dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods This pooled retrospective analysis combined advanced NSCLC patients from 3 studies conducted in the United States and France: Liquid Biopsies in Patients Presenting Non-Small Cell Lung Cancer (LIBIL) study (NCT02511288); Liquid Biopsy (NCT02666612), and Reveal study (NCT02906852). Patients were included in the analysis if they met the following criteria: 1) stage IIIB/IV NSCLC, with a positive InVisionFirst 1 -Lung ctDNA liquid biopsy for an actionable alteration (common sensitizing and resistant EGFR mutations, BRAF V600E mutation or ALK/ROS1 fusion) with or without concurrent tissue biopsy, and 2) treated with an appropriate targeted therapy, namely FDA-approved TKIs according to the mutation identified. Patients were prospectively enrolled between 2015 and 2018 and eligible patients were consented by the Institutional ethic committee that approved the studies.
Structure determination of Drosophila melanogaster SERINC The fly possesses a single SERINC ortholog (referred to here as DmSERINC, and also known as TMS1) that shares 36% amino acid sequence identity with human SERINC5 (Supplementary Table  1 ). Ectopic expression of DmSERINC during production of Nefdeficient HIV-1 virions strongly suppressed their infectivity (Extended Data Fig. 1 a-c ), confirming conservation of functional restriction by the insect ortholog. To structurally characterise the SERINC family of proteins, we produced recombinant human SERINC5 and DmSERINC in human and yeast cells, respectively. When subjected to chromatography through a 24-ml Superdex-200 sizing column, SERINC5 migrated as a single peak with elution volume of 12 ml. Imaging negatively-stained material from this peak revealed featureless micelles, which failed to produce discrete 2D averages (Extended Data Fig.
2ad ). By contrast, separation of DmSERINC resulted in an additional species eluting at 9. 8 ml, containing well-defined assemblies with prominent six-fold symmetry (Extended Data Fig. 3a-d ). Encouraged by these results, we acquired micrograph movies of frozen-hydrated DmSERINC from this fraction. Image processing of single particles resulted in a 3D reconstruction to an overall resolution of 3. 3 Å with a local resolution of 2. 8-3 Å for the majority of the protein (Extended Data Fig. 3e-g ). Atomic coordinates were built ab initio into the cryo-EM map revealing the details of the homo-hexameric DmSERINC structure (Fig. 1a, b , Table  1  and Supplementary Video 1). Each of the six identical DmSERINC subunits consists of ten transmembrane helices (TM) arranged into two subdomains revealing a tertiary fold that is ~35 Å by ~50 Å in the membrane plane and ~50 Å traversing the membrane (Fig.
4j ). Searching structural databases with ProFunc 24 did not reveal similarity of either conserved pocket to any of the known functional sites. The surface of subdomain B features an elongated groove formed by TM5, 7, 8 and the Cterminal end of TM4, which is an appropriate size, shape, and location to accommodate a lipid moiety, potentially allowing a head group to access the hydrophilic cleft between the subdomains (Extended Data Fig. 6a, b ). When we embedded the protein structure in a lipid bilayer in silico, the groove became readily occupied by lipids during molecular dynamics (MD) simulations, and the cryo-EM map revealed presence of an acyl chain bound at this site (Extended Data Fig. 6c, d ). We conducted a screen of lipids and found that addition of phosphatidylserine, cholesterol or sulfatide increased DmSERINC and SERINC5 protein thermostability, while cardiolipin stabilised DmSERINC only (Extended Data Fig.
The structure of human SERINC5 Composed of two subdomains, the DmSERINC structure is suggestive of conformational flexibility. To determine if the conformation observed in DmSERINC represents that of the bona fide HIV-1 restriction factor, we imaged SERINC5 in detergent micelles doped with phosphatidylserine, which was found to increase thermostability of the protein (Extended Data Fig. 6g ). To aid in single particle image alignment of this 51 kDa protein, we acquired cryo-EM data in the presence of the antigen binding fragment (Fab) of a monoclonal antibody that recognizes SERINC5 ECL4. The resulting 3D reconstruction at a local resolution of 6. 5-7 Å contains a single molecule of SERINC5 in a conformation very similar to that of DmSERINC protomers within the hexamer (Fig. 3 , Extended Data Fig.
Cryo-EM grid preparation and data collection Four μl of hexameric DmSERINC at 0. 5 mg/ml, freshly isolated by size exclusion chromatography, was applied onto glow-discharged holey carbon 400-mesh Quantifoil R1. 2/1. 3 grids (Quantifoil) for 1 min, with 100% humidity at 20°C, before blotting for 3-4 sec and plunge freezing into liquid ethane using Vitrobot Mark IV (FEI). Data were collected on a Titan Krios microscope operating at 300 keV and equipped with a Gatan postcolumn energy filter, selecting a 20-eV window, on a GIF Quantum K2 direct electron detector (Gatan) operating in counting mode. A total of 5,807 movies were recorded using single electron counting mode with a magnified pixel size of 1. 38 Å and a defocus range of -1. 6 to -4 μm. The total electron dose was 50 e -/Å 2 , spread over 30 frames (Table  1 ).
Freshly isolated SERINC5 in LMNG micelles supplemented with DPPS and equimolar amount of Fab was subjected to size exclusion chromatography through a Superdex 200 Increase 3. 2/300 column. Four μl of isolated SERINC5-Fab complex was applied onto nonglow discharged C-flat 400-mesh R1. 2/1. 3 holey carbon grids (EMS) for 30 sec under 100% humidity at 20°C, blotted for 7 sec, and vitrified by plunging into liquid ethane using Vitrobot Mark IV. Data were collected on a Titan Krios microscope at 300 keV equipped with a Falcon 3 detector. A total 7,165 movies were recorded in single electron counting mode with a pixel size of 1. 09 Å and a defocus range -1. 6 to -4 μm, using total electron doze of 33. 6 e -/Å 2 over 30 frames (Table  1 ).
Cryo-EM image processing and 3D reconstruction Micrograph movies were aligned with dose weighting applied as implemented in MotionCor2  35  and the contrast transfer function (CTF) parameters were estimated from the frame sums using Gctf-v1. 06  36. Movies exhibiting ice contamination were discarded at this stage, leaving 4,238 (DmSERINC) and 7,021 (SERINC5-Fab complex) movies for further processing. A sub-set of particles semi-automatically picked in EMAN2 Boxer  32  were used to generate the starting 2D class averages, which, upon low-pass filtering to 20 Å, served as templates for auto-picking of the entire dataset (Extended Data Fig. 3e ). The initial DmSERINC particle dataset was picked with Relion-2. 1  33  resulting in 1,857,080 particles. Reference-free 2D classification was performed in cryoSPARC using 40 online-EM iterations into 200 classes.
These particles were used for 3D auto-refinement in Relion-3 with a soft mask around the entire particle using C1 symmetry, including the detergent micelle resulting in a 3D reconstruction to an overall resolution of 8. 2 Å and a local resolution throughout the protein density of 6. 5-7 Å (Extended Data Fig. 2e-g ). Resolution is reported according to the gold-standard Fourier shell correlation (FSC), using the 0. 143 criterion  37  (Table  1 , Extended Data Figs 2f,g and 3f,g). Local resolution was estimated using blocres tool from BSOFT package  38.
Infected cells were incubated at 37°C for 42 h before scoring number of infected cells per each well using an Ensight plate reader. Neutralization was measured by calculating the residual infectivity of treated virus samples considering the infectivity of the untreated sample as 100%. Fitted sigmoidal curves and the IC 50 values were obtained using Prism (Graphpad) with the least square variable slope method using the dose-normalized response protocol. Neutralizations were performed in quadruplicate with each combination of virus and antibody to be analyzed and data shown are the average with the error bars representing the 95% confidence interval as calculated by Prism. Neutralizing antibodies 4E10 (ARP3239) and 2F5 (ARP3063) were obtained from the UK National Institute for Biological Standards and Control.
Instrument settings were as follows: capillary voltage 1. 5 kV, source temperature 40°C, S-lens RF = 200, maximum injection time 150, HCD voltage 200 V, desolvation voltage -10 (positive mode), with in-source trapping on. Data were acquired between 2,000-15,000 with 10 microscans, and results were processed using XCalibur 2. 2 software (Thermo Fisher Scientific). DmSERINC peak assignments were done manually using in-house software as well as with UniDec software  57.
The mass spectrometer was operated at a source temperature of 80°C and a spray voltage of 2. 6 kV. Spectra were collected in positive ion mode. Peptide identification was performed by MS E58 using an identical gradient of increasing acetonitrile in 0. 1% v/v formic acid over 11 min. The resulting MS data were analysed using Protein Lynx Global Server 3. 0 software (Waters) with an MS tolerance of 5 ppm. Mass analysis of the peptide centroids was performed using DynamX 3. 0 software (Waters). Only peptides with scores exceeding 6. 4 were considered. The first round of analysis and identification was performed automatically by the DynamX 3. 0 software, however, all peptides (deuterated and non-deuterated) were manually verified at every time point for the correct charge state, presence of overlapping peptides, and correct retention time.
Deuterium incorporation was not corrected for backexchange and represents relative, rather than absolute changes in deuterium levels. Changes in H/D amide exchange in any peptide may be due to a single amide or a number of amides within that peptide. The DynamX 3. 0 software plots the standard deviation for every peptide. The error band shows the standard deviation of the plotted uptake or difference for each peptide. When there are multiple exposures, as in this experiment, for a given peptide, the maximum standard deviation is plotted for each peptide. A sigma multiplier of 1 is applied to the standard deviation to produce the grey error bar plotted in Extended Data Fig. 7b-e. Sequence conservation mapped onto the DmSERINC structure, with invariant residues in dark blue and most variable in red; the outer and inner plasma membrane surfaces are depicted as chocolate and olive dotted planes, respectively.
Acknowledgements We thank the  UK Biological Services Division at the National Institute of Biological Standards and Control  for their expertise in animal husbandry for the production of the antibody;  R. Peat  and the  Cell Services Platform (Crick Institute)  for upscaling of the hybridoma culture and purification of the antibody;  J. Diffley  for advice on codon-optimisation for protein expression in yeast;  J. Frigola  and  G. Coster  for the generous gift of JF1 cells and pGC014;  R. Carzaniga  for the maintenance of Vitrobot and Tecnai G2 microscope and user training;  P. Walker ,  A. Purkiss  and  M. Oliveira  for computer and software support;  M. Silva dos Santos  for assistance with lipid preparations;  D. Wu  ( University of Oxford ) for lipidomics support; the  UK National Institute for Biological Standards and Control , and depositor  H.
Katinger , for providing anti-HIV-1 4E10, 2F5 and p55/p24 antibodies;  A. Engelman  ( Dana-Farber Cancer Institute ) and  J. Luban  ( University of Massachusetts ) for comments on the manuscript. PJS and RAC were funded by  Wellcome Trust  ( 208361/Z/17/Z ). Research in PJS's lab is supported by the  MRC  ( MR/S009213/1 ) and  BBSRC  ( BB/P01948X/1 ,  BB/R002517/1 ,  BB/S003339/1 ). This project made use of time on ARCHER and JADE granted via the  UK High-End Computing Consortium  for Biomolecular Simulation, HECBioSim (http://hecbiosim. ac. uk), supported by  EPSRC  (grant no. EP/R029407/1 ). This research was funded by  US National Institutes of Health  grant  P50 AI150481  and the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001061 ), the  UK Medical Research Council  ( FC001061 ), and the  Wellcome Trust  ( FC001061 ).
B). DNA amplification was performed using Bakt_341F (5 -CCT ACG GGN GGC WGC AG-3 ) and Bakt_805R (5 -GAC TAC HVG GGT ATC TAA TCC-3 ) primers  [60]  targeting V3 and V4 regions of the 16S rRNA gene. Bacterial 16S rDNA libraries were sequenced on the MiSeq platform using MiSeq Reagent Kit v3 (600-cycles) (Illumina). The libraries containing fungal ITS2 region were sequenced on the MiSeq platform using MiSeq Reagent Kit v2 (500-cycles) (Illumina). All datasets were deposited in the National Center for Biotechnology Information (NCBI) Sequence Reading Archive (SRA) and are available under the PRJNA674969 bioproject.
Bioinformatic Procedures Raw reads were demultiplexed and quality checked by FastQC v. 0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were trimmed against primer sequences using cutadapt v. 2. 9  [61]. Then, reads were processed (sequence quality control, including denoising, trimming, and chimera removal) and amplicon sequence variants (ASVs) were generated using DADA2 v. 1. 14. 1  [62]. To assign the taxonomy to ASVs, the naive Bayesian classifier on the Silva v. 132 trainset and UNITE 8. 2 database  [63]  were used for bacterial and fungal datasets, respectively. ASVs assigned to chloroplasts (for bacterial dataset) or unclassified at the kingdom level (for bacterial and fungal datasets) were removed. Bacterial ASVs were analyzed directly. Fungal ASVs were clustered into operational taxonomic units (OTUs) using Swarm v2  [64]  with clustering threshold value d = 2.
ITSx software  [65]  was used for the extraction of ITS2 highly variable subregion. Data were rarefied to the minimum library size and then transformed to centered log-ratio (CLR). Alpha diversity analysis (calculation of richness (Chao 1) and diversity (Simpson) indices) was performed using MicrobiomeAnalyst tool  [66]. Significant differences in alpha-diversity indices between sample types were tested with Kruskal-Wallis tests at the OTU/ASV level, p-value < 0. 01). Minimum count filter (abundance value = 4) was applied before beta-diversity analysis. Nonmetric multidimensional scaling (NMDS) plots were constructed using the Bray-Curtis dissimilarity index with permutational multivariate analysis of variance confirmation. The above-described procedures of OTU generation were applied for the Microdochium sp.
Phylogenetic Classification of Microdochium sp. Isolates Nucleotide sequences of ITS2 of Microdochium species (M. nivale, M. majus, M. bolleyi, M. poae, M. colombiense) were collected from the NCBI nucleotide databank. The sequences that displayed >90% identity and coverage with the sequences of the isolated strains were considered. Then, 164 bp fragments of ITS2 of Microdochium species were used for multiple sequence alignment (ClustalW 1. 6 algorithm for non-coding sequences); the alignment was visualized by WebLogo3 [67]. The phylogenetic tree was built using the Bayesian inference method implemented in the MrBayes program (v3. 2. 6)  [68]. The alignment and phylogenetic tree visualization were performed using the MegaX  [69].
For the control plants, 8 mm plugs of sterile PSA were used instead of the mycelial plugs. Control and infected plants were grown at 20 • C with a 16 h light/8 h dark cycle photoperiod for 20 days. A total of 12-16 biological replicates were analyzed for each of the experimental variants. Twenty days after infection, the number of plants displaying necrosis on leaves and stems, as well as plants showing brownish root pigmentation, were counted and the length of roots and shoots, the quantity of roots and leaves, and the fresh weight of roots and shoots were measured. The results were analyzed using XLSTAT Statistical Analysis Software (2020. 4. 1. 1018). Pearson's correlation analysis was performed to assess the correlation between the development of different symptoms or between the degree of virulence and enzymatic activities, growth rate, and attribution to the particular OTU.
From the pool of reads obtained for amplicons synthesized using ITS2-specific primers, after the read filtration, dereplication, and chimera removal, 1296 amplicon sequence variants (ASVs) were generated. Then, 366 ASVs were excluded from the subsequent analysis because their sequences did not correspond to fungi. A total of 930 ASVs assigned to fungi were clustered into 678 operational taxonomic units (OTUs). The clustered 678 OTUs (Tables  S3  and S4 , Supplementary Materials) comprising the "fungal dataset" belonged to 250 fungal genera. A total of 2448 ASVs were generated from the pool of the processed reads obtained for amplicons synthesized using 16S rRNA-specific primers. Then, 609 ASVs assigned to fungi and plants were excluded from the subsequent analysis. The 1839 ASVs assigned to bacteria (Tables  S5  and S6 , Supplementary Materials) were analyzed directly without clustering.
The revealed 1839 In total, 3,945,809 (Table  S1 , Supplementary Materials) and 2,839,056 (Table  S2 , Supplementary Materials) reads were obtained for the amplicons synthesized using ITS2-and 16S rRNA-specific primers, respectively. From the pool of reads obtained for amplicons synthesized using ITS2-specific primers, after the read filtration, dereplication, and chimera removal, 1296 amplicon sequence variants (ASVs) were generated. Then, 366 ASVs were excluded from the subsequent analysis because their sequences did not correspond to fungi. A total of 930 ASVs assigned to fungi were clustered into 678 operational taxonomic units (OTUs). The clustered 678 OTUs (Tables  S3  and S4 , Supplementary Materials) comprising the "fungal dataset" belonged to 250 fungal genera. A total of 2448 ASVs were generated from the pool of the processed reads obtained for amplicons synthesized using 16S rRNA-specific primers.
Then, 609 ASVs assigned to fungi and plants were excluded from the subsequent analysis. The 1839 ASVs assigned to bacteria (Tables  S5  and S6 , Supplementary Materials) were analyzed directly without clustering. The revealed 1839 ASVs comprising the "bacterial dataset" belonged to 223 bacterial genera. Chao1 and Simpson indices were calculated to estimate the richness and diversity, respectively, of the fungal and bacterial communities in GL-, DL-, and R-samples of snow mold-affected rye (Figure  2 ). The Chao1 estimator  [80, 81]  calculates the estimated true species diversity of a sample. Simpson's diversity index is used to calculate a measure of diversity, considering the number of species, as well as its relative abundance. An increase in the Simpson index in the range from 0 to 1 indicates an increase in the contribution of dominant species to the population of the community.
NMDS analysis of the fungal dataset showed that the samples of aboveground plant parts formed a mutual cluster distinct from the R-sample cluster (PERMANOVA, p-value < 0. 001). In turn, for the bacterial dataset, each sample type formed a distinct cluster (Figure  3 ). This means that the fungal communities were more or less similar in green leaves and desiccated leaves, while the bacterial communities were significantly different. The most abundant phylum within the fungal dataset was Ascomycota: from 88. 1% in GLsamples to 96. 8% in R-samples. The Basidiomycota represented 1. 5% (R-samples) to 11. 4% (GL-samples). Mortierellomycota, Mucoromycota, and Olpidiomycota constituted less than 1% of the fungal dataset (Table  S4 , Supplementary Materials). At a genus level, two genera dominated in the DL-and GL-samples: Microdochium-some species of which cause the snow mold disease (36.
7% and 16. 5% of the fungal dataset in DL-and GL-samples, respectively) and Mycosphaerella-which includes well-known phytopathogenic species (33. 5% and 42. 1% of the fungal dataset in DL-and GL-samples, respectively) (Figure  4 ). In addition, the members of fungal genera Tetracladium, Lasionectria, Cistella, Phenoliferia, Nectria, Oculimacula, and Penicillum were represented in the DL-and/or GL-samples (Table  S7 , Supplementary Materials). In the R-samples, the members of Microdochium and Mycosphaerella were also present (9. 8% and 7. 2%, respectively); however, the dominant genus was Pseudogymnoascus (27. 6%). In addition, the members of fungal genera Cistella, Lasionectria, Nectria, Oculimacula, Penicillum, and Tetracladium (that were also revealed in DL-and/or GL-samples), as well as Chalara, Gibberella, and Leochumicola (that were not revealed in DL-and/or GL-samples), were represented in the R-samples (Figure  4 , Table  S4 , Supplementary Materials).
GL-samples-207 ASVs, and DL-samples-83 ASVs. The diversity (Simpson index) of fungal community was lower in GL-and DL-samples compared to R-samples. Furthermore, the intragroup variability of the Simpson index was large for GL-samples pointing to a greater variability of the fungal community in green leaves compared to roots or desiccated leaves. For the bacterial community, the diversity in GL-samples was as high as in R-samples, while it was lower in DL-samples (Figure  2 ). constructed on the basis of Bray-Curtis dissimilarity (Figure  3 ). NMDS analysis of the fungal dataset showed that the samples of aboveground plant parts formed a mutual cluster distinct from the R-sample cluster (PERMANOVA, p-value < 0. 001). In turn, for the bacterial dataset, each sample type formed a distinct cluster (Figure  3 ).
This means that the fungal communities were more or less similar in green leaves and desiccated leaves, while the bacterial communities were significantly different. The most abundant phylum within the fungal dataset was Ascomycota: from 88. 1% in GLsamples to 96. 8% in R-samples. The Basidiomycota represented 1. 5% (R-samples) to 11. 4% (GL-samples). Mortierellomycota, Mucoromycota, and Olpidiomycota constituted less than 1% of the fungal dataset (Table  S4 , Supplementary Materials). At a genus level, two genera dominated in the DL-and GL-samples: Microdochium-some species of which cause the snow mold disease (36. 7% and 16. 5% of the fungal dataset in DL-and GL-samples, respectively) and Mycosphaerella-which includes well-known phytopathogenic species (33. 5% and 42. 1% of the fungal dataset in DL-and GL-samples, respectively) (Figure  4 ).
3%) was not identified at the genus level (Table  S9 , Supplementary Materials). The presence of a large amount of nonidentified bacteria is typical of rhizosphere communities  [82, 83]. Since our study emphasized snow mold disease, we gave special attention to the composition of representatives of the Microdochium genus in the diseased plants. Three different OTU variants related to Microdochium genus were found (M. OTU1, M. OTU2, and M. OTU3, referred to as OTU4, OTU8, and OTU57 in the raw dataset; Tables  S1  and S10 , Supplementary Materials) within the fungal dataset (Figure  5 ). Since our study emphasized snow mold disease, we gave special attention to the composition of representatives of the Microdochium genus in the diseased plants. Three different OTU variants related to Microdochium genus were found (M.
OTU1, M. OTU2, and M. OTU3, referred to as OTU4, OTU8, and OTU57 in the raw dataset; Tables  S1  and S10 , Supplementary Materials) within the fungal dataset (Figure  5 ). All three variants together were revealed only in R-samples (8. 6%, 0. 7%, and 0. 4% of fungal dataset for M. OTU1, M. OTU2, and M. OTU3, respectively). In the GL-and DL-samples, M. OTU3 was absent. M. OTU1 and M. OTU2 were represented rather equally in GL-samples (9. 7% and 6. 8%, respectively); on the other hand, in the DL-samples, M. OTU1 was strongly dominant compared to M. OTU2 (30. 6% and 3. 6%, respectively) (Figure  5 ). This means that Microdochium species/strains belonging to M. OTU1 have an advantage over the representatives of M. OTU2 as the host plant tissues collapse.