dataesr/scientific-paragraphs-categorization huggingface dataset:
These maps include post-Born corrections  [50]. The convergence maps were generated using an equal area pixelisation scheme in HEALPix 13 format  [51]. The set of maps we use in this study are generated at N side = 4096 and were crosschecked against higher resolution maps constructed at a higher resolution N side = 8192, 16384 for consistency. These maps constructed at different resolution were found to be consistent with each other up to the angular harmonics ≤ 2000. For our study, we have used high resolution maps N side = 4096. These maps were degraded to various lower resolution N side = 2048. In figure  1  we show one such map for the source redshift z s = 0. 5 (left panel) and the corresponding phase map (right panel). We analysed these maps using publicly available software TreeCorr 14 for the computation of two-and three-point correlation functions.
The two-point correlation functions are shown in figure  2  for various redshifts. 12 http://cosmo. phys. hirosaki-u. ac. jp/takahasi/allsky_raytracing. 13 https://healpix. jpl. nasa. gov. 14 https://rmjarvis. github. io/TreeCorr/_build/html/index. html. -6 -  LCF for κ. The line correlation function Π 2 (θ) defined in eq. (3. 8b) is being plotted as a function of θ In figure  3. From left to right we show results for source redshifts z s = 2. 0, 1. 0 and 0. 5. We have presented the mean results estimated from ten all-sky maps. No noise was included in our study. Degraded maps with Nside = 2048 and 512 were respectively used to estimate the correlation function for the range θ s = 1 -10 and 10 -100. The fitting function presented in  [45]  was used to compute the theoretical predictions.
Panels from left to right correspond to z s = 2. 0, 1. 0 and 0. 5 respectively We use the fitting function proposed by  [45]  for the bispectrum. The simulation results show an ensemble average computed from 10 all-sky maps using publicly available software TreeCorr  [48]. From left to right we show results for source redshifts z s = 2. 0, 1. 0 and 0. 5. The theoretical predictions correspond to N side = 2048. The dashed-lines correspond to results computed using perturbation theory and the solid lines are obtained using a non-perturbative fitting function. We use the fitting function proposed by  [45]  for the bispectrum. The simulation results show an ensemble average computed using 10 all-sky maps. The three-point correlation function L 2 (θ) was computed from the phase maps using publicly available software TreeCorr  [48].
The dots represent the cumulant correlators defined in eq. (3. 11) computed from one all-sky phase map by cross-correlating the squared phase map 2 (θ) with itself (θ). We have used the publicly available software TreeCorr. Theoretical predictions correspond to the one computed using the fitting function proposed by  [45]  for the bispectrum. The deviations from the theoretical predictions at smaller angular scales is an effect of pixelisation. We have generalised both Π 2 (θ) and L 2 (θ) to higher-order. Next, we have adopted the cumulant correlator typically used for 3D density field and 2D convergence maps for statistics phases. We showed that available theoretical models can reproduce the numerical results with reasonable accuracy and our results can be used to select the range to retain in order to maintain a given level of theoretical accuracy.
Figure6. The lines correspond to the cumulant correlator C 21 (θ) defined in eq. (3. 11). is being plotted as a function of θ (in arcmin). From left to right we show results for source redshifts z s = 2. 0, 1. 0 and 0. 5. The theoretical predictions correspond to N side = 2048. We use the fitting function proposed by [45]  for the bispectrum (solid lines). The dots represent the cumulant correlators defined in eq. (3. 11) computed from one all-sky phase map by cross-correlating the squared phase map 2 (θ) with itself (θ). We have used the publicly available software TreeCorr. Theoretical predictions correspond to the one computed using the fitting function proposed by [45]  for the bispectrum. The deviations from the theoretical predictions at smaller angular scales is an effect of pixelisation.
Acknowledgments DM was supported by a grant from the  Leverhulme Trust  at  MSSL  when this work was initiated. RT is spported by  MEXT/JSPS KAKENHI  Grant Numbers  20H0 5855 ,  20H0 4723  and  17H0 1131. DM would like to thank  Peter Taylor  for many useful discussions regarding implementation of the software TreeCorr. We would like to thank  Alexander Eggemeier  for many useful discussions.
Data reduction and time-series extraction The data were reduced using version 3. 8 of HARPS Data Reduction Software (DRS). DRS RVs were extracted from the Cross-Correlation Function (CCF) obtained using an M4 mask. We also extracted RVs using two alternative pipelines which have been shown to outperform the DRS for M-dwarfs: SERVAL  (Zechmeister et al. 2018  ) and HARPS-TERRA (Anglada-Escud é & Butler 2012 ). SER-VAL and HARPS-TERRA both compute a template spectrum from  the observations themselves, rather than using a pre-existing digitized mask to compute the CCF as in the DRS. In the case of SERVAL , the template is constructed by co-adding the observed spectra in the barycentric rest frame. In the case of HARPS-TERRA , the template is initially computed by co-adding high-SNR observ ations, allo wing preliminary estimates of the RVs to be made, then a new, higher SNR template is produced by co-adding all the spectra in the stellar rest frame, and the RVs are e v aluated again.
M O D E L L I N G T H E RV S In this section, we describe the method used to analyse the data, which involved jointly modelling activity and planet signals in the RVs and the activity indicators simultaneously, using the multidimensional Gaussian Process (GP) model introduced by  Rajpaul et al. ( 2015 )  as implemented in the pyaneti https:// github. com/oscaribv/ pyaneti. package (Barrag án et al. 2022b , hereafter B22 ).
Results We report fitted values and upper and lower uncertainties for each parameter in the final column of Table  3  , using the median and 16th and 84th percentiles of the marginal posterior distributions. The inferred model is shown alongside the data in Fig. 4. The GP hyper-parameters are well constrained, and consistent with prior information about the rotation and activity behaviour of AU Mic. The GP period is P GP = 4. 857 + 0. 004 -0. 003 d, consistent with the rotation period P rot = 4. 863 ± 0. 010 d reported by P20. The GP evolution time-scale λ e = 108 ± 15 d is 20 times the rotation period and is consistent with estimates from previous studies where a GP was used to describe the RVs only ( P20 , K21 , C21 ). The inverse harmonic complexity λ p = 0. 449 + 0. 049 -0. 043 is relatively low.
This is consistent with the prediction MNRAS 512, 3060-3078 (2022)  The jitter terms, σ RV and σ DLW , can be used as diagnostics of the ef fecti veness (or otherwise) of our model at explaining the full data set. In both cases, the posteriors medians are ∼10 times smaller than the amplitude of the dominant activity term ( B V and A α , respectively), but roughly twice the nominal measurement uncertainties. This illustrates the fact that our activity model explains most of the RV and DLW variations, but is not perfect. A widely used test of the quality of a model fit is to compare the root-mean-square (RMS) before and after subtracting the best-fitting model. Caution must be used in interpreting such a comparison, as a low residual RMS can result from o v erfitting as readily as from a particularly good model.
Planet masses From our estimates for K b = 5. 8 ± 2. 5 m s -1 and K c = 8. 5 ± 2. 5 m s -1 , we directly obtain mass estimates for the two planets of M b = 11. 7 ± 5. 0 M ⊕ and M c = 22. 2 ± 6. 7 M ⊕. This represents a 3. 4 σ detection of planet c, but only a tentative, 2. 3 σ detection of planet b. MNRAS 512, 3060-3078 (2022) K21 reported a mass estimate of 17. 1 ± 4. 6 M ⊕ for planet b, which is consistent at the 1 σ level with our estimate. We note that K21 did not include AU Mic c in their analysis, since the second planet was not confirmed at the time. More recently, C21 reported a mass of 20. 1 ± 1. 6 M ⊕ for planet b and a 5 σ upper limit for planet c of M c < 20. 1 M ⊕ , using a combination of data from multiple optical and near-IR spectrographs, and a model which includes only the RVs, but exploits the chromaticity of the activity signal.
TTV analysis As AU Mic b and c are close to a 2:1 mean-motion resonance, transit timing variations (TTVs) can be used to constrain their masses and eccentricities. Szab ó et al. ( 2021 )  and  Gilbert et al. ( 2021 )  report tentative TTVs for AU Mic b at the level of 3-4 min and 80 s, respectively, using TESS and CHEOPS observations. Although the measurement of transit times is some what dif ficult because of A U Mic' s frequent flaring, these results indicate that strong TTVs, with amplitudes in excess of ∼10 min, are not present in the system. This can be used to provide a sanity check on the mass and eccentricity estimates derived in this work and in previous RV analyses, and to moti v ate future TTV follow-up. Using the TTVFast package  (Deck et al. 2014 )  we forward modelled the TTVs expected from a two-planet configuration as observed in the AU Mic system.
We then repeat the same e x ercise, creating another 100 synthetic data sets, but this time, injecting two Keplerian signals with the median planet parameters obtained from the real data set, as reported in Table  3. Again, we modelled these synthetic data sets using pyaneti https:// github. com/oscaribv/ pyaneti with the same configuration as described in Section 3. 4. The resulting posteriors are shown in the right column of Fig. 6. In this case, we are interested in two distinct questions. First, how often are AU Mic b and AU Mic c detected at a given confidence level from these simulations? For this purpose, we consider that a detection has occurred if the median of Downloaded from https://academic. oup. com/mnras/article/512/2/3060/6547782 by CNRS -ISTO user on 27 June 2022 the posterior is larger than 2 σ , where σ is half the interval between the 16.
5th and 83. 5th percentiles. We find that AU Mic b is detected 42 per cent of the time, and AU Mic c 88 per cent of the time. This confirms that, if the planet masses are similar to the maximum a posteriori values we derived from the real data, more data are needed for a robust detection of planet b. Second, we asked what fraction of the time the reco v ered semi-amplitude is within 2 σ of the injected value? We find that this happens 91 per cent and 89 per cent of the time, for AU Mic b and AU Mic c, respectiv ely. F or Gaussian posteriors, we would expect these numbers to be around 95 per cent. Again, accounting for Poisson counting errors, this result is thus consistent with what we expect.
C O N C L U S I O N S We have analysed data from an intensive monitoring campaign using HARPS with multidimensional GP framework ( B22 ), and measured the masses of AU Mic b and c to be 11. 7 ± 5. 0 M ⊕ and 22. 2 ± 6. 7 M ⊕. We detect planet c at 3. 4 σ confidence and planet b at 2. 3 σ confidence. Our results are insensitive to the choice of RV timeseries (DRS, SERVAL , HARPS-TERRA ) and to the specific activity indicators used, though this choice affects the degree of confidence in the detections slightly. Our mass measurements, combined with literature radius estimates, indicate that AU Mic b has a significant H/He envelope and may have an internal heat source. On the other hand, we find that AU Mic c is denser and compatible with either a rocky core surrounded by a ∼2 per cent H/He envelope or a pure H 2 O composition.
These results are in tension with current coreaccretion models, which predict that planet c, with a more massive core, should have the larger H/He envelope of the two. We provide a possible, speculativ e e xplanation for this discrepanc y involving a giant impact, but stress that additional observations are needed to further refine the mass measurements and constrain the obliquity of planet c. The RV signals of the two planets around AU Mic are around 50 times smaller than the activity signals. Disentangling the former from the latter is very challenging, as highlighted by the discrepancies between our results and those of other teams working on the same system using different instruments and methods, which need to be better understood. Further observations of this system and independent analyses of the available data sets are needed to resolve these discrepancies.
DATA AVA I L A B I L I T Y The HARPS spectra used in this study are available on the ESO archive. 4 The radial velocities and activity indicators extracted with the DRS, HARPS-TERRA , and SERVAL are listed in Table  2  , a machine-readable version of which is provided in the Supplementary Material. The code used to perform the evolutionary analysis is available at MESAplanet ht tps://github. com/jo276/MESAlanet. 5  Pyaneti is available at pyaneti https:// github. com/oscaribv/ pyan eti. 6.
The raw sequencing reads were evaluated for their GC content distribution, quality distribution, base composition and average quality score at each position, and stored in NG6  [42]. The reads were de novo assembled using the RS Hierarchical Genome Assembly Process in SMRT analysis version 2. 3. 0 software (Pacific Biosciences, Menlo Park, CA, USA). For the RB84, Ae109, MF1 and NC1 strains, DNA-seq libraries were prepared using an Illumina TruSeq DNA v. 2 Library Prep Kit following the manufacturer's instructions. An Agilent Bioanalyzer was used to assess the quality of the libraries, and the libraries were quantified by qPCR using the Kapa Library Quantification Kit. DNA-seq experiments were performed on an Illumina HiSeq2000 Sequencer using a paired-end read-length of 2 × 100 pb with the HiSeq v.
3 Reagent Kit at the GeT-PlaGe core facility in Toulouse. All the reads were quality-checked and stored in NG6  [42]. The reads of Ae109, RB84, MF1 and NC1 were assembled using MaSurCa  [43]  and the assembly metrics were calculated using assemblathon_stats. pl script (http://korflab. ucdavis. edu/datasets/ Assemblathon/Assemblathon2/Basic_metrics/assemblathon_stats. pl). Proteomes were predicted using the Augustus-based Braker 2 pipeline  [44]  trained with predicted proteins from a previous assembly, and RNASeq reads of zoospores and mycelium from A. euteiches  [41]. Proteomes predictions were benchmarked using BUSCO software with the Stramenopiles reference gene set  [45]. Repeated sequences were de novo identified using the RepeatModeller pipeline and the repeated sequence was then masked in the genome using RepeatMasker 4.
1. 0 (http://www. repeatmasker. org). Satellite DNA was predicted using the bioinformatics algorithm as described in a previous publication  [37]. Tandem Repeat Finder  [46]  was used to find tandemly repeated sequences in the genome; the resulting sequences were sorted by size to keep the ones with sizes from 100 bp to 500 bp. The monomeric sequences were blasted  [47]  against the genome (-perc_identity 85 -qcov_hsp_perc 90). The monomeric sequences that occurred less than 100 times were excluded from the analysis. Blastclust pairwise analysis was used to cluster monomeric sequences (identifying at 75%) to form families. Sequences of families were loaded into CLC Main Workbench software (Qiagen) to obtain the consensus sequence of the family. Sequences were aligned using ClustalW and the Neighbor-Joining method was used to build and visualize the phylogenetic tree in CLC Main Workbench software.
Mitochondrial Genome Assembly A random subsample of 10% of self-corrected PacBio reads was used in the perl-based software Organelle_PBA (https://github. com/aubombarely/Organelle_PBA) as described in  [49]  to generate a circular contig. The Aphanomyces astaci mitochondrial genome (NCBI RefSeq: NC_032051,  [50] ) was used as the reference. Gene annotation was done on the MITOS webserver in May 2021 (http://mitos. bioinf. uni-leipzig. de) with further manual curation. The contig was visualized using the OGDRAW webtool  [51].
euteiches previously generated by RNASeq technology  [41, 58]. We checked the completeness of the genome assembly using a Benchmarking Universal Single-Copy Ortholog (BUSCO)  [45]  based on 100 highly conserved Stramenopile proteins (100 proteins, odb10 dataset) and identified 98% complete genes, 2% fragmented genes and zero missing (Table  1 ). The two fragmented genes were EF-hand domain and mitochondrial-processing peptidase subunit beta, both of which harbor catalytic domains. A total of 23,027 genes from the A. euteiches V2 (this study) were identified (Table  S1a ), representing an increase of 2404 genes when compared with V1. Comparison of proteomes from both assemblies showed that over 20,000 proteins from V2 (88. 5%) had protein models identical (identifying at ≥ 75%) to those in V1.
In our work, 2647 new protein models were predicted and 1756 protein models from V1 were not supported in V2 (Figure  1c ). Among the new set of predicted proteins, only around 25% (i. e. , 647 proteins) harbor a predicted PFAM domain mainly related to nucleic acid interactions, such as 'integrase', 'endonuclease', 'transposase', 'RNAse', 'Helicase' and 'CENPB DNA-binding' (Table  S1b ). All the data were stored in an updated version of the AphanoDB database in August 2021 (https://www. polebio. lrsv. ups-tlse. fr/aphanoDB/) [48]. This resource is dedicated to "omics" studies on the Aphanomyces genus and provides tools such as a genome browser, gene annotation facilities and Basic Local Alignment Search Tool (BLAST) to facilitate analysis [48].
A total of 23,027 protein-coding genes were identified in the V2 assembly of the A. euteiches genome, which is a significantly higher number than in V1. Among the newly predicted proteins present in the V2 assembly, 44% do not possess a putative functional PFAM domain, while the rest harbor PFAMs related to nucleic acid modification (i. e. , integrase, endonuclease, transposase, etc. ). Overall, the proteome exhibits numerous ankyrin repeats and WD-40 repeats. The ankyrin repeat is one of the most frequently observed amino acid motifs in protein databases, probably because this protein-protein interaction motif is involved in a diverse set of cellular functions  [86]. Similarly, in eukaryotes, WD-40 repeat proteins generally mediate supramolecular interactions and participate in the assembly of complexes involved in different cellular processes  [87].
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/jof8010088/s1, Table  S1 : A. euteiches (pea strain ATCC201684) genome annotation (PacBio); Table  S2 : Oomycete secretomes; Table  S3 : A. euteiches strains' annotation (Ae109, RB84, MF1, NC1 strains, Illumina). submission at ENA as follows: ATCC201684 (PacBio); PRJNA769534|MF1; PRJNA767766|NC1; PRJNA767769|RB84; PRJNA767773|Ae109; PRJNA767593.
Data Availability Statement: Assemblies, sequencing and annotation are available in the publicly accessible repository AphanoDB v2. 0, updated in August 2021 (http://www. polebio. lrsv. ups-tlse. fr/ aphanoDB/). The genome assembly of A. euteiches ATCC201684 (Illumina/545 combination) is also available via the EMBL|European Nucleotide Archive (ENA) (https://www. ebi. ac. uk/ena/browse) under study number PRJEB24016. For the other genome assemblies, data are currently under.
Genome Version Version V1 [Ref] Illumina/454 Version V2 (This Study) PacBio Total contig length (Mb) 56. 9 72 GC content (%) 47. 69 47. 27 Protein-coding genes 20,623 23,027 Average exons per gene 3. 7 3. 5 Mean gene size (kb) 1503 1447 N50 (kbp) 275,164 1,005,788 N90 (kbp) 69 39 Gene density (nb genes/Mb) 343 320 Coverage 146x 148x Number of scaffolds 349 420 BUSCO complete/ fragmented/duplicated /missing 83. 1%/3. 8% (Alveolata-Stramenopiles dataset) 98%/2%/0%/0% (Stramenopiles dataset).
Purpose: Purpose: This study provides a comprehensive analysis of research trends on the etiology, mechanisms, potential risk factors, diagnosis, prognosis, surgical and non-surgical treatment of varicocele, and clinical outcomes before and after varicocele repair. Materials and Methods: Materials and Methods: Varicocele studies published between 1988 and 2020 were retrieved from the Scopus database on April 5, 2021. Original studies on human varicocele were included, irrespective of language. Retrieved articles were manually screened for inclusion in various sub-categories. Bibliometric data was subjected to scientometric analysis using descriptive statistics. Network, heat and geographic mapping were generated using relevant software.
Data source Scopus is a bibliographic database containing abstracts and citations for academic journal articles. It covers approximately 21,000 titles from over 5,000 publishers, including 20,000 scientific, technical, medical, and social journals. An advantage of using the Scopus database is the ability to automatically analyze the literature search and generate graphs and tables based on bibliometric metrics (e. g. number of documents by year, author, affiliation, journal, country, type of document, subject area).
Data retrieval strategy Data was collected on April 5, 2021 and included scientific articles from 1988 to 2020 (33 years). Articles published in 2021 were excluded by using the Scopus filter. The keyword "varicoc*" was searched as an entry term in 'TITLE-ABSTRACT-KEYWORDS' fields to retrieve all relevant publications. Additional keywords ("man" OR "men" OR "adolescent*" OR "patient*" OR "human") were used to exclusively limit the search to human studies. The query AND NOT TITLE ("review*" OR "meta-analysis") AND (LIMIT-TO (DOCTYPE, "ar")) was used to retrieve only original studies. We used the asterisk '*' after the word to include all variants of the term. All retrieved publications were saved as commaseparated value (CSV) files and subsequently converted to Microsoft Excel files. The extracted studies were manually screened to exclude non-relevant articles.
Only original studies on humans were included, while reviews, meta-analyses, and other types of publications (case-reports, editorials, etc. ) were excluded by using Scopus filters and manual screening. Afterward, the collected articles were manually validated by independent researchers for inclusion in different subcategories: varicocele in adolescents or adults, prevalence, pathophysiology, etiology and risk factors, prognostic/diagnostic studies, surgical and non-surgical interventions, impact of varicocele or treatment on semen parameters, seminal OS, sperm DNA damage and/or SDF, testicular histology, testicular inflammation and cytokines, hormones, vasculature and blood flow, reproductive outcomes (pregnancy rate, PR; live birth rate, LBR) in natural conception or ART (Fig. 1 ). As each article can be included in more than one subgroup, data is reported as the number of publications and percentage out of the total number of publications identified.
Network, heat, and geomap analysis Network and heat maps were generated by using the VOSviewer software (freely available at https://vosviewer. com)  [30]. The relatedness of each node was calculated by using co-authorship as criteria, while their size reflected the number of publications. Geographic Step 1 Step 2 Step 3 Step 5 Step 6 Step 7 Step 4 Original human studies on varicocele Fig. ASRM 2006  [38]  ASRM 2008  [39]  EAU 2012  [62]  ASRM 2014  [40]  WHO 2010  [71]  EAU 2005  [36]  EAU 2002  [35]  Fig.
Limitations Despite interesting data emerging from the current study, few limitations are noted. First, our study utilized a single bibliographic database (Scopus), and therefore varicocele articles which were not indexed in this database are excluded. Future studies could endeavor to incorporate other databases such as Web of Science and PubMed. Second, although our study was able to analyze separately the publications concerning adolescents from those concerning adults, it was not able to analyze the trends in the studied parameters among males with advanced age. Finally, an inherent limitation of the scientometric method is the retrospective nature of this analysis.