dataesr/scientific-paragraphs-categorization huggingface dataset:
The red and blue dots represent performances respectively obtained by TBProfiler and Mykrobe softwares under the same cross-validation process. Full-size  DOI: 10. 7717/peerj. 6857/fig- 3  Overall, while it did not improve the performance of TBProfiler and Mykrobe, the Lasso-penalized logistic regression strategy allowed to reach the same level of performance while providing a probabilistic prediction, hence allowing to adjust, to some extent, the desired levels of sensitivity and specificity, and relying on much shorter list of mutations.
Models integrating lineage effects and novel mutations identified by TBProfiler We then aimed to evaluate the relevance of considering novel covariates in the model to take into account lineage effects, and to evaluate whether the novel mutations identified by TBProfiler during its genotyping process could bring additional predictive power. For this purpose, we applied the same procedure as above, using a one-hot encoding scheme to include the lineage information in the models: a set of five mutually exclusive binary covariates were defined to encode the presence of a given genome in one of the four major lineages (1-4) or to another one. We therefore considered two configurations using Mykrobe data (using lineage covariates or not in addition to the detected markers), and four using TBProfiler data (using lineage covariates or not in addition to known markers only, or to known and novel markers).
CONCLUSION We performed a large scale evaluation of two widely recognized softwares to predict antibiotic resistance in M. tuberculosis. The size of the considered dataset was much larger than in prior studies done in a similar setting  (Schleusener et al. , 2017; Macedo et al. , 2018; Phelan et al. , 2016; Kohl et al. , 2018) , hence providing a more precise estimation of their prediction performance. This study nevertheless has several limitations. First, as opposed to the aforementioned ones, this study cannot be considered as a proper independent validation study, as some of the genomes considered have been involved in the development of these tools. However, it allows to draw general conclusions about the respective merits of each tool, as well as their current limitations, as recently described in a similar setting for Staphylococcus aureus  (Mason et al.
(2017). It is considered trustworthy enough by the WHO to correct phenotypes determined phenotypically: isolates harboring these mutations were systematically considered as resistant in World Health Organization (2018a), even if they were identified as susceptible by phenotypic testing. In the dataset considered in this study, less than 2% of susceptible strains harbor at least one of these mutations for most drugs, except fluoroquinolones and capreomycin, where this figure rises to 3-4%, and ethionamide, where it rises to 19%, most probably due to the fact that the confidence in the underlying mutation is moderate. Further details about this analysis are provided in Supplementary Materials. Definitively claiming that these susceptible samples are false negatives is probably hazardous since (i) while deemed highly confident, these mutations may not be perfectly specific and (ii) calling these mutations present depends on the genotyping pipelines embedded in TBProfiler and Mykrobe, and in particular on the allele frequency threshold considered.
While probably much less sensitive than a whole-genome distance criterion and harder to interpret, this preliminary analysis, described in Supplementary Materials, indicates that close isolates are indeed probably present in the dataset (especially within the samples coming from the original Mykrobe study  (Bradley et al. , 2015) ), but suggests that this issue is probably marginal. An interesting perspective of this work could amount to consolidating this analysis after a preliminary step of genome assembly, in order ultimately to refine the estimation of the predictive performances after the exclusion of such close isolates. These limitations acknowledged, this large-scale study confirms the overall good performance of these tools, hence the relevance of NGS for resistance prediction in M.
tuberculosis, in agreement with World Health Organization (2018b). This study moreover revealed that an important fraction of the catalog of mutations they embed is of limited predictive power. This was especially the case for TBProfiler, where around two third of the 1,195 mutations included in its catalog were actually never detected in the large dataset considered in this study (more than 6,500 genomes). It also showed that both softwares achieve different trade-offs in terms of sensitivity and specificity, which is directly related to the nature of the mutation catalogs they embed, TBProfiler including a larger catalog than Mykrobe, leading in general to a higher sensitivity for a lesser specificity, but also to the fact that they do not fully agree in terms of genotyping. Finally, this study revealed strong lineage effects for some antibiotics, with much lesser performance in some lineages than others.
These results are consistent with World Health Organization (2018b), who reported important differences in resistance performance prediction across countries in a multi-country surveillance project. We therefore believe that the predictive performance of these softwares should be quantified in a lineage per lineage basis, to allow the user to better appreciate the predictive value of the result obtained depending on the lineage inferred from his sample. We demonstrate moreover that standard machine learning approaches operating from the set of markers detected by these softwares provide an interesting alternative strategy. Indeed, they seek for signatures involving the smallest number of mutations (e. g. , 165 TBProfiler mutations altogether for the 10 antibiotics considered, out of 1,126 candidates from the original catalog), while preserving the prediction performance.
Cell culture Human, female HeLa cells were cultured in DMEM medium with GlutaMAX (Gibco) supplemented with 10% fetal calf serum, 1% antibiotics (Zell Shield, Minerva Biolabs) and were incubated at 37°C in a 5% CO2 humidified atmosphere. Human, female hTERT-RPE1 cells (ATCC Cat# CRL-4000, RRID:CVCL_4388; hereafter RPE1 cells), were cultured in DMEM/F12 (Gibco) supplemented with 10% fetal calf serum and 1% antibiotics (Zell Shield) and were incubated at 37°C in a 5% CO2 humidified atmosphere. RPE1 cells stably expressing EGFP-PHOSBP were selected using G418 (Sigma). Surviving colonies were isolated using cloning cylinders (Bel-Art), expanded and further sorted by FACS (FACSAria III, BD Biosciences). RPE1 cells stably expressing EGFP-PHOSBP, EGFP-P4MSidM were cultured in medium supplemented with G418 (500 µg/ml).
Bioinformatic analysis For disorder prediction, we used the following sequences of human ORPs (Uniprot access number): OSBP1 (P22059), OSBP2/ORP4 (Q969R2), ORP5 (Q9H0X9), ORP8L (Q9BZF1), ORP10 (Q9BXB5), ORP11 (Q9BXB4), ORP6 (Q9BZF3), ORP7 (Q9BZF2), ORP1 (Q9BXW6), ORP3 (Q9H4L5), ORP9 (Q96SU4). Of note, our ORP4 sequence (gift from N. Ridgway) started at M39 (as referred to UNIPROT Q969R2:ORP4-OSBP2 sequence), therefore, in all this study M1 corresponds to M39 of the UNIPROT reference sequence. Order/disorder composition of different ORPs was determined with "Predictor of Natural Disordered Regions (PONDR®)" web server (http://www. pondr. com/) using VL3-BA and VSL2 predictors. Amino acids composition of selected domains was determined using Expasy/protparam web server (https://web. expasy. org/protparam/) and plotted as pie charts to highlight similarities and divergences between domains (N-terminus, PH domain, ORD).
Limits of selected domains are detailed in Figure  S2A. For phylogenetic analysis, protein sequences similar to human OSBP (P22059) were obtained from different species of higher eukaryotes in the UniProt database. To identify a single sequence corresponding to respective OSBP homolog, all sequences from each species were aligned to that of the human OSBP using Clustal Omega  (Sievers et al. , 2011). Only sequences that contained complete PH and ORD domains were used for phylogenetic analysis. The phylogenetic tree was created with the Phylogeny. fr server  (Dereeper et al. , 2008). From the dataset of OSBP homologs involved in the phylogenetic analysis, the N-termini, PH and ORD domains were separately aligned to that of corresponding human OSBP domains using Clustal Omega to obtain a percent identity matrix as indicator of domain conservation.
Rabbit polyclonal antibody against OSBP (Atlas Antibodies Cat# HPA039227, RRID:AB_2676401)) and mouse monoclonal V9 Vimentin antibody (Sigma-Aldrich Cat# V6389, RRID:AB_609914 ) were from Sigma-Aldrich. Secondary Alexa Fluor-conjugated antibody (Thermo Fisher Scientific Cat# A32723, RRID:AB_2633275) were from Invitrogen and secondary HRP-conjugated antibody were from Jackson ImmunoResearch (Jackson ImmunoResearch Labs Cat# 111-035-047, RRID:AB_2337940). Egg PC, brain PS, brain PI(4)P, liver PI, liver PE, dansyl-PE (1,2-dioleoyl-sn-glycero-3phosphoethanolamine-N-(5-dimethylamino-1-naphthalenesulfonyl)), rhodamine-PE [1,2dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-(lissamine rhodamine B sulfonyl)], and DGS-NTA(Ni) [1,2-dioleoyl-sn-glycero-3-[(N-(5-amino-1-carboxypentyl) iminodiacetic acid).
Acknowledgments We thank  N. Leroudier ,  D. Deblay ,  J. Cazareth  and  F. Brau  for technical assistance,  A. Di Cicco  for cryo-EM image acquisition and for fruitful discussions, and  A. Patel  and  A. Copic  for comments on the manuscript. This work was supported by  CNRS ,  Institut Curie  and the  Agence Nationale de la Recherche  ( ANR-15-CE11-0027-02  and  ANR-11-LABX-0028-01 ). D. J. was supported by a  PhD fellowship  from the  Signalife PhD Programme. We thank the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ).
We fit most infrared to millimeter emission line fluxes within a factor better than 3, simultaneously with SED, PAH features and radial brightness profiles extracted from images at various wavelengths. However, some line fluxes may deviate by a larger factor, and sometimes we find puzzling data which the models cannot reproduce. Some of these issues are probably caused by foreground cloud absorption or object variability. Our data collection, the fitted physical disk parameters as well as the full model output are available to the community through an online database (http://www. univie. ac. at/diana).
These are just a few examples. Exploiting these feedback mechanisms can help to break certain degeneracies as known, for example, from pure SED-fitting. Our data collection is available in a public database  (DIOD, Dionatos et al. submitted 2018) , which includes photometric fluxes, low and high-resolution spectroscopy, line and visibility data, from X-rays to centimeter wavelengths, and respective meta-data such as references. The database is online at http://www. univie. ac. at/ diana, together with our fitted stellar and disk properties and detailed modeling results, which are also available in an easy to use format at http://www-star. st-and. ac. uk/ ∼ pw31/DIANA/SEDfit. This makes our work completely transparent and reproducible. The predictive power of these models can be tested against new observations, for example unexplored molecules, other wavelength ranges or new instruments.
However, we can check this later, when absorption and scattering by the disk is included, and can adjust in this case. We use a large collection of optical and near-IR photometry points in combination with low-resolution UV spectra, UV photometry points, and X-ray measurements. We fit the photospheric part of each dataset by standard PHOENIX stellar atmosphere model spectra  (Brott & Hauschildt 2005) , with solar abundances Z = 1, after applying a standard reddening law  (Fitzpatrick 1999)  according to interstellar extinction A V and reddening parameter R V. A standard value of R V = 3. 1 is applied to all stars if not stated otherwise. All photometric data in magnitudes have been converted to Jansky (F obs filter ) based on instrument filter functions and zero-point data kindly provided by Pieter  Degroote (priv.
(2000 Valenti et al. ( , 2003)). The details are described in  (Dionatos et al. submitted 2018, see Fig. 3 therein). These spectra were then de-reddened according to our A V to obtain the stellar UV-spectra as seen by the disk. These UV input spectra are included in our database DIOD. The de-reddened data is used to replace the photospheric model in the UV, and possible gaps between UV spectral data and photospheric model spectrum are filled by powerlaws. X-ray data was collected from XMM-Newton and Chandra. A physically detailed X-ray emission model was fitted to these observations  (Dionatos et al. submitted 2018) , from which we extracted a high-resolution X-ray emission spectrum as seen by the disk by not computing the last modeling step, namely the reduction of the X-ray fluxes by extinction.
These X-ray emission spectra are also available in our database DIOD. As a side result, the modeling of the X-ray data provided estimates of the hydrogen column densities towards the sources, which is useful to verify our results for A V. The stellar properties, and in particular the assumed visual extinction A V , have a profound influence on the disk modeling results. The stellar parameters must be carefully adjusted and checked against UV and optical data to make sure that this part of the spectrum is properly reproduced by the model. A blind application of published stellar parameters can lead to substantial inconsistencies. If A V is overestimated, for example, one needs to assume larger values for L , which would then make the disk warmer and brighter in the infrared and beyond. A more substantial de-reddening would result in a stronger UV spectrum as seen by the disk, causing stronger emission lines, etc.
6 µm destroys the otherwise fine fit for MWC 480, possibly a problem related to the variability of the object. maximum (FWHM) of the line has been measured, we also compute χ 2 FWHM = FWHM mod -FWHM obs σ FWHM 2. ( 9 ) If velocity profiles are available, we use again Eq. (3) to compute χ 2 velo , and if a line map is available (converted to a radial line intensity profile by averaging over concentric rings), we use Eq. (3) to calculate χ 2 map. Finally, these components are added together to compute χ 2 line,k =          χ 2 flux if only line flux is observed 1 2 χ 2 flux + 1 2 χ 2 FWHM if line flux and FWHM are observed 1 2 χ 2 flux + 1 2 χ 2 velo if line flux and line profile are observed 1 3 χ 2 flux + 1 3 χ 2 velo + 1 3 χ 2 map if line flux, profile and a map are observed , (10) where, for the total χ 2 line , we still need to average over all observed lines k in the dataset.
USco J1604-2130 Mgas = 2. 1 × 10 -3 M , M dust = 4. 0 × 10 -5 M , dust settling α settle = 2. 3 × 10 -3 Mid-and near-IR CO lines (wavelength below ∼ 100 µm) have an ever increasing contribution also from the small inner disk: ∼ 40% for the low J CO v = 1 -0 lines increasing to 100% for J > 20 and ∼ 60% for the low J CO v = 2 -1 lines increasing rapidly with J to 100%. However, line fluxes for ro-vibrational CO lines from our model are below 10 -18 W/m 2. Due to the low inclination of 10 o , the CO sub-mm lines are narrow with typical FWHM of 1. 4 km/s (Table  15 ). 9. TW Hya probably is the best studied protoplanetary disk around a T Tauri star. Practically every suitable instrument developed in the past 30 years was pointed at this object, producing datasets of varying quality and scientific usefulness, yet the shape of the disk around TW Hya is still debated (see e.
So, while enlarging the disk mass and simultaneously decreasing the carbon or oxygen abundances may be a tempting option to improve some line fits, it might worsen the fits in other spectral regions, for example the mid-IR and sub-mm and water lines  (Kamp et al. 2013). Kama et al. (2016a)  showed in a more detailed parameter study using the DALI code  (Bruderer et al. 2009 ) that the flaring angle and tapering radius both affect the carbon fine structure and CO sub-mm lines in the same way as the carbon elemental abundance. In addition, the differences in quoted observed line fluxes from various papers amount to a factor 2-3 as well. Clearly more work is needed to reconcile the remaining model discrepancies. get a disk gas mass of M disk = 3. 3 × 10 -2 M sun which is consistent with the HD derived disk mass but about an order of magnitude higher than the CO disk mass estimate of  McClure et al.
All model parameters are offered in an easyto-use format, see App. A, and all users of MCFOST, MCMax or ProDiMo can download setup files to re-run our disk models. This ensures the transparency of our modeling work, and their re-use in future applications. The use of the DIANA disk models beyond the project has already started. Stolker et al. (2017)  used the DIANA SED model for the interpretation of multi-epoch VLT/SPHERE scattered light images of HD 135344B. They use our model to illustrate that a small misalignment between the inner and the outer disk (2 •. 6) can explain the observed broad, quasistationary shadowing in north-northwest direction. The effects of inclined inner disks on line fluxes would be worth studying in the future. As a second example,  Muro-Arena et al. (2018)  used the DIANA SED model of HD 163296 to analyze the combined VLT/SPHERE and ALMA continuum dataset for this object.
Even though clear ring structures are seen and the model is eventually refined to show a surface density modulation, the average surface density profile stays the same as in the DIANA SED model. This illustrates that in some cases, the detailed disk substructures are minor modulations of the global 2D disk models. These substructures are key for the planet formation processes, but have only little effect on global observables such SEDs and unresolved line observations that arise from an extended radial region of the disk. Lastly, the MWC 480 and Lk Ca 15 disk models can serve as an interesting starting point for the analysis of the full ALMA spectral scan (ALMA community proposal 2015. 1. 00657. S searching for complex molecules, led by Karin  Öberg et al. ). These examples illustrate the potential of the 2D disk models presented in this paper for future data analysis and interpretation, even in the realm of observed 3D disk substructures.
The ModelOutput. tgz files contain all model output data as listed above (and more) in numerical tables where each line corresponds to a (r, z)-point in the model. These data can be used, for example, to make your own plots, for example to look for the various ice-lines. The ModelSetup. tgz files contain all numerical input and data-files necessary to recompute the model. These files are included in the format required for PRODIMO, MCFOST and MCMAX. The data-files include the observational data in the "model-friendly" input format requested by PRODIMO. B. UNCERTAINTIES IN DISK PARAMETER DETERMINATION Our derived values of the model parameter concerning disk shape, gas, dust and PAH parameters are subject to large uncertainties, partly due to measurement errors and calibration uncertainties of the astronomical instruments used, but partly also due to well-known fitting degeneracies, in particular if the disk model is only fitted to SED data.
Acknowledgements The authors acknowledge funding from the  EU   FP7-2011  under Grant Agreement no. 284405. JDI gratefully acknowledges support from the  DISCSIM  project, grant agreement  341137 , funded by the  European Research Council  under ERC-2013-ADG. Astrophysics at  Queen's University Belfast  is supported by a grant from the  STFC  ( ST/P000312/1 ). OD acknowledges the support of the  Austrian Research Promotion Agency (FFG)  to the project  JetPro , funded within the framework of  Austrian Space Applications Program (ASAP)   FFG-854025. This paper makes use of the following ALMA data: RECX 15: ADS/JAO. ALMA# 2011. 0. 00133. S (RECX 15 ) and ADS/JAO. ALMA# 2011. 0. 00629. S ( DM Tau  and  MWC 480 ). ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan)  and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
92 CY Tau small yes 18 30 1 1 7 5 5 3 1. 94 RECX 15 small yes 16 26 1 - 10 1 2 - 0. 84 Nphot = number of selected photometric data points. Nspec = number of low-resolution spectra, for example Spitzer/IRS, Herschel/PACS, Herschel/SPIRE, ISO/SWS or ISO/LWS. Nimage = number of continuum images or visibility data files, for example from NICMOS, SUBARU, SMA, ALMA or MIDI. Nlines = number of observed line fluxes (including upper limits). Nvelo = number of high-resolution line velocity profiles, for example VLT/CRIRES, SMA, ALMA, and two NIRSPEC observations probing full series of CO fundamental lines for BP Tau and CY Tau. Nmaps = number of spatially resolved line datasets, converted to line-integrated intensity profiles, for example SMA, ALMA.
Campaign of measurements in church of Cetățuia Monastery The existing heating system is composed from static heaters (Fig. 5 ) placed in the pews (Fig. 3 ). The main purpose of this research being is to evaluate the microclimate created in the church and also the heritage conservation condition. There has been done an analysis in CFD environment to evaluate the physical arrangement of temperature inside the hole volume and the air movements caused by static heating system. It had been created a campaign of measurements for the main parameters, temperature ( 0 C) and air relative humidity (%), the aim been also to validate the plan of measurements with the simulation of indoor climate done in Autodesk CFD 2019 software. [5] [6]  For evaluating the microclimate in the church of Cetățuia Monastery there have been utilized equipment as thermal imaging camera from Flir, USB Data Logger DS 100 for temperature (T -0 C) and relative humidity (rH -%).
Also it can be seen a variation of humidity with peak loads in a quick time. The mean value of humidity is around 48,20 % rH in a scale between minimum of 35% and maximum of 64%, with a standard deviation of 4,745 (Fig. 13 , Fig. 14 ). The indoor climate condition described in figure 10 to 14 relief that there is maintaining a minim level of comfort, the maximum values of temperature and humidity are recorded during the liturgical service when people gather are in high number. Very quick variation of this parameters in a short period of time can have a bad influence over the frescos and wooden objects in the church. [7]  There were calculated the comfort index PMV and PPV for the indoor microclimate based on the activity and data taken from the measurement (Fig. 15 ). From the Figure  15  and 16 it can be observed that the level of comfort is medium to high with a degree of satisfaction, approximately of 65 %, this showing that the heating system is assuring a comfortable environment for the churchgoers.
Microclimate modelling Within the research, the CFD package of Autodesk CFD, version 2019 was used. Software used is Autodesk 2019 -capable of solving heat, air and water vapour simulation, the analysis was run in steady state. The 3d model was made in Autodesk Inventor 2019 (Fig. 17   The model was build using Cartesian coordinates, in which a non-structural grid was applied. The idea behind the non-structural grid was to properly model the indoor climate. Within the CFD model, the equation for Flow, Turbulence, and Energy were solved. The discretization schemes for these equations are: Standard, Simple and the First Order scheme for Momentum, Turbulence Kinetic Energy, Turbulence Dissipation rate and Energy (Fig. 18 ). The boundary conditions imposed for the analysis for the walls, ceiling and floor as for the heating system is presented in the table 1.