dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , TRI 3810. Others had different accession numbers but similar or identical accession names, e. g. , TRI 8018 (RIEBESEL ST. 47-51) and TRI 24963 (RIEBESEL 47/51). In both cases, the profile the percentage of bases covered on chromosome 1B differed, indicating different genotypes, possibly due to different introgressions (Supplementary Figure  S3 ). Additionally, four extreme examples of accessions with two genotyped SSD lines and at least 100-Mb differences between them on one chromosome were detected from the GBS data (see Supplementary Figure  S4 ). The accession TRI 7878 was a mixture of a tetraploid and hexaploid wheat. Accession TRI 16401 (DUCK) had one SSD line with a substitution of 2D by a 2A-like chromosome. The accession TRI 10859 had one SSD line with 1RS. 1BL. The accession TRI 7025 (RED ROCK) had one SSD line with an introgression on 2BS.
Comparison of different low-coverage data Finally, introgression predictions based on WGS and GBS data were directly compared. To reduce the number of examples, only samples with at least one large introgression were considered (23 samples). Different levels of homogeneity can be expected for genebank accessions and cultivars, so the samples were divided into genebank accessions and cultivars. Each recently released cultivar should be a single genotype (genetically homogeneous or identical), as seed purity is required for the release process. At the same time, genebank accessions, which can be landraces and old cultivars, can be a mixture of several genotypes, as described above. Therefore, genebank accessions were distinguished as accessions with the same descent (SSD-PGR) and accessions with unknown descent.
Thus, it could be a case of confounding. In the remaining five cultivars, introgressions were located on chromosome 6D in AREZZO, on chromosome 6B in CHIRON, on chromosome 6A and 6D in GLADIATOR, and on chromosome 4D in MAGISTER and RYWALKA. In summary, 11 samples showed differences in detected introgressions between GBS and WGS data (four accessions with unknown SSD-PGR, two with the same SSD-PGR, and five cultivars). In all cases, the differences were related to predicted introgressions based only on WGS data but not GBS data. Often, the difference between the expected percentage of bases covered in the wheat chromosome and the observed percentage of bases covered in the introgression was visible but smaller compared with other introgressions. LG MAGIRUS was another example where an introgression was detected from the WGS data but not the GBS data.
This was because of the small difference between the expected and observed percentage of bases covered. The introgression was located on chromosome 1B. Still, the size did not exceed the threshold of 300 Mb due to the similarity between the introgressed donor sequence and the replaced sequence of chromosome 1B (Supplementary Figure  S3 ). Since WGS data are better suited for identifying introgressions than are GBS data, and WGS is much more expensive than GBS, the question arises as to what sequencing depth is needed to predict introgressions from WGS data. Hence, we used publicly available WGS data for cultivars that harbor large introgressions and varied the sequencing depth, i. e. , the number of raw reads per sample, from 0. 5 to 25 million reads. Three cultivars were selected to cover different introgression donors, and introgressions were predicted independently for each sequencing depth (Supplementary Figure  S8 ).
, 2003; Placido et al. , 2013). We are unaware of reported phenotypes for other samples with large introgressions. Detailed phenotyping of accessions with large introgressions could uncover novel beneficial loci or alleles. In addition, several accessions with introgressions in the D genome were identified, and these could increase diversity in the D genome for use in breeding programs  (Mirzaghaderi and Mason, 2019). All identified introgressions at 1-Mb resolution for each sample will now be available as a resource for breeders and scientists. Directly comparing introgressions identified from GBS and ulcWGS data for genebank accessions and cultivars, 11 samples with large differences were identified. In all 11 cases, the ulcWGS data led to the discovery of additional introgressions. The difference between the expected and observed percentage of bases covered was visible for these introgressions but was smaller than for other introgressions.
This indicated that those introgressions were more similar to the original DNA fragment. With ulcWGS, we could detect these minor differences in the percentage of bases covered because the whole genome is considered, not just a small portion, and no (methylation-sensitive) restriction enzymes are used. Our results show that the detection sensitivity depends not only on the sequence data type, sequencing depth, and introgression donor, but also on the introgression size and detection method. Alignment of short sequence reads from a genebank accession with an introgression to the reference genome sequence without this introgression reveals an unexpectedly high or low percentage of covered bases, which was used to predict introgressions. These profiles of percentage bases covered indicate that introgressions could result in increased or decreased coverage on some chromosomes, with decreased coverage reflecting the absence of the expected region.
In contrast, increased coverage may indicate that an introgression (of another region) is more similar to this genomic region. Variant calling using a reference genome sequence is based on mapped reads and could be problematic in such regions, leading to either heterozygous or incorrect variant calls or an increased number of missing values. For this reason, increased or decreased coverage may impact variant calling and all downstream analyses, including imputation, population genetics, and genome-wide association studies. Differences in introgressions identified in different plants with the same genebank accession number confirm that genebank accessions can be heterogeneous  (Keilwagen et al. , 2014; Keilwagen et al. , 2022). However,  Schulthess et al. (2022a)  only analyzed two independent SSD lines for accessions with a clear phenotypic difference.
Even for cultivars like RIEBESEL ST. 47-51, neither the accession number nor the accession name can be used to identify duplicates, as demonstrated here. For these reasons, computational methods based on single-nucleotide polymorphisms (SNPs) and genetic distances have been proposed  (Singh et al. , 2019; Cseh et al. , 2021; Sahu et al. , 2022; Schulthess et al. , 2022a). Still, these methods might have problems with introgressions leading to missing variant calls due to low coverage in the corresponding genomic regions as described above. For instance,  Schulthess et al. (2022a)  used SNP data and clustered accessions based on their proportion of pairwise difference. Using this method, they defined, for instance, cluster C2 with 766 identical genotypes. Based on our introgression analyses, this cluster contained several samples with medium-size introgressions of at least 100 Mb, comprising 12 samples with large introgressions on 1B and one with a large introgression on each of chromosomes 2A, 2B, 2D, 5A, and 5D (Supplementary Table  1 ).
These samples cannot be duplicates, and removing them from genebank collections might cause a loss of genetic diversity and potentially beneficial alleles. Hence, duplicate detection should not be based on a single SSD line per accession number. In addition, a pure SNP-based approach might miss introgressions, which might be particularly important for pre-breeding and breeding programs. For these reasons, multiple randomly selected seeds/plants from each existing accession number and each new genebank entry should be analyzed individually using a combination of introgression identification and variant calling to detect potential duplicates. The number of analyzed plants per accession will determine the detection threshold for the fraction of this accession that might be a different genotype.
Data availability statement Publicly available datasets were analyzed in this study. The datasets analyzed for this study can be found in the European Nucleotide Archive (ENA): https://www. ebi. ac. uk/ena/browser/ view/PRJEB41976 (GBS genebank collection), https:// www. ebi. ac. uk/ena/browser/view/PRJEB48738 (WGS genebank collection), https://www. ebi. ac. uk/ena/browser/view/PRJEB48988 (WGS genebank collection) and https://www. ebi. ac. uk/ena/ browser/view/SAMEA5374255 (Chinese Spring GBS). Details for the data used can be found in Material and Methods as well as in Tables  S1  and S2.
This mode is then subtracted from the function f (t) to get a new function f 1 (t) = f (t)a 1 e νt. The process is then repeated with this newly-obtained function until desired N strongest modes are obtained. This technique works very well for weakly-chaotic systems like the Solar System when variables can be decomposed into quasi-periodic modes over a sufficiently short period of time. It has been proven that this algorithm converges towards the true frequencies much faster than the classical Fast Fourier Transform  (Laskar 2005). Therefore, it is a good tool to study the chaotic diffusion of the fundamental frequencies. In this work, we used a routine naftab written in the publicly-available computer algebra software TRIP  (Gastineau & Laskar 2011) , developed at IMCCE, to apply directly the frequency analysis.
Evolution of the parameters The parameters of our fitting models are extracted by the method of least squares, implemented by the routine curve_fit in the scipy package in Python. To retrieve the statistical distribution (mean and standard deviation) of the parameters of a given model, we implement a bootstrap approach based on Eq. (  16 ), with the assumption that pointwise standard errors of the KDE estimated by MBB are independent. We remark that, with such an assumption, the variance of the fitting parameters tends to be underestimated. The time evolution of the mean of the parameters of the fitting models is shown in Fig. 10 , along with their ±2 standard error, for both past and future. It turns out that the evolution of σ 2 0 is robustly fitted from 200 Myr to 5 Gyr in the past by a power law function σ 2 0 (t) = a T b , (20) where T = t/(1 Gyr), as shown in Fig.
Newark-Hartford data In  (Olsen et al. 2019) , the astronomical frequencies were retrieved from a long and well-reserved lacustrine deposit. The frequency signals of the Neward-Hartford data are very similar to that of the astronomical solution La2010d, which was taken among 13 available astronomical solutions  (Laskar et al. 2011a). Having 120 000 astronomical solutions at hand, we can  derive a more precise statistical analysis of this result. The fundamental frequencies from the geological record are obtained in the following way. The data were dated by the relatively stable 405-kyr cycle (g 2g 5 ) and additionally verified by a zircon U-Pb-based age model  (Olsen et al. 2019). A frequency analysis (Sect. 2. 2) was performed on the geological data as well as on the eccentricity of the astronomical solution La2010d to retrieve their strongest frequencies.
The frequency analysis of geological data are cross-checked with that of astronomical solution to identify its orbital counterpart. For example, the third strongest frequency 12. 989 /yr from Newark data is identified with the second strongest frequency 12. 978 /yr from La2010d, which is g 3g 5. The frequency g 3 is then derived by summing the obtained combination with g 5 , which is nearly constant. The same goes for g 1 , g 2 and g 4. Definite values of astronomical fundamental frequencies are thus determined. Unfortunately, the uncertainty estimation of the geological frequencies is yet not available. Our density estimation of the frequencies is obtained by combining all our samples in a conservative approach specified in section 3. 4. Figure  12  compares the secular frequencies (right) and their combinations (left) extracted from NH data with our density estimation around the same time in the past.
Acknowledgements. NH is supported by the PhD scholarship of  CFM Foundation  for Research. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  ( Advanced Grant   AstroGeo-885250 ) and from the  French Agence Nationale de la Recherche (ANR)  (Grant  AstroMeso ANR-19-CE31-0002-01 ).
Here we used an Oxford-Nanopore-Technology (ONT)-based sequencing approach to obtain the S. latifolia genome, in order to study the repeat-rich Y chromosome. We also used highquality genome assemblies of closely related non-dioecious Silene species as outgroups to make inferences about the evolution of the S. latifolia sex chromosomes. We sequenced mutants with Y deletions for three sexual phenotypes (hermaphrodites and asexuals with early/intermediate and late anther development arrest) in order to pinpoint candidate sexdetermining genes/regions. Furthermore, we generated expression data at two critical stages for male and female flower development to help identify sex-determining genes.
The structure and gene content of the sex chromosomes To assemble the complex S. latifolia genome, we produced an inbred population from 17 generations of brother-sister crosses to reduce heterozygosity (see Material and Methods for the details of the sequencing, assembly and annotation methodologies). We selected one male and one female individual from this inbred population for sequencing. The male was sequenced using ONT at 100X coverage to ensure 50X coverage for both sex chromosomes (Table  S1 ). Assembly was performed using an initial hybrid long-read approach including Flye (Figure  S1 ). The ONT contigs were polished using 200X of short Illumina reads and then scaffolded using Bionano optical mapping data followed by Omni-C contact data (see Table  1  for genome statistics and Table  S2  for more details).
Funding: Agence Nationale de la Recherche (ANR)  grant  ANR-20-CE20-0015-01  (GABM,  ABe ). Fédération de Recherche "Biodiversité, Eau, Environnement, Ville & Santé" (FR BIOEENVIS) of University of Lyon 1  grant (GABM, JB) The  Czech Science Foundation  grants  21-00580S  and  22-00364S  (PJ).
Ex vivo patch-clamp recordings of IPN neurons Patch pipettes (5-8 MΩ) were stretched from borosilicate glass capillaries (G150TF-3, Warner instruments) using a pipette puller (Sutter Instruments, P-87, Novato, CA) and filled with a few microliters of an intracellular solution adjusted to pH 7. 2, containing (in mM): 116 K-gluconate, 20 HEPES, 0. 5 EGTA, 6 KCl, 2 NaCl, 4 ATP, 0. 3 GTP and 2 mg/mL biocytin. Biocytin was used to label the recorded neurons. The slice of interest was placed in the recording chamber and viewed using a white light source and a upright microscope coupled to a Dodt contrast lens (Scientifica, Uckfield, UK). Neurons were recorded from the dorsal (IPDL) and rostral (IPR) parts on the IPN. Whole-cell configuration recordings of IPN neurons were performed using an amplifier (Axoclamp 200B, Molecular Devices, Sunnyvale, CA) connected to a digitizer (Digidata 1550 LowNoise acquisition system, Molecular Devices, Sunnyvale, CA).
Signal acquisition was performed at 10 kHz, filtered with a lowpass (Bessel, 2 kHz) and collected by the acquisition software pClamp 10. 5 (Molecular Devices, Sunnyvale, CA). Nicotine tartrate (30 μM in aCSF) was locally and briefly applied (200 ms puffs) using a puff pipette (glass pipette ~3 μm diameter at the tip) positioned about 20-30 μm from the soma of the neuron. The pipette was connected to a Picospritzer (PV-800 PicoPump, World Precision Instruments) controlled with pClamp to generate transient pressure in the pipette (~2 psi). Nicotine-evoked currents were recorded in voltage-clamp mode at a membrane potential of -60 mV. All electrophysiology traces were extracted and preprocessed using Clampfit (Molecular Devices, Sunnyvale, CA) and analyzed with R.
Fundamental research prize from the Fondation Médisite for neuroscience (AM). Fourth- year PhD fellowship from  Fondation pour la Recherche Médicale  ( FDT201904008060  to RDC and  FDT20170437427  to SM). Fourth- year PhD fellowship from the  Biopsy Labex (CN). Fourth- year PhD fellowship from the  Memolife Labex  (JJ). Visualization: SM. Supervision: PF and AM. Funding acquisition: PF and AM.
Assessment of parasite mitochondrial morphology Mixed asexual blood stages cultures of a wildtype (3D7) strain and the PNPLA2-iKD line were maintained as previously described  (Trager and Jensen, 1976; Trager et al. , 1980). Parasite cultures (200 µl of compact infected RBCs) were collected by centrifugation (10 min at 1200 rpm at room temperature). Cultures were resuspended in media with or without 2. 5 mM GlcN and incubated for 48 h. These cultures were then collected by centrifugation (10 min at 1200 rpm at room temperature) and resuspended in prewarmed RPMI containing 100 µM Mitotracker ™ and incubated for 45 min. After the incubation, specimens were examined with an Axio Imager; ZEISS, ×100 magnification. Images were treated using ImageJ software.
After washing, membranes were incubated with the respective alkaline phosphatase-conjugated secondary antibody (Sigma-Aldrich) for 1 h at RT and developed in a solution of nitroblue tetrazolium chloride (NBT) and 5-brom-4-chlor-3-indoxylphosphate (BCIP; Sigma-Aldrich) for 5-30 min at RT. Blots were scanned and processed using the Adobe Photoshop CS software. Band intensities were measured using the ImageJ programme version 1. 51f.
, 2008)  in the presence of butylhydoxytluene, PC (C21:0/C21:0), C13:0 or C15:0 FA standards (Avanti). Lipid extraction, separation and analyses were all performed as previously described in  Flammersfeld et al. , 2020 (Flammersfeld et al. , 2020). FA methyl esters were detected by GC-MS (Agilent 5977A-7890B) according to the method described in  Ramakrishnan et al. (2012)   (Ramakrishnan et al. , 2012)  and quantified by Mass Hunter software (Agilent). Each FA was quantified according to a calibration curve generated with authentic fatty methyl ester standards.
Schizont cultures of the PfPNPLA2-HA-KD parasite line were treated with 2. 5 mM GlcN for 72 h to determine knockdown of PfPNPLA2. Lysates were immunoblotted with rat anti-HA antibody to detect PfPNPLA2-HA (~240 kDa); immunoblotting with rabbit anti-Pf39 antisera (39 kDa) was used as loading control (bottom panel). Quantification of PfPNPLA2-HA levels following knockdown. PfPNPLA2-HA levels were evaluated between the GlcN-treated and untreated PfPNPLA2-HA-KD parasites in three independent Western blots based on the band intensity as estimated with ImageJ software (right panel). Pf39 levels were used for normalization (set to 100%). (C) Using a mouse anti-PfPNPLA2 antibody (green) for immunolabelling, PfPNPLA2 is detectable in perinuclear regions in schizonts and gametocytes of stages II-V. Counter labeling of the asexual blood stages was done using rabbit anti-PfMSP1 antisera and gametocytes with rabbit anti-Pfs230 antisera (red).
Furthermore, previous data show the protein localizing close to the apicoplast  (Burda et al. , 2021)  leading us to the idea that this protein would have a dual function. To determine the importance of PfPNPLA2 for parasite survival, we performed growth assays upon presence or absence (+GlcN) of the protein. Agreeing with previous phenotypic scores predicted for the protein  (Nolan et al. , 2017; Singh et al. , 2019) , knocking PfPNPLA2 down in the PfPNPLA2-HA-iKD line, by treatment with 2. 5 mM GlcN, had no significant effect on intraerythrocytic replication during a 72 h period in regular in vitro culture conditions, when compared to untreated PfPNPLA2-HA-iKD parasites and both treated or untreated WT NF54 parasites (Figure  2A ). Furthermore, quantification of ring, trophozoite and schizont stages, after GlcN treatment, at different time points, did not show any major effect on parasite stage development of PfPNPLA2-HA-iKD and WT NF54 parasites (Supplementary Figure  3 ), suggesting that the lack of PfPNPLA2 does not delay the blood stage cycle.
Funding This work and CYB, YYB, SS, and NQ were supported by  Agence Nationale de la Recherche, France  (Project ApicoLipidAdapt grant  ANR-21-CE44-0010 , Project Apicolipidtraffic grant  ANR-23-CE15-0009-01 ), The  Fondation pour la Recherche Médicale  ( FRM EQU202103012700 ),  Laboratoire d'Excellence Parafrap, France  (grant  ANR-11-LABX-0024 ),  LIA-IRP CNRS Program  ( Apicolipid  project), the  Université Grenoble Alpes  ( IDEX ISP- IRGA Apicolipid ),  Région Auvergne Rhone-Alpes  for the  lipidomics analyses platform  (Grant  IRICE Project GEMELI ), the  CEFIPRA  via a  Collaborative Research Program   Grant  (Project  6003-1 ) The work was further supported by the following grants of the  Deutsche Forschungsgemeinschaft  to GP:  PR905/15-1 ,  SPP 1580 PR905/3-2 ,  SPP 2225 PR905/19-1.
Measurement of bone microarchitectural parameters To determine the microarchitecture of the femurs, we used a high-resolution X-ray Micro-CT device (Quantum FX Caliper, Life Sciences, Perkin Elmer, Waltham, MA, United States) provided by the PIPA Platform, EA2496 in Montrouge, France. The X-ray source was set at 90 V and 160 μA. Samples up to 65 mm in diameter and 200 mm in length were imaged with a Full 3D high-resolution system. Raw data were obtained by rotating both the X-ray source and the flat panel detector 360° around the sample, with a rotation step of 0. 1° and a scan time of 3 minutes. The image projections were then automatically reconstructed (RigakuSW software, Caliper) into a Dicom stack of files using standard back-projection techniques. The multiplanar reconstruction tools used in this analysis allow images to be displayed in axial orientation and allow to visualize bone microstructure.
Scans for the trabecular bone were obtained from the distal femoral growth plate. The different aspects of the trabecular bone were finally quantified at a 3D isotropic voxel size of 10x10x10 μm 3. The structural indices were evaluated using numerous parameters including the ratio of segmented bone volume to the total volume of the region of interest (BV/TV, %) and trabecular number (Tb. n, 1/mm). Trabecular thickness (Tb. Th, µm) was calculated on 3D images using CtAn® Skyscan software and a method previously described by Hildebrand and Ruegsegger  (29, 30). Cortical thickness and diameter (mm) were measured manually on the image at the mid-diaphysis level. Regarding cortical bone area (B. Ar, mm 2 ) and moment of inertia (mm 4 ), these parameters were measured at the mid-diaphysis level as previously described  [21].
Statistical analysis All results obtained in this study were expressed as mean ± SD along with the number of independent experiments. Comparison of results between the different experimental groups receiving the NP or LP diets was performed using a one-way analysis of variance (ANOVA) and a Tukey multiple comparison test. In addition, a t-Test was done to compare the Sham NP with the Sham LP. Differences were considered significant when p was less than 0. 05. All statistical analyses were performed using Prism® Version 6. 05 (GraphPad Software Inc. ).
Discussion In the present preclinical study, we propose a new model of bone fragility induced by ovariectomy combined with moderate restriction of dietary protein supplied to mice. This experimental situation aimed to mimic the clinical situation of postmenopausal women with insufficient dietary protein intake. Such a situation can also be observed in "real life", as aged women often have lower dietary protein intake when compared with younger women  [23]  in a context of increased protein requirement  [24]. Indeed, in individuals over 65 years, the dietary protein requirement is estimated to be 1. 0-1. 2 g protein per kg body weight per day  [25] , which is higher than the recommended protein intake for healthy young adults (that is 0. 8 g protein per kg body weight per day  [26] ). The data obtained clearly show that moderate protein restriction further deteriorates bone quality that is already impaired by the ovariectomy procedure.