dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic Relationship Matrix and Genetic Analysis The whole-genome sequencing data of all 198 accessions were available and used to estimate the genomic relationship matrix  (Kajiya-Kanegae et al. , 2021). Only the biallelic sites in all accessions with a minor allele frequency (MAF) ≥ 0. 025, missing rate < 0. 05, and linkage disequilibrium < 0. 95 were extracted, and the imputation of missing genotypes was applied. Genotyping data identified 425,858 SNPs. Genotypes for individual alleles were represented as -1 (homozygous for the reference allele), 1 (homozygous for the alternative allele), or 0 (heterozygous for the reference and alternative alleles). The genomic relationship matrix G was estimated as G = XX T / c, where X is an n × m scaled marker genotype matrix (n and m are the numbers of lines and markers, respectively), and c is the normalization constant  (Endelman and Jannink, 2012).
Genetic heritability was estimated for all traits using the genomic best linear unbiased prediction (G-BLUP) model: g = m1 + Zu + ε (6) where g is a vector of genotypic values estimated using Equations 4 and 5, m is the mean, u is a vector of random genetic effects that follows N(u | 0, σ u 2 G), ε is a vector of residuals that follows N(ε | 0, σ ε 2 I), σ u 2 and σ ε 2 are the genetic and residual variances, respectively, and Z is a design matrix. The R package rrBLUP (ver. 4. 6)  (Endelman, 2011)  was used to solve Equation  6. After solving the mixed model, the genomic heritability was estimated as h 2 = σ u 2 /(σ u 2 + σ ε 2 ).
Cross-Validation Schemes Cross-validation was repeated three times for the combination of a cross-validation scheme and a prediction model. (1) Cross-validation of genotypes (CV1). CV1 corresponded to the prediction of LAI dynamics for untested genotypes. Data from a subset of genotypes in any treatment or year were excluded from the training data (Figure  3A ). The prediction model built using the training dataset was evaluated for the left-out genotypes. Ten-fold crossvalidation was used to randomly select 19-20 left-out genotypes. (2) Cross-Validation over combination of genotype and environment (CV2). The combination of a treatment and a year was considered as an environment: there were a total of six environments (two treatments × 3 years). Here, the predicted LAI dynamics were evaluated for genotypes and environments left out from the training dataset.
A 10-fold cross-validation of genotypes and leave-one-environment-out cross-validation were applied simultaneously to get rid of data of test genotypes in one environment from training dataset (Figure  3A ). (3) Cross-validation with a focus on the late growth period (CV3). The growth cycle was split into early and late growth, with an equal number of observations for the two periods. CV3 was similar to CV2, but data of early growth period of test genotypes in a test environment was included in training data, and prediction of the LAI dynamics were evaluated over the late period (Figure  3A ).
The R package MTM (ver. 1. 0. 0) was used to solve Equation 7 based on the Markov chain Monte Carlo (MCMC) method. TMGP consisted of two steps, that is, MGP of the LAI dynamics model parameters and the calculation of the GF using the predicted parameters. MGP and TMGP were expected to improve the prediction accuracy compared to GP by exploiting phenotypic data from environments included only in the training dataset. However, since the GF was measured repeatedly in each environment, it was difficult to include all the phenotype data (152 measurements in total by adding up observation dates in all the environments). Thus, a strategy was applied where the training of prediction models was repeated for each observation date, and ten additional variates were selected from the whole data to support the prediction every time.
The TGPG included three steps (Figure  3B ). The first two steps were the same as those of the TGP, where the LAI dynamics model parameters were predicted without using the data from the first half of the growth period. However, for TGPG, the distributions of the MCMC values of the LAI dynamics model parameters were used instead of the average value of the samples used in the TGP. As a result, 60,000 samples of the predicted GF dynamics were obtained for each genotype corresponding to the prior distribution when no GF measurements on the genotype were available. Then, the GF data from the first half of the growth period were exploited using the approximate Bayesian computation (ABC) method, and the 60 GF dynamics that minimize the Euclidian distance between the predicted GF dynamics and the actual GF observations were selected.
ACKNOWLEDGMENTS We are grateful to the technical staff of the  Arid Land Research Center, Tottori University , and  Izumi Higashida  for the management of the field experiments. We would like to thank  Yuji Sawada  for being involved in the field experiments,  Kosuke Hamazaki  for curating whole-genome marker data, and  Sawako Maruyama  for curating the phenotype data. This manuscript was published in agriRxiv (https://agrirxiv. org)prior to the submission in Frontiers in Plant Science (Toda et al. , 2021b).
2017). The first spectroscopic evidence of ram pressure events in a z = 0. 7 cluster, detected using the MUSE IFU spectrograph, is reported in  Boselli et al. (2019)  where two long tails of ionized gas departing from two galaxies close to the cluster center have been revealed through their [O II] emission. These two galaxies show a very large line-of-sight velocity with respect to the host cluster, and still display a tail in the plane of the sky, due to an edge-on stripping. Similarly, using the Keck Cosmic Web Imager IFU data  Kalita & Ebeling (2019)  resolved the gaseous and stellar kinematics of a jellyfish galaxy in the intermediate redshift cluster A1758 (z ∼ 0. 3). A few galaxies in the CIZA J2242. 8 + 5301 (a. k. a. the Sausage) cluster (z ∼ 0. 2) analyzed by means of GMOS IFU spectroscopy have been suspected to be ram-pressure-stripped galaxies  (Stroe et al.
The presence of a diffuse faint radio halo in the southern part also points toward this conclusion  (Xie et al. 2020). The analysis of the H ɪ emitting galaxies in A370 by  Lah et al. (2009)  shows, indeed, an asymmetric redshift distribution with a small tail at lower redshift. Table  1  gives the main cluster features, together with the scale that we will use in what follows. Our aim is to identify, using MUSE data cubes, two types of galaxies: star-forming emission-line galaxies with clear signatures of ram pressure stripping (hereafter RPS) and passive galaxies that were recently quenched which can be poststarburst or post-star-forming (hereafter PSB, for simplicity). According to this operative definition there is no overlap between the two samples, as only RPS galaxies do show more or less evident signatures of stripped gas tails.
With this aim, three of us (B. P. , B. V. , and M. G. ) inspected the MUSE data cubes (here shown in Figure  1 ) and the HST RGB images (F435W+F606W+F814W). RPS galaxies were identified searching for extraplanar, unilateral tails/debris with emission lines in the MUSE data cubes and/or unilateral tails/ debris from HST images that were confirmed to belong to the galaxy from the MUSE redshifts. PSB galaxies were chosen to be generally lacking emission but to display evident strong Balmer lines in absorption from the MUSE data. All galaxies in the MUSE+HST data were searched in particular for these signatures. Probable mergers and weak RPS cases have also been tagged but are not included in this paper. Our final sample comprises 10 RPS/PSB galaxies in A2744 and nine RPS/PSB galaxies in A370 (one of which possibly belonging to a secondary substructure).
Data Analysis From the MUSE datacube, we extracted a cubelet containing each selected galaxy over an area encompassing all the interesting features that we detected by visually inspecting the cube. We then corrected the cube for Galactic extinction using the values estimated at the galaxy position from  Schlafly & Finkbeiner (2011)  and the extinction law by  Cardelli et al. (1989) , and we smoothed it with a 5 × 5 pixel boxcar kernel, corresponding to the typical point-spread function (PSF) size of ∼1″. The spectral resolution of the observations varies from R ∼ 2000 to R ∼ 4000, with a spectral range between 4750 and 9350 Å. The corresponding velocity resolution is 75-150 km s -1 sampled with ∼55 km s -1 pixels. On the spatially smoothed cube we then performed a first run of emission lines spectral fitting using a purposely-devised software described below, and a determination of the galaxy stellar kinematics using the pPXF software  (Cappellari & Emsellem 2003)  binning the data in Voronoi bins with a signal-to-noise ratio (S/N) = 5.
With the redshifts derived from emission and absorption lines we then used the SINOPSIS code  (Fritz et al. 2017)  to extract the best fitting mix of Single Stellar Populations (SSP) that best reproduces the stellar features and produced a second version of the MUSE cube containing only the gas emission lines. SSPs used by the SINOPSIS code are given by S. Charlot & G. Bruzual (in preparation) and adopt a  Chabrier (2003)  IMF with stellar masses between 0. 1 and 100 M e and metallicities between 0. 004 and 0. 04. On the emission only cubes we measured again the emission lines fluxes and finally corrected them for internal extinction using the observed value of the Balmer decrement and the  Cardelli et al. (1989)  extinction law. In order to measure the emission lines, we developed our own software (HIGHELF; M.
Radovich et al. 2021, in preparation) , that is able to fit the entire set of emission lines included in the observed redshift range. HIGHELF is based on the LMFIT Python library (https://lmfit-py. readthedocs. io/), and is designed to fit a user-defined set of emission lines with either one or two Gaussian components. It starts by computing the zero-moment of the cube, i. e. , the integral of the flux within the entire observed wavelength range, and the noise given by its variance. This constitutes the initial S/N of each spaxel. Then, for each spaxel an initial estimate of the galaxy redshift is refined by an iterative procedure where different subsets of lines are fitted simultaneously, and the optimal result is found. Finally, each emission line is fitted independently with the Gaussian mean value (velocity) and standard deviation (velocity dispersion) as free parameters.
The set of emission lines that are included in the observational setup at the clusters redshift is given in Table  2. For the subsequent analysis, we selected spaxels with an S/ N (as derived by HIGHELF) > 2. Each measurement was then flagged according to the S/N in the given line, the error in the velocity and in the velocity dispersion, the difference between the velocity/velocity dispersion values with respect to their mean value (values larger than 3σ are flagged). We also compared the HIGHELF measurements with those obtained with the KUBEVIZ  (Fossati et al. 2016 ) software that is being commonly used for low redshift IFU analysis, and found that the two are in agreement within the errors. For the lines that were outside the observed range at low redshift we visually checked that the automatic HIGHELF measurements are correct: in the case of the [O II] (3727, 3729) doublet, we also verified that the software was able to deblend it.
2021) , and one is too faint and small to say anything (A370 6295). To summarize, the population of blue galaxies seen in projection toward the central regions of these two clusters is dominated by two classes of objects: those currently+recently undergoing RPS (RPS+PSB), and those that are already completely passive. Together, these two classes account for 85% of all blue cluster galaxies. The remaining 15% of the cases, apart from a merger, are only moderately disturbed by some mechanism that cannot be securely identified. Note. Galaxy ID, coordinates, redshifts, and HST magnitudes come from  Richard et al. (2021). We give in the last column the spectral classification from the analysis of their MUSE data cubes. Excluding the totally passive systems, which are likely to be the end product of RPS+PSB evolution, the fraction of blue galaxies that is seen to be undergoing (RPS) or have recently undergone (PSBs) RPS is at least 76% and becomes 90% if we include the uncertain weaker cases.
Conclusions In this paper we have presented the characterization of 13 RPS galaxies located in the central region of the two intermediate redshift (∼0. 3) clusters A2744 and A370. Six more galaxies (PSBs) appear to have been affected by the same physical mechanism and will be discussed in a separate paper  (A. Werle et al. , 2022, in preparation. ). To infer the presence of the RPS we have made extensive use of the MUSE-GTO data, which, despite covering only the inner cluster regions, have been fundamental to characterize the galaxies and their stripped tails, while HST data can only suggest the presence of rampressure-stripped candidates. At low redshift, ram pressure stripped galaxies with tails have been observed over a wide range of clustercentric distances, up to the cluster virial radius  (Yagi et al.
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 833824, GASP project). We acknowledge funding from the agreement ASI-INAF n. 2017-14-H. 0 (PI A. Moretti), as well as from the INAF main-stream funding program. B. V. and M. G. also acknowledge the Italian PRIN-Miur 2017 (PI A. Cimatti). We acknowledge Y. J. for the stimulating discussion. This research made use of APLpy, an open-source plotting package for Python (Robitaille & Bressert 2012; Robitaille 2019). Software: pPXF, SINOPSIS, HIGHELF, KUBEVIZ.
05, Fig. S1  in the Supplement), thus giving credibility to our measurements which comprised only five to six depths in the photic layer. Mesozooplankton were sampled using vertical WP2 hauls (∅ 57 cm, 50 µm mesh size, Hydro-Bios, Germany) hoisted at 0. 5 m s -1 from 300 m to the water surface during nighttime (19:00-06:00). The southeastern Mediterranean Sea is an extremely oligotrophic region, with very low zooplankton densities, especially in the large-size fraction  (Koppellmann et al. , 2009). It was therefore stressed that the standard 200 µm underestimates the mesozooplankton abundance and community structure in this region  (Feliú et al. , 2020 ) and therefore we used the 50 µm mesh size. Filtered volume was measured using a mechanical flow meter (Hydro-Bios, Germany). The raw oceanographic data are publicly available at the ISRAMAR oceanographic database website (http://isramar.
06 zoo , where T is seawater temperature (average value for 0-300 m: background 18. 8 • C, cyclone 17. 8 • C, anticyclone 20. 0 • C) and chl a is food availability (mg C m -3 ) estimated from the integrated chl a values. W zoo is the average carbon concentration per zooplankter, set to 0. 01072 mg Cperindividual based on data collected from the background station in 2019-2020 (Guy-Haim, unpublished data). Phytoplankton was regarded as food following  Calbet et al. (1996). ZCD was compared to the phytoplankton stock and to primary production to estimate the potential clearance of phytoplankton by zooplankton. N and P excretion and oxygen consumption rates for an average zooplankter with weight W zoo were estimated using the multiple regression model by  Ikeda (1985)  based on carbon weight and temperature: ln Y = a 0 + a 1 ln W zoo + a 2 T , where Y represents N or P excretion or oxygen uptake.
DNA was amplified with the following primer sets amended with CS1/CS2 tags: (i) the V4 region of the 16S rRNA gene (ca. 300 bp), 515Fc/806Rc  (Apprill et al. , 2015; Parada et al. , 2016) ; (ii) the 18S rRNA gene (200-500 bp), 1391F, EukBr  (Amaral-Zettler et al. , 2009) ; and (iii) the mitochondrial cytochrome c oxidase I (COI) gene (ca. 300 bp), mlCOIintF, jgHCO2198. Library preparation from the PCR products and sequencing of 2 × 250 bp Illumina MiSeq reads were performed by HyLabs (Israel). The COI and 18S rRNA gene amplicon reads were submitted to NCBI Sequence Read Archive BioProject PRJNA667077.
Bioinformatic analyses of marker gene amplicons Demultiplexed paired-end reads were processed in a QI-IME2 V2020. 6 environment  (Bolyen et al. , 2019). Reads were truncated based on quality plots, checked for chimeras, merged and grouped into amplicon sequence variants (ASVs) with DADA2  (Callahan et al. , 2016) , as implemented in QI-IME2. The 16S and 18S rRNA amplicons were classified with a scikit-learn classifier that was trained on the Silva 138 database or BLAST against the Silva 138 database (0. 9 minimum identity cutoff, performed best for the analyses of 18S gene amplicons of microbial zooplankton). COI amplicons were classified with BLAST (0. 9 minimum identity cutoff) against the merged NCBI/BOLD database  (Heller et al. , 2018) , which was transformed into QIIME2 format. Downstream statistical analyses, calculation of alpha diversity indices (the richness estimator ACE, Abundance-based Coverage Estimator, and the biodiversity estimators Shannon and Simpson), beta diversity (non-metric multidimensional scaling, NMDS, based on the Bray-Curtis dissimilarity) and plotting were performed in R (R Core Team, 2018) using packages phyloseq (McMurdie and Holmes, 2013), ampvis2  (Andersen et al.
Genetic material was deposited in NCBI Sequence Read Archive BioProject PRJNA667077 (https://www. ncbi. nlm. nih. gov/bioproject/?term=PRJNA667077, Guy-Haim, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-18-693-2022-supplement. Author contributions. NB, TGH, MRB, AL, BH and ER conceptualized the study. IG, TO, TGH and AL were responsible for data curation. NB, TGH, MRB, RK, ARM, GSV, AL, and ER carried out the formal analysis. Project administration was carried out by AL and GSV. NB, TGH, RK, MRB, AL, IG, TO, BH and ER wrote the original draft. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. We would like to thank the R. V. Bat-Galim captains and crew for help at sea. The authors also thank  Briac Le Vu ,  Evangelos Moschos  and  Alexandre Stegner  for producing the AMEDA map and  François Carlotti  and  Marc Pagano  for their help with zooplankton rate calculations. Financial support. This research has been supported by the  Israel Science Foundation  (grant no. 1666/18 ), the National Monitoring Program of Israel's Mediterranean waters, the  Israel Science Foundation  (grant no. 1666/18  to  Ayah Lazar ) and the  Ministry of Science and Technology (MOST)  (grant no. 3-17933  to  Tamar Guy-Haim). Rainer Kiko  received funding from the  Deutsche Forschungsgemeinschaft  as part of the  Sonderforschungsbereich 754  " Climate-Biogeochemistry Interactions in the Tropical Ocean " and by a  Make Our Planet Great Again  grant of the  French Agence Nationale de la Recherche  under the " Programme d'Investissements d'Avenir ", reference  ANR-19-MPGA-0012.
Characterization using DSC analysis A differential thermal analysis was carried out using a heat flux DSC, Thermal Analysis Q200. The heat flux absorbed and restored by the PCM was measured by imposing a temperature cycle on a sample of 3. 5 mg of paraffin RT70 HC. The sample was heated from 30 • C to 90 • C with a constant heating rate of 8 K min -1. Then, the temperature was maintained for 5 min to erase the thermal history of the PCM, before being cooled at the same cooling rate to reach 30 • C. From the DSC curve, we measured that the PCM starts melting at 69. 5 • C and the crystallization phase begins at 68. 8 • C (Fig. 3 ), indicating a small supercooling of 0. 7 K. The enthalpy of melting and solidification, obtained from the integration of the DSC curve, was close to the value of the manufacturer data sheet: 260 kJ kg -1.
Acknowledgements We thank  Hanno Hildenbrandt  for helping us debug the model and increase simulation efficiency. We thank the  Center for Information Technology of the University of Groningen  for providing access to the Peregrine high performance computing cluster. J. J. K. was supported by an  Adaptive Life  grant by the  University of Groningen. A. B. was supported by  DFG  grant no. BE6684/1-1  ( Research Unit "So-long " -FOR 2218). D. E. was supported by  DFG  grant no. KO1895/19-1  (to Judith Korb) and by  NWO  grant no. 823. 01. 006  (to F. J. W. ). F. J. W. acknowledges funding from the  European Research Council (ERC Advanced  Grant No. 789240 ).
Empirical analysis and computational experiments In this section, large-scale instances of problem (12a)-(12f) are constructed and solved based on the decomposition procedure presented in Section 5. 1 , the linearization and strong valid inequality presented in Section 5. 2 and the γ -bounds presented in Section 6. Instances reflects an investment setting in which a MIC attempts to invest in the U. S. stock market relying on local brokers who are tasked with selecting stocks from listed enterprises belonging to different industries (i. e. , our set M ). We use financial data from U. S. listed enterprises in the time horizon 1999-2014 (as described in Section 7. 1 ). With a view to assessing the impact of the incomplete information, the different follower types are built by assuming increasing order of uncertainties.
5] , one should set γ ∈ (0 , 0. 5] (from constraint (17d), this mirrors the need for a minimum expected portfolio return from each local market k ∈ M , that depends linearly on z k ). By contrast, for β ∈ (0. 5 , 1] , one should set γ ∈ (0. 5 , 1]. Table  7  reports the total number of selected markets based on the multi-scenario reformulation and the γ -bound transformation. These quantities are averaged over the 16 year horizon of the analyzed data set. Consistently with Fig. 1  , setting γ < 0. 5 is a good strategy when using the γ -bound approximation for highly risk averse MICs (i. e. when β < 0. 5 ), as the number of selected markets perfectly matches. In the same vain, when the worst-case expected losses aversion is low (i. e. when β = 0. 99 ), the best γ -bound approximation is obtained with γ = 0.
Materials and methods All patients followed (day hospital endocrinology department, radiotherapy department, neurosurgery department) for clinical NFPA at the Bordeaux University Hospital between January 2007 and January 2018 were screened. The cut-off date for the data was fixed at January 1st, 2021. Inclusion criteria were: age ≥ 18 years old, first-line surgery, histologically proven benign pituitary adenoma, > 6 months follow-up. Exclusion criteria were: pituitary carcinoma, NFPA without initial surgery or cerebral radiotherapy before surgery. Progression was defined as the need for further treatment (radiotherapy or surgery). Each treatment modification was decided by the multidisciplinary tumor board including neurosurgeon, radiation oncologist, neuroradiologist and endocrinologist. Salvage treatment was decided in case of objective radiological progression on MRI.
Follow-up and statistical analysis As recommended, the postoperative residual tumor status was analyzed following a 3-12 months post-operative MRI  [29]. Prior to any treatment decision, all patients were assessed by a multidisciplinary tumor board including endocrinologists, neurosurgeons, neuroradiologists, and radiation oncologists. All statistical analyses were carried out using XLSTAT ® software. Chi-square analysis was used to compare categorical variables, t-test to compare continuous variables, and Mann-Whitney test to compare continuous variables concerning time ranking. Two-sided p-values below 0. 05 were considered statistically significant. Survival analysis was done according to the Kaplan Meier method and compared according to the Cox model.