9. 4 (SAS Institute, Cary, NC, USA).