dataesr/scientific-paragraphs-categorization huggingface dataset:
49 This is an area of ongoing work (ie, double/debiased machine learning, cross-validated TMLE and cross-fit estimators). 44, 50, 51 Furthermore, TMLE and the SuperLearner were originally developed in R. 35, 46  Outside R, there is a Python library implementing TMLE and the SuperLearner named zEpid,  52  and a SAS library implementing the SuperLearner. 53 Also, there is a user written program for Stata (eltmle). 45 However, eltmle is not completely native to Stata but rather calls the SuperLearner R package to calculate the predictions of the treatment and outcome models. More work is required to continue implementing and improving the TMLE framework in other statistical software. 35 ausal inference is a growing field in rapid developments. Modern causal inference methods allow machine learning to be used when strong assumptions for parametric models are not reasonable.
Study population and design The present study was conducted within the framework of the MeDALL consortium and included cohorts with DNA methylation data measured at birth and/or during childhood. The study consisted of 2 phases, discovery and replication. In discovery and replication, allergic disease end point/IgE data were collected at the same time as DNA methylation analyses. In the discovery phase, whole blood DNA methylation was measured in participants of 3 cohorts, namely, Barn/Children, Allergy, Milieu, Stockholm, Epidemiology (Sweden), Infancia y Medio Ambiente (INMA) (Sabadel, Spain), and Prevention and Incidence of Asthma and Mite Allergy (PIAMA) (The Netherlands) (   E1 ). Furthermore, we investigated potential shared or disease-specific DNA methylation markers by comparing 3 nonoverlapping groups of case patients who had a single allergic disease, namely, asthma only, rhinitis only, or eczema only.
DNA methylation measurements In the discovery analysis, DNA methylation was measured by Illumina Infinium HumanMethylation450 BeadChips (Illumina, San Diego, Calif), whereas in the cohorts contributing to the replication, it was measured by either Illumina Infinium HumanMethylation450 BeadChips or iPlex (Agena Biosciences, Hamburg, Germany). We applied the same quality control of DNA methylation data and covariates as published previously. 8 nctional follow-up of significant DNA methylation findings We searched for the effects of genetic variants on DNA methylation variation (cis and trans methylation quantitative trait loci) by using the public Biobank-Based Integrative Omics Studies (BIOS) consortium database (the BIOS QTL browser, http://genenetwork. nl/biosqtlbrowser/). We associated blood CpG methylation with whole genome gene expression by RNA sequencing in the BIOS consortium data set.
22 CpG sites were annotated by using Genomic Regions Enrichment of Annotations Tool. 23 Functional enrichment analysis was done by overlapping our replicated CpG sites with histone marks and chromatin states of 27 blood cell types in the Roadmap Epigenome Project. 24 Gene expression sets associated with replicated CpG sites were interrogated for cell type specificity using the 500 Functional Genomics Project data set. 25, 26.
*These authors contributed equally to this work as first authors. The Mechanisms of the  Development of Allergy (MeDALL) European Union  project was supported by the  Seventh Framework Programme  (grant agreement  261357 ). The  Biobank-Based Integrative Omics Studies (BIOS) Consortium  was funded by  BBMRI-NL , a research infrastructure financed by the  Dutch government  ( NWO 184. 021. 007 ). Cohort-specific funding is described in the Online Supplement (at www. jacionline. org). The funders of the study had no role in design of the study, data gathering, analysis, interpretation, writing of the article, or in the decision to submit it for publication. M. G. N. was supported by a  European Research Council   Advanced Grant  ( 833247 ) and a Spinoza Grant from The  Netherlands Organization for Scientific Research.
We analyse the far-infrared properties of ∼ 5,000 star-forming galaxies at z < 4. 5, drawn from the deepest, super-deblended catalogues in the GOODS-N and COSMOS fields. We develop a novel panchromatic SED fitting algorithm, Stardust, that models the emission from stars, AGN, and infrared dust emission, without relying on energy balance assumptions. Our code provides robust estimates of the UV-optical and FIR physical parameters, such as the stellar mass (M * ), dust mass (M dust ), infrared luminosities (L IR ) arising from AGN and star formation activity, and the average intensity of the interstellar radiation field ( U ). Through a set of simulations we quantify the completeness of our data in terms of M dust , L IR and U , and subsequently characterise the distribution and evolution of these parameters with redshift.
INTRODUCTION Corresponding author: Vasily Kokorev vasilii. kokorev@nbi. ku. dk * https://github. com/VasilyKokorev/stardust/ One of the main mechanisms driving galaxy evolution is the interaction between the interstellar medium (ISM), primarily consisting of gas and dust, and the radiation field induced by stellar activity. In this context, dust poses challenges in the detection of the UV/optical emission of galaxies and in the interpretation of these observations in terms of physical properties (e. g. star for-mation rate (SFR), stellar mass M * , etc), but also is an important tracer of star formation and ISM in the FIR observations. At the same time, dust shields cold molecular hydrogen from ionising photons, and facilitates the collapse of molecular gas and subsequent star formation  (Goldsmith 2001; Krumholz et al.
COSMOS 'Super-Deblended' Catalogue We supplement our study with the 'Super-Deblended' catalogue (SDC2 hereafter) presented in  Jin et al. (2018). The catalogue covers 1. 7 deg 2 in COSMOS, in the same bands as in SDC1, plus additional MAMBO data at 1. 2 mm  (Bertoldi et al. 2007). In practice, the deblending methodology remains identical to that adopted in SDC1, with one primary difference: an additional step selecting a highly complete sample of priors in K s band from the UltraVista catalogues  (McCracken et al. 2012). The resulting 24 µm detections are then combined with the mass-limited sample of K s sources in order to fit the remaining bands in the catalogue. The final input dataset contains 195,107 priors, with 13% of them having spectroscopic confirmation. The authors highlight that only 11,220 objects are in fact detected over the 100-1200 µm range.
Similarly to GOODS-N, we impose the goodArea = 1 flag to only include sources that are present in the UltraVista Data Release 4 area  (McCracken et al. 2012). We note that for their catalogue, Jin et al. (  2018 ) used a combination of  Laigle et al. (2016)  and  Muzzin et al. (2013)  (M13 hereafter) catalogues. The M13 catalogue has an advantage in that it does not completely remove the sources around saturated stars, which has a positive effect on the number counts, however the reduced quality of the photometry could lead to unreliable estimates for photometric redshifts (z phot ), as well as any parameters extracted by fitting optical templates. To be on the safe side, our analysis of SDC2 will only focus on the Laigle et al. 2016 sources, which narrows down the number of objects in the input catalogue to 186,549 and the total area to 1.
38 deg 2. The COSMOS 2015 catalogue  (Laigle et al. 2016) , also comes with a plethora of UV-optical photometry, spanning an additional ∼ 20 bands, as well as photometric redshifts and stellar mass estimates by LePhare  (Arnouts et al. 1999; Ilbert et al. 2006). We exploit these data by cross-matching the same COSMOS 2015 UV-optical photometry that was used to derive M * and redshift to SDC2, thus extending our photometric coverage. In total, the merged catalogues consist in ∼ 40 bands. For posterity, in Figure  1  we present the UltraVista Data Release 2 map  (Laigle et al. 2016; Davidzon et al. 2017 ) (L16 area), with the regions where the star subtraction took place being clearly identified. In addition to that, on the West border of the survey there exists a number of sources falling outside of the L16 area.
Basic Description of the The χ 2 distributions for the main derived parameters. The shaded red areas enclose solutions for which ∆χ 2 ν = 1. To model the extensive photometric coverage of the galaxies in our sample we develop a new, panchromatic SED fitting tool: Stardust. The code performs a multicomponent fit that linearly combines stellar libraries with AGN torus templates and IR models of dust emission arising from star-formation (SF-IR). This approach, which is very similar to that presented in  (Liu et al. 2021) , has a number of key differences compared to the currently existing SED fitting codes such as MAGPHYS, CIGALE and SED3FIT. First, the three components (stellar, AGN and SF-IR) are fit simultaneously yet independently from each other, without assuming an energy balance between the absorbed UV/optical radiation and the IR emission.
The other advantage of Stardust is related to the χ 2 minimisation approach to select the best fit models. Instead of finding the solution from a vast library of precompiled templates, we devise an optimisation method to find the best linear combination of a much smaller set of 'basic' templates (similar to eigenvectors in principal component analysis). This is the same approach adopted in the photometric redshift fitting code EAZY  (Brammer et al. 2008 ). In our case, the basic templates are divided in three classes and the linear combination includes a sum of templates from these classes. The models used to create these templates are the following: • Stellar library. We incorporate an updated version of the Stellar Population Synthesis (SPS) models described in  Brammer et al. (2008). Although UV and optical photometry is not always available, the inclusion of the stellar component in the code is important in the NIR regime.
05. After all of the models are created, synthetic photometry is performed by convolving each mock redshifted SED with a filter transmission curve. We consider all filters redder than MIPS 24 µm available in SDC2, for a total of 9 bands and set all recovered fluxes to a S/N=3 significance level. At each redshift the algorithm calculates the rest-frame wavelength for each available band, producing a grid of possible rest-frame wavelengths of the last detection (λ last ) after accounting for our selection criterion that requests the availability (and the detection) in at least two bluer bands. For each λ last it then randomly selects two additional bands bluewards of λ last , producing a final set of three photometric data points. We then fit each set, and 50 permutations by varying the original fluxes by 10 %, with our code to extract M dust and L IR estimates.
2018) , and impose the same selection criteria to the mock photometry as those applied to the real catalogues. Following Section 4. 1 we also request that the simulated sources have λ last ≥ 150 µm. If a galaxy of given M dust fulfils these criteria at a given redshift, the algorithm moves to a lower M dust until the object is rejected by our selection. By following these steps at different redshifts we thus obtain a limiting M dust as a function of z, that we coin lim(M dust )(z). In the process we also consider the scatter of the U -z relation of  Béthermin et al. 2015 , in order to account for the variation of U 9 The SEDs of the most extreme outliers can be retrieved here https://github. com/VasilyKokorev/sdc ir properties/. among MS galaxies at a given redshift. The derived lim(M dust )(z) can then be converted to lim(L IR )(z), via: U = L IR,DL07 125 M dust , (1) as described in  Draine & Li (2007).
This is done to preserve the original mass cuts described in  Liu et al. (2018)  and  Jin et al. (2018) , and therefore the mass completeness and homogeneity of the catalogue. In total, out of 4,991 sources, we find 21 that we consider to be catastrophic fits (χ 2 ν > 100), these only comprise 0. 4 % of the entire output catalogue and are subsequently removed. The average χ 2 per degree of freedom of the entire dataset was computed to be equal to 0. 98. The distribution of the FIR properties of the whole sample, their medians and associated uncertainties are presented in Figure  7  and summarised in Table  1. The catalogue containing the extracted FIR properties is described in the Appendix and is publicly available along with the best fit SED for each object. We also calculate the position of the galaxies in our sample with respect to the MS, by converting the AGNfree L IR,DL07 estimates to SFR  (Kennicutt 1998 ) and using the functional form of the main-sequence as presented in  Schreiber et al.
For fitting we use the Python packagescipy. optimize. curve fit, which finds the solution based on the least-squares method. We also consider how the extreme outliers can affect our results and thus only fit the medians in a given redshift bin. The best fit values are as follows: a 0 = -0. 90 a 1 = 1. 31 b = -0. 98 c 0 = -0. 23 c 1 = 0. 11 d = -0. 64, with the uncertainties being computed from the covariance matrix. We then used the functional form given by Equation 2 to re-normalise all galaxies to lie on the main sequence (∆MS = 1), and M * = 5 × 10 10 M , in order to directly compare with our best-fit function in 2 dimensions. The normalised data and the best fit relation presented in Figure  11  are in very good agreement with the collection of similar trends drawn from the literature (e.
The grey hexbins contain the data from 'M dust -robust' sample, and are normalised by the number count. Both the data and the derived relations have been re-scaled to ∆MS=1 and M * = 5 × 10 10 M. White diamonds show median positions of the Horizon AGN starforming galaxies at that redshift, normalised in the same way as our data. effects of the sample. The objects that end up in our sample go through several selection stages, both before and after the deblending procedure. These include both the brightness and mass cuts of the parent catalogue  (Laigle et al. 2016) , the availability of infrared coverage  (Jin et al. 2018 ) and the selection criteria imposed in our study. As such, it is not possible to robustly assess the properties and the number of objects that end up being 'lost'. Therefore, we select an alternative approach in computing the DMF for our objects, namely utilising the derived f dust parametrisation along with the available SMFs in the literature.
Also, since M dust is not an explicit paramater of HAGN galaxies, we used a constant δ GDR = 100 to convert the M gas values, as derived from the simulations, to M dust. We then use the M gas , M * , and M dust of the simulated galaxies to infer f gas and f dust. The median values and their scatter, renormalised to MS (∆MS = 1) and M * = 5 × 10 10 M in four redshift bins, are presented and compared to the real data in Figure  11  and Figure  14. We find a good agreement between the theoretical predictions and our observationally driven trends (in the < 0. 2 < z < 0. 5 range at least) indicating that the HAGN simulation can successfully reproduce the baryonic components of the galaxies and its evolution with redshift. Conversely, the agreement of our results with both theoretical and observational studies, provides an extra indirect validation for the performance of our new SED fitting code.
Subsequently, we perform a synthetic photometry on all three components separately in all observed bands where data is available. This is done by convolution of the filter transmission curves with the model SEDs. The resultant synthetic fluxes for each template and available observed band are then all combined into a two-dimensional matrix and passed onto   [-10. 75,-11. 37,-11. 90,-12. 53,-12. 05, -12. 47,-8. 37,-8. 60,-8. 50,-8. 57,-8. 93,-8. 90] a Please refer to  Brammer et al. 2008  for a more detailed description of the creation and selection of these basis set templates. See Blanton & Roweis 2007 for a methodology regarding the SFH. the non-negative least square (nnls) algorithm in the python scipy. optimize. nnls package, that finds the best solution vector for that object. The nnls is a simple minimisation algorithm that in our case takes the form of, min ||A b,t x t -y b || 2 where x ≥ 0, (A7) with A being the uncertainty weighted template matrix, y as the signal to noise for each band respectively, x the solution vector and ||.
B. STELLAR MASS COMPARISON To test the robustness of Stardust -derived M * estimates we start by first comparing them to the M * as given by the original catalogue. The SDC2 takes its M * directly from the COSMOS 2015 catalogue  (Laigle et al. 2016) , which uses SED fitting code LePhare  (Arnouts et al. 1999; Ilbert et al. 2006)  to constrain both the photometric redshift, M * , as well as a host of other parameters. Similarly to our approach, LePhare relies on  Bruzual & Charlot (2003)  SSP libraries to fit galaxy SEDs. We begin our fitting procedure by carefully correcting all the COSMOS 2015 aperture fluxes to total fluxes, as well as correcting for the MW extinction, as prescribed in  Laigle et al. (2016). We then cross-match these sources to SDC2, by using the K-band, and fit the entirety of available 36 bands with our code.
templates. We show the comparison between the two methods in Figure  21. There is a very good agreement between the M dust derived with our code and CIGALE, with the dif-ference having a mean of 0. 09 dex and median of 0. 02 dex. The derived values of L IR are however in tension, with a mean of 0. 20 dex and median of 0. 11 dex. We attribute the significant outliers (> 1σ) to cases where the energy balance method in CIGALE has failed to account for the extra FIR flux. We also compare the Stardust and CIGALE computed M * , and find that the two agree within 0. 1 dex, albeit with a significant 0. 3 dex scatter. As we have already discussed in Section 3, in certain environments the stellar and the dust emission could be spatially disconnected, thus the energy balance might not be the best physically motivated option.
5 hours. Due to how the linear combination is performed within Stardust, defining an exact number of models attempted is not possible. However, considering that the 12 optical templates have been constructed as a basis set of ∼ 3, 000 models described in  Brammer et al. (2008) , combining that with 2 AGN templates and ∼ 4, 800 DL07 models, results in roughly 30 × 10 6 total effective model combinations. Our code then takes 14 minutes in total to fit the same 288 objects, which is approximately 11 times faster than CIGALE, for the same number of CPU cores.
Figure11. Derived relations for f dust as a function of z/tage. The dashed purple line shows the fit to our data, while solid coloured lines display literature results. The shaded purple region denotes the 16 th and 84 th percentile confidence intervals of our fit. Starred labels denote literature calculations where a direct comparison was not available and a δGDR = 100 was assumed. The grey hexbins contain the data from 'M dust -robust' sample, and are normalised by the number count. Both the data and the derived relations have been re-scaled to ∆MS=1 and M * = 5 × 10 10 M. White diamonds show median positions of the Horizon AGN starforming galaxies at that redshift, normalised in the same way as our data.
Figure14. Derived relations for fgas (top) and τ depl (bottom) as a function of z/tage. The dashed purple line shows the fit to our data, while solid coloured lines display literature results. The shaded purple region denotes the 16 th and 84 th percentile confidence intervals of our fit. The grey hexbins contain the data from 'M dust -robust' sample, and are normalised by the number count. Both the data and the derived relations have been re-scaled to ∆MS=1 and M * = 5 × 10 10 M. White diamonds show median positions of the Horizon AGN star-forming galaxies at that redshift, normalised in the same way as our data.
ACKNOWLEDGMENTS We would like to thank the anonymous referee for a number of constructive suggestions. We thank  Yohan Dubois  and  Clotilde Laigle  for providing us with HAGN data and their input towards the discussion of simulations,  Jenifer Millard  for providing us with the DMF data, and  Benjamin Magnelli  for the help in understanding the dust mass density evolution. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No. 140. GEM acknowledges the  Villum Fonden  research grant  13160  "Gas to stars, stars to dust: tracing star formation across cosmic time," grant  37440 , "The  Hidden Cosmos ," and the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under the grant No. 140. FV acknowledges support from the  Carlsberg Foundation  research grant  CF18-0388  " Galaxies: Rise And Death ".
1e ). We use the recorded trajectories as a database, which we can post-select and synchronize in order to study the average particle motion starting from various initial configurations (see Methods). In Figs. 1f, g and h we show different initial distributions of particles for latter use. Fig. 1f  shows the particle distributions when all recorded trajectories are synchronized to start at the same initial time. We observe more particles at the borders than in the bulk because of the particle flux from outside the device. Since this flux cannot be controlled, we cannot directly choose the initial particle distribution. We overcome this limitation by post-selecting trajectories so that they start either from the top left and bottom right cells (Fig. 1g ), or from a uniform distribution over all cells (Fig.
B. Microfabricated model substrate for topological guidance: design and fabrication The microscale features that define the masks are designed and microfabricated on a silicon wafer, and later transferred by replication to a thin structure of polydimethylsiloxane (PDMS). The microchannel design is created with a computeraided design (CAD) software (AutoCAD, Autodesk) and is made of ratchet-like structures that favor directional trajectories of the active particles  [46]. Direct writing laser lithography (DWL 66FS, Heidelberg Instruments) is used to produce an AZ resist master (AZ 1512HS, Microchemicals GmbH) of 1. 5 µm thickness on the silicon wafer. A PDMS replica from the rigid mold/master is produced to obtain the open channel microfluidic device with the desired features. For reusability purposes, the master is silanized with trichloro (1H,1H,2H,2Hperfluorooctyl)sylane (Sigma-Aldrich) by vapor phase for one hour at room temperature, this operation reduces the adhesion of PDMS to the substrate.
C. Setup fabrication A circular well of 2 mm height and 1. 5 cm diameter is designed with a CAD software and post-treated with Slic3r and Repetier softwares in order to generate the G-code required for 3D printing it with a Cellink's Inkredible+ 3D printer. Polydimethylsiloxane (PDMS) SE1700 cross-linker and monomer are mixed at a ratio of 1:20, and the viscous solution is added to the cartridge used in the 3D printer. The pneumatic extrusion of the 3D printer allows to print the PDMS-based structures on clean crystal slides (60 × 24 mm) at a pressure of 200 kPa. Once the well structure is printed, the slides are left overnight at 65 • C for curing, to obtain a stable structure. To ensure good sealing of the 3D-printed well, a mixture of 1:10 PDMS Sylgard 184 is added around the outer side of the well, followed by a curing period of four hours at 65 • C.