dataesr/scientific-paragraphs-categorization huggingface dataset:
Addition of Ion Mobility to MSI with plasma-based post-ionization The experimental setup has allowed us to utilize trapped ion mobility for plasma-based post-ionization, and we obtained a TIMS-enabled dataset on mouse brain with representative single spectra shown in Supporting Information Figure  S8. Given the rich lipidomic spectra generated from tissue (e. g. , Figure  3 ), ion mobility is expected to aid in the resolution of inevitable isobaric and isomeric species. Here we will present two advantages of ion mobility applied to MSI using plasma-based post-ionization, namely (i) the resolution of isomeric [PE+H] + and [PC-CH3+2H] + ions; and (ii) enabling the extraction of ion images for additional molecular features among complex isobaric mixtures that would otherwise remain unresolved in the absence of ion mobility.
All ion images are generated using a +/-6 ppm mass tolerance, +/-0. 006 1/K0, total-ion current normalization and hotspot removal (99% quantile). Scale bar 2mm. One of the surprising observations from the lipid standards outlined above was the low intensity observed for intact PC lipids and the detection of the fragment [PC-CH3+2H] +. These fragments resemble the native lipid class known as dimethyl-PE (DMPE, or PE-NMe2) that exist as intermediates in the enzymatic conversion between PC and PE, and are isomeric with PE lipids containing an additional two methylene groups on the acyl chains (e. g. , [PC(34:1)-CH3+2H] + is isomeric with [PE(36:1)+H] + ). Given the high abundance of PE and PC lipids in most tissues, these isomeric overlaps are expected throughout an MSI dataset. Figure  5a  shows the ion mobility heat map with two isomeric features present at m/z 746.
Value of the Data • Pesticide-free production systems can substantially contribute to pesticide risk reduction -a topic of societal and policy relevance. We here provide data on the adoption of pesticidefree wheat production in Switzerland, which gives a comprehensive overview of potential adoption determinants and barriers for pesticide-free production. • Researchers, policymakers and food-value chain actors aiming to understand the determinants and barriers of pesticide-free production can profit from this dataset. • This data can be used in the future to provide further inisghts on socio-economic, environmental and farmers intrinsic drivers for the adoption of sustainable production practices in agriculture.
Data Description We collected survey data from IP-SUISSE wheat producers in Switzerland. More specifically, we focused on the adoption of a pesticide-free but non-organic wheat production standard in the growing season 2019/20. The standard was first introduced as a pilot in the growing season 2018/19 and was opened up for public participation in the growing season 2019/20. The survey was designed based on a conceptual adoption model discussed with farmers, extension service experts, and farm advisors. The initial survey was pre-tested with ten producers. It was sent out to the entire population of IP-SUISSE wheat producers (4749). IP-SUISSE producers already produce wheat under an extensive production scheme of the producer organization IP-SUISSE, which prohibits the use of fungicides and insecticides in wheat production.
The pesticide-free production scheme goes one step further and prohibits the use of all synthetic pesticide in wheat production. The survey data comprise 1105 complete responses (response rate of 23. 3%) of producers. Survey data include information on adoption as well as potential adoption in the future. Moreover, expectations and perceptions of the program and farm and producers' characteristics were surveyed. We further collected producers' risk preferences, farming objectives, attitudes, goals, and non-cognitive skills such as self-efficacy and locus of control. Survey respondents were distributed all over Swiss wheat production regions (see Fig. 1  ). We combined the  Weed pressure Regional weed abundance, according to Info Flora. (Info Flora, 2019)  [5]  Herbicide resistances The spread of herbicide resistances per municipality.
Acknowledgments We thank  Ladina Knapp  and  Cordelia Kreft  for helpful comments on survey implementation and various farmers, extension service experts, and IP-SUISSE experts for helpful feedback on survey design, especially  Martin Bertschi ,  Simon Briner , and  Sandro Rechsteiner. We thank  Leonie Vidensky  for help with translation and data preparation. We acknowledge the financial support for the  PestiFreeWheat  project by the  ETH Foundation.
We obtained 6 × 1800 s exposures on J1959, with the pointing dithered several arcseconds between integrations. These were interspersed with flat field exposures and spectrophotometric flux standards. All data were processed with the instrument's 2dfdr software. This included spatial flattening via the (non-Hα) sky lines. The six individual frames were combined into a final flux-calibrated position-velocity data cube. Pulsar bow shocks are nonradiative, so the optical emission line flux is dominated by Balmer lines, with Hα by far the strongest. To isolate this Hα emission, we generated a median filtered continuum image from 6530 to 6600 Å and subtracted this from each velocity channel. This largely removes the field stars, although of course the Hα photospheric absorption results in negative stellar features (Figure  2 ).
The radio timing data consisted of times of arrival (TOAs) recorded between MJD 55903 (2011 December 8) and 59361 (2021 May 27). For each NRT observation we extracted one TOA per 128 MHz of bandwidth to be sensitive to potential DM changes, and per 10 minutes of observation to get sufficient signal-to-noise while having short integration times compared to the orbital period. In order to exclude data recorded during eclipses or ingress/egress phases we conservatively discarded TOAs with orbital phases ranging from 0. 17 to 0. 38, with 0. 25 corresponding to superior conjunction. The TOA data were then analyzed using the TEMPO2  (Hobbs et al. 2006 ) and ENTERPRISE  (Ellis et al. 2019 ) pulsar timing libraries. After a first step of the analysis where we modeled the rapid orbital period variations with nine orbital frequency derivatives with TEMPO2, we then fixed the latter parameters as well as the longitude of periastron passage and the orbital eccentricity, and used ENTERPRISE to fit for the astrometric, rotation, and other binary parameters.
The VLBA data were reduced using a ParselTongue  (Kettenis et al. 2006 ) pipeline that has previously been used for numerous VLBA pulsar data sets (e. g. ,  Deller et al. 2019)  and can be obtained from https://github. com/dingswin/ psrvlbireduce/. The source J200352+232800, separated by ∼3°from J1959, was used as the primary phase and delay reference calibrator, while J1925+2106 was used to calibrate the instrumental bandpass. For the measurement of parallax and proper motion at ∼1. 5 GHz as attempted here, the precision of the relative astrometry of the target source is typically dominated by the residual direction-dependent ionospheric propagation effects between the phase calibrator source and the target. Accordingly, and as been used in most recent pulsar astrometry campaigns, we made use of a secondary "in-beam" phase calibrator source located closer to J1959 on the sky, which was identified in preliminary MSPSRπ observations.
The resultant position time series for the pulsar can be obtained from https://github. com/dingswin/publication_ related_materials/tree/main/J1959+2048. We followed the approach taken by  Deller et al. (2019)  and  Ding et al. (2021)  to fit the parallax and proper motion of J1959 from this set of positions, namely, (1) adding an estimated systematic astrometric uncertainty to each epoch based on the empirical estimator described by  Deller et al. (2019) , and then (2) using a bootstrap process  (Efron & Tibshirani 1991)  to infer the parallax and proper motion (and their uncertainties). After applying the estimated systematic uncertainty, the reduced χ 2 of a simple least squares fit to the full data set of 14 epochs was 2. 1, indicating that the per-epoch astrometric uncertainty may have been somewhat underestimated; however, the use of a bootstrap sampling process provides robustness against this.
We wish to thank  Andrei Tokovinin  and  Ceasar Briceno  at  SOAR  for assistance with the SAMI observations. At the AAT,  Andy Green  assisted with the KOALA data acquisition; we also thank  Newton Cheng  for assistance with their reduction. R. W. R. was supported in part by  NASA  grant  80NSSC21K0896. The Nançay radio Observatory is operated by the  Paris Observatory , associated with the  French Centre National de la Recherche Scientifique (CNRS) , and partially supported by the  Region Centre in France. We acknowledge financial support from the " Programme National Hautes Energies" (PNHE)  of  CNRS/INSU, France. The authors acknowledge use of the  Very Long Baseline Array  under the  US Naval Observatory 's time allocation. The  Very Long Baseline Array  (VLBA) is operated by the  National Radio Astronomy Observatory (NRAO).
TATOOINE observed double-lined (SB2) binary star systems. With spectra from both stars visible within a spectrograph, complex deconvolution methods need to be applied to recover the individual components' velocities with high precision and accuracy. Konacki et al. (2009)  found their best binary target was HD 9939 ( = 7). Their ten radial velocity measurements on this target from Keck HIRES have uncertainties of 1 -4 m s -1 , but they find a residual root mean squared (RMS) scatter of 7 m s -1. This RMS is calculated after the data have been fitted for binary Keplerian models, which we refer to as residual RMS for the remainder of this paper. To achieve this level of RMS scatter on an SB2 is impressive, though combining all their data on this target yielded a 99% confidence detection limit of ∼ 1 M Jup  (Konacki et al.
We also test our detection protocol by injecting circumbinary planets into our HARPS data and retrieve them with kima. We show that, after removing the binary signal, we repeatedly achieve a detection limit for circumbinary planets at masses as low as Neptune's, paving our way to actual detections. This paper is organised as follows. In Sect. 2 we describe our observing strategy for the BEBOP survey along with statistics of data gathered to date. Second in Sect. 3 we provide a brief overview of the kima diffusive nested sampling package, along with justification of its use within the BEBOP survey. In Sect. 4 the data analysis and simulation methods used are described. In Sect. 5 we display the results of our detection limit and simulation recovery analysis, and discuss the effect of the results on the BEBOP survey before concluding in Sect.
DESCRIPTION OF OUR OBSERVATIONAL PROTOCOL AND DATA COLLECTION The current BEBOP sample consists of a total of 113 single-lined eclipsing binaries which do not exhibit strong stellar activity, or the presence of a tertiary star. 54 in the Southern hemisphere, with HARPS, and 59 in the Northern hemisphere, with SOPHIE; observations still ongoing in each. The Southern sample was established from the EBLM programme, which identified low-mass eclipsing binaries from WASP false-positives  (Triaud et al. 2013; Triaud et al. 2017; Swayne et al. 2021).
Since most our systems have V magnitudes between 9 and 12, photon noise rarely reaches down to the 1 m s -1 long-term stability of the instrument  (Lovis & Pepe 2007; Mayor et al. 2009) , removing the need for simultaneous calibration. Calibration of the instrument is done at the start of night using Fabry-Pérot and Th-Ar calibration lamps as is now standard on HARPS  (Coffinet et al. 2019). The data is analysed on a bi-monthly basis for quality control (for instance verifying whether the residual residual RMS around the binary solution is low enough to allow exoplanet detections). Exoplanet candidate identification are done only once a year. We chose on purpose to perform those candidate searches rarely in order to avoid falling into observer's bias. A homogeneously observed dataset also provides more robust detection limits.
Choice of targets & observation summary For this paper we select three systems from within the full BEBOP South sample. We choose our Southern sample for this exercise as we have obtained more high precision data with a longer baseline than what is currently available in BEBOP North. We select these three from the sample for several reasons. The first is that no planetary or stellar activity signal are currently visible. Second, we select amongst the systems with the lowest radialvelocity uncertainty, and lowest RMS scatter, after having removed the contribution of the secondary star to the radial-velocities of the primary (that we refer to as residual RMS). We do this to specifically demonstrate BEBOP's ability to recover circumbinary planets with signals of a few m s -1. Of our 41 Southern targets with more than the average of 23 spectra we have identified 17 with a residual RMS < 10 m s -1 , corresponding to roughly 42% of the sample.
While 27 of these targets (66%) have an RMS < 17 m s -1. Three systems are selected from within these, which we now describe: 1) EBLM J0310-31 (J0310-31 thereafter) has been part of the preliminary survey for BEBOP on HARPS, and of the first extensive observing campaign. This is the target for which the largest number of spectra has been obtained. In total, 65 RV data points are available, obtained between 2017-07-09 and 2020-01-02. These measurements also have the lowest mean uncertainty of the survey  RV = 2. 13 m s -1. J0310-31's residual RMS = 3. 16 m s -1 , which is one of the smallest we obtain so far. All these make J0310-31 the ideal target to test our procedures to compute detection limits, as well as perform injection-recovery tests on. See figure  1  for a phased plot of our RV data for J0310-31, along with our best fit model and residuals.
2) EBLM J1928-38 (J1928-38 for short). Its residual RMS = 2. 96 m s -1 , better than J0310-31, close to its mean photon noise uncertainty  RV = 2. 67 m s -1. Only 25 measurements have been obtained on J1928-38 so far, which makes it more representative of the current state of the survey than J0310-31 is. These were collected between 2018-06-04 and 2019-09-14. 3) EBLM J1540-09 (henceforth J1540-09) has  RV = 3. 86 m s -1 , and RMS = 3. 75 m s -1 , for 41 available spectra, observed from 2017-04-20 to 2020-03-06. These targets were analysed using the kima RV analysis package (see next section). Their parameters can be found in Table  1. All data can be accessed in Tables  A5 to Table A7. All measurements for these three systems are used, with no outliers found within those three datasets.
g. Dvorak et al. 1989; Holman & Wiegert 1999; Doolin & Blundell 2011; Mardling 2013). HDBSCAN is applied on the remaining posterior samples, highlighting dense regions in parameter space. The cluster corresponding to the Keplerian signal is plotted with the Corner package (Foreman-Mackey 2016). Parameters are determined as the 50 th percentile of the cluster, with 1 uncertainties estimated from the 14 th and 84 th percentiles. To decide between competing models that fit our data we use the Bayes factor. The Bayes factor (BF, here onwards) is the ratio of the Bayesian evidence of the two competing models, and provides a measure of the support in favour of one over the other (Robert E. Kass; Adrian E. Raftery 1995; Trotta 2008). Table  2  adapted from  Trotta (2008)  shows how the BF is measured and introduces the Jeffreys' scale  (Jeffreys 1961 ) as a measure of evidence strength.
Model setup With kima we assume independent, static Keplerian orbits for the binary and any circumbinary planets. This neglects the fact that the orbits of the planet and binary will evolve through three-body interactions (e. g. Martin & Triaud 2014; Kostov et al. 2014). Martin et al. (2019)  however found such interactions to be negligible with respect to the CORALIE BEBOP survey. We will briefly verify if this assumption still holds with our more precise HARPS data in Sect. 4. 4, where we test the injection and retrieval of N-body simulated RV signals. The Keplerian models we fit to the data are defined by the following parameters. For the binary we have  bin (the orbital period),  A (the semi-amplitude of the primary star, caused by the secondary),  bin (the binary's orbital eccentricity),  bin (the argument of periastron) and  bin (the starting phase of the orbit).
First we remove the best fit binary model from J0310-31's observed radial-velocity measurements, producing residuals. Then we use rebound to simulate a radial-velocity model of the binary and putative planet at each epoch of observation. We add this model to the residuals. Doing this preserves the scatter, uncertainties and observational cadence obtained in reality. kima is ran once again on each N-body simulated dataset, and Bayes factors computed and compared to those resulting from the injected static Keplerian signals. We discuss the result of this test in Sect. 5. 3. All Bayes factor values are plotted in Fig. 2  as coloured triangles offset to the right, and can also be found in table  A2.
g. GJ 876;  Correia et al. 2010) , and would likely be the case for circumbinary systems, as they have been for HD 202206  (Correia et al. 2005; Couetdic et al. 2010) , a system comparable to a circumbinary configuration. To better understand the non-Keplerian signal, we visualise the amplitude of the Newtonian interactions in Fig. 6. We use rebound to simulate nightly RV data, for  p = 6. 32 m s -1 at  p = 74. 59 days (the shortest period and highest mass planetary signal simulated in this work, producing the largest Newtonian perturbation), over the timespan of our data on J0310-31. Figure  6  depicts the residuals after fitting and removing the Keplerian binary and planetary signals from the N-body simulated dataset. The residuals show that N-body model diverges from the Keplerian model due to apsidal precession (  bin ).
Detection limits in the presence of planetary signals Detection limits thus far are all generated from our current data on systems purposefully chosen with no candidate planetary signals. The ultimate goal of the BEBOP survey is to detect RV signals of circumbinary planets in our data. Once a system is found to have a planetary signal, a detection limit is required to rule out the presence of additional companions above our calculated mass limit. To this end, we calculate the detection limit for J0310-31 with our N-body simulated data-set containing an injected 4. 74m s -1 , 486 d signal. A signal of this strength is easily detected in the data (see Tab. A2 for its Bayes factor value). We identify the injected signal as in Sect. 4. 4, with  p varying uniformly from 0 to 3, as is standard for the survey.
CONCLUSIONS We analyse high-precision radial-velocities obtained as part of a large scale, ongoing, radial-velocity survey that seeks circumbinary planets using radial-velocities obtained with HARPS, ESPRESSO and SOPHIE, in both hemispheres, on single-lined eclipsing binaries. This survey is called BEBOP (Binaries Escorted By Orbiting Planets). We then detail an observing and Bayesian analysis protocol and test it on data collected for three single-lined binaries within the survey. Our analysis shows for the first time, a repeated ability to detect circumbinary planets with masses between Neptune's and Saturn's, for orbital periods within 1000 days with as few as 25 spectra in the span of a year. Figure  11  displays a mass vs period plot of the detection limits from this work (blue) along with confirmed exoplanets (grey circles; NASA exoplanet archive 4  ), transiting circumbinary planets (magenta diamonds), and Solar System planets for comparison.
ACKNOWLEDGEMENTS We would like to thank the staff at  ESO 's  La Silla  observatory for their hard work and assistance throughout this project, especially during the COVID pandemic. We also thank all of the observers who took part in the HARPS timeshare and were instrumental in collecting data for this projects. We particularly thank  X. Dumusque  and  F. Bouchy  for their work organising the timeshare. This research received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n  • 803193/BEBOP ) and from the  Leverhulme Trust  (research project n •  RPG-2018-418 ). MRS would like to acknowledge the support of the  UK Science and Technology Facilities Council (STFC). We thank  H. Rein  for helpful discussion regarding this work.
This work made use of the Astropy, numpy, pandas, scipy, corner, and matplotlib packages. We also use of label_line. py by  Luke Zoltan Kelley. The French group acknowledges financial support from the  French Programme National de Planétologie (PNP, INSU). ACMC acknowledges support by  CFisUC projects  ( UIDB/04564/2020  and  UIDP/04564/2020 ),  GRAVITY (PTDC/FIS-AST/7002/2020 ),  ENGAGE SKA  ( POCI-01-0145-FEDER-022217 ), and  PHOBOS  ( POCI-01-0145-FEDER-029932 ), funded by  COMPETE 2020 and FCT, Portugal.
A to G index computing and stratification Mesothelioma RNA-seq reads included in the analysis were: the TCGA-Mesothelioma cohort (n = 87) downloaded from the NCBI database of Genotypes and Phenotypes (dbGaP) in 2019, under phs000178. v10. p8; the Malignant Pleural Mesothelioma cohort from the Bueno study (n = 223) downloaded from the European Genome-phenome Archive (EGA) in 2020, under EGAS00001001563 (EGAD00001001915 and EGAD00001001916); the genetically characterized pleural mesothelioma primary cultures (n = 64) provided by Didier Jean's team in 2022 for which RNA-Seq was performed as described in Ref. [19] ; the human embryonic stem cell-derived mesothelium (n = 10) downloaded from the NCBI Gene Expression Omnibus (GEO) in 2020, under GSE113090 (GSM3096389-GSM3096398). The choice of using human embryonic stem cell-derived mesothelium as control was dictated by the fact that RNA-Seq data on normal mesothelial cells or pleura are not available and RNA editing is tissue-specific  [20].
RNA-seq reads were preprocessed using FASTP (0. 20. 0). The first six bases at the beginning of each read were deleted to remove priming bias  [21]  introduced during Illumina RNA-seq library preparation. Sequencing adapters and low-quality ends (averaged quality lower than 20 in sliding windows of 4 bp, moving from 5 0 to 3 0 , and from 3 0 to 5 0 , respectively) were trimmed. Reads longer than 48 nt were trimmed back to 48 nt, in order to achieve uniform max read length across different datasets and comparable RNA editing index values. Trimmed reads with average quality above 20 and length between 18 and 48 bp were aligned to the human reference genome (Genomic Data Commons (GDC) GRCh38. d1. vd1 Reference Sequence, https:// gdc. cancer. gov/about-data/gdc-data-processing/gdcreference-files) using STAR (2.
7. 8a) with 2-pass mode. PCR duplicates were marked using PICARD   (2. 22. 8). Primary alignments were extracted using samtools  (1. 11)  and were used for computing the A to G index and C to T index by applying the python package RNAEDITINGINDEXER (https://github. com/a2iEditing/ RNAEditingIndexer). Variants in the aligned, duplicate-marked RNA-seq reads were identified using GATK (v3. 8. 1. 0) following RNA-seq best practices' workflows. In detail, mapping quality reassignment, splitting spliced aligned reads into multiple supplementary alignments and clipping mismatching overhangs were performed using 'SplitNCi-garReads' with options '-rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_ READS. ' Base quality recalibration was performed using 'BaseRecalibrator' with dbSNP release151_GRCh38p7 downloaded in 2018 as the true variant set.