dataesr/scientific-paragraphs-categorization huggingface dataset:
VLTI/GRAVITY observations AU Mic was observed with GRAVITY and the Auxiliary Telescopes (ATs) on 2016 June 16 during the ESO science verification run of the instrument. GRAVITY is a near-IR interferometric beam combiner operating in the K-band (λ = 1. 990-2. 450 µm). The observations were carried out using the highest spectral resolution (R ∼ 4000) and the telescope baselines A0-G1-J2-K0 (projected baselines range from 48 m to 110 m). Three datasets of 300 s each (10 × 30 s) interleaved by sky exposures were recorded. To monitor the interferometric transfer function, the calibrator HD 155276 (K0. 5IIICN0. 5, K = 3. 93 mag, θ LD = 0. 799 ± 0. 078 mas) was observed (taken from SearchCal. ). However, HD 155276 was observed about 6 h before AU Mic, and therefore the calibration of the squared visibilities is likely unreliable.
Angular diameter of AU Mic To measure the diameter, we only used the #1 PIONIER observations because this is the only dataset with long enough baselines to allow a reliable measurement. In addition, PIONIER operates in the H band and therefore provides a better spatial resolution than GRAVITY. There is no bright companion orbiting AU Mic reported, and therefore we do not expect the angular diameter measurement to be biased by the presence of a low-contrast component. We first estimated the uniform disc diameter θ UD by fitting the calibrated squared visibilities with the following formula: |V(u, v)| 2 = 2 J 1 (x) x 2 (1) where J 1 (x) is the first-order Bessel function, x = πθ UD B/λ, λ the wavelength, B = √ u 2 + v 2 the baseline length and (u, v) are the spatial frequencies. We measured θ UD = 0.
These detection limits are not better than the ones derived by  Lannier et al. (2017)  combining RVs and direct imaging at separation r > 0. 2 au. This is because direct imaging has a better sensitivity than interferometry at these angular separations. However, for the innermost region, at 0. 2 au, which is the Non co-planar Fig. 6. Same as Fig. 5  but only interferometric and direct-imaging observations. spatial scale probed by interferometry, we find that 99% of the companions down to ∼0. 5 M jup can be detected. This is actually similar to using RVs alone because of the lack of high-dynamic range of the current interferometric instruments and our limited datasets. In this specific case (i. e. for co-planar orbits of the edgeon disc), interferometry does not bring additional constraints when precise RVs are available.
Acknowledgements. A. G. acknowledges the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-15-CE31-0012-01  (project  UnlockCepheids ). A. G. acknowledges financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU, France. A. G. also acknowledges financial support from the  ANID-ALMA  fund No. ASTRO20-0059. The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  695099  (project  CepBin ), grant agreement no. 951549  (project  UniverScale ) and from the  National Science Center, Poland  grants  MAESTRO UMO-2017/26/A/ST9/00446  and  BEETHOVEN UMO-2018/31/G/ST9/03050. A. G. acknowledges support from the  IdP   II 2015 0002 64  and  DIR/WK/2018/09  grants of the  Polish Ministry of Science and Higher Education , from the  BASAL Centro de Astrofísica y Tecnologías Afines (CATA)   AFB-170002 , and from the  Millennium Institute of Astrophysics (MAS)  of the  Iniciativa Científica Milenio del Ministerio de Economía ,  Fomento y Turismo de Chile , project  IC120009.
Partitioning heuristic In Fig. 6 , we compare the performance of our partitioning heuristic to First Fit  [6] , Best Fit  [8]  and ETF  [9]  heuristics on generated task sets with different number of cores  (2, 4 and 8). We determine the worstcase response time of each DAG task when partitioned with different methods. Then, we compute the ratio of the response times obtained by First Fit, Best Fit and ETF heuristics over the one obtained by our partitioning heuristic. For this purpose, we use SimSo a simulation tool developed by Chéramy et al. [21]  to evaluate real-time scheduling algorithms. It supports single and multiprocessor architectures. It also supports several models and scheduling policies. However, it does not include DAG task model and priority on sub-task level. Therefore, we have adapted the source code 2  to add precedence constraints inside tasks and to specify a static priority for sub-tasks.
The implemented method for FE analysis is described in 2. 2. and 2. 3. sections. The design and the generation of the strut-based lattices are performed using the computational environment of the software Abaqus/ CAE 2018. A Python script is developed to enable and automate the design procedure of the strut-based lattices. The strut-based lattices consist of symmetric and periodic struts. The imported data to the script are the parametric shape and the number of the basic solids that constitute the unit cell, their orientation, and their geometric characteristics, such as the length and the radius. Then, the appropriate periodic boundary conditions are applied to the lattice structure through a homogenization method, which is described in detail in the Numerical Periodic Homogenization section.
Conclusions In the present study, a comprehensive qualitative and quantitative approach is performed to numerically design, analyze, and finally select the suitable lattice type as the internal architecture of an implant. A broad database of TPMS-based and two strut-based unit cells with various volume fractions is designed for this investigation, which is carried out with the aid of finite element analysis and the application of a periodic homogenization method in the elastic regime. The following conclusions have been reached: • The direct relation of various normalized effective mechanical properties to the design parameter t of the TPMS equations makes straightforward the design procedure. • The most isotropic behavior is exhibited by Gyroid sheet and IWP sheet topologies. Their Zener ratios are quasi-constant and insensitive to volume fraction variation compared to other studied topologies.
PCA is available in most data processing toolboxes (Matlab, Scikit, Hyperspy  [45] …) and has been applied to denoise SI datasets with success due to its simplicity of use. However, it has some limitations. As the data are noncompletely linear, relevant information may be retained in the low variance components, consequently be lost and lead to artefacts as shown in  [46]. The number of retained components must be chosen with great care and constitutes a sensible parameter of this denoising technique. Moreover, owing to the orthogonality constraint, the components found by the decomposition process cannot be identified with the real components of the system, that is the spectra of the different elements of the probed sample. Another limitation is that each individual spectrum is processed independently of spatial positions and the spatial correlations between pixels are not taken into account for denoising.
7. Building a 3D data cube either as collection of parallel EELS spectra recorded at different points (x,y) on the specimen in a STEM microscope or as a stack of energy filtered images with energy DE (from  [42] ). The individual bit of useful information (0D) is the intensity recorded in an energy-loss channel of width dE at DE and on a pixel size of area dx x dy at (x,y). C. Colliex: Eur. Phys. J. Appl. Phys. 97, 38 (2022)  The response of the electron population of the solid appears in the two major energy ranges: -In blue, from typically 50 eV to highest values (a few thousand eV), i. e. in the far UV up to the X-ray spectral domain, the core energy-loss domain. It corresponds to the excitation of the atomic core-shell electrons, in the present case of the 1s electrons of oxygen atoms (O-K edge) and of Mg atoms (Mg-K edge) respectively at 532 eV (see insert O-K) and 1305 eV.
This model-based quantification of EELS spectra such as accessible in Hyperspy  [45] , has been extended to include the fine structure of the characteristic edges  [60]  and consequently used to extend the analysis of SI from elemental to bond mapping in complex nanostructures  [61]. A public software, EELSMODEL, has also been made available to solve issues of quantifying EELS spectra by model fitting (http://www. eeelsmodel. ua. ac. be). With the access to corrected-probes down to the 0. 1 nm diameter, 2D atomically resolved elemental maps have now become a common result following their first demonstration about 12 years ago in a bunch of papers  [62] [63] [64]. In this last paper, the possibility of access to bonding maps through the information contained in the fine structures attached to an atomic edge was clearly pointed out.
A definite progress in the mathematical description of surface plasmons in a wide panel of nanostructures within this BEM approach is due to Hohenester and Trügler  [116]  who have developed a software (MNPBEM), that is a Matlab well adapted to the simulation of theoretical maps to be compared to experimental ones. An alternative method for the simulation of surface plasmon maps is the Discrete Dipole Approximation (DDA), which accounts for the volume of the particle as a discrete number of polarizable volume elements  [117]  instead of the surface charges. In  [114] , the agreement between the experimental and DDA simulated dependence of the surface plasmon resonances of triangular platelets as a function of their aspect ratio R = L/t (where L is the edge length and t the thickness) has been fully demonstrated.
Another advantage brought by contiguous assemblies is that SV detection across multiple strains can now be achieved by pairwise reciprocal Whole Genome Alignments (WGA) between target genome assemblies and the reference genome  (Goel et al. , 2019; Nattestad and Schatz, 2016; O'Donnell and Fischer, 2020). In S. cerevisiae, the most recent long-read datasets containing 8 strains employed a WGA-based SV detection tool, MUM&Co  (O'Donnell and Fischer, 2020) , and discovered on average 330 SVs per genome  (Bendixsen et al. , 2021). Compared to previous SV detection efforts, this is three times more than the average across other publications with longread datasets and SV detection based on more classical mapping methods. Population genomics is entering this new era where numerous contiguous genome assemblies are available, the detection of SVs is more efficient and more complex genomes and their haplotypes are also accessible.
RESULTS The ScRAP is a large panel of telomere-to-telomere genome assemblies in S. cerevisiae The ScRAP includes 142 strains that cover the broad geographical and ecological distributions of the species as well as various ploidy and heterozygosity levels (Fig. 1A  and B, Supp. Table  1 ). The full panel is composed of 3 distinct datasets. (i) 100 newly sequenced and de novo assembled genomes. These 100 strains were sampled in all previously defined phylogenetic clades  (Peter et al. , 2018)  and comprised genomics features such as large SVs, horizontal gene transfers  (HGTs)  and introgressions (Supp. Table  2 ). (ii) 18 re-assembled genomes using previously available raw Nanopore read data  (Istace et al. , 2017). (iii) 24 publically available complete genome assemblies, including the S288C reference genome  (Bendixsen et al.
Fig. 2A , Supp. Table  1 ). Additionally, 14 phased diploid assemblies of our newly sequenced genomes show an unprecedented level of contiguity with an average of 25 scaffolds per haplotype. Seven of them are completely assembled into a single telomere-to-telomere haplotype-phased scaffold per chromosome. Based on genome size, the newly sequenced genomes are the most complete, being on average 120 kb and 80 kb larger than the re-assembled and publically available genomes, respectively (Supp. Fig. 2B ). The number of chromosomes fully assembled from telomere to telomere is also higher in the newly sequenced genomes as compared to the two other datasets (Supp. Fig. 2C ). Noticeably, 47/100 haploid/collapsed assemblies include terminal telomeric repeats at all 32 chromosome ends while only 3/24 public assemblies (12.
, 2009)  on the SNP dataset. The genetic composition of the YJM454 (ABH) strain shows a mixed origin between the 'American' and the 'Mosaic beer/cider' clades that explains its phylogenetic positioning instability (Fig. 1D ). The general topology of both trees is also consistent with previous phylogenetic reconstructions. The ScRAP covers all main phylogenetic clades as they have been previously defined from large population genomics studies  (Duan et al. , 2018; Peter et al. , 2018) , thereby providing referencequality genomes across the entire species phylogenetic diversity. Noticeably, the 'Alpechin' lineage branches as a sister taxon to the 'Wine European 2' clade within the larger population of wine European strains. However, the Alpechin clade has a unique genomic composition, different from that of the wine European strains as it carries abundant S.
The most striking difference that we found between two haplotypes from the same isolate concerns the wine European MC9 strain for which the branch length of the haplotype 2 (AIS_HP2) is disproportionately longer as compared to all other terminal branches (Fig. 1C ). This was due to chromosome-scale introgressions in this genome where the two homologues of the chromosomes VI and VII originate from two highly diverged species (see below). We applied a strict molecular clock model to the ScRAP dataset to time the major founding events of the species history (see STAR METHODS). In brief, we used 100 and 365 generations per year as lower and upper bounds  (D'Angiolo et al. , 2020)  and mutation rate 2. 31 x 10 -10 /bp/generation as estimated from homozygous and heterozygous diploid mutation accumulation lines  (Tattini et al.
For 51 out of 142 isolates, we obtained the transcriptome, consisting of the expression levels for 6,445 transcripts and a total of 1,876 SVs, encompassing a similar proportion of the different SV types than the entire dataset (Supp. Fig. 8A ). To explore the effect of SVs on gene expression, we first defined the entire set of SVgene pairs, representing all the SVs located in the coding sequence or in the intergenic region neighboring a gene (see STAR METHODS). Overall, we captured 2,808 SV-gene pairs with more than half of the pairs involving insertion and deletion events. We then compared the expression of genes associated or not with a given SV in our population (Supp. Fig. 8B , see STAR METHODS). We found that 124 SVgene pairs (4. 4%) (Supp. Table  7 ), encompassing a set of 97 unique SVs, showed significant differential expression changes (Table  1 ).
, 2015)  and 909 from  (Peter et al. , 2018) ) was re-analysed to detect both simple and complex aneuploidies (see STAR METHODS). The proportion of aneuploid chromosomes estimated to be complex in this larger dataset is between 10-18% (44/423 -85/464). In terms of strains, 15-23% (36/248 -59/248) of aneuploid strains are predicted to contain at least one complex aneuploidy (Supp. Table  9, Supp. Table 10 ). Lower boundaries of 10% of complex aneuploid chromosomes in 15% of the strains are conservative estimates as they only consider Centromere-Related (CR) events i. e. regions where the coverage deviation covers a centromere (for instance chrVIII in AGA in Fig. 4A ). The upper boundaries of 18% of complex aneuploidies in 23% of aneuploid strains are less conservative estimates as they also include chromosomes with large (>100kb) Non-Centromere Related (NCRs) coverage deviations, if the same strain includes another CR event (for instance chrXVI in AGA in Fig.
Notably 3/5 aneuploid chromosomes not reported previously, were complex aneuploidies. More globally, the 1,011 survey reported 343 aneuploid chromosomes in 200 strains  (Peter et al. , 2018). Reanalysing the same dataset here, we found 120 additional cases where 35 of them (29%) are made of complex calls, showing that complex aneuploidies were prevalently lacking in initial analysis. Interestingly, we found that complex aneuploidies involve significantly larger chromosomes as compared to simple aneuploidies (Fig. 4B ). Additionally, there is a clear positive correlation between the involvement of the different chromosomes in complex aneuploidies and their size, showing that larger chromosomes are more often involved in complex aneuploidies than the smaller ones (Fig. 4C ). Finally, a general pattern of increasing proportion of simple aneuploidies with increasing global ploidy of the strains has been reported  (Gilchrist and Stelkens, 2019).
Nuclear chromosomes Raw fastq files were treated with porechop (v0. 2. 3) to remove both adapters and barcodes (github. com/rrwick/Porechop), downsampled with Filtlong (v0. 2) for a maximum of 40X per strain (github. com/rrwick/Filtlong) then assembled by both Canu (v2. 0)  (Koren et al. , 2017)  and SMARTdenovo (https://github. com/ruanjue/smartdenovo). This generated two assemblies per strain that were treated separately. The contigs were polished using the same fastq data used for assembly. First both the Canu and Smartdenovo assemblies are polished by  Racon (v1. 4. 3) for one and three rounds respectively  (Vaser et al. , 2017). This is followed by two rounds of Medaka (v1. 2. 2) (github. com/nanoporetech/medaka). The Nanopore polished contigs were then polished using Illumina data with  Pilon (v1.
Mitochondrial chromosomes The mitochondrial de novo genome assemblies were constructed from the Illumina paired-end reads with a pipeline derived from  (Tao et al. , 2019). Short reads were down-sampled to sets of 500,000, 600,000, 700,000 and 800,000 paired-end reads with seqtk (https://github. com/lh3/seqtk). Each dataset was de novo assembled with A5-miseq  (Coil et al. , 2015)  and mitochondrial contigs were identified through similarity searches to the S. cerevisiae mitochondrial reference sequence (accession number KP263414. 1). For each strain, a representative assembly was selected based on the number of contigs and their length. The one-contig assemblies were subjected to circlator for circularization  (Hunt et al. , 2015). For the non-circularisable V90, inspired by the L90 and N90, is the minimum number of phased blocks to cover 90% of all phased variants.
tDNAs and Ty elements We defined a consistent set of 100 haploid or homozygous genome assemblies for the analyses of the dynamics of tDNAs and Ty elements by first excluding the diploid, triploid and tetraploid phased assemblies because they contained numbers of annotated tDNA and Ty copies that were proportional to their ploidy and therefore difficult to compare with haploid and collapsed genome assemblies (Supp. Fig. 17 , Supp. Table  11 ). We also removed 8 haploid genomes from one specific study  (Bendixsen et al. , 2021)  because they contained a much lower number of tDNAs than all other genomes of the dataset (Supp. Fig. 22A ), probably indicative of local assembly errors. We finally excluded collapsed assemblies from heterozygous genomes because they showed some discrepancies with their cognate phased assemblies, suggesting possible assembly problems in these complex regions and also because they contained the most extreme numbers of tDNAs per genome (Supp.
Telomere detection We developed a python package called Telofinder to determine the chromosomal location and size of telomere sequences in yeast genomes assemblies (https://telofinder. readthedocs. io/en/latest/). Telomere detection is based on calculation of both the DNA sequence entropy and the proportions of the "CC", "CA" and "AC" dinucleotides in a 20 bp sliding window. Telofinder outputs two csv and two bed files containing the telomere calls and their coordinates, either as raw output or after merging consecutive calls. We run Telofinder on all 394 de novo and 24 previously available nuclear genome assemblies with the -s option set to -1 to scan entire genome sequences.
Aneuploidy detection in a dataset of 909 strains The aneuploidy detection pipeline using  (Peter et al. , 2018, 011)  and  (Strope et al. , 2015)  illumina data is available at https://github. com/SAMtoBAM/aneuploidy_detection and consist of the following steps: 1. Illumina reads were aligned with bwa mem and the coverage calculated using bedtools genomecov (-d -ibam). 2. Coverage was binned the median coverage in 30kb bins with a 10kb slide using both bedtools makewindows and map and remove regions covering 15kb (half a window) from each chromosome end (to reduce telomere mapping/variation issues). 3. Coverage was normalized to the genome wide median and regions extracted if they deviated by +-0. 7*(1/n). 0. 7 gives some leniency to coverage deviation considered sufficient for a change in copy number.
During the manual curation, 34 complex stayed complex, 347 simple remained simple, 4 complex were removed as false positive aneuploidies (due to the impact of the 'smiley-effect'), 32 moved from complex to simple, 10 moved from simple to complex. The move from simple to complex was always due to the threshold of size (the size difference of these ten CRs was  96, 90, 82, 80, 72, 72, 66, 66, 50 and 40kb). All examples of this reclassification from simple to complex were distinct. 9. Identify any NCRs >100kb which are present within a strain containing an aneuploidy detected above. Label as complex aneuploidy-related and use in the less conservative estimate of complex aneuploidy count. The newly generated large aneuploidy dataset overlaps, by strain and chromosome, 88% (303/343) with that of the  (Peter et al.
, 2018)  dataset. This leaves only 40 aneuploidies (12%) not re-detected. Of this 40, manual inspection identified that 9 were clearly false positives in  (Peter et al. , 2018) , 8 came from the same strain and are an issue of defining whether 8 chromosomes were lost or the other 8 were gained, 1 contains a large increase in coverage close to the centromere but not covering and 3 contain a slight change in coverage but are well below the set threshold for indicating a change in copy number. Therefore, only 19 missed aneuploidies are likely true false negatives in the new dataset. The overlap between datasets additionally shows 120 previously undetected aneuploidies in the new dataset. Of these 120, 35/120 (29%) are complex aneuploidies, as compared to 9/303 (2%) found within the overlap.
Using these rules, 91645 (95. 6%) were considered validated, which when split by SV type ranged from 78-99% for translocations and contractions, respectively (Supp. Fig. 27 ). Additionally, an average of 95. 7% of all variants within each strain were validated. Notably however, genomes that were not assembled within the frame of this project had a lower average within-strain validation due to only containing a single assembly per strain. Furthermore, many from  Bendixsen et al. (2021)  have genetically diverse East Asian origins and therefore reducing the likelihood of sharing an SV with another strain within this cohort (Supp. Fig. 28 ). The total non-redundant dataset contains 4809 SVs. For diploid genomes, validated SVs were considered homozygous if found in at least one genome from both HP1 and HP2 for the same strain.
SNP-based nuclear tree The input vcf file (matrixSam. snp. vcf. gz) was generated using GATK4's HaplotypeCaller and GenotypeGVCFs (v4. 1. 4. 1) with bwa-mem (v1) aligned illumina reads. The resulting multi-sample VCF was then filtered for variants with a Qualityby-Depth (QD), StrandOddsRatio (SOR), FisherStrand (FS), Mapping Quality (MQ), MappingQualityRankSum (MQRankSum) and/or ReadPosRankSum (RPRS) of more than two standard deviations from the average. Lastly, variants were removed from regions labeled as repetitive by RepeatMasker and/or in  (Jubin et al. , 2014)  to generate the final VCF. We used the vcf2phylip python script (https://github. com/edgardomortiz/vcf2phylip) (versions: 2. 8; options: -I $input_vcf -resolve-IUPAC -o S288C -fasta -output-prefix) to convert the vcf file into the fasta format.