dataesr/scientific-paragraphs-categorization huggingface dataset:
02; (iv) the low base-quality-weighted alternate-allele frequency ratio between tumor and normal samples when there was at least one read supporting alternate allele in the normal sample, (AF_ALT_tumor × BQ_ALT_tumor) / (AF_ALT_normal × BQ_ALT_normal) <4, where AF_ALT stands for the alternate allele frequency estimated from allelic depth and BQ_ALT the alternate allele base quality; or (v) the low reference base quality in either tumor or normal sample (<20 in the Phred scale). The resulting mutations were annotated using Variant Effect Predictor (v99)  65  and CADD (v1. 4, RRID:SCR_018393)  66  , including population frequency in control databases (gnomAD, RRID:SCR_014964)  67. Samples were considered to be mutationnegative if they lacked previously reported somatic variants in NF2 (or chromosome 22q loss), TRAF7, KLF4 (K409Q), AKT1 (E17K), PIK3CA, PIK3R1, AKT3 (E17K), SMO (L412F/W535L), SUFU, PRKAR1A (A17D), POLR2A (Q403K/L438_H439del), SMARCB1, or SMARCE1  11, 12, 14, 28, 68, 69.
Sample clustering. We summarized read count data per transcripts to genes using tximport R package  75  and then normalized read counts using regularized logarithm transformation (rlog) implemented in DESeq2  76. We then performed multidimensional scaling (MDS) and identified that the first dimension was highly correlated with experimental bias, namely, the logarithm of the ratio of intronic and target bases (Pearson correlation, r = -0. 98), as well as the standard deviation of normalized expression values (r = 0. 975), and the mean normalized expression values (r = -0. 923). Therefore, we removed this unwanted variation by regressing the normalized expression values onto the MDS first dimension for each gene. We used the residuals obtained as such to cluster samples. We then selected, from 60,669 genes, 14,687 (24.
We adopted the normalized lmQCM (local maximal Quasi-Clique Merger) framework  80  to construct GCN. In contrast to WGCNA framework  81  , this method allows for a gene to belong to multiple network modules. (1: module detection) We identified network modules using localMaximumQCM function implemented in lmQCM R package  80  (steps 1-12 of the algorithm described in ref. 80, see below for a correction) with default parameters for λ = 1 and t = 1, while γ = 0. 3 was determined by examining a curve of the number of modules against γ values as in Fig. 1  of ref. 80. We discard modules with size <4 after this step. (2: module reduction) Identified modules were examined for overlaps and modules with the highest overlap were merged as described below (step 13 of ref. 80). (3: module characterization) We calculated the module eigengene for each module that is the first principal component of the gene-wise standardized expression matrix of the genes in the module  81.
05. We also performed the gene set overrepresentation analysis using clusterProfiler  83  and MSigDB (msigdb_v2022. 1. Hs) for each module. We considered module members were significantly enriched for a given ontology/ pathway if Benjamini-Hochberg corrected P-value < 0. 05. Modifications to lmQCM implementation. We modified the original implementation in the localMaximumQCM function implemented in lmQCM R package  80  , which discards a local maximal edge if both nodes have been members of any of the previously identified modules even when two nodes belong to different modules, to make an edge being discarded only when both nodes belong to the same module (correctly reflecting the step 3 of ref. 80). In addition, we implemented an alternative algorithm for the overlap-based module merging step (step 13 of ref.
Quenching step lasted for 30 sec and was performed with Quenching Buffer (BU08-L, Lunaphore Technologies). Imaging step was performed with Imaging Buffer (BU09, Lunaphore Technologies) with exposure times set at 4 min for all primary antibodies and secondary antibodies at 2 min. Imaging step is performed with an integrated epifluorescent microscope at 20x magnification. Image registration was performed immediately after concluding the IF staining and imaging procedures by COMET™ Control Software. Each seqIF™ protocol resulted in a multi-stack OME-TIFF file where the imaging outputs from each cycle are stitched and aligned. COMET™ OME-TIFF file contains DAPI image, intrinsic tissue autofluorescence in TRITC and Cy5 channels, and a single fluorescent layer per marker. Markers were subsequently pseudocolored for visualization of multiplexed antibodies using the Viewer software (Lunaphore Technologies).
4 million paired reads. For meningiomas with IHH tandem duplications, data from 2 patients (MN-52323 and MN-61891) were combined, generating a total of 785. 1 million paired reads. Fragments were mapped to MboI restriction sites using a digested reference genome generated by the 'digest_genome. py' script of Hi-C Pro. Iterative correction and eigenvector decomposition (ICE) normalization were performed on all datasets with a bin size (or resolution) of 50 kb. Topologically associated domains were inferred by using SpectralTAD  38. Chromatin interaction loops were identified using FitHiChIP  37  at a 1% false discovery rate (FDR). We used H3K27ac ChIPseq peak calls from MN-52323 and MN-52407 for IHH tandem duplication and non-IHH data, respectively, as input for FitHiChIP. We adopted a "peak to all" option so that only pairs of bins in which at least one bin overlapped H3K27ac peak.
Acknowledgements The authors gratefully acknowledge the contributions of  Boris Krischek  , who contributed resources for this study but unfortunately passed away before publication. Automated multiplexed sequential immunofluorescence was enabled through a gracious gift from the  Stephen M Coffman Charitable Trust  to the  Malnati Brain Tumor Institute of the Lurie Cancer Center at Northwestern University. We thank the  Onconeurotek tumor bank (Brain Institute, Paris ). This study was supported by  Gregory M. Kiez  and  Mehmet Kutman Foundation  and  Yale School of Medicine  funds. M. W. Y and V. E. C. were supported by  NIH   Medical Scientist Training Program  Grant  T32GM007205 , as well as  NIH/ NCI   NRSA F30CA213666  and  F30CA183530-03  respectively. C. L. was supported by the  GuangDong Basic and Applied Basic Research Foundation  ( 2021A1515111051 ).
Data availability The publicly available RNA-seq and ChIP-seq data used in this study are available in the Gene Expression Omnibus database under accession code GSE85135  14. The de-identified sequencing datasets generated as a part of this study, including RNA sequencing, whole-exome sequencing (new drivers), H3K27ac ChIP-seq and H3K27ac Hi-ChIP, are deposited in the European Nucleotide Archive, under accession code PRJEB55424. The remaining data are available within the Article, Supplementary Information, Supplementary Data or Source Data file. Source data are provided with this paper.
Genetic analyses Dried plant material was obtained from a total of 193 individuals initially identified as T. glomerata (60 specimens), T. hispanica (15 specimens), T. nidifica (51 specimens), T. salina (50 specimens), T. normaniana (9 specimens), and 13 morphologically ambiguous Tolypella sp. Genetic data for the atpB, psbC and rbcL plastid genes presented in this study were obtained by three different working groups: A) the University of Rostock, B) the New York Botanical Garden and C) the Norwegian Institute for Water Research by the following methods. Method A) Genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany), following the manufacturer's protocol. Amplification of the plastid genes rbcL, psbC, and atpB was performed with 10 PCR cycles with one minute each of annealing at 94°C, extension at 55°C, and denaturation at 72°C, followed by one minute each for denaturation (94°C), annealing (52°C), and polymerisation (72°C) in 25 cycles.
3. 1 cycle sequencing kit (Biosystems, Applied Biosystems, Thermo Fisher Scientific Oslo, Norway) according to the manufacturer's instructions. Complete sequences of all three plastid genes could not be generated for every sample analyzed due to the differing qualities of the specimens (age, storing conditions, drying conditions after collection, etc. ). Therefore, four different datasets were used for the phylogenetic analyses in order to obtain as much information as possible for all specimens. The first dataset included the three plastid gene sequences from 88 individuals, whereas three additional data sets were compiled for each plastid gene separately; the atpB dataset with a total of 1034 positions for 94 samples, the psbC dataset with a total of 1104 positions for 125 samples, and the rbcL dataset with a total of 1265 positions for 189 samples.
As outgroup, which is defined as closely related taxon or group outside of the taxon investigated, two different sequences of T. porteri were used. In order to estimate evolutionary divergence, pair-wise uncorrected p-distances and the number of substitutions were conducted using MEGA version7  (Kumar et al. , 2016). To uncover phylogenetic relationships, Bayesian inference (BI) and maximum likelihood (ML) trees were constructed, with evolutionary substitution models evaluated in MEGA v. 7. The method selected the same best-fitting evolutionary model (GTR +G+I) for each of the four datasets. The ML algorithm was conducted in MEGA v. 7 with 1000 bootstrap replicates. BI trees were performed with MrBayes 3. 2. 6 (e. g. Ronquist and Huelsenbeck, 2003)  with a random starting tree and two independent runs of one cold and three heated chains, each using default parameters.
Oospore analyses The terminology of oospore characters in this study is based on Soulie ́-Märsche and Garcı ́a (2015). Descriptions of membrane ornamentation follow those of  Frame (1977)  and  Urbaniak et al. (2012). Altogether, 712 mature oospores were harvested from herbarium specimens, fresh plant material and sediment samples. Oospores were collected from 12 specimens of T. glomerata, 19 of T. nidifica, 18 of T. salina and 10 of T. sp. The individual numbers of oospores examined and the appropriate pre-treatments are given in Table  S2. Oospores were stored in well plates for eventual reexamination and are part of the Rostocker oospore database  (Holzhausen et al. , 2015). For stereomicroscopic analysis, oospores were photographed in lateral, apical, and basal views with a mounted camera.
Genetic analyses The phylogenetic analyses of the plastid gene sequences in each dataset recovered the 196 Tolypella individuals into five general clades that were denoted as 'T. glomerata', 'T. nidifica/salina', 'Tolypella sp. ', and two distinct 'T. hispanica' clades ('T. hispanica I' and 'T. hispanica II'; Figure  1 ; Table  S4 ). Supporting values for each cluster were given in the sections below. The gene sequence similarities of Tolypella individuals within each group were generally over 99. 8% (Table  1 ). However, support for their phylogenetic placements were unresolved or weakly to moderately supported in the single gene analyses. Phylogenetic resolution and support were greatest in the three-gene analyses. The results of the network analyses were comparable to the phylogenetic trees, with the same clusters recovered in both approaches (Figures 1, 2; Table  S1 ; Figures  S1-S3 ).
With an average of about 3. 85 bp substitutions, the genetic variability within the group was relatively high compared to the intragroup variability shown by the other taxa. However, differences in their sequence data were not regionally correlated; European and North American specimens showed identical sequences. In contrast, 8 substitutions were observed between samples collected in Greece (GR02-UW22) and Italy (RB13_16-1 and RB12_14-1). 'T. nidifica/salina' A second cluster (labelled 'T. nidifica/salina') consisted of 110 individuals which have traditionally been assigned mainly to the taxa T. nidifica (44 specimens) and T. salina (50 specimens). Additionally, two T. normaniana, and 14 morphologically ambiguous T. sp. were found in this cluster (Table  S4 ). They were collected in Austria, France, Germany, Greece, Italy, Norway and Sweden.
Interestingly, the specimen originally determined as T. glomerata f. littorea from France (KGK4867) and two of nine sequenced T. normaniana (T70/T71) clustered within T. nidifica/ salina. Sequence data for each of the atpB, psbC and rbcL genes could not be obtained for every specimen in this cluster. rbcL sequence data was obtained from 188 specimens, whereas sequences for atpB and psbC were obtained from 38 specimens (Table  S4 ). Overall, however, there was little genetic variation within the 'T. nidifica/salina' cluster when comparing each of the gene sequences. Depending on the dataset, between 95. 2 and 97. 6% of the analysed specimens had identical sequences. Minor genetic differences were observed in this group; two T. sp. collected from Italy (rbcL, Figure  S1 ), and a T. normaniana from Norway (psbC, Figure  S3 ) differed by a single nucleotide substitution each.
Regional differences were not reflected in the sequence data with identical haplotypes throughout Europe. Consistent nucleotide differences were found only among two T. sp. collected in Austria (AU03-Tg01, AU03-Tg03). 'Tolypella sp. ' A third cluster (labelled 'Tolypella sp. ') consisted of 21 individuals which have been classified as morphologically ambiguous specimens due to the presence of vegetative characters of more than one Tolypella taxon mentioned above. The specimens were partly originally determined as T. glomerata f. humilor. They were collected in Denmark, France, Germany, Great Britain, Greece, Italy, Norway, Portugal, and Sweden. 'Tolypella sp. ' revealed unique sequence data and differed from 'T. nidifica/ salina' and 'T. glomerata' by averaging 19. 4 and 27. 9 bp substitutions respectively (Table  1 ).
nidifica and T. salina. But these are mainly caused by local separations. However, Italian T. nidifica did not differ from French T. salina or Italian/ French T. sp. This is in strong accordance with the results of sequence data. Differences in oospore ornamentation patterns were not reflected by sequence data of plastid genes (Table  S2 ). Tolypella hispanica is the only dioecious taxon in the section Tolypella and, by including sexuality as a taxonomically relevant parameter at species level, easily distinguished from all other European taxa. Several studies demonstrated that sex separation occurred independently in various groups of the Characeae  (Proctor, 1980; Meiers et al. , 1999; Pe ́rez et al. , 2016). Phylogenetic data of this study revealed that this taxon consists of two well-separated lineages, representing two cryptic taxa.
Acknowledgments The authors are thankful for the constructive suggestions of the two reviewer,  Maria A. Rodrigo  and  Hidetoshi Sakayama , and the handling editor  S. Pasta. The authors want to thank  Peter Gisch  from the  Biological Station Neusiedler See  for the provision of physico-chemical data. They are also grateful to  Didier Desmots  from the  National Natural Reserve of the Müllembourg salt marshes, Isle of Noirmoutier ,  Barbara Tuner  and  Karl-Georg Bernhardt  for collecting specimens and environmental data. Moreover, the authors are thankful to  Claudia Lott  ( University of Rostock ) for preparing genetic analyses in Rostock and  Arne Schoor  for laboratory and technical assistance. The laboratory equipment was partly supported by the  European Fund for Regional Development (EFRD).
These clusters usually contain genes encoding one or two key enzymes that generate the backbone of the molecule and a varying number of genes encoding accessory enzymes that decorate the initial molecule. Genes coding for transporters or transcription factors can also form part of the BGC  (Keller, 2019). This colocalization in the genome facilitates the identification of BGCs, and several bioinformatic tools and databases (e. g. SMURF, antiSmash, MIBiG, CusProSe) have been developed to mine and annotate the ever-increasing number of sequenced fungal genomes released in public databases  (Blin et al. , 2021; Khaldi et al. , 2010; Oliveira et al. , 2023; Terlouw et al. , 2023). Analysis with these tools has shown that a single fungal genome can contain more than 80 non-redundant BGCs  (Han et al.
Comparative analyses of the biosynthetic gene cluster 16 The protein sequences of the genes belonging to the BGC16 of C. higginsianum IMI 349063 (CH63R_05468 to CH63R_05483) as predicted in  Dallery et al. (2017)  were used to query the NCBI nr database using cblaster v1. 3. 9  (Gilchrist et al. , 2021)  with the search module and default parameters but limiting the investigations to fungi with -eq "txid4751[orgn]" for the Colletochlorin part of BGC16, or limiting the investigations to Colletotrichum spp. with the parameter -eq "txid5455[orgn]" for the entire BGC16. The BGCs identified were retrieved with the module extract_clusters and used as input for clinker software v0. 0. 23 using default parameters  (Gilchrist and Chooi, 2021). Manual editing of the cartoons was performed to represent the contig ends where appropriate.
Cloning of reporter genes and biosynthetic genes The gene predictions of CH63R_05468 to CH63R_05471 were manually inspected for correctness using RNA-Seq datasets previously published  (O'Connell et al. , 2012). The CDS of CH63R_05468 appeared to be composed of six exons instead of only five in the initial prediction. Each gene was then synthesized without stop codons and with optimization of the codons for S. cerevisiae and exclusion of common restriction enzyme sites using the GenSmart™ Codon Optimization tool (Genscript Biotech B. V. , Netherlands). The optimized sequences were deposited at Genbank with the following accession numbers: OR389982, OR389983, OR389984 and OR389985. Each coding sequence was individually cloned into the EcoRV linearized pHYX137 and subsequently assembled together following the strategy described by  Hoefgen et al.
Data processing of untargeted metabolomic data The. d data files (Bruker Daltonics) were converted to. mzXML format using the MSConvert software (ProteoWizard package 3. 0;  Chambers et al. , 2012). MzXML data processing, mass detection, chromatogram building, deconvolution, samples alignment and data export were performed using MZmine-2. 37 software (http://mzmine. github. io/) for negative data files. The ADAP chromatogram builder  (Myers et al. , 2017)  method was used with a minimum group size of scan 9, a group intensity threshold of 1000, a minimum highest intensity of 1000 and m/z tolerance of 10 ppm. Deconvolution was performed with the ADAP wavelets algorithm using the following setting: S/N threshold 10, peak duration range = 0. 01-2 min RT wavelet range 0. 02-0. 2 min, MS2 scan were paired using a m/z tolerance range of 0.
05 Da and RT tolerance of 0. 5 min. Then, isotopic peak grouper algorithm was used with a m/z tolerance of 10 ppm and RT tolerance of 0. 1min. All the peaks were filtered using a feature list row filter keeping only peaks with MS2 scan. The alignment of samples was performed using the join aligner with an m/z tolerance of 10 ppm, a weight for m/z and RT at 1, a retention time tolerance of 0. 2 min. Metabolites accumulation was normalized according to the weight of dried extract for the relative quantification. Molecular networks were generated with MetGem software  (Olivon et al. , 2018 ; https://metgem. github. io) using the. mgf and. csv files obtained with MZmine2 analysis. The molecular network of ESIdatasets was generated using cosine score thresholds of 0. 60.
Metabolite annotation of untargeted metabolomic data Metabolite annotation was performed in three consecutive steps. First, the obtained RT and m/z data of each feature were compared with our library containing the 6 standards based on their RT and m/z. Second, the ESI-metabolomic data used for molecular network analyses were searched against the available MS 2 spectral libraries (Massbank NA, GNPS Public Spectral Library, NIST14 Tandem, NIH Natural Product and MS-Dial), with absolute m/z tolerance of 0. 02, 4 minimum matched peaks and minimal cosine score of 0. 60. Third, not-annotated metabolites that belong to molecular network clusters containing annotated metabolites from steps 1 and 2 were assigned to the same chemical family and annotation was carried out on the basis of MS/MS spectrum comparisons.
Selection of a biosynthetic pathway to test the expression system Previously, we isolated several members of the Colletochlorin family of secondary metabolites from C. higginsianum and we proposed a hypothetical biosynthetic pathway based on the isolated molecules and the plausible expected enzymatic activities  (Dallery et al. , 2019). In order to identify the biosynthetic gene cluster (BGC) responsible for producing Colletochlorins, we looked for putative halogenases (InterPro signature IPR006905) in the C. higginsianum IMI 349063 genome. The only BGC with an IPR006905 signature was BGC16, comprising genes CH63R_05468 to CH63R_05483 in the original prediction (Dallery et al. ,   2020 ) reported the heterologous production of Higginsianins as well as other decalin-containing diterpenoid pyrones by expressing 8 of the 16 genes in BGC16.
Acknowledgments This work has benefited from the support of  IJPB 's " Plant Observatory -Chemistry and Metabolism " platform. The authors would like to thank  Axel A. Brakhage  and  Maria Stroe  ( Hans Knoll Institute, Jena, Germany ),  Nancy DaSilva  ( UC Irvine, California ) and  Verena Siewers  ( Chalmers Univ. Of Technology, Gothenburg, Sweden ) for kindly providing the pV2A-T plasmid, the BJ5464-NpgA strain and the XI-3-pCfb2904-bccpr1 plasmid, respectively. The EasyClone-MarkerFree Vector Set was a gift from  Irina Borodina  ( Addgene  kit # 1000000098 ).
Purpose: To quantify the effect of meniscoplasty suture-saucerization on volume and surface coverage of lateral discoid menisci. Methods: This retrospective study included all consecutive 10 patients treated between 2014 and 2019 who had magnetic resonance imaging before and after surgery and 15 controls. The MITK 3M3 semiautomatic software was used to segment the meniscus and cartilage before and after surgery to measure the percentage of meniscus coverage on the tibial cartilage. Results are compared to control patients without knee pathology matched on sex and age with Student t test. Results: Discoid meniscus surface and volume before surgery were respectively 597 mm 2 (range, 550-887 mm 2 ) and 2,822 mm 3 (1,571-3,407 mm 3 ), representing 74. 5% (56%-89%) of the tibial cartilage surface.
Meniscus Segmentation The MITK 3M3 semiautomatic software allows one to measure a surface and volume of structures by segmentation (lateral tibial cartilage and lateral meniscus). Manual highlighting of structures of interest was needed on 10 to 15 slices (Fig 1C ), allowing the calculation of cartilage coverage, surface, and volume (Fig  1B ). To quantify inter-and intraobserver (orthopaedic surgeon) reproducibility, a second set of measurements was performed 1 year after the first one in the pathologic group and quantified using the k coefficient.
This may explain the moderate intra-and interobserver reproducibility regarding the treated meniscus, where suture leads to segmentation imprecisions. MITK 3M3 software is simple and free to use in all settings. It is less reproducible regarding estimation of meniscal volume than other parameters. Other teams used deep learning methods or machine learning methods to segment the meniscus in adult patients with interesting results. [23] [24] [25]  Presurgical meniscus coverage for the 2 cases of meniscus luxation was probably underestimated. Indeed, when the meniscus moves behind, its projected surface is sometimes out of the corresponding tibial surface (Supplementary Fig  2). This shows the importance of articular position during MRI. To be extremely reliable, the MRI would need to be performed in a position where the meniscus is not in luxation (hyperflexion for posterior disinsertion and hyperextension for anterior disinsertion).