dataesr/scientific-paragraphs-categorization huggingface dataset:
For the Connolly surface generation, the calculations used 240 dots and a probe of 0. 4 nm (SI Figure30). BDQ was first simulated with the mycobacterial membrane alone to confirm association with the membrane (SI Figures2,31). The Mtb a-and c-subunits of F-ATPase, BDQ's target in the mycobacterial membrane, was modelled using SwissModel (56)  based on a structure from Mycobacterium smegmatis (25)  (PDB: 7JGC, Figure 4C and SI Figures 32A.
(E) Density of the phosphates (orange) and BDQ over the course of the simulations where the antibiotic started in either the periplasmic leaflet (green) or the cytoplasmic leaflet (blue). (F) Snapshot of a single simulation containing a Mtb ATPase model and 8 x BDQ models showing the main positions BDQ occupied. Phosphates are shown in orange, BDQ shown in blue, c-subunits are shown in grey, and the a-subunit is shown in cyan. The lipid sticks are shown in the colors illustrated in Figure 1A. (G) Comparison of the highest occupancy sites identified with PyLipID (surface) and BDQ from the Cryo-EM structure (PDB: 7JGC) (sticks). (H) PMF of BDQ moving through a mycobacterial plasma membrane with the error shown in grey. A schematic of the PMF is shown as an insert.
Acknowledgements C. M. B. is supported by an  MRC studentship  ( MR/N014294/1 ). R. A. C. is funded by  Wellcome  ( 208361/Z/17/Z ). Research in P. J. S. 's lab is funded by  Wellcome  ( 208361/Z/17/Z ), the  MRC  ( MR/S009213/1 ) and  BBSRC  ( BB/P01948X/1 ,  BB/R002517/1  and  BB/S003339/1 ). M. C. is supported by the  CNRS-MITI  grant "Modélisation du vivant" 2020. W. I. is funded by  NSF  ( MCB-2111728 ). E. F is a  Sir Henry Dale Fellow jointly  funded by the  Wellcome Trust and Royal Society  ( 104193/Z/14/Z  and  104193/Z/14/B ). M. G. would like to acknowledge the.
This implied that the order of WGD events happened in these species was carrot, celery, coriander, and Angelica which was also consistent with the previous 4dTV analysis. Comprehensive comparison between A. sinensis (QH) and A. sinensis (GS) genomes. A total of 41,040 high-confidence genes were predicted, which is 2,163 fewer than the published genome annotation of 43,202 genes. To evaluate the integrity of the gene set, both gene sets were first compared using the same BUSCO version and parameters. A proportion of complete genes of 96. 41% was found in A. sinensis (QH), while A. sinensis (GS) had only 88. 10%. Second, common databases, including the InterproScan  33  , Gene Ontology (GO)  34  , KEGG  35  , SwissProt  36  , TrEMBL, KOG, and nonredundant protein NCBI databases, were used to functionally annotate these two gene sets.
Raw reads were preprocessed to obtain clean data using SOAPnuke 55 (version 1. 5. 6). Kmer analysis method was used for A. sinensis genome size estimation. Kmerfreq from GCE v1. 0. 2  56  were adopted to obtain the 17-mer distribution from clean NGS reads with the parameter of -k 17 -L300. The output file was calculated and visualized using line_diagram. pl in kmerfreq package to estimate the genome size and heterozygosity rate. Genome assembly and evaluation. The draft genome was assembled using the raw reads generated by the PacBio and Illumina sequencing platform. First, PacBio long reads were corrected and the genome framework was assembled using the NextDenovo (version 2. 3. 0) with default parameters. Primary contigs were polished with illumine short reads using NextPolish 57 (version 1.
3. 0) to correct any remaining errors, yielding a final draft genome assembly. To generate a chromosome-level genome assembly, Hi-C technology was used to anchor the contigs onto chromosomes. First, the high-quality paired-end Hi-C sequencing reads were mapped to A. sinensis draft genome by BWA 58 (version 0. 7. 17). Then, HiC-Pro  59  was used for repetitive reading removal, classification, and quality assessment. Raw counts of Hi-C links were aggregated and separately by using Juicer 60 and 3D-DNA  61. Finally, based on the threshold of contact frequency, the contigs were clustered into 11 pseudo-chromosome linkage groups and the results were carefully manual checked using the Juicebox 62 (version 1. 11. 08). To evaluate the consistency and nucleotide accuracy of the genome assembly, The short reads were mapped to the assembled genome using BWA  58  software.
Genome annotation Transposable element annotation. Repetitive elements in the A. sinensis genome were identified using both homology alignment and ab initio prediction methods. De novo libraries of repetitive elements were constructed using LTR_Finder 66 , PILER  67  , and RepeatScout  68. These libraries, combined with the Repbase 69 database, were used to search for repeat sequences in the genome using RepeatMasker  70. Tandem repeats were predicted using Tandem Repeat Finder (TRF). Protein-coding gene prediction and gene functional annotation. Three methods were combined for protein-coding gene prediction in A. sinensis: homology-based, transcriptome-assisted, and ab initio prediction. For homology-based prediction, annotated protein sequences from Arabidopsis thaliana, Coriandrum sativum, Apium graveolens, Daucus carota, Glycine max, Helianthus annuus, Lactuca sativa, Oryza sativa, Solanum lycopersicum, and Vitis vinifera were mapped to the A.
sinensis genome using TBLASTN (v2. 2. 29+) with an E-value cutoff of 1e-5. BLAST hits were concatenated using Solar software (v0. 9. 6) and genomic regions corresponding to 1000 bp upstream and downstream of each candidate gene were extracted for gene structure prediction using Genewise  71 (v2. 4. 1). For transcriptome-assisted prediction, RNA-seq data from 25 samples were mapped to the assembled genome using HISAT2 72 (version 2. 21) and potential transcripts were assembled using StringTie 73 (version 1. 3. 3b). Transcriptome proteins were predicted using Transdecoder (version 5. 5. 0). For ab initio prediction, AUGUSTUS 74 (version 3. 2. 1), SNAP  75  , and glimmerHMM  76  were used on the repeat-masked genome with specific parameters obtained by training with homology-based gene sets. All gene models were integrated into a consensus set using EvidenceModeler 77 (version 1.
1. 1) with weights assigned to each type of evidence. Functions of predicted proteins were obtained by BLAST against InterproScan 33 , Gene Ontology (GO)  34  , Kyoto Encyclopedia of Genes and Genomes (KEGG)  35  , SwissProt  36  , TrEMBL, KOG, and non-redundant protein NCBI databases. Non-coding RNAs. Canonical small non-coding RNAs, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), were identified in the A. sinensis genome using BLAST against various RNA libraries. tRNAs were predicted using tRNA scan-SE  78  (version 1. 3. 1) while snRNAs and microRNAs (miRNAs) were identified by alignment with BLAST followed by searching with INFERNAL 79 (version 1. 1. 2) against the the Rfam database  80  (v12. 0). Distinguish of high confidence and low confidence genes.
The high confidence (HC) gene set was supported by homology with gene sets from Lactuca sativa, Apium graveolens, Daucus carota, Helianthus annuus, Solanum lycopersicum, Glycine max, Arabidopsis thaliana, Vitis vinifera, and Oryza sativa (identity ≥40% and coverage ≥50%) or by transcriptome data (FPKM ≥ 1 in 25 sequenced RNA samples). HC genes had complete open reading frames (ORFs) and lacked transposable element (TE) sequences. In contrast, the low confidence (LC) gene set was not supported by homology or transcriptome data and may have contained TEs. A total of 41,040 genes were identified as HC while 27,767 were identified as LC. BUSCO analysis showed that the completeness of the whole gene set was 96. 41%, with the HC gene set at 95. 71% and the LC gene set at 0. 81%, indicating the high quality of the HC gene set.
Transcriptome sequencing and gene differential expression analysis. RNeasy Mini Kits (QIAGEN Sciences Inc. , USA) were used to isolate total RNA from the tissues according to the manufacturer's instructions. The main steps were as follows: (a) total RNA sample extraction; (b) library construction; and (c) sequencing and bioinformatics. The raw reads were filtered by SOAPnuke 55 (version 1. 5. 6) and then aligned to the genome using Bowtie2  81. Gene expression was quantified using RESM  82  and gene differential expression analysis was conducted using the DESeq2 package  83. The functional enrichment analyses of Gene Ontology and KEGG pathway data were carried out by in-house Perl scripts. Gene families. The protein sequences of A. sinensis (QH) and other six plant representative species, including A.
The ML tree was estimated using IQ-TREE v. 1. 5. 5  93  under the WAG model, and supports were evaluated with ultrafast bootstrapping testing (1000 replicates)  94. Gene trees support ratios for this ML tree were obtained using ASTRAL v. 5. 5. 9 with parameters setting: -t 1  95. For the coalescent approach, single ML gene trees were inferred using RAxML with the PROTCATWAG model. The species tree was inferred using ASTRAL with parameters setting: -t 1 -gene-only, and branch lengths for this tree were obtained using IQ-TREE with the same settings as with the "supermatrix" approach. Divergence time estimation. Divergence times for each orthologous gene were estimated using Bayesian inference in MCMCTree v. 4. 9j package  96  with the uncorrelated relaxed clock model (clock = 2). The ML estimates of the branch lengths were calculated using BASEML using the Empirical+F protein substitution model.
The prior on the overall substitution rate across loci (μ) was set to G (1, 20), and the prior for the degree of rate variation across branches (σ 2 ) was set to G (1, 4. 5). The time unit was set to 100 Ma, and the parameters for the birth-death process were set as λ = μ = 1 and r = 0. The MCMC analyses were run for 1 million generations sampled every 100 generations after a burn-in of 50 000 iterations. The chain convergence was assessed by running MCMC analyses twice, and the effective sample size of all parameters was confirmed to be >200 using Tracer v. 1. 7 (http://tree. bio. ed. ac. uk/software/tracer/). Three fossil calibration points were used: the divergence time of V. vinifera and A. thaliana is 107 million years ago to 135 million years ago, the divergence time of S. lycopersicum and A.
thaliana is 111 million years ago to 131 million years ago, the divergence time of S. lycopersicum and D. carota is 95 million years ago to 106 million years ago. Finally, all orthologous gene trees were integrated using DensiTree. v2. 2. 7 software  97. Whole genome comparisons and gene family analysis. Wholegenome comparisons, visualization of the alignments and genetic variation identification were performed using MUMmer 98 (version 4. 0). Protein sequences from 6 species were selected for gene family analysis: P. notoginseng, D. carota, A. graveolens, C. sativum, A. sinensis (GS), and A. sinensis (QH). We used P. notoginseng as the outgroup and clustered the genes with OrthoFinder 37 v2. 5 (default parameters). We extracted 3189 single-copy genes and used them to construct a phylogenetic tree.
To analyze the evolution of gene families, CAFE  85  was used to calculate the number of expansions and contractions of gene families. Gene collinearity was detected using MCScanX 86 and visualized using NGenome-Syn v1. 41  99. Transcription factor (TF) genes were identified by the iTAK 21 pipeline. Identification of genes involved in coumarin, lignan, phthalide, and terpenoid biosynthesis. The seed sequences used for orthologous gene identification were collected and are listed in Supplementary Data 5 and Tables  S14  and S15. The protein sequences of A. thaliana, P. notoginseng, carrot, celery, coriander, A. sinensis (GS), and A. sinensis (QH) were used to perform BLASTP v2. 11. 6 (E-value < 1e-50) analyses to identify orthologous genes. Multiple sequence alignment was performed using muscle 100 , and pruning was performed using trimal 101 (-gt 0.
6. 3 software (AB Sciex). The self-built database MWDB (Metware Biotechnology Co. , Ltd. , Wuhan, China) and the public database of metabolite information, as well as secondorder spectra, were used for the qualitative analysis of the primary and secondary mass spectrometry data. Metabolite quantification was achieved using data from QQQ mass spectrometry in multiple reaction monitoring (MRM) mode. Raw mass spectrometry data was processed using Analyst 1. 6. 3 software (AB Sciex, Framingham, MA, USA) and the IntelliQuan algorithm  104. The function of identified metabolites was annotated using the KEGG database  35. VIP (Variable Importance in Projection) values were calculated using R package MetaboAnalystR 105. Significantly regulated metabolites between different groups were determined by the parameters of VIP ≥1 and Log2FC (fold change) ≥1.
Acknowledgements The work was financially supported by the  QingHai Science and Technology Department  ( 2020-SF-132 ),  Science, Technology and Innovation Commission of Shenzhen Municipality of China  under grant NO. JCYJ20180507183534578  and special funds for  Science, Technology, Innovation and Industrial Development of Shenzhen Dapeng New District  (Grant No. PT202101-28 ). The funding agencies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Data availability The supporting data for this study, along with the genome assembly and annotations, have been deposited in the CNGB Sequence Archive (CNSA) 108 of the China National GeneBank DataBase (CNGBdb) under accession number CNP0003198. The raw sequence data have also been deposited in the Genome Sequence Archive 109 in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA013096). These data are publicly accessible at the respective databases. All other data are available from the corresponding author on reasonable request. The source data for graphs and charts in the main figures can be found in the Supplementary Data 6 file.
g. ,  Dreger & Helmberger, 1993). The structural model used here is based on fitting fundamental mode dispersion of Rayleigh and Love waves  (Beghein et al. , 2022) , although our results are similar if we use the model of  Kim, Stähler, et al. (2022) , which fits both fundamental mode and overtone data (Figure  S7  in Supporting Information S1). Although using models constructed from surface wave dispersion enables accurate fitting of Rayleigh and Love waveforms, the model does not incorporate constraints on crustal layering below InSight. A structural model based on joint inversion of surface wave dispersion and receiver functions could potentially improve moment tensor estimations because waveform predictions would include converted and reflected body wave phases from discontinuities below InSight.
Acknowledgments Our moment tensor inversion was based on a modified version of the MTUQ code package (https://github. com/uafgeotools/ mtuq), and we thank the developers for making this tool openly available. Seismic data from the S1222a event, and other marsquakes, are available for download through the IRIS Data Management Center. This paper is InSight contribution number 290. The authors acknowledge the  NASA , the  CNES , their partner agencies and Institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS , ETHZ, IC, and  MPS-MPG ) and the flight operations team at  JPL ,  SISMOC ,  MSDS ,  IRIS-DMC , and  PDS  for providing the SEED SEIS data. D. K. acknowledges support from the  ETH+  funding scheme ( ETH+02 19-1 : " Planet Mars "). N. S. and V. L. were supported by  NASA  Grant  80NSSC18K1628.
C. B. and J. L. were funded by  NASA InSight PSP  Grant  80NS-SC18K1679. P. L acknowledges support from  CNES  and  ANR  ( ANR-19-CE31-0008-08 MAGIS ,  ANR-18-IDEX-0001 ). JCEI acknowledges support from  UKSA  Grant  ST/ W002515/1. We thank  Göran Ekström  and  Julien Thurin  for their thorough and constructive reviews that helped improve this manuscript.
This fact has left the question of the existence of common molecular regulatory mechanisms and targets in the diverse MNDs un-answered. A major difficulty in comparing available transcriptomic studies is that the datasets were obtained from heterogeneous, and often late-stage or post-mortem samples. This prevents robust comparisons and the identification of direct vs. indirect targets. Another challenge associated with the identification of relevant regulated mRNAs is that SMN, FUS and TDP-43 are multifunctional and may exhibit distinct sets of target RNAs in the nucleus and the cytoplasm, raising the need for compartment-specific studies. To overcome previous limitations and unambiguously assess the existence of transcripts commonly regulated by SMN, FUS and TDP-43, we decided in this study to systematically identify the direct and indirect neuronal RNA targets of these proteins.
RNA-seq data analysis Following quality assessment using FastQC version 0. 11. 5 (https:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/), all raw sequencing data was processed with in-house perl scripts to filter out reads with unknown nucleotides, homopolymers with length ≥ 50 nt or an average Phred score < 30, and trim the first 10 nucleotides  [45]  Remaining reads were aligned to the BDGP D. melanogaster Release 6 genome assembly build  [46]  using the STAR aligner version 2. 5. 0  [47]  with the following options: -outFilterType BySJout -alignSJover-hangMin 8 -alignSJDBoverhangMin 5 -alignIntron-Max 100,000 -outSAMtype BAM SortedByCoordinate -twopassMode Basic -outFilterScoreMinOverLread 0 -outFilterMatchNminOverLread 0 -outFilterMatchNmin 0 -outFilterMultimapNmax 1 -limitBAMsortRAM 10000000000 -quantMode GeneCounts.
Gene counts were determined using the htseq-count function from HTseq (version 0. 9. 1) in union mode and discarding low quality score alignments (-a 10), using the Flybase R6. 19 annotation of gene models for genome assembly BDGP6. For RIP-seq data analysis, gene counts were normalized and tested for DE using the DESeq2  [48]  package of the Bioconductor project  [49] , following removal of genes with less than 10 counts. mRNAs associated with each protein were identified by performing a differential expression analysis (DEA) for each condition vs the corresponding control pull-down. Transcripts with a positive log2 FC and an adjusted p value for DEA lower that 0. 05 were considered to be bound by the target protein. DEA for RNA-Seq gene counts was performed with the limma Bioconductor package  [50]  using the voom method  [51]  to convert the read-counts to log2-cpm, with associated weights, for linear modelling.
The design formula (~ hormone + Cond, where hormone = treated or non-treated and Cond = Caz, Smn or TBPH RNAi) was used to consider hormone treatment as a batch effect. Differential gene expression analysis was performed by comparing RNAi samples for each target protein to control (always early RNAi) samples. Genes showing up or down-regulation with an adjusted p value < 0. 05 were considered to be differentially expressed. Altered splicing analysis (ASA) was performed on the RNA-seq aligned data using rMATS version 4. 0. 2  [52]  with flags -t paired -nthread 10 -readLength 66 -libType fr-firststrand. For the purpose of the downstream analysis, the union of all genes showing any kind of altered splicing using the junction count and exon count (JCEC) analysis with a FDR < 0. 05 in the comparison between each target gene RNAi versus control RNAi was compiled as a single dataset.
Normalized RNA-Seq data of adult fly brain tissue was retrieved from FlyAtlas2 database in November 2020 (www. flyat las2. org;  [53] ). Neuronal transcripts were filtered applying an expression threshold of > 1 FPKM (Fragments Per Kilobase per Million). This gene set was then used to filter the final gene lists from RIP-seq, DEA and ASA. The full universe of 8,921 neuronal genes is annotated in Supplementary Data 5. Clustering analysis was performed using the heatmap function from ggplot2 R package (default parameters)  [54]  and correlation plots were generated using lattice R package. Intersection analyses of RNA-Seq and RIP-seq datasets were performed using UpSetR and SuperExact-Test R packages  [55].
Network analysis and generation of the library of functional modules Drosophila physical Protein-Protein Interaction (PPI) data was retrieved from APID repository (http:// apid. dep. usal. es;  [56, 57]  in December 2019. The original unspecific network was filtered to include only interactions between proteins expressed in adult fly brain tissue as described in previous section. The neuronal network was then simplified to remove self-loops and isolated proteins using the igraph R package  [58]. Bioconductor GOfuncR R package  [59]  was used to evaluate the functional enrichment of brain network as compared to the unspecific network-Gene Ontology Biological Process, hyper-geometric test, FDR = 0. 1 on 1000 randomizations. Finally, the functional modules were defined by selecting groups of physically interacting proteins annotated under the same enriched term.
For this purpose, cellular fractionations were performed prior to independent anti-GFP immunoprecipitations, thus generating paired nuclear and cytoplasmic samples (Fig. 1A ). As shown in Fig. 1B , relatively pure nuclear and cytoplasmic fractions were obtained from head lysates and GFP-tagged proteins could be efficiently immuno-precipitated from each fraction. For each paired nuclear and cytoplasmic pull-down, co-precipitated RNAs were extracted and used to prepare mRNA-seq libraries. Pull-downs from flies expressing GFP were used as control. Three independent replicate datasets were generated for each protein, except for GFP-Caz, for which one nuclear pull-down sample did not pass quality control for library generation. The raw sequencing dataset, composed of 23 libraries containing between 17.
7 and 64. 6 million total reads (Supplementary Data 1), was submitted to the European Nucleotide Archive (ENA) with the study accession code PRJEB42798. RIP-seq datasets were analyzed to identify mRNA molecules enriched in GFP-fusion versus GFP control pull-downs with an average of 13,500 genes detected across all samples, ranging (Supplementary Data 1). Sequencing datasets clustered primarily depending on the nuclear versus cytoplasmic natures of the extract, and secondly depending on the protein used for pull-down (Supplementary Fig. S2A ). Differential expression analysis (DEA) was performed between each of the six pulldown conditions and the corresponding GFP control, with transcripts displaying positive enrichment (adjusted p value < 0. 05) considered as associating with the target protein (Supplementary Data 2).
The seven transcripts found in the overlap of the top diagram ("All") are not detected in the overlap of cytoplasmic and nuclear interactomes because they correspond to mRNA molecules present in distinct compartments (See figure on next page. ) Although Caz, Smn and TBPH fusion proteins were expressed specifically in neurons via the elav promotor, a certain degree of RNP complex re-association may occur in head lysates during the different experimental steps, as previously described  [66]. To discard any non-neuronal transcripts that may have co-precipitated with target proteins, the dataset resulting from the DEA was filtered to include only genes with reported expression in the adult fly brain (see Methods), corresponding on average to 70% of the enriched transcripts (see Supplementary Fig.