dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on the strong synteny between peach (Prunus persica) and almond (Prunus dulcis), the Illumina 18K peach SNP array was used in a GWAS analysis of a collection of Italian almond germplasm  [11]. The authors obtained an average marker density of 1 SNP per 424 kb across the almond genome. With the development of an almond-specific array, this should improve the marker density and cover the whole almond genome, in particular its almond-specific regions. The whole genome sequences of Prunus dulcis cv "Texas"  [12]  and Prunus dulcis cv "Lauranne"  [13]  were published and are available on the GDR database (https://www. rosaceae. org/analysis/295 accessed on 1 January 2021), and recently another whole genome cv "Nonpareil" was also published  [14]. In addition to the sequencing of the "Texas" genome, the "almond genome" consortium has resequenced several almond cultivars using paired-end Illumina sequencing, allowing the detection of a high number of SNPs and InDel variants.
Array Design 2. 1. 1. SNPs Array Selection Alignment of the NGS reads of the 81 almond accessions to the "Texas" reference genome  [12]  and SNP calling with GATK (Section 3. 2) allowed us to detect 11,683,655 SNPs. We then applied three different steps (Figure  1 ), to select SNPs for the chip. In the first step with the filters described in Section 3. 2, we obtained 576,968 SNPs with the threshold of less than 20% missing data (NA) and 71,843 SNPs for less than 10% and we retained the 10% NA filter. We did not discard the A/T and C/G SNPs although they require two probes per SNP in the Axiom probe sets. In the second step, we included the three additional SNPs published as trait markers of interest and detailed in Section 3. 2 and Table  1. The final filter on step 3 was based on the p-convert score estimated by Affymetrix.
Validation of the Three Additional SNPs In the almond diversity panel analysis, the three additional SNPs were classified in the category Poly High Resolution (PHR) and were considered "best recommended" (Figure  S1 ). For the SNP marker AX-599403222 of the RMja gene [16], only the two resistant accessions R1107 (Alnem-1) and R1109 (Alnem-88) were genotyped Homozygote with the alternative allele, which was consistent with their resistant phenotypes  [17]. Three wild accessions and seven almond accessions were genotyped heterozygote for this SNP marker. As the RMja gene is a dominant gene, these genotypes would be resistant, which will be validated by an inoculation test with root-knot nematodes. All other almond accessions were genotyped homozygous susceptible. This is consistent with published data  [18]  and results obtained in resistance tests of four almond accessions (Ferragnès, Ferrastar, Dorée and Pointue d'Aureille) against M.
Comparison of Array Data from Three Replicates of the "Ferrastar" Variety To assess the repeatability of the genotyping, we tested three "Ferrastar" samples. The differences of allelic state between each replicate of Ferrastar were the consequence of the natural error rate belonging to the Axiom technology. There were 0. 90% and 0. 95% error rates between the first replicate and the two other replicates and 1. 01% between the second and the third replicate (Table  6 ), which is considered a low error rate.
Genotyping and SNP Analysis Genomic DNA was extracted from young leaves according to the protocol of Antanaviciute et al. [38]. The amount of DNA required for genotyping with the array was 50 µL/genotype at 10 ng/µL. Genotyping was performed on an Axiom GeneTitan system on the INRAE Gentyane platform at Clermont-Ferrand (https://gentyane. clermont. inrae. fr/ accessed on 1 November 2021). After genotyping accessions, genotype calling and QC metrics were performed with SNP Axiom Analysis Suite v5. 1 (Affymetrix) using diploid threshold configurations and default DishQC settings (DQC ≥ 0. 82 and call rate > 0. 97). The software classified SNPs into six categories: Poly High Resolution (PHR) when we observed a good resolution for the two homozygous clusters and at least two occurrences of the minor allele, Mono High Resolution (MHR) when the SNP passes all thresholds except the number of minor alleles.
Conclusions We have developed and validated a new Axiom 60K almond SNP array (PdSNP_V1) and our studies have shown that the genotyping is reliable, with few missing data and erroneous points. Based on F2 progeny, we found only a few errors indicating departures from Mendelian inheritance of homozygote SNP loci. The PdSNP_V1 array could be used for almond diversity analysis, association studies and genetic mapping for breeding purposes, as a large number of breeding genitors were included in the set of accessions resequenced for the detection of SNPs. Our data showed that this array is also effective for peach species and some wild Amygdalus species. The PdSNP_V1 array would be suitable for Prunus rootstock breeding, notably for linkage mapping of peach × almond progenies. The PdSNP_V1 array is the first developed almond.
Acknowledgments: We thank  Charles Poncet ,  Pauline Izorche  and the staff of the  Gentyane platform (INRAE Clermont-Ferrand)  for the genotyping and  Naïma Dlalah  ( INRAE GAFL ) for the DNA extraction of the accession samples. We thank  Frederique Bitton  for helping with the extraction of the axiom71mer and  Véronique Decroocq  for the provision of clones of Prunus fenzliana.
Data Availability Statement: The probe set sequences and positions of all SNPs included in the almond chip from this study have been deposited in the GDR database under ID code tfGDR1059 and the data will be available for download at https://www. rosaceae. org/publication_datasets (accessed on 28 December 2022). The Axiom 60K almond SNP array (PdSNP_V1) is commercially available with Thermo Fischer Scientific, with request to the author before 2024.
Acknowledgments We would like to thank  Guanlin Li ,  David Demory , and  Jacopo Marchi  for feedback with the bet-hedging framework. This work was enabled by support from the  National Science Foundation, USA  ( DEB -1934554  to JTL and DS,  DBI -2022049  to JTL and  DEB -1934586  to JSW),  US Army Research Office  Grant ( W911NF-14-1-0411  and  W911NF-22-1-0014  JTL) and the  National Aeronautics and Space Administration, USA  ( 80NSSC20K0618 JTL ). JSW was supported, in part, by the  Chaires Blaise Pascal program of the Île-de-France region.
Acknowledgements The authors thank  T. Kampfrath  and  G. Bierhance  ( Freie Universität Berlin ) for very fruitful discussions. This work was supported by a grant overseen by the  French National Research Agency (ANR)  as part of the " Generic Project Call -2021 "  Programme  ( ANR-21-CE24-0011 TRAPIST ), public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( Labex NanoSaclay , reference:  ANR-10-LABX-0035 SPICY ) and the program  ESR/EquipEx+  (Grant No. ANR-21-ESRE-0025 ). The authors acknowledge financial support from the  Horizon   2020 Framework Programme  of the  European Commission  under  FET-Open Grant  No. 863155  ( s-Nebula ) and Grant No. 964735  ( Extreme-IR ). This work was done within the C2N micro-nanotechnologies platforms and partly supported by the  RENATECH network and the General Council of Essonne.
We use the SPH code Phantom  (Price et al. 2018) , which offers two methods for modelling gas and dust mixtures. The first is a 2-Fluid algorithm which treats the gas and dust components separately, solving a different set of governing equations for each component and computes a drag coupling term  (Laibe & Price 2012; Price & Laibe 2020 ). This method computes the stopping time explicitly and the time stepping is chosen so that the drag interaction is timeresolved (making this method relatively computationally expensive). The second method is a 1-Fluid algorithm where one set of equations is solved for the gas-dust mixture, including an evolution equation for the dust fraction  (Price & Laibe 2015). This method employs a terminal velocity approximation and thus does not resolve the drag interaction.
ACKNOWLEDGEMENTS The authors warmly thank  Daniel J. Price  for insightful comments and suggestions that improved the manuscript and  Timothee David-Cléris  for thorough discussions. We also thank the anonymous referee for comments that improved the clarity of the manuscript, in particular Figure 2. H. A. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  101054502 ). R. N. acknowledges support from  UKRI/EPSRC  through a  Stephen Hawking Fellowship  ( EP/T017287/1 ). J. -F. G. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  823823  ( DUSTBUSTERS ) and from the  ANR (Agence Nationale de la Recherche) of France  under contract number  ANR-16-CE31-0013  ( Planet-Forming-Disks ), and thanks the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) for its financial support within the Plan France 2030 of the French government operated by the  ANR.
MeerKAT and the TRAPUM processing infrastructure MeerKAT is a radio interferometer located in the Karoo, South Africa, consisting of 64 antennas with 13. 5-m ef fecti ve diameter. Here, we give a brief description of MeerKAT and the TRAPUM infrastructure used to perform our pulsar search observations. For a full technical description of the instrument, we refer the reader to  Jonas & MeerKAT Team ( 2016 ). At the time of data taking (between 2020 June and 2021 February), two receivers were available: the L -band receiver operating between 856 and 1712 MHz and the Ultra High Frequenc y (UHF) receiv er between 544 and 1088 MHz. This surv e y was conducted at L band, but follow-up observations of new pulsars were also made at UHF. At the L -band centre frequency, a coherent tied-array beam produced using all 64 MeerKAT antennas has a typical full width at half-maximum of the order of a few arcseconds.
Furthermore, the data rate of complex voltages from the antennas is too high to record while observing. Efficiently searching the several-arcmin localization region of an unassociated Fermi -LAT source in a single pointing therefore relies on the ability to form and record the Stokes intensities from a large number of coherent beams simultaneously. This capability is provided by the Filterbanking BeamFormer User Supplied Equipment (FBFUSE), a 32-node, GPU-based, software beamformer developed by the Max Planck Institute for Radio Astronomy  (Barr 2018 ; Chen et al. 2021 ). FBFUSE coherently sums the channelized complex voltages in real time, using sky position-dependent complex weights computed by the purpose-built Mosaic software 5 from a delay model provided by the MeerKAT MNRAS 519,  5590-5606 (2023)  Science Data Pipeline.
FBFUSE can produce channelized time series for up to 864 coherent beams, as well as one incoherent beam produced by summing the (real-valued) Stokes intensities from each antenna. FBFUSE's beamforming algorithm requires a multiple of four antennas, and not all antennas are available for all observations. As such, we used either 56 or 60 antennas during our observations, depending on availability. The channelized data from each coherent beam are then recorded on to a distributed 3. 5-PiB file system accessible from the Accelerated Pulsar Search User Supplied Equipment (APSUSE) instrument, a second, 60-node computer cluster, with two NVIDIA GeForce GTX 1080 Ti GPUs per node, on which the pulsar search takes place. The number of beams that can be stored is limited by the data rate at which APSUSE can record, and so down-sampling in time from the native data rate is necessary for a large number of beams to be recorded.
All of our observations used the 4096-channel MeerKAT F-engine channeliser mode, but beamformed spectra were downsampled in time by a factor of 16 from the native time resolution (4096 / 856 MHz = 4. 785 μs) to give 76-μs time resolution. With these time and frequency resolutions, up to 288 coherent beams could be formed and recorded. Additionally, downsampling in frequency by a factor of 2 allowed for 480 coherent beams with 2048 frequency channels. Both of these recording modes were used in our surv e y. The full set of filterbank files for 480 coherent beams constitute 46 TiB of data per hour of observ ation, allo wing at most 73 h of data in this format to be stored for processing on APSUSE. It was therefore necessary to process these data quickly, identify promising candidates for further follow-up, and delete the raw data to ensure there was sufficient storage space for other TRAPUM projects to continue observing.
The data from the beam containing the strongest detection of each candidate cluster were then folded using the PulsarX software, 7  and folded candidates were scored by the Pulsar Image-based Classification System (PICS) machine-learning classifier  (Zhu et al. 2014 ). Candidates surpassing a conserv ati vely lo w PICS-score threshold of 10 per cent , typically a few hundred per pointing, were then grouped for visual inspection. As mentioned previously, the full set of raw data could not be stored indefinitely, except for data from beams containing high-confidence pulsar candidates. Reduced data products, i. e. candidate lists, and folded data for every candidate, were retained from all observations.
Follo w-up obser v ations To confirm the pulsar nature of the detected candidates, we performed dedicated follow-up observations with MeerKAT at both L band and UHF, and checked for archi v al data from pre vious search observations of the Fermi -LAT sources in which they were found. Three pulsars (J1757 -6032, J1803 -6707, and J1823 -3543) were re-detected in archi v al data from the Parkes radio telescope. The five high-confidence pulsar candidates detected in the first pass were remo v ed from the scheduled second surv e y pass, so that the y could be observed in a dedicated set of confirmation observations along with candidates from the second pass. In these confirmation observations, we employed a very dense tiling (with 90 per cent o v erlap) with a smaller number of beams around the location of the coherent beam in which the candidate was detected most strongly in the initial surv e y observations.
Phase-connected timing solutions for two MSPs, described in detail below, were obtained using the Dracula algorithm 10  (Freire & Ridolfi 2018 ); this was necessary given the sparsity of the timing data in both cases. Table  1. List of surv e y observations of Fermi -LAT sources. P (psr) is the probability predicted by the Random Forest classifier of each source being a gamma-ray pulsar (see Section 2. 4 ). S opt , S 50 , and S 95 are sensitivity estimates (see Section 3. 4 ) from the radiometer equation for a 500-Hz pulsar, with DM = 100 pc cm -3 and a 15 per cent duty cycle, based on optimistic (no losses due to finite frequency, acceleration, and DM trial spacings, and for a pulsar lying at the centre of a coherent beam), realistic (median losses), and conserv ati ve (95th centile losses) assumptions, respectively.
Light-curve modelling to estimate properties of the companion such as its temperature, irradiation, radius, and the binary inclination angle will be performed in a dedicated follow-up project (Phosrisom et al. , in preparation). As with PSR J1526 -2744, we folded the gamma-ray data using the radio ephemeris to check for gamma-ray pulsations. Significant pulsations are detected within the 1-yr interval in which the radio ephemeris is valid, with a weighted H -test of H = 51, corresponding to a 5. 6 σ detection using the false-alarm probability calibration from  Bruel ( 2019 ). These pulsations are shown in Fig. 7. Ho we ver, these pulsations quickly disappear when extrapolating to earlier data. This is not unexpected, as Taylor-series models for orbital period variations lack predictive power, and the spin-down rate is not measured precisely enough in the radio data to fold the 13 yr of Fermi -LAT data.
We have not been successful in extending the ephemeris by timing the gamma-ray data, likely as a result of the pulsar's faint gammaray flux and the large amplitude of the orbital phase variations. A MNRAS 519, 5590-5606 (2023) 6. Optical light curves for PSRs J1036 -4353 and J1803 -6707 obtained using ULTRACAM + NTT. Observations are folded on the orbital period using the radio timing ephemerides. The pulsar's superior and inferior conjunctions are marked with dotted and dashed vertical lines, and labelled SC and IC, respectively. The two peaks in the light curve for PSR J1036 -4353 are due to ellipsoidal deformation of the companion star in the pulsar's gravitational field, while the single peak in the light curve for PSR J1803 -6707 indicates strong heating from the pulsar. longer radio timing baseline will hopefully solve this problem by providing a more precise spin-down rate measurement, and a longer phase-connected radio ephemeris that will fold more Fermi -LAT data to provide better statistics with which to build a gamma-ray pulse profile template to search with.
PSR J1036 -4353 This pulsar is both the fastest-spinning (with ν ≈ 595 Hz), and the most highly-accelerated (with a ≈ 15 m s -2 ) pulsar found by our surv e y. F ollo w-up observ ations have re vealed this to also be a redback system. A preliminary orbital solution revealed an ∼6. 2 h orbit and a minimum companion mass of 0. 23 M. Several non-detections have occurred during our timing campaign in the half of the orbit in which the pulsar is behind its companion, likely due to eclipses by material from the redback companion. A single Gaia source lies within its SeeKAT localization region. Like PSR J1803 -6707, we observed this source with ULTRACAM, revealing a double-peaked light curve, shown in Fig. 6  , very typical of redback binary systems. Modelling of this data will also be presented in a future paper (Phosrisom et al.
One final advantage to our surv e y, o v er projects using singledish telescopes, is the rapid and precise localization that can be obtained from a multibeam detection using SeeKAT. This has two significant scientific benefits: rapid localizations enable immediate multiwavelength follow-up and catalogue searches; and precise knowledge of a pulsar's location greatly assists in obtaining a phaseconnected timing solution to fully exploit the scientific potential of a new MSP disco v ery. In the absence of an interferometric localization, it often takes lengthy timing campaigns to reveal the location of a pulsar, as astrometric parameters can be highly degenerate with a pulsar's spin-down rate (and sometimes orbital parameters) until a data set spanning several months has been obtained. The SeeKAT positions for PSRs J1803 -6707 and J1036 -4353, were precise enough to unambiguously link these pulsars to their Gaia counterparts, establishing these as redback binary systems, and providing sub-milliarcsecond astrometric uncertainties that are sufficiently precise that these parameters no longer needed to be fit for when building a timing solution.
694745 ). MB, AP, and AR gratefully acknowledge financial support by the research grant 'iPeska' (P. I. Andrea Possenti ) funded under the INAF national call Prin-SKA/CTA appro v ed with the Presidential Decree 70/2016. EDB, CJC, DJC, WC, MK, VVK, LN, PVP, and AR acknowledge continuing valuable support from the  Max-Planck Society. LV acknowledges financial support from the  Dean's Doctoral Scholar Award  from the  University of Manchester. VSD, TRM, and ULTRACAM acknowledge the support of the  STFC. SMR is a CIFAR Fellow and is supported by the  NSF Physics Frontiers Center  awards  1430284  and  2020265. We w ould lik e to thank  Matthew Kerr ,  Guillem Mart í-Devesa , and  David Smith  for re vie wing this paper on behalf of the  Fermi -LAT collaboration.
Due to climate change, years with positive temperature anomalies are becoming more frequent in Europe, requiring high-resolution climate data to plan for climate change mitigation and adaptation. However, many regional climate models (RCMs) simplify the representation of groundwater processes, leading to biases in simulated extreme heat events. Here, we study the characteristics of summer heat events in a unique dataset from the regional Terrestrial Systems Modeling Platform (TSMP) simulations, compared to an ensemble of EURO-CORDEX climate change scenario control simulations, for the historical time period 1976-2005. Our results show that in TSMP, the impact of groundwater coupling on the frequency of hot summer days depends on the considered time period and the region, associated with respective evaporative regime.
to more operational groundwater parameterizations  (Schlemmer et al. , 2018). Barlage et al. (2015) ,  Keune et al. (2016)  and  Furusho-Percot et al. (2022)  showed that the energy flux partitioning and, hence, near surface air temperatures are strongly influenced by groundwater, which also affects the representation of extreme heat events. In fact, many RCMs overestimate the frequency, duration, and intensity of heat waves (e. g. ,  Vautard et al. , 2013; Lhotka et al. , 2018). To study the evolution of future heat events in Europe, here we first investigate the evolution of past heat events over the European continent in a multi-model RCM ensemble driven by CMIP5  (Taylor et al. , 2012 ) GCM control simulations. The RCM ensemble is complemented with a new dataset from the regional Terrestrial Systems Modelling Platform (TSMP), which allows for an integrated groundwater-to-atmosphere simulations, thus closing the terrestrial water cycle from bottom to top.
Land surface, subsurface hydrology, and energy states were initialized with the moisture conditions of 1st of December 2011 from a spun-up evaluation run driven by ERA-Interim reanalysis  (Furusho-Percot et al. , 2019) , which influenced the first years of the simulations and discarded in the analyses due to hydrodynamic spin-up. Forcing data and the lateral boundary conditions for the TSMP atmospheric component model were provided by the Max-Plank Institute's MPI-ESM-LR r1i1p1 GCM experiment, with a resolution of T63L47  (Giorgetta et al. , 2013). Detailed information on CLM and ParFLow specifications used in the TSMP setup has already been described earlier in  Furusho-Percot et al. (2019) ;  Hartick et al. (2021). In addition to these studies, we improved the subsurface geology using geological information from different databases as follows.
We defined the soil texture in the top 10 layers of ParFlow from the Food and Agriculture Organization soil database  (FAO, 1988). For the 5 lowest soil layers, we prescribed hydrogeology from the International Hydrogeological map of Europe (IHME) dataset  (Duscher et al. , 2015)  and GLobal HYdrogeology MaPS (GLHYMPS)  (Gleeson et al. , 2014; Gleeson, 2018). As a proxy for alluvial aquifer systems in ParFlow, we used a pan-European River and Catchment Database  (Vogt et al. , 2007)  with the assumption that alluvial aquifers lie underneath or in close proximity of existing rivers. The TSMP output constitutes a dataset of all terrestrial essential climate variables at a 3-hourly time step.
We assume that a day with the mean daily temperature exceeding the 90th percentile is a hot day (e. g. ,  Sulikowska and Wypych, 2020) , highlighted in dark red in Fig. 1. A series of hot days constitute a heat event. A heat event is interrupted if the mean daily temperature drops below the 90th percentile. The cumulative number of hot days during a heat event corresponds to the TG90p heat index, introduced by the European Climate Assessment and Dataset (ECA&D) project, complementing the core set of indices determined by the Expert Team on Climate Change Detection and Indices (ETCCDI) (e. g. ,  Frich et al. , 2002; Zhang et al. , 2011). TG90p describes the number of days with T G ij > T G in 90, (1) where T G ij is the mean daily temperature on day i of period j, and T G in 90 is the i-day 90th percentile of mean daily temperature calculated for a 5-day window centred on each calendar day of the 30-year reference period n.