dataesr/scientific-paragraphs-categorization huggingface dataset:
If necessary, the pipetting robot can be used to perform a treatment on bioreactor samples (dilution, fixation, extraction, purification…) before loading into the measurement device. If reactive experiment control is not needed, treated samples can also be stored on the robot deck for offline measurements (the OT-2 temperature module can help the conservation of temperature-sensitive samples). b On the software side, ReacSight enables full platform integration via a versatile instrument control architecture based on Python and the Python web application framework Flask. ReacSight software also provides a generic event system to enable reactive experiment control. Example code for a simple use case of reactive experiment control is shown. Experiment control can also inform remote users about the status of the experiment using Discord webhooks and generates an exhaustive log file.
a A simple ODE model of the light-driven gene expression circuit is fitted to the characterization data of Fig. 2d. Fitted parameters are γ m = 2. 09 h -1 , σ = 0. 64 RPU h -1 , and γ FP = 0. 475 h -1 •k m was arbitrarily set to equal γ m to allow parameter identifiability from protein median levels only. b Strategy for the real-time control of gene expression. Every hour, cytometry acquisition is performed, and after gating, deconvolution, and FSC-normalization, data are fed to a model-predictive control (MPC) algorithm. The algorithm uses the model to search for the best sequence of duty fractions for 10 duty cycles of period 30 min (i. e. a receding horizon of 5 h) in order to track the target level. c Real-time control results for four different target levels, performed in parallel in different bioreactors (custom setup).
We verified that cytometry data were reproducible week-to-week with these fixed settings. Single-color strains described above were used together with the autofluorescence control strain to obtain 'spectral' signatures of the three fluorophores mCerulean, mNeonGreen, and mScarlet-I, and autofluorescence levels for each channel. These signatures were also highly reproducible week-to-week (Supplementary Fig. 7 ). To convert raw cytometry data into fluorophore concentrations in relative promoter units (RPU  43  ), we used a pipeline described in Supplementary Note 2. In essence, it uses data from single-color strains with pTDH3-driven expression for normalization. This pipeline was implemented in Python, mainly using NumPy  44  functions. The Opentrons OT-2 pipetting robot is used to wash the wells of the cytometer plate between samples, and dilute and load samples.
We used the OT-2 Python API versions 1 and 2 (see https://opentrons. com). Model-predictive control. For real-time control of gene expression using light (Fig. 3a, c ), model-predictive control using the two-variables, three-parameters ODE model described in Fig. 3a  was used. For state estimation upon arrival of cytometry data, the FP estimate was set equal to the fluorescence measurement (median of gated, deconvolved data) and the mRNA estimate was simply an 'openloop' estimate based on simulating the history of light induction. This first state estimate corresponds to the state of the system at the time of sampling. To account for the time interval (and the concomitant light induction profile) between the sampling time and the data arrival time (typically 10-15 min), the model was used to obtain the corresponding updated state estimate.
A threshold of 1 mCerulean RPU was used to assign each cell to its genotype. Size gating was performed as described in Supplementary Fig. 13  (parameters: size threshold = 0. 5 and doublet threshold = 0. 5, less stringent than for experiments of Figs. 2  and 3 ) to discard dead or dying cells. Dynamic control of the two-strain consortium. For the original attempt presented in Fig. 4 , a simple sigmoidal model describing the steady-state growth rate difference between the two strains as a function of OD was fitted on previous characterization data corresponding to different OD setpoints. Every 2 h, cytometry data were automatically acquired. To assign a genotype to each cytometry event, the GRN-B and ORG-G channels were used in combination, exploiting the fact that the histidine auxotroph strain is GRN-B positive and ORG-G negative (Supplementary Fig.
We used the software of the vendor (Tecan Spark Control, v2. 2). The incubation periods lasted 300 s, during which there was a first round of linear shaking (10 s, amplitude 5 mm, 390 rpm frequency), then still incubation, OD measurement, then two rounds of still incubation (20 s) and linear shaking (10 s, amplitude 5 mm, 390 rpm frequency) and finally still incubation for the rest of this cycle. OD 600 measurements were carried out every 5 min. For long-lasting experiments ("turbidostat mode", Fig. 5b ), if the median of the measured ODs was higher than a chosen threshold (0. 05 or 0. 1), the cultures were diluted by adding 100 µl of media with the chosen antibiotic concentration (same as at beginning of experiment), mixing, and then taking out 100 µl minus an estimate of the evaporated volume since last dilution.
Acknowledgements The authors would like to thank  Cosmin Saveanu ,  Emmanuel Frachon , and  Alain Jacquier  for the gift of the 16-vessel custom bioreactor setup and for guidance regarding its functioning, enabling us to customize and modernize it even further. We also received guidance and help from  Albane Imbert  (heading the  Institut Pasteur Fab Lab)  for the custom design and fabrication of Plexiglas and 3D-printed pieces. This work was supported by  ANR  grants  CyberCircuits  ( ANR-18-CE91-0002 ; S. S. -C. ),  MEMIP  ( ANR-16-CE33-0018 , S. S. -C. ),  Inception  ( ANR-16-CONV-0005 , V. G. ), and  Anoruti  ( ANR-20-PAMR-0001 , V. G. ), by the  H2020 Fet-Open COSY-BIO  grant (grant agreement no. 766840 , C. A. , S. S. -C. ) and by the  Inria IPL  grant COSY (G. B. ).
Code availability In the ReacSight Git repository (https://gitlab. inria. fr/InBio/Public/reacsight), we provide (i) software supporting the ReacSight strategy (a full platform software template and examples of instrument APIs with corresponding Flask interfaces), (ii) code enabling automated gating and deconvolution of cytometry data and code enabling modelpredictive control of gene expression using light, (iii) scripts performing data processing, analysis and plotting for figures, and (iv) a movie showing how to use ReacSight to set up experimental platforms (Supplementary Movie 1).
12. 9 6c 129 Fig. 3Closing the loop: real-time control of gene expression using light. a A simple ODE model of the light-driven gene expression circuit is fitted to the characterization data of Fig. 2d. Fitted parameters are γ m = 2. 09 h -1 , σ = 0. 64 RPU h -1 , and γ FP = 0. 475 h -1 •k m was arbitrarily set to equal γ m to allow parameter identifiability from protein median levels only. b Strategy for the real-time control of gene expression. Every hour, cytometry acquisition is performed, and after gating, deconvolution, and FSC-normalization, data are fed to a model-predictive control (MPC) algorithm. The algorithm uses the model to search for the best sequence of duty fractions for 10 duty cycles of period 30 min (i. e. a receding horizon of 5 h) in order to track the target level. c Real-time control results for four different target levels, performed in parallel in different bioreactors (custom setup).
We reduce the NIRSpec MOS spectroscopic data following our customized procedures, elaborated in the Methods section. The middle and bottom rows of Fig. 2  show the resultant 2D and 1D prism spectra of RXJ2129-z8HeII. We detect pronounced emission features of the [O iii] doublets and Hβ line complex at λ obs ∼ 4. 5µm, and a clear continuum break at λ obs ∼ 1. 1µm with prominent continuum flux redward of the break. The identification of this continuum break as the Lyman break occurring at the Lyα wavelength (λ rest = 1216Å) is in excellent agreement with both the photometric redshift of RXJ2129-z8HeII measured from broadband photometry and the strong [O iii] +Hβ lines in the prism spectrum. Fitting simultaneously the entire spectroscopic (continuum + lines) and the photometric data sets, we confirm RXJ2129-z8HeII as an extreme emission line galaxy with strong continuum emission at z spec = 8.
1623 +0. 0007 -0. 0008 (see Fig. 3 ), when the Universe is ∼613 Myr years old. This puts RXJ2129-z8HeII deep in the epoch of reionization (EoR), when the IGM was mostly neutral hydrogen  3, 4. We employ the BAGPIPES software  9  to conduct detailed spectro-photometric analysis of both the broad-band photometry and the full NIRSpec spectrum that we obtained for RXJ2129-z8HeII. Performing full spectrum fitting is highly critical since it is the only appropriate approach to extract the detailed information of stellar population from the low-resolution prism spectroscopy. According to the up-to-date lens model produced by the GLAFIC software  10, 11  , we estimate that the magnification for our object is µ = 2. 26 ± 0. 14. After correcting for magnification, we obtain the following physical picture of RXJ2129-z8HeII: it is a very low mass (log(M * /M ⊙ ) ∼ 7.
We utilize the pPXF software to perform accurate emission line analyses. The emission line fluxes and equivalent widths 2 (EWs) are measured by fitting the stellar continuum and emission lines simultaneously using the BC03 stellar population library  20  and the Gaussian component describing each line (see the left panel of Fig. 4 ). From the NIRSpec prism data, we detect 5 , we estimate the ionizing photon production efficiency to be log(ξ ion [erg -1 Hz]) ∼ 25. 50 following Ref. 23 , in agreement with the expectation at z ≳ 8 if sub-L * galaxies are the major sources of reionization  14. Notably, the He ii emission is the only UV line clearly detected in RXJ2129-z8HeII. This line has the intrinsic (corrected for the magnification) flux f He ii = 64 ± 10 × 10 -20 erg s -1 cm -2 and EW He ii = 19 ± 3Å.
1" 8, 33-40. Among this cohort, RXJ2129-z8HeII has by far the steepest slope, highlighted by the star. 2] [43] [44] [45]. The dotted and dashed lines denote the population-averaged correlation of M UV and β at z ∼ 7  18  and z ∼ 8  19. The shaded regions correspond to the parameter space where the escape fraction of the ionizing radiation ( f LyC esc ) is above 5, 10, and 20 percent, respectively, derived by Ref. 7  from the LzLCS dataset. This strongly suggests that RXJ2129-z8HeII is hitherto the most promising spectroscopically confirmed galaxy that enables significant LyC leakage into the IGM to actually contribute to the cosmic reionization. He ii/C iii] and He ii/C iv flux ratios, as compared to local galaxies  28, 30  , galaxies at the cosmic noon epoch  24  , and CR7 at z = 6. 6  29.
Methods Cosmological model. Throughout this paper, we adopt the standard concordance ΛCDM model with Ω m = 0. 3, Ω Λ = 0. 7, H 0 = 70 km s -1 Mpc -1 , and the AB magnitude system  48. JWST/NIRCam data reduction and mosaicing. The NIRCam imaging data were acquired by a Director's Discretionary program (DD-2767; PI: P. Kelly), whose primary goal is to measure the light curves and spectra of a strongly lensed supernova SN 2022riv at z = 1. 52  49, 50  discovered in the RXJ2129 field, by a HST SNAP program (GO-16729; PI P. Kelly). The exposure times are 4982 seconds for the F150W/F356W filters, and are 2061 seconds for the other four NIRCam filters (F115W, F200W, F277W, and F444W). We reduce these NIRCam images using the standard JWST pipeline version 1. 8. 1 and making use of the jwst_1013. pmap context.
We follow the standard three stages of the image data reduction pipeline calwebb_detector1, calwebb_image2 and calwebb_image3 to calibrate each image and build the mosaic image of each band. Furthermore, we remove the "snowball" 3  and 1/f noise are using the scripts from https://github. com/chriswillott/jwst, and the "wisps" 4  are removed using the default wisps templates  5. We produce the imaging mosaics on 60 and 30 mas pixel scales, astrometrically aligned to the GAIA DR2 astrometry frame. The image reduction script is uploaded to https://github. com/ chengchengcode/jwst-pipeline-note/blob/main/F150W-snowballflag-wisps-1overf. ipynb. The data doi is 10. 17909/2dxj-z303. JWST/NIRCam and HST/ACS-WFC3 photometry. For comprehensive photometry of RXJ2129-z8HeII, we not only use the 6-band imaging mosaics produced above, but also include the publicly released HST imaging mosaics from the Cluster Lensing and Supernova Survey (CLASH; Ref.
For filters with no detections, we report the 2-σ upper limits. The NIRCam photometry normalized at their individual F200W flux is shown in Fig. 5. Photometric redshift estimates. We use the EAzY software  53  to estimate the photometric redshift from the broad-band photometry presented in Table  2. All together, we use the HST filters ACS/F775W, ACS/F814W, WFC3/F105W, WFC3/F125W, WFC3/F140W, WFC3/F160W, as well as the JWST NIRCam filters F115W, F150W, F200W, F277W, F356W and F444W. We adopt the standard set of galaxy SED templates eazy_v1. 1_lines. spectra. param, which includes star-forming We also perform extensive tests to verify the photometric calibration of NIRSpec prism spectra. On one hand, we cross-check the flux levels of our extracted spectra and that of our broad-band photometry from NIRCam imaging in the similar wavelength regime.
They show good agreement with each other within 10% (see the upper panel of Fig. 3 ). On the other hand, we conduct detailed investigation of the wavelength calibration by performing line identifications in low-z galaxies showing multiple emission lines across their entire prism spectra, observed in the same MSA setting whose spectra are extracted in the same fashion. We find that the wavelength offset between the best-fit centroid and that expected for the mean redshift shows a scatter <0. 004µm, much smaller than the instrument resolution. So we conclude that our flux and wavelength calibrations are sufficiently accurate. Spectro-photometric analyses. We employ the BAGPIPES software  55  to perform the spectral energy distribution (SED) fitting of both our spectroscopic and photometric observations simultaneously.
16, 0. 01). BAGPIPES utilizes the nested sampling algorithm to perform efficient Bayesian inference and obtain posterior distribution of parameters. We take the [16th, 50th, 84th] percentiles of the parameter posteriors as the median and 1-σ CI, and show the 1D posterior distributions of some key parameters in Fig. 7. For each set of sampled parameters, BAGPIPES produces realistic galaxy SED through its model_galaxy module. We thus overlay the median and 1-σ CI of such model realizations in orange curve and band in the upper panel of Fig. 3 , which shows that they are faithful fits to the observations. We also present the median constraints and 1-σ uncertainties of some key physical parameters in Table  1. Here, M * stands for the stellar mass of RXJ2129-z8HeII. SFR S represents the SFR averaged over a few 100 Myrs time scale.
8, -2. 6]. M UV = m F140W + µ -5 log(d L /10 pc) + 2. 5 log(1 + z) (2) Emission line fitting and measurements. We perform the stellar population analysis and emission line fittings to the prism data by using the pPXF code  59   3. The inset in the left panel of Fig. 4  shows a zoom-in view of our fitting result to the source spectrum at λ rest ∈ [1600, 1700], where He ii and the oxygen auroral lines O iii]λλ1661,1666 are marked in vertical dashed lines. We verify that the detection of the He ii line is robust against negligible contamination from the oxygen auroral lines. Bayesian inference of ISM properties. We apply a well-established Bayesian forward-modeling inference framework to the measured line fluxes derived from our dedicated emission line profile fitting presented in Table  3. We constrain jointly three key ISM properties: gas-phase metallicity (12+log(O/H)), nebular dust extinction (A N V ), and de-reddened Hβ flux ( f Hβ ), through the following likelihood function following Refs.
We adopt the cluster lens model of RXJ2129 constructed using the GLAFIC software  10, 11  , updated from the initial work of Ref. 66. In total, 22 individual images of 7 multiply lensed background galaxies spectroscopically confirmed by Ref  67  are used as the strong lensing constraints. The macroscopic mass model consists of one cluster-scale dark matter halo in the elliptical Navarro-Frenk-White profile  68  , combined with galaxy-scale halos modeled using the pseudo-Jaffe profile according to the scaling relation of the velocity dispersion and luminosity of cluster member galaxies  69. The best-fit model is derived using the Markov chain Monte Carlo sampling process with a χ 2 minimization assuming a positional uncertainty of 0. 4". A hundred additional realizations are also created to bootstrap the 1-σ statistical uncertainties for magnifications.
As a consequence, we obtain the best-fit and 1-σ CI of the magnification estimates of RXJ2129-z8HeII to be 2. 26 and [2. 12, 2. 40]. We also double check with an independent lens model built by the Lenstool software  67  , and derive consistent results. Source morphology. As shown in the multiple image stamps displayed in the upper panels of  equivalent width [O iii] +Hβ nebular emission lines (also see Table  3 ). Indeed we observe a more extended light profile in the rest-frame optical than that in the UV. We thus model the image of.
Acknowledgements This work is based on observations made with the  NASA/ESA/CSA James Webb Space Telescope , associated with the program JWST-DD-2767. The specific observations analyzed can be accessed via https://doi:10. 17909/2dxj-z303. The data were obtained from the  Mikulski Archive for Space Telescopes at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. XW is supported by  CAS Project for Young Scientists in Basic Research , Grant No. YSBR-062. JG acknowledges support from the  Youth Innovation Promotion Association of the Chinese Academy of Sciences  (No. 2022056 ). MO acknowledges support by  JSPS KAKENHI  grants  JP20H00181 ,  JP20H05856 , and  JP22H01260.
MNRAS 520,  3201-3217 (2023)  France-Hawaii Telescope (CFHT) and cover a large wavelength range, from 370 to 1040 nm, with a spectral resolution of 65 000. Each observation consists of four subexposures, each of them taken in a specific polarimeter configuration. The data reduction pipeline of ESPaDOnS is Libre-Esprit  (Donati 1997  ) included in the UPENA package. We normalized the spectra through a semiautomatic PYTHON code  (Martin 2017 ). The signal-to-noise ratio (S/N) of the spectra ranges from 143 to 537 with an average value of 376, measured at 503 nm. Rele v ant information regarding the observations is gathered in Table  1. Note that, when checking the night reports from the CFHT, we noticed large differences in the S/N between subexposures for the observation #2 (2297769). This is not the case for the other observations and could be the evidence of some transient (unknown) issue.
Finally, we estimated the errors on the luminosity and the surface gravity by propagating the errors from the abo v e mentioned parameters in the formula used by  Stassun et al. ( 2019 ). As a check on the fundamental parameters provided in the TIC, we retrieved the Geneva six-colour photometry for this star from the Lausanne General Catalog of Photometric Data  (Mermilliod, Mermilliod & Hauck 1997 ) , which is maintained online at the Masaryk University in Brno, Czech Republic. 1 (No Str ömgren photometry of this star appears to be available. ) These data were used to obtain values of T eff = 11376 ± 57 K and log g = 4. 22 ± 0. 08, employing the program CALIB. F  (K ünzli et al. 1997 ) , as described in more detail by  Silaj & Landstreet ( 2014 ). The underlying calibrations for fundamental parameters derived from Geneva photometry with CALIB.
In fact, we need to obtain two rather different abundance tables. For the precise measurement of magnetic field strengths using the LSD method, our analysis ef fecti vely fits the line absorption and polarization profile of each spectral line with a simple model and then averages these fits to obtain a high-S/N measurement of circular polarization. For this purpose, we need an abundance table that leads to a line list that includes lines that approximately reproduce the observed strength of lines when computed with a code that ignores the effects of a magnetic field, because the selection process for lines in such line data archives as the Vienna Atomic Line Database (VALD;  Piskunov et al. 1995 ; Pakhomov, Piskunov & Ryabchikova 2017 )  is carried out ignoring the effects of a magnetic field.
Espadons Abundance fitting for both line lists has been done using the polarized line synthesis program ZEEMAN  (Landstreet 1988 ; Wade et al. 2001 ; Bailey et al. 2011 ; Landstreet 2011  ). The program is built around forwarding computation of the emitted spectrum from an assumed model atmosphere, also assuming an initial abundance table, and if desired a simple model of the stellar magnetic field. The program takes as input a stellar atmosphere interpolated in a grid of ATLAS non-magnetic atmosphere models computed for T eff and log g values close to those assumed for the star. In its simplest form, the program solves the four coupled equations of transport for polarized radiation propagating through the atmosphere of the specified atmosphere at typically 60 sample points on the visible hemisphere to compute local emergent intensity spectra.
A2  , the y hav e different magnetic signatures. Thus, we used the following reasoning to refine the rotation period. Between the first observation on 2011 August 19 and the mid-day of all other observations, the star rotated N = 1012 times. Thus, a small error on the rotation period will have a strong impact on the phase of the 2011 MiMeS data point. Such an error can be estimated based on the misfit between the phase of this point and the expected phase from the dipolar fit. The expected period phase of V352 Peg, φ exp , when the 2011 observation was recorded, can be calculated from the dipole magnetic field model given by equation ( 2 ). It is estimated to be φ exp = 0. 08968. The MiMeS point phase found with the rotation period of 2. 63605 d is φ obs = 0. 90131. It represents a difference in phase of φ = 1φ obs + φ exp , thus an error on the rotation period of e = P × φ N = 4.
( 2018 ). This is a PYTHON implementation of the code presented by  Donati et al. ( 2006 )  and uses the maximum entropy method of  Skilling & Bryan ( 1984 ) , which both minimizes the χ 2 and maximizes the entropy to obtain a unique solution. We have made one major modification to the code of  Folsom et al. ( 2018 ). In Ap stars, the Stokes I variability is pre-dominantly driven by chemical spots, which produce variations in line strength, while the DI of  Folsom et al. ( 2018 )  was based on mapping brightness variations due to temperature spots. These effects have some similarities, but brightness variations do not change the equi v alent width of a line while chemical spots do. Thus we have modified the code to map relative-line strength, as a proxy for chemical abundance, instead of relative brightness.
Spectral lines from Cr, Fe, and Ti are used to reconstruct the surface distribution of these elements. To this aim, LSD profiles are produced from a subset of lines extracted from the original LSD line mask (see Section 4. 1 ). We used around 2000 lines of Cr, 3000 lines of Fe, and 190 lines of Ti. Results are presented in Fig. 10. The surface distribution of Cr, Fe, and Ti is inhomogeneous. As expected from the clear variations observed in the line profiles, chemical spots are present at the surface of V352 Peg. The Stokes I and V profiles are very well-fitted for  Iron and Chromium. For Titanium, the ZDI code struggles to fit the profiles at phases 112. 85, 47. 90 and -968. 92. These phases should coincide with the Southern magnetic hemisphere entering in the line of sight, around phase 0.