dataesr/scientific-paragraphs-categorization huggingface dataset:
In the near future we expect to conduct a similar study with a set of samples consisting of highly absorbing particles to disentangle the effect of both size and absorption on the measured scattering pattern. We present the experimental phase functions, DLP, and δ L curves for the forsterite samples at 514 nm. The experimental data are obtained at the IAA-Cosmic Dust Laboratory  (Muñoz et al. 2011). In Section 2 we present a brief description of the experimental apparatus and scattering-matrix formalism. The samples processing is conducted at the facilities of the Funceramics group at ICV-CSIC. Sample synthesis and characterization are provided in Section 3. The experimental data are presented in Section 4. We evaluate the performance of the Mie spherical model for reproducing the scattering pattern of natural dust grains in Section 5.
This work has been funded by the  Spanish State Research Agency and Junta de Andalucía  through grants LEONIDAS ( RTI2018-095330-B-100 ) and  P18-RT-1854 , and the  Center of Excellence Severo Ochoa  award to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). T. J. acknowledges the  European Science Foundation (ESF)  and the  Ramon y Cajal Program of MICINN  for their financial support.
The Tse6 prePAAR-PAAR model shows that the His residue of the prePAAR AARxxDxxxH motif is properly positioned to participate to the tetrahedrally coordination of the Zn 2+ atom  21. The P. laumondii Rhs1 prePAAR region also bears a PAAARxxDxxxH motif that comprises the PAAR/PAAAR signature and the coordinating His residue (Supplementary Fig. 2c ). Interestingly, our XL-MS data fully agree with this model as several contacts between Rhs prePAAR and PAAR were observed (Fig. 2e ). Taken together, the current model proposes that the Rhs1 NT prePAAR and PAAR regions form a bona fide PAAR conical structure that sits on the VgrG spike, such as canonical PAAR subunits, hence anchoring Rhs1 to the tip of the T6SS needle. While the position of Rhs1 on the needle is now better defined, it remains to be determined whether Rhs1 is loaded onto the VgrG spike during T6SS biogenesis.
2 × 10 5 intensity with 3 to 8 charges were selected for HCD fragmentation with a normalized collision energy (NCE) set up to 26. Data analysis. All raw data were processed using MassSpec Studio V2. 4  60. Data were analyzed using the "E-Value Generator" algorithm with PercentEValue-Threshold set at 0. 1. Cross-linked peptide identifications were confirmed manually and discrepancies in the cross-linked sites were resolved to ensure that 2 chromatographic peaks did not share the same pair of sites. Purification of EagR-Rhs1 complex for structural studies. E. coli BL21(DE3) cells freshly transformed with pRSF-EagR-ST and pCDF-Rhs-FL carrying the D1338N, Y1351A and Y1375A mutations to avoid autocleavage and toxicity were grown overnight in LB and diluted 1/100 into 4 L of LB supplemented with kanamycin and streptomycin.
1 software  61. Two thousand particles were manually picked and submitted to 2D classification in order to generate initial templates, which were in turn used for template-based picking. 2,623,687 particles were extracted with a 280-pixel box size resized to 96 pixel and submitted to several rounds of 2D classification, resulting in 1,036,869 selected particles, which were re-extracted with a box size of 280 pixels with original pixel size. A global 3D refinement resulted in a 3. 72 Å resolution map. The refinement was repeated with a mask on the Rhs1 barrel region, which led to a 3. 46 Å consensus map. After per-particle CTF refinement and Bayesian polishing, this same dataset retrieved a 3. 1 Å map, but in which the C-terminal region of the Rhs1 barrel was suboptimally defined. This map was used for a 3D classification without sample alignment using a mask on the Rhs1 barrel and a separation in eight classes, which retrieved two major classes and six classes containing poorlyaligned particles.
The alignment was then treated with JalView 2. 11. 1. 4 to generate a consensus sequence logo. Transmembrane segments were predicted using TMHMM server v. 2. 0  64  and hydrophobicity was analyzed by ProtScale (Expasy) using Kyte & Doolittle scale  65. VgrG and EagR 3D models were obtained using Galaxy Homomer 66 tool on GalaxyWEB server using the P. aeruginosa VgrG1 (PDB: 4MTK; unpublished) and EagR (PDB: 1TU1; unpublished) structures as templates, and by imposing trimeric (VgrG) and dimeric (EagR) oligomeric states. Rhs1 NT model was made using AphaFold2  67. Structures were visualized and figures were prepared using Chimera  68.
Acknowledgements We thank the members of the Cascales, Fronzes and  Chamot-Rooke  laboratories for discussions and support,  Amaury Payelleville ,  Julien Brillard  and  Alain Givaudan  ( DGIMI, Montpellier, France ) for material and discussions,  Pascal Mansuelle  and  Régine Lebrun  ( Proteomic facility of the Mediterranean Institute of Microbiology, Marseille ) for mass spectrometry analyses and N-terminal Edman sequencing,  Artemis Kosta  ( Microscopy facility of the Mediterranean Institute of Microbiology, Marseille ) and  Eric Durand  for technical assistance and advices for preliminary EM analyses, and  Moly Ba ,  Isabelle Bringer ,  Annick Brun ,  Audrey Gozzi  and  Mathilde Valade  for technical support. This work was funded by the  Center National de la Recherche Scientifique , the  Aix-Marseille Université , the  Bordeaux Univ.
, the  Institut Pasteur  and by grants from the  Agence Nationale de la Recherche  ( ANR-17-CE11-0039 ,  ANR-20-CE11-0017 ), the  Fondation pour la Recherche Médicale  (FRM,  DEQ20180339165 ), and the  Fondation Bettencourt Schueller  to EC. DJ was supported by a  FRM post-doctoral fellowship  ( SPF201809007142 ). This work has benefitted from the  facilities and expertize of the Biophysical and Structural Chemistry Platform (BPCS)  at  IECB.
Data availability The final cryo-EM density map of the P. laumondii Rhs1-EagR complex has been deposited to the Electron Microscopy Data Bank (EMDB) under the accession code EMD-13587. The final atomic model was deposited into the Protein Data Bank (PDB) under the accession code 7PQ5. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028652. The data are provided in the Supplementary Information files or can be obtained from the corresponding authors upon request. Uncropped gels and Western-blots are shown in Supplementary Fig. 12.
Phylogenetic analysis When positive PCR products were obtained from areas where PPR had never previously been detected (Kebbi, Katsina, Rivers, Enugu and Abia states; see the Results section), one positive sample per state was selected based on the brightness of the amplicon on the gel, and sent to GENEWIZ (United Kingdom) for purification and sequencing in both forward and reverse directions. The sequences were submitted to the NCBI GenBank database (Table  1 ). Forward and reverse deoxyribonucleic acid (DNA) sequences were assembled using BioEdit and trimmed to remove poor-quality portions of the sequences (final size = 255 base pairs [bp]). Corrected sequences were aligned with a dataset of PPRV N gene sequences publicly available in GenBank using MEGA 6 representatives of the four genetic lineages.
Type indicates if samples were collected from a flock or in a market. N is the number of positive samples or total samples tested. Accession number, GenBank accession number. Multiple accession numbers are given for one location when multiple non-identical sequences were obtained from PPRV-positive samples. Accession numbers are repeated when the same sequence was obtained from several locations. The total accession number refers to the total number of non-identical sequences obtained. Lg, lung; Ln, lymph node. http://www. ojvr. orgOpen Access.
ACKNOWLEDGMENTS This project was supported by  NTHU  Project No. 106N505CE1. In addition, L. -U. L. , Y. -H. Y. , C. -W. C. , and G. H. acknowledge funding from the  Ministry of Science and Technology, R. O. C. , by Grant Nos. 105-2112-M-007-022-MY3  and  106-2923-M-007-001-MY4 , and H. -Y. L. , K. -H. M. C. , S. -W. H. , and J. K. by Grant Nos. 105-2112-M-007-014-MY3 ,  108-2112-M-002-021 ,  109-2112-M-002-028 , and  109-2622-8-002-003.
In their work,  Meguerdichian et al. (2021)  propose a command-line pipeline that takes as input the annotations of both genes and TEs for a given genome, then detects and reports the positional relationships between each TE insertion and their closest genes. The results are processed into an R script to provide tables displaying some statistics and graphs to visualize these relationships. This tool has the potential to be very useful for performing preliminary analyses before studying the impact of TEs on gene functioning, especially for biologists. Indeed, it makes it possible to identify genes close to TE insertions. These identified genes could then be specifically considered in order to study in more detail the link between the presence of TEs and their functioning. For example, the identification of TEs close to genes may allow to determine their potential role on gene expression.
Reviewed by anonymous reviewer, 2021-03-23 09:37 Title : Pipeline to detect the relationship between transposable elements and adjacent genes in host genome In this work, C. Meguerditchian and colleagues propose a pipeline to retrieve, from a list of transposable elements (TEs) coordinates and a list of gene annotations, a list of overlapping and closest upstream / downstream genes. It is true that this step is probably the first one to apply when searching for TEs with a potential impact on gene expression. However, different tools are already available to manage such analysis, and I would only cite the Bedtools suite (https://bedtools. readthedocs. io/en/latest/), with the tools "intersect" or "closest". In addition, and contrary to the postulate of the authors that "none existing tools can reveal the relationship between TEs and host coding sequences", several works attempted to address this question and even went further by taking into account expression data or functional data such as GO terms (for example, LIONS  (Babaian et al.
Statistical Analysis Data were analyzed using SPSS (version 20. 1, SPSS Inc. Chicago, IL, USA). The primary outcome measures were changes in the VAS, ODI, and RDQ scales over time as determined by using a repeated measure ANOVA. The model included the pattern of covariance between repeated observations to account for correlations between observations within each patient. For the secondary outcome analysis, unpaired Student t-tests or Mann-Whitney tests were used for continuous variables, and chi-square tests or Fisher's exact test was used for categorical variables. The following baseline characteristics of patients experiencing treatment failure after GRNB were examined for statistically significant differences: The sex, age, BMI, BMD, underlying disease, and medications, such as steroids.
Investigating correlation of ACE2 expression with other genes, we observed negative correlation of ACE2 with interferon-stimulated genes (ISGs). Using an independent HCV infected liver biopsy dataset we replicated the same effect and observed down-regulation of ACE2 after treatment with pegylated IFN-. We observed the same pattern in gastrointestinal tract where inflammation driven ISGs were negatively correlated with ACE2 expression in two independent cohorts. The interferon-associated downregulation of ACE2 was also identified in lung tissue in a murine model of SARS-CoV-1 infection. Performing a genome-wide eQTL analysis for ACE2 expression in the virus infected liver biposies, we found that ACE2 expression is associated with host genetic variation in the IFNL region on chromosome 19q13.
We also observed that genes involved in extracellular structure organisation were enriched among genes positively correlated with ACE2 expression (Supplementary Figure  1  and Supplementary Table  4 ). We used an independent data set of liver biopsies 9 from 28 patients (6 non-HCV infected controls and 22 HCV infected cases, GSE84346) and replicated concordant correlation signs for 162 of the 168 genes negatively correlated with ACE2 (Figure  1c  and Supplementary Table  5 ). , binomial test). In this replication cohort 18 patients had two biopsies taken, one before and another after treatment with pegylated IFN- at different time points (N 4 hours =4, N 16 hours =3, N 48 hours =3, N 96 hours =3, N 144 hours =5). At all time points we observed a down-regulation of ACE2 expression (Figure  1d ), with the biggest median drop at 16 hours post injection.
Pearson's correlation coefficient is shown for each gene. (d) ACE2 expression in paired liver biopsy samples from before and after pegylated IFN- treatment taken at different time points post treatment. (e) Log fold change (log 10 (post-treatment / pretreatment)) of ACE2 expression after pegylated IFN- injection. At all time points we observe a reduction in mean ACE2 expression with the biggest median drop at 16 hours. The reported pvalues are from paired t-tests for each time point. Combining all time points, downregulation of ACE2 after IFN- treatment is highly significant (p=6. 5x10 -5 , paired ttest). To further explore the negative correlation of ISGs with ACE2 expression in a known site of SARS-CoV-2 replication, we explored the relationship between ACE2 and ISGs expression in the gastrointestinal (GI) tract in a gene expression study of terminal ileum biopsies in inflammatory bowel disease (IBD) in treatment-naive young donors (RISK cohort 14 , GSE57945).
In intestinal biopsies, there was a striking decrease of ACE2 expression with increasing severity of inflammation that was independent of the abundance of transcriptional markers of epithelial identity  15  (Figure  2a  and Supplementary Figure  2a ) and ISGs had increasing expression with rise in disease activity and were negatively correlated with ACE2 expression (Figures  2b, 2c  and Supplementary Figure  2b ). Genes associated with epithelial cell structure and function were enriched among genes that were positively correlated with ACE2 in both liver and intestine, while genes associated with type I interferon signalling pathways were enriched among genes that negatively correlated with ACE2 expression in both tissues (Supplementary Figure  3 ). These data were supported by analysis of a second independent IBD cohort  16  (GSE137344, Supplementary Figure  4 ).
Since the pattern of gene expression incorporating down-regulation of ACE2 in presence of ISGs was consistent in two models of viral chronic infection and/or inflammation in different tissue, we addressed whether a similar pattern of gene regulation was observed in lung tissue using data from mouse models of SARS-CoV-1 infection 17 (GSE59185). Indeed we observed in SARS-CoV-1 infected lung the same associated down-regulation of ACE2 in the presence of up-regulation of classical ISGs (Figure  2d ). In the context of chronic HCV infection, ISG induction varies considerably between individuals with some patients showing constant ISGs expression at high levels while others show almost no detectable induction of innate immune system  7, 8. This differential expression of the ISGs is strongly associated with genetic variants in the IFNL locus on chromosome 19q13.
Finally, the genetic data add weight to the idea of a careful exploration of IFN-λ pathways in therapy for SARS-CoV-2  40. incubation, the columns were washed with 350uL of Buffer RW1 and centrifuged at 10000 rpm for 15 seconds. Flow-through was discarded and 500uL of Buffer RPE was added to the spin columns. Columns were then centrifuged again at 10000 rpm for 15 seconds and flow-through was discarded. An additional 500uL of Buffer RPE was added to the spin columns and columns were centrifuged at 10000 rpm for 2 minutes. Finally, spin columns were transferred into new microcentrifuge tubes and 30uL of RNasefree water was added directly to the column membrane. Columns were then centrifuged at 10000 rpm for 1 minute to elute the RNA. RNA yield was quantified using a NanoDrop spectrophotometer.
92 CollectRnaSeqMetrics. jar. Three samples were excluded after QC checks due to low sequencing depth which left 195 samples for analysis. Genes were filtered using the criteria of having a count per million (CPM) of 1. 25 in at least 10 samples to remove low expressed genes. Function cpm from edgeR  48  version 3. 20. 9 was used to calculate the CPM values. After removing low expressed genes we were left with 14882 genes. To normalise for library size and gene length, transcripts per million (TPM) values were calculated from unique mapped read counts and log 10 (TPM+1) was used in the analysis. For the replication cohort 9 (GSE84346), the read counts were downloaded from GEO. Gene expression data for 46 liver biopsy samples from 28 individuals were available for this data set. 22 individuals had chronic HCV infection while 6 individuals did not have HCV infection and were enrolled as controls.
Host genotyping Host genome-wide genotyping was performed on 567 patients from the BOSON cohort as described previously  20. Briefly, genomic DNA was extracted from buffy coat using Maxwell RSC Buffy Coat DNA Kit (Promega) as per the manufacturer's protocol and quantified using Qubit (Thermofisher). DNA samples from patients were genotyped using the Affymetrix UK Biobank array  20. Both liver RNA trascriptomic and human genome-wide SNP data were obtained on a total of 190 patients of mainly White self-reported ancestry infected with HCV subtype 3a. After quality control and filtering of the human genotype data, approximately 330,000 common SNPs with minor allele frequency greater than 5% were available for analysis.
Statistical analysis For the BOSON Cohort, Log 10 (TPM+1) values were calculated and used to estimate Pearson's correlation coefficient between ACE2 and all other genes. The qvalue package in R was used to calculate false discovery rate. We used FDR of 1% and correlation coefficient of >0. 3 or <-0. 3 to decide on genes significantly correlated with ACE2. To test for enrichment we used enrichGO function from the clusterProfiler package  49  , limiting the analysis to GO "biological process" class and maximum gene set size of 500. An independent liver biopsy dataset (GSE84346) was used to replicate the negative correlation of ACE2 with 168 genes found in the BOSON cohort. We used the Log 10 (TPM+1) values from the 28 baseline liver biopsy samples and calculated Pearson's correlation coefficient between ACE2 and the 168 genes.
Assuming a null hypothesis of no association between correlation coefficients signs in the two data sets, one can use a binomial test to assess this null hypothesis where the number of trials (n) is 168 and the probability of a negative correlation sign was estimated from the BOSON cohort by dividing the number of genes with negative correlation with ACE2 divided by total number of genes (14882). To test the hypothesis of down-regulation of hepatic ACE2 expression when treated with pegylated interferon-alpha, we used the 18 individuals with liver biopsy samples taken before and after the treatment. We used one-sided paired t-test to perform hypothesis testing for each of the time points and across all time points. To test for association between autosomal human SNPs and ACE2 expression (Log 10 (TPM+1)), we performed linear regression using PLINK  50  version 1.
9 using additive and dominant genetic models adjusted for the human population structure by adding the first five genetic principal components as covariates. We also added host cirrhosis status, age and sex as covariates to the analysis. To assess the impact of age per 10 years increase, we divided the age by 10 before adding it as a covariate. For 190 patients both host genome-wide genotyping data and hepatic ACE2 expression data were available. We used a significance threshold of 5x10 -8. To test for association between IFNL4 SNP rs12979860 genotypes and expression of CTSL, CTSB and TMPRSS2 genes, we used linear regression and added the first five host genetic principal components as covariate to account for population structure. Patient cirrhosis status, age and sex were also addes as covariates in this analysis.
GENESIS cohort: GENESIS is funded by the National Institute of Diabetes and Digestive and Kidney Diseases and managed by Emory University for the recruitment of self-identified African American subjects with IBD  53. We used a subset of 195 GENESIS cohort subjects with ileal transcriptomic profiles as an additional replication cohort to test for negative correlation of ACE2 expression with interferon-stimulated genes expression. Pearson correlation tests between normalized expression values for ACE2 and four ISGs confirmed that this pattern of negative correlation is also observable in a cohort enriched for African American ancestry. This dataset includes 158 IBD patients along with 37 controls. Subjects with ileal inflammation were included as IBD, while non-IBD controls did not have ileal inflammation.
Data analysis for SARS-CoV-1 mouse model The mouse lung tissue microarray data were downloaded from GEO using the accession number GSE59185  17. In case of multiple probes per gene, they were collapsed into a single feature, which resulted in 21217 features. Three lung tissue samples were from mice infected with wild type virus and three lung tissue samples were from mock infection. We then used LIMMA  54  to perform differential gene expression analysis between these two conditions. The log fold change and the p-values were used to make a volcano plot. The genes highlighted as red on the volcano plot are 17 genes which were significantly negatively correlated with ACE2 in the human liver and were part of the GO term "response to type I interferon" (GO ID: 0034340). References:.
Acknowledgements The authors would like to thank  Gilead Sciences  for the provision of samples and data from the BOSON clinical study for use in these analyses. The authors would also like to thank  HCV Research UK  (funded by the  Medical Research Foundation ) for their assistance in handling and coordinating the release of samples for these analyses. The authors would also like to thank ISARIC-4C investigators for their discussion and contributions to this study. This work was funded by a grant from the  Medical Research Council.
Boson patient cohort: For this study, we used patient data from the BOSON cohort that has been described elsewhere  41. All patients provided written informed consent before undertaking any study-related procedures. The BOSON study protocol was approved by each institution's review board or ethics committee before study initiation. The study was conducted in accordance with the International Conference on Harmonisation Good Clinical Practice Guidelines and the Declaration of Helsinki (clinical trial registration number: NCT01962441).
The analysis was performed by accessing the Gene Ontology Annotation (GOA) Database for Biologic processes, Cellular components, Immune system processes and Molecular function, the Reactome pathways database (https://reactome. org/) and the KEGG database (https:Resources for statistical analysis and data visualization: Prism version 8. 0 (GraphPad Software) Excel for Mac Version 15. 32 (Microsoft) Cytoscape 3. 7. 1 (https://cytoscape. org/) Cytoscape 3. 7. 1 plugin ClueGO (Version 2. 5. 6) Cytoscape 3. 7. 1 plugin CluePedia (version 1. 5. 6) Cytoscape 3. 7. 1 plugin Venn and Eluler Digrams (Version 1. 0. 3) Morpheus (https://software. broadinstitute. org/morpheus/) R (Version 3. 6. 1) RStudio (Version 1. 2. 5001) were ranked based on their Pearson's correlation coefficient with ACE2 for Specific statistical tests applied in this study are described in the respective each patient subgroup.