dataesr/scientific-paragraphs-categorization huggingface dataset:
• Data Calibration deals with all calibration and monitoring efforts that are required to remove instrument signature from the scientific exposures during the data-reduction process. It thus provides the necessary calibration files so that the 4MOST Data Management System performs to specification. Different aspects include 1) Science CCD characterization like bias, dark current and read noise, pixel-to-pixel flats and linearity through internal LEDs, 2) fibre spectrum flatfielding and tracing through twilight and night time attached Calibration Illumination Unit continuum light source exposures, 3) wavelength and projected fibre image calibrations through night time attached Calibration Illumination Unit Fabry-Perot light source exposures and Fabry-Perot and Thorium-Argon light exposures through the simultaneous calibration fibres during science exposures, 4) sky subtraction modelled using the spectra from about 10% of the fibres dedicated to empty sky regions both spread over the 4MOST field-of-view and along the slits of the spectrographs, and 5) Flux calibrations that will be achieved by a combining the various twilight and attached night time flatfield exposures with measurements of White Dwarf and other standard stars and Gaia BP and RP low-resolution spectroscopic measurements.
• Science Calibration deals with all calibration and monitoring efforts that are required to ensure proper scientific exploitation of the 4MOST data products by the high-level data-analysis pipelines, provided by each Survey that utilizes 4MOST. Observations will include for instance Gaia Benchmark and asteroseismology stars, well studied open and globular cluster stars, and stars and galaxies with accurate parameters in the literature. A special sample of stars covering a large range in parameter space will be observed early on during the 4MOST Start-up Phase to be fully characterised using classical spectral modelling methods and then used as a training set for a Machine Learning based stellar pipeline. 4MOST will observe targets that are also covered by other spectroscopic surveys such as RAVE, Gaia-ESO, GALAH, GAMA, WEAVE, MOONS, DESI, in particular in deep, repeat field areas.
Operational Rehearsals In parallel to the instrument development and verification effort, the 4MOST Project is conducting Operational Rehearsals (OpRs) to test and perform quality assurance on the entire data flow and the software modules. Initial OpRs were focussed on completing all interfaces and the data flow with all systems involved. The Fourmost operations Rehearsals Observational data Generator (FROG) is used on a computer cluster to run TOAD (Section 2. 2), generating the many artificial data frames needed to simulate several nights worth of observations. Because it takes several days to generate one night of artificial data, current OpRs tend to be more focussed on particular streams. For instance, one OpR concentrates mainly on testing the front-end observation planning in Garching and its interface to the observatory to send an Observing Block with command instructions for the instrument and telescope.
In another OpR only the primary backend section is tested where calibrated 1D spectra are extracted from the artificial 2D data and feedback information on target completion status is generated and send to the front-end observations planning. During earlier OpRs it was discovered that the original operations model with the Level 2 (L2) enhanced data products pipelines distributed at different physical locations created bottlenecks in the data flow. While the amount of data that needed to be transferred is not huge by modern standards, the transfers of the large number of individual files created enough interruptions that the pipelines that need to create the stellar, galaxy, and AGN physical parameters were not reliable enough on the time scales required. The operations model was hence changed, where the L2 pipelines are still being developed by teams distributed across the globe, but the execution of all the code takes place in the 4MOST Data Processing Centre (4DPC) located at the IoA in Cambridge.
In this way the position where the maximum flux is received for each stellar position can be determined, the relative distortions between the fibre positions, the sky target positions, and the calculated metrology measurement positions can be calculated and any necessary corrections in the telescope and ray tracer model of the metrology system can be optimised. In parallel with commissioning, the different elements in the operations data flow will be tested step by step in the final Operations Rehearsal to verify the operations software. Initially the instrument will be controlled by OBs generated on the mountain, but the first data will stream quite early to the Data Management System in the intended fashion for quick data analysis and to support instrument verification. Once the instrument behaviour is largely calibrated, the designated Visitor Mode operations from MPE in Garching, Germany will be tested and the remote scheduling will be tested.
The different pipelines will start analysing the data to optimise their parameters and to deal with unexpected features in the data. Once all elements work individually, a few nights of constant operations will be performed to fully test the functioning of the data flow. Once this is functioning, we will start the last verification step before starting the first five-year 4MOST survey. During this last Operations Start-up Phase the aim is to fully characterise the instrument in a variety of different observing conditions in order to optimise the survey strategy parameters (exposure times, next field selection, etc. ), estimate the survey efficiency, and to verify that all science goals of the different Surveys can be expected to be achieved in five years of observations. In the first few weeks of this period, we will target special areas with for instance good quality data in the literature, where we will expose longer than we expect to do during normal operations to establish optimal exposure times, determine best magnitude limits for the different target classes, and in general to vary observing strategy parameters to establish settings for the most efficient survey.
Microbiome data processing Raw sequences were processed using Qiita  [25]  and were quality filtered to generate amplified sequence variants using Deblur  [26]. Sequences were aligned in QIIME2 version 1. 9. 1  [27]  using MAFFT  [28]  to construct a phylogenetic tree using FastTree2  [29]. Taxonomy was assigned using the q2-feature-classifier against the 99% Greengenes 16S rRNA gene reference database (version 13-8)  [30]. If the Greengenes database is updated this may result in identification of further diversity  [30, 31]. QIIME2 version 2019. 4. 0 was used to calculate core diversity metrics, i. e. Shannon indices and weighted UniFrac distance matrices measures  [27]. Amplicon sequence variants (ASVs) were classified as either pathogens or fermenters (anaerobes) based on their known clinical relevance in clinical microbiology labs and CF literature  [4, 32]  (Table  S1  and S2 ).
Metagenomic sequencing of patient sputum samples DNA from sputum samples was isolated using the Dneasy PowerSoil Pro Kit (Qiagen), according to the manufacturer's protocol. Isolated DNA was quantified by Qubit. Metagenomics was performed by CosmosID Inc. (Rockville, MD) according to their standard protocols. DNA libraries were prepared using the Nextera XT library preparation kit (Illumina), with a modified protocol. Library quantity was assessed with Qubit (Thermo-Fisher). Libraries were then sequenced on an HiSeq platform 2x150bp (Illumina). Unassembled sequencing reads were directly analyzed by CosmosID metagenomic software (CosmosID Inc. , Rockville, MD) via highperformance data-mining k-mer algorithm and highly curated dynamic comparator databases that disambiguate short reads. Taxonomy was assigned via the CosmosID database.
DNA was extracted from 50 µL of the sample via Quick-DNA Miniprep Plus kit (Zymo Research Corp) followed by qPCR and 16S rRNA gene sequencing as described above. This experiment was repeated with 5 replicates for validation. The pH estimation was obtained by measuring the average RGB color values from the phenol red media dye using Image J software and comparing it to ASM with phenol red standard buffered from pH 5-8.
Statistical analysis Alpha diversity for microbiome and metabolome were calculated with the Shannon diversity index. Beta diversity for the microbiome was calculated using the weighted UniFrac distance while Bray-Curtis was used for the metabolome. Permutational Multivariate Analysis of Variance test (PERMA-NOVA) individual and mixed effect model, examining interactions between and nested effects, were calculated from the relative abundances via the Bray-Curtis method with 999 permutations. Subsequent stepwise model selection was used to determine which effects/mixed effects had the greatest influence on ASV presence/absence. Kruskal-Wallis tests were done to determine significance across various measures as the data was not assumed to be normal. Post hoc Mann-Whitney tests with applied Bonferronic correction (Tables  S4-S13 ) were performed to examine differences across the antibiotic treatments.
Prevalence measures on ASVs were also examined (Fig. S9 ). PERMANOVA and the standard homogeneity condition was performed with the R vegan package (v. 2. 5-7)  [38]  via adonis() and betadispr(). Kruskal-Wallis tests and post-hoc Mann-Whitney tests were performed using the R dplyr package (v. 1. 0. 2)  [39]. Data visualization was done using the R package ggplot2 (v. 3. 3. 5) [40]  and phyloseq (v. 1. 30. 0)  [41]. A PCA was calculated via the R package stats (v. 3. 6. 2) and visualized via R package factoextra (v. 1. 0. 7). Prevalence was measured and visualized using the R package microbiome (v. 1. 17. 41)  [42].
ACKNOWLEDGEMENTS We would like to thank  Carla Jentz , the  Spectrum Health ,  UCSD clinical teams  for help with sample collection for this study. This project was funded by the  National Institute for Allergy and Infectious Diseases  grant  R01AI145925  awarded to PI Quinn. L-AG and RAQ wrote the manuscript. L-AG, RR, and RAQ conceived the research, conducted the experiments, analysis, and data interpretation. DVG, CM, KAN assisted in data acquisition, interpretation, and statistical analysis. KAN carried out the qPCR analysis. Sputum samples were collected was done by AC-B and JM. BS and JMF assisted with setup and data collection in the initial phases of the experiment. IK and TZ performed all the mathematical modelling analysis. RAQ, MM, DC, IK, and TZ conceived the work and provided significant revisions as well as insight into data interpretation.
DATA AVAILABILITY The 16S rRNA gene amplicon microbiome data was deposited in the Qiita  [25]  database as the project numbers 12992 and 14086. The metagenomic data was deposited to the NCBI database under the SubmissionID: SUB11489970 and BioProjectID: PRJNA839435. The code for simulating the mathematical model is available at https://github. com/zhangtianyu-msu/WinCF_Antibiotic_Code.
The CV analysis for planar electrodes is highly matured  [2]  and one may derive kinetic information by routinely fitting of experimental data with appropriate models using commercial software. For analyzing porous electrodes, the geometry needs to be properly approximated. One approach is considering arrays of electrode units, with the geometry chosen according to the type of porous material studied  [3, 4]. Very recently, Tichter and co-authors improved this formalism by representing porous electrodes as randomly distributed arrays of microelectrodes for wider variety of geometries  [5, 6] ; they developed a software, Polarographica, for simulating CV of porous electrodes. Considerable work has also been made to analyze kinetics from EIS of porous electrodes. DeLevie is often referred as pioneer of the field in the 1970s  [7, 8]  with the introduction of transmission line analogy.
Experimental apparatus and protocols for macro-felts All electrochemical experiments were performed with a VSP-300 potentiostat (Bio-Logic, France) interfaced by EC-Lab® software (Bio-Logic, France). For macro-felt experiments, a custom-made PVC cell to perform cyclic voltammetry and electrochemical impedance spectroscopy on pieces of graphite felts was designed and built (see Fig. S1 ). Graphite felts were used as electrodes (SGL 4. 6 GFD, thickness of 4. 6 mm): two large cylindrical pieces (Ø 5 cm) served as counter electrodes (CE) while one smaller cylindrical piece (Ø 1 cm) was the working electrode (WE). The felts were cut with punches from large pieces of felt as received from providers for better reproducibility. No screening standard was performed regarding the felt homogeneity. Both commercially available non-activated (n.
EIS experiments for graphite felts were carried out applying the following protocol: first, the formal potential of studied redox couple was estimated from cyclic voltammetry by taking the average of oxidative and reductive peak potentials. Then, staircase potential electrochemical impedance spectroscopy (SPEIS) was performed with EC-Lab® software in a range of potentials centered on formal potential and including it: at each potential step, potential is applied for enough time to reach pseudo-equilibrium (typically ten minutes), then oscillates around its pseudo-equilibrium value while measuring the response current at different frequencies. For ferrocyanide, 50 mV potential steps and five minutes of buffer time were used. For TEMPOL compound, mV potential steps were applied using a buffer time of five minutes.
5 min. The analysis of EIS spectra was done by performing Complex Nonlinear Least Squares, that is to say by fitting the experimental Nyquist diagram with a model. The models used are detailed in a subsequent part. The optimization was done by programming a small custom-made routine with MATLAB® software based on the use of the optimization function "fmincon". Constraint tolerance, function tolerance and optimality tolerance were all set to 10 -8. The maximum number of function evaluations was set to 200 and algorithm used was "sqp". Before optimization, the parameters were weighted with their approximate order of magnitude to ease convergence. The relevant range of frequencies usable for analysis was typically below 5 kHz for graphite felts. BET surface of graphite felts was measured using 3 g of graphite felt.
Cyclic voltammetry model for porous electrodes In order to estimate kinetic constants based on cyclic voltammograms measured on porous macro-felts, the model developed by Tichter and co-authors was used  [6]. This model, available under the form of a free software called Polarographica, is based on the representation of porous materials as randomly spaced arrays of microelectrodes. The spacing of microelectrodes is handled with a statistical distribution that can be divided into several intervals of inter-electrode distances. Cyclic voltammetries are then calculated for each of these intervals, each time based on corresponding average distance, and their sum is weighted according to the statistical distribution to give the resulting voltammogram. Among the different geometries available, the array of cylindrical electrodes is the one that should best represent carbon and graphite felts.
∂C R ∂r ) r=r0 = k 0 [ (C R ) r= r0 exp ( (1 -α)nF R g T ( E -E 0 ′ ) ) - ( C * R + C * O -(C R ) r= r0 ) exp ( - αnF R g T ( E -E 0 ′ ) )] (13) determined as a function of parameters to optimize. Then, typically the third cycle was used for the fitting: the calculated current points at corresponding input potentials were compared to experimental current points with least square minimization method. Optimized parameters were charge transfer kinetic constant k 0 , as well as geometric surface area A in order to include the measurement error of fibers' lengths as well as the variability of their radii. The whole optimization procedure was coded with MATLAB®.
This is well correlated by the increase in specific surface area measured by BET method: indeed, specific surfaces measured were about 0. 5 m 2. g -1 for nonactivated felt and 1. 4 m 2. g -1 for activated one (about 500 cm 2. cm - and 1400 cm 2. cm -3 ). Care should be given however to the meaning of these values, as these correspond to values referring to N 2 adsorption, and might represent a surface not readily available to bigger molecules used in this study. Using Polarographica software, manual fits of the cyclic voltammograms of the two systems on non-activated felts are presented in Fig. 2 , estimated values for k 0 and total surface area A total are displayed below   the graphs. The fit reproduces quite well the experimental data, especially around formal potential. Far from this value, important deviations are observed, which may arise from the non-faradaic current that is not considered in the model.
Comparison between macro-felts and micro-fibers The charge transfer kinetics of two redox systems on activated and non-activated carbon felts are studied at two different scales: macro-felt and micro-fiber. For each scale, CV and EIS are carried out and analyzed by corresponding models. At macro-felt scale, CV analysis is based on Polarographica software and EIS data are fitted with a modified macrohomogeneous model. At micro-fiber scale, CV is fitted by considering non-Nernstian charge transfer kinetics and 1D semi-infinite diffusion in cylindrical coordinates, and EIS is fitted with a modified Randles circuit adapted to cylindrical geometry. For macro-felts, the separation between oxidation peak and reduction peak is an indication of charge transfer kinetics on the electrode. From the results in Fig.
Acknowledgements This work was carried out under  Higreew  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement no. 875613. The authors are grateful to  Dr. Tim Tichter  and  M. Sc. Jonathan Schneider  from  Physikalische und Theoretische Chemie, Institut für Chemie und Biochemie, Berlin, Germany , for insightful discussion as well as for providing us with their excellent software: Polarographica. The authors wish to thank  M. Sc. Oihane Zugazua  and  Drs. Maddalen Agirre ,  Ana Catarina Lopes  and  Eduardo Sánchez-Díez , from  CICEnergigune , for providing the electrolytes studied in this paper.
N/A Field visits to meetings of regional innovation agencies on and with eHealth entrepreneurs and care organizations. Although the three cases had dissimilar data collection methods, the document analysis (mostly based on key scientific articles and policy documents) as well as information gained during interviews with a wide range of different stakeholders, provided a historical perspective and knowledge of the state-ofthe-art. This was laid down in a historical account that was validated in workshops, during which also anticipated future developments, challenges and needs were articulated. Triangulating the outputs of these multiple methods provided a rich and coherent overview of the emergence and potential future evolutions of the three cases. These case overviews were then examined for the market formation processes; whether these were present and how they played out.
2006, p. 333)  [DemandArt]. Other dentists then began experimenting with 3D printing  (Scherer 2015)  to create models of patients' teeth based on medical imaging  [NewUse]. This evolved into the direct printing of dental prostheses, requiring feedback loops with 3D printing professionals and new companies entering the dentistry field 2 https://wohlersassociates. com/GARPA. html (accessed 15/12/201) in order to develop additive manufacturing capabilities. One such companies was Materialise (Belgium), which developed a software system (the Materialise Mimics TM software 3 ) to translate medical imaging data into a 3D system, thereby enabling the design of dental prosthetics  (Liu et al. , 2006). Such support systems were essential to enable 3D printing to be effectively used for medical applications.
The advent of printing with metal rather than plastic, particularly the cobalt-chrome that meets the high-quality standards of dentistry, made direct manufacturing of dental prostheses possible. De facto dominant combinations of printers, materials and design software emerged  (Robinson et al. , 2019)  [DomDesign], which were distinct from the combinations that became dominant in other 3D printing markets, like aerospace and automotive industries  (Frazier, 2014)  (Figure  2 ). The emerging dominant design broadened the usual practice of dental prostheses design, production and fitting, enabling dentists themselves to customize prostheses, like orthodontic models, splints and dentures, and have them   Cheaper testing would allow ubiquitous diagnosis and customized treatment. As such, genetic testing forms a pivotal technology in transitioning towards more personalized healthcare  (Hamburg & Collins, 2010).
They have promoted paths for use of toxicoepigenetic data in risk assessment and encouraged debates about bottlenecks via workshops, sessions at major toxicology conferences, and review articles and opinion pieces in specialized and interdisciplinary journals. Here, we examine these efforts, using a qualitative approach to identify barriers to the process of including epigenetic data in risk assessment. We analyzed this literature and interviewed a dozen experts active in these debates, specifically, scientists with positions in academia (n = 3), industry (n = 2), regulatory agencies (n = 2), an environmental NGO (1), and risk assessors in governmental agencies (n = 2). All but one were in the US. (The study was certified exempt by the Institutional Review Board (IRB) of the University of California, Los Angeles (UCLA), (IRB No.
We analyzed the data from interviews using an inductive approach, identifying themes that emerged out of the interviews. We compared interview data with reviews and meeting reports that specifically address the intersection between toxicoepigenetics and risk assessment, so as to identify recurring themes, issues, and ways to move forward that are currently under discussion in the field. In the present perspective article, we will highlight the gaps between toxicoepigenetic data produced by basic science and the standards for regulatory science that constitute the knowledge basis of risk assessment. Then, we will outline several ways to address these barriers and indicate areas in which toxicoepigenetics could most readily contribute to risk assessment.
• The principles of the Red Book, along with the regulatory frameworks that were adopted in the past decades, have encouraged the development of a community of professionals (in particular, regulatory toxicologists) who participate in the production of data on chemicals following standardized tests capable of documenting risks. Over time, the Organization for Economic Co-operation and Development (OECD) has managed to harmonize local testing protocols into international test guidelines that are presently the gold standard for the regulatory assessment of chemicals. 18, 23 he   2 ). While epigenetic studies have been performed on many toxicants (130 in 2021), the bulk of them focuses on a small number of chemicals, namely certain metals and metalloids (arsenic, lead, cadmium, methylmercury, nickel, chromium), bisphenol A (BPA), and polycyclic aromatic hydrocarbons (PAHs) (Figure  3 ).