dataesr/scientific-paragraphs-categorization huggingface dataset:
Out of a number of FDA approved drugs, we decided to dock tipranavir (Fig. 6 ) and darunavir (Fig. 7 ) as they were reported to inhibit also protease dimerization in addition to enzymatic activity  16. We employed the program Autodock Vina 34 (see Methods section). We docked both inhibitors to each of the three binding pockets -S2 A, B and C. The binding affinities obtained by docking were -5. 8 kcal/mol, -7. 3 kcal/mol and -7. 8 kcal/mol for darunavir, respectively. Tipranavir shows higher binding affinities than darunavir: -7. 8 kcal/mol, -7. 9 kcal/mol and -9. 2 kcal/mol, respectively. For both inhibitors structure S2C displays the highest docking affinity: starting from the best docking pose we proceeded to estimate more accurately the binding free energy to the latter conformation of the partially open dimer, comparing it with that for the catalytic cavity of the closed dimer.
22, based on the weighted histogram analysis method, as implemented in the software Metagui  50. Parallel growth of each bias profile is observed after 28 ns of simulation per replica, therefore the trajectory data between 28 and 68 ns is employed for reconstructing the landscape. 150 structural clusters were obtained by the k-medoids algorithm, where each cluster is visited at least 10 times during the simulation (with trajectories being recorded every 5 ps). The robustness of the results were checked by changing the number of clusters between 100 and 200, obtaining very similar results, as well as by comparing the free energy difference between states S1 and S2 computed by WHAM with that estimated by the relative populations in the unbiased replica, obtaining a deviation smaller than 0. 7 kcal/mol.
Docking. Molecular docking was performed using the software AutoDock Vina  34. Ligands had fully flexible torsion degrees of freedom, while the receptor side chains were inflexible. Protein and ligand input files were prepared by AutoDockTools  51. The initial 3D structure of darunavir was extracted from the PDB file 3S53  52  , whereas tipranavir was taken out of the PDB file 3SPK  53. Non-polar hydrogen atoms of the protein and the ligands were merged. The center of grid was placed at the X = -2. 036 Å, Y = -1. 903 Å and Z = 0. 031 Å (with respect to the geometry provided as supporting information). Grid dimensions were 20 Å × 18 Å × 20 Å and the spacing between the grid points was set to 1 Å. The exhaustiveness parameter of the global search was set at 8. Twenty binding modes were generated for each inhibitor and each binding site.
Body temperature was maintained at 37-37. 5°C with a heating pad. A fluid-filled catheter was placed in the abdominal aorta via the femoral artery. A fluid-filled catheter was introduced via the left jugular vein into the right atrium to measure central venous pressure (CVP). A catheter-tipped transducer (Millar Instruments Inc, Houston, USA) was introduced into the left ventricle (LV) via the right carotid artery. Arterial, venous and LV pressures, as well as airway peak pressure were recorded simultaneously with a data acquisition system (IOX, EMKA Technologies). Maximal +dP/dt (+dP/dt max ), and Tau, the relaxation time constant, were calculated simultaneously from the LV pressure curve to assess left ventricular systolic and diastolic function  [11] [12].
Statistical analysis Data are expressed as the median with interquartile range (IQR). An ANOVA test for repeated measures analysis of variance was performed for comparisons over time between and within groups. One-way ANOVA analysis of variance, followed by a Student-Newman-Keuls post hoc test, was performed for pairwise comparisons. A p-value<0. 05 was considered statistically significant. Intra-observer reproducibility of echocardiographic parameters was assessed in ten rats: echocardiography was performed twice on each rat with a one-hour interval between measurements. Reproducibility was defined as the mean difference between two measurements, expressed as a percentage (absolute difference divided by the average of the two observations).
It should be noted that this data set is not evenly sampled in terms of central meridian longitude (CML). Because of the tilt of the Jovian magnetic field, some sub-Earth longitude ranges provide a better visibility of the aurorae than others, depending on the hemisphere. Hence, CMLs between 110 and 230 • S III have been preferentially observed in the north, while CMLs between 300 and 120 • S III have been preferred for the south. Additionally, the favored orientation for the northern aurorae's observations is such that, most of the time, the magnetic anomaly (around ∼ 100 • S III ) is located in the dusk sector, making it challenging to disentangle local time effects from the consequences of localized variations in the magnetic field amplitude in the ionosphere for the current system. Estimating the emitted power corresponding to an auroral feature on HST images is easier than estimating their brightness as seen from above because the power does not need to be corrected for limb brightening.
Conclusions While early models of the corotation enforcement current system that gives rise to the main auroral emission at Jupiter assumed symmetry around the magnetic axis, observations and further developments of these models now outline the importance of local time dependence. The present paper aims to characterize the local time variations in the main emission on a statistical basis, based on the large data set of FUV auroral observations carried out by the Hubble Space Telescope. Our study focusses on the dawn-dusk variation in the FUV emitted power in the dawn and dusk sectors of the main emission. Our results show that the dusk side is generally ∼ 3 times brighter than the dawn side, at least in the southern hemisphere. In the northern hemisphere, the ratio appears to be closer to unity, which is probably due to the combination of the limited CML coverage and the presence of a localized magnetic anomaly.
Acknowledgements. B. Bonfond is funded by the  Fund for Scientific Research (F. R. S-FNRS). B. Bonfond ,  J. Gustin ,  D. Grodent ,  A. Radioti , and  J. -C. Gérard  are supported by the  PRODEX program  managed by  ESA  in collaboration with the  Belgian Federal Science Policy Office. C. Tao  was supported by a  JSPS (Japan Society for the Promotion of Science) Postdoctoral Fellowships for Research Abroad. This research is based on observations with the  NASA/ESA Hubble Space Telescope , obtained at the  Space Telescope Science Institute , which is operated by  AURA  for  NASA. It is based on publicly available observations acquired with the  NASA/ESA Hubble Space Telescope (program IDs 10140 ,  10507 ,  10862 ) and obtained from the  Space Telescope Science Institute  (https://archive. stsci.
Most bacterial PTMs are dynamic and reversible. This allows the cell to exploit them as regulatory devices. It also means that the full understanding of the cellular roles of different PTMs necessitates global, quantitative and time-resolved studies. One seminal paper in this topic reports a novel proteomics approach for such quantitative studies: the Intensity Based Absolute Quantitation (iBAQ). Using this approach, the authors have quantified the expression and the occupancy of various PTM sites in the proteome of Escherichia coli  (Soufi et al. , 2015). The authors report remarkable differences in expression and occupancy of PTMs sites under different growth conditions. The dataset comprised 2300 proteins, which is close to 90% of the expressed proteome. This is an important landmark, since proteomics in general has not yet attained the same level of coverage that global transcriptome studies can achieve.
This is something that was demonstrated by providing kappa statistics of the performance of the technique versus a panel of expert pathologists. 1 ost research studies to date have been conducted on general-purpose research instruments and there is a need to develop and optimise commercial products to be trialled and validated. In this context, reproducibility of systems is a vital technical challenge to be addressed, as is transferability of datasets between systems. There is currently a huge gap between the research community and commercial/industrial partners. Big medical diagnostic companies need to be on board and engaged in the drive towards strategic and targeted technological development. Critically, while the research environment has demands on demonstrating the reproducibility of data, the demands of clinical deployment are substantially more rigorous.
Studies must, therefore, be extended to use of a clinically appropriate scale and statistical analysis to be considered meaningful. The statistical and ethical plan needs to be outlined for each study, which should include significant sample sizes (∼150 patients) and blind datasets, while outcomes should detail sensitivity, specificity, AUC etc. To date, the proof-of-concept has been adequately demonstrated, but there is a need to develop large (randomised controlled) clinical trials for the technology to be adopted by the clinical community. However, such trials carry considerable cost implications, taking into account implementation costs and the cost of developing further studies; the cost of (randomised) clinical trials and the cost of bringing something to market. The field of diagnostic applications of spectroscopy could potentially learn from the successes and failures of other technologies in the clinical field, and engaging with large transnational organisations such as the European Clinical Research Infrastructures Network (ECRIN: http://www.
Only then can large databases be established for both translational and research purposes. There is also a need for extensive validation and prospective testing of data pre-processing protocols as well as classification and regression models. In this context, using patient data, it is impossible to know what is the 'correct' result. Simulated datasets could play a key role in validating data pre-processing methodologies, ensuring that the spectral integrity is preserved. 34, 35 However, while they can play a similar role in validating classification algorithms, correlation with disease pathology and patient prognosis still relies upon consensus clinical standards. The use of archived tissue banks for retrospective studies may play a critical role in establishing such a clear correlation. There is much scope for instrumental development, in collaboration with the instrument companies.
45 evertheless, such models represent an exciting new development for in-vitro models which better mimic in-vivo conditions, and the emergence of IR tomographic image reconstruction using synchrotron sources to image these structures holds great promise. 46 The usefulness of Raman microspectroscopy 'optical sectioning' should also be emphasised. Whereas diagnostic applications rely largely on classification or regression algorithms, in-vitro applications can potentially exploit the full analytical capabilities of biospectroscopy. In this context, maintaining the integrity of the spectral information during data processing is imperative. As in the case of exvivo measurements, data preprocessing methodologies can, and should, be validated using simulated datasets. Ideally, such datasets should include spectral variability due to all potentially confounding experimental factors since limits of detection may ultimately be determined by such factors.
47 ost-processing and analysis protocols can similarly be validated and optimised to ensure that they produce the correct result. 33, 35 Simulated datasets can be employed to explore and develop the limits of biospectroscopy as an analytical technique, for example, to minimise the limits of detection of and to maximise the specificity of regression algorithms and feature selection based on spectral biomarkers. 47 With properly validated analytical techniques, biospectroscopy could aspire to the realisation of its potential as a truly label-free, highcontent screening technique based on the field of 'spectral-ohmics'. Achieving such goals, as well as those of clinical translation, relies much on continued instrumental development. Increased signal throughput and novel sampling techniques, such as those afforded by Quantum Cascade Lasers in IR  [48] [49] [50]  and Bragg filters in Raman,  51  may significantly reduce sampling times over large areas.
Through dereplication analysis, seven known Mammea coumarins were identified in a fraction obtained from Mammea neurophylla dichloromethane bark extract selected for its ability to prevent advanced glycation end-product (AGE) formation. Among them, a careful examination of the NMR dataset of pedilanthocoumarin B led to a structural revision. Inspection of LC-DAD-MS n chromatograms allowed us to predict the presence of four new compounds, which were further isolated. Using spectroscopic methods ( 1 H-, 13 C-and 2D-NMR, HRMS, UV), these compounds were identified as new benzoyl substituted 4-phenylcoumarins (iso-pedilanthocoumarin B and neurophyllol C) and 4-(1-acetoxypropyl)coumarins cyclo F (ochrocarpins H and I).
Dereplication of Known Mammea Coumarins By comparison of UV spectra, as well as mass spectra and fragmentations (Table  1 ) and by interlocking literature data via the Scifinder database  [8] , seven known coumarins were presumably detected in the fraction and associated with peaks 8. 1 to 8. 11 (Figure  S1 ). As far as peaks 8. 1, 8. 4 and 8. 8 to 8. 11 were concerned, mass fragmentation of the quasi-molecular peak in positive ionization mode suggested a 4-(1-acetoxypropyl)coumarin (Mammea E type coumarins), as a loss of both 42 and 60 u was observed to form a stable ion. In negative mode, the loss of 60 u confirmed this substitution. Their UV spectrum (λmax = 222, 295, sh 325-330 nm) indicated 4-alkyl-5,7-dihydroxycoumarins exhibiting an 8-acyl substituent  [9] [10] [11]. For 8. 10 and 8.
Neurophyllol C (9) The HRMALDI-TOFMS of Compound 9 exhibited a pseudomolecular ion [M -H] -at m/z 441. 1355, which corresponds to the molecular formula C27H23O6 (calcd. for [M -H] -: 441. 1344). LC-DAD-MS n analyses suggested a 4-phenylcoumarin bearing a benzoyl group at C-6 and a 2-hydroxy-3-methylbut-3-enyl moiety at C-8. This was confirmed by the NMR dataset of 9, which appeared similar to pedilanthocoumarin B  (7) , except that no signal corresponding to a prenyl group was observed. Instead, the presence of a 2-hydroxy-3-methylbut-3-enyl moiety at C-8 was deduced from characteristic signals  [15] : two methylenic proton signals at δH = 3. 07 (1H, dd, J = 8. 0 and 15. 0 Hz, H-1‴) and 3. 26 (1H, dd, J = 2. 5 and 15. 0 Hz, H-1‴), an oxymethine proton signal at δH = 4. 48 (1H, dd, J = 2. 5 and 8.
Fraction VIII Profiling LC-PDA-ESIMS analyses were performed on a Waters 2795 apparatus (Waters, Guyancourt, France) equipped with a Waters 2487 UV detector and coupled with an Esquire 3000 PLUS ESI ion trap mass spectrometer equipped with an electrospray source (Bruker, Wissembourg, France) assisted by the HyStar ® software (Bruker Daltonics, Wissembourg, France). A 10-µL sample was directly injected onto a Lichrospher 100 RP18 column (150 × 4. 6 mm, 5 µm, Merck, Darmstadt, Germany). The column was set at 20 °C. Separation was achieved using an acidic water/MeOH system. The mobile phase was as follows: water (HCOOH-0. 1%)/MeOH from 30:70 to 20:80 v/v in 45 min. The flow rate was 1 mL/min with a split of 90% before mass analysis. For the study, the wavelength was set at λ = 290 nm. For each peak of interest, the UV spectrum was obtained by scanning the sample in the range of 210 to 400 nm.
Acknowledgments This work was supported by a grant from the  Vietnamese government (Bach Tai Dang). We thank  B. Siegler  and  I. Freuze  for their assistance in NMR spectroscopy and EIMS, respectively. We are very grateful to the North and South Provinces of New Caledonia who facilitated our field investigation. We express our thanks to  J. -M. Veillon  of the  Botany and Plant Ecology Department, IRD, Nouméa , for his assistance in the botanical determination and  B. Fogliani  for plant collection.
The slides were then stained with 5% aqueous aniline blue mixed with 2% acetic acid (pH = 3. 5) for 5 min, washed with distilled water, and air-dried once more. The staining solution was prepared by adding 5 g of aniline blue (Water Blue, Fluka, Sigma-Aldrich, St. Louis, MO) to 100 mL PBS, filtering, and adjusting to a pH of 3. 5 with a solution of 2% glacial acetic acid (Merck, Darmstadt, Germany). The samples stained with aniline blue were then used to perform a computerized morphometric analysis using a computer-aided cell analysis system (Software Motic Image Advanced version 3. 0, Motic Spain, S. L. U. Barcelona, Spain) coupled to a Motic BA 210 optical microscope (Motic Spain, S. L. U. Barcelona, Spain) (Figure  1 ). The smears were examined using a 100× oil immersion objective (bright field).
Methods Information on the incidence of T1D and country characteristics were searched for in the 194 World Health Organization (WHO) member countries. T1D incidence was extracted from a systematic literature review of all papers published between 1975 and 2014, including the 2013 update from the International Diabetes Federation. The information on country characteristics was searched in public databases. We considered all indicators with a plausible relation with T1D and those previously reported as correlated with T1D, and for which there was less than 5% missing values. This yielded 77 indicators. Four domains were explored: Climate and environment, Demography, Economy, and Health Conditions. Bonferroni correction to correct false discovery rate (FDR) was used in bivariate analyses. Stepwise multiple regressions, served to identify independent predictors of the geographical variation of T1D.
Incidence of T1D data by country The country T1D incidence was obtained thanks to a systematic review following the PRISMA recommendations  [18]  (S1 Checklist). All relevant original papers published in English between 1975 and 2014 including reviews (Diamond  [16]  and Eurodiab studies  [19] [20] [21] , the International Diabetes Federation (IDF) atlas  [22, 23]  The T1D incidence value per 100. 000 persons/year for individuals in the age group 0-14 years for both sexes used here for each country was obtained as follows. 1) When only one dataset was available for a given country, the figure obtained from this study was used, even if the study was not nationwide. 2) If more than one dataset was available including nationwide and local studies, the most recent published nationwide study in the country was used.
Country indicators Three open public databases from the World Health Organization (WHO)  [24] , the United Nations (UN)  [25]  and the World bank (WB)  [26]  were used to search indicators in four domains: Climate and environment (land use and physical environment), Demography, Economy (including health resources and expenses), and Health. We only kept indicators with less than 5% missing values in the 80 countries for which we could estimate T1D incidence (see Results, below). In total we used 77 indicators: two (latitude and longitude) were obtained thanks to Google Maps  [27] , 29 were retrieved from the WHO  [24] , three from the UN  [25]  and 43 from the WB  [26]. When several values were available for one indicator, the value obtained in 2012, or on the date closest to 2012 was chosen (see Fig.
Results Incidences were retrieved for 80 countries; they varied widely between continents, countries and regions (see S2 Table and S1 Database for the incidences). Among children aged 0-14 years the lowest nationwide incidences ( 1 per 100. 000/year) were in Eastern Asia (China), South-East Asia (Thailand), Melanesia Oceania (Papua New Guinea), and South America and the Caribbean (Dominican Republic, and Paraguay). The highest nationwide incidences (! 30 per 100. 000/year) were in Northern Europe (Finland, Sweden, Norway). The correlations between the incidence and the 77 selected independent variables grouped into four domains are shown in Fig. 1  (for details and sources of information see S1 Database). Forty-one of 77 variables survived the Bonferroni correction and were significantly correlated with incidence at a significant p-value 0.
Discussion Our work explored whether the information available in public databases could help to study the environmental part of the country-to-country variations of T1D incidence. We found 77 variables with less than 5% missing values in the 80 countries for which the systematic review we carried out provided an estimate of the 0-14 T1D incidence. These 77 variables explored four domains (Climate and environment, Demography, Economy, Health). In each of these domains, a stepwise multiple regression analyses identified the subset of variables of the domain which were independent predictors of the variation in the geographical T1D incidence. Then, analyzing these 4 subsets of domain variables, we identified five final independent environmental indicators: four of the five were known as risk factors of T1D: UV radiation  [34] [35] [36] , health expenditure per capita  [34] , coverage of hepatitis B vaccination  [37]  and mean BMI  [38].
Within the Health domain, seven independent predictors were identified, among which two were in the final list: mean BMI in male >20, that had already been identified as risk factor  [38] , and hepatitis B immunization coverage in children <1 year of age, also recently proposed as a potential protector factor  [37]. A first limitation of this study is that we could only rely on a relatively short set of variables retrieved in the public databases that met the condition of an acceptable fraction of missing data that we set arbitrarily at 5%. A second limitation was the differences in the temporality of the T1D data collected and the predictors. The mean year of the studies we used to estimate T1D incidence was 1997, while the mean year of the data for the 77 selected predictors was 2009. An important drawback, shared by all ecological studies, is that the correlations cannot be directly interpreted in terms of causality of the disease.
allelefrequencies. net/ [51] , a public electronic repository for allele frequency of populations; we found that information on the haplotype DRB1Ã0405-DQA1Ã0301-DQB1Ã0302 for which the T1D Odd Ratio was extremely high (11. 37 reported by the Type 1 Diabetes Genetics Consortium Families  [50] ) was available in only 6 countries. Moreover, the genetic characteristics of a country are much less homogeneous than the environmental characteristics, because of the presence of various ethnic groups within the same country, while many of the environmental variables that we studied can be considered as identical (latitude, geographical characteristics) in the subgroups of a country. In the future, it can be expected that analyses similar to this one, taking advantage of the increased availability and quality of public databases characterizing the human populations would be a path to help identify some of the still unknown causes, and networks of causes, of TID.
National Income, BMI: Body Mass Index, CVD: Cardiovascular Diseases, CA: Cancer, DM: Diabetes Mellitus, CHRD: Chronic Respiratory Diseases, Adj. R2: Adjusted-R 2. Red: positive correlations, blue: negative correlations. (a) Age-standardized estimate; (b) Per 100,000 individuals; (c) Coverage among 1year-olds (%). See S1 Database for the entire database. doi:10. 1371/journal. pone. 0118298. g001.
Gross National Income, BMI: Body Mass Index, CVD: Cardiovascular Diseases, CA: Cancer, DM: Diabetes Mellitus, CHRD: Chronic Respiratory Diseases, Adj. R2: Adjusted-R 2. Red: positive correlations, blue: negative correlations. (a) Age-standardized estimate; (b) Per 100,000 individuals; (c) Coverage among 1year-olds (%). See S1 Database for the entire database. doi:10. 1371/journal. pone. 0118298. g001.