dataesr/scientific-paragraphs-categorization huggingface dataset:
Loss of ESX-1 has been linked to attenuation in M. bovis BCG  35  and there is evidence for attenuation in ESX-5 mutants in M. tuberculosis as well  36. Not only are the new ESX-5 genes which we identified actively transcribed, but they are also differentially expressed compared to the known ESX-5 genes. This strongly suggests that esxN. 2 and esxJ. 3 may have some new functionalities compared to their previously paralogs, including a potential a role in M. tuberculosis pathogenesis. Our results also suggest that the RNAseq reads properly attributable to esxJ. 3 may have been misassigned to esxJ. 1 using the current Genbank H37Rv genome reference, potentially confounding analysis of esx gene functionality (Figure  S4k ). Regions with similar genomic organization to R3 were previously observed in Mycobacterium marinum  27  , Mycobacterium microti  29  and M.
Future studies will be needed to determine how well Bact-Builder works for performing de novo assemblies of class II and class III bacterial genomes. Our approach provides several improved and complete reference genomes for the second largest cause of death from an infectious disease. The updated genomes of TMC102, NR123 and H37Rv1998 are quite similar as they all contain R1-R10. However, we did note small differences between each isolate and have deposited updated sequencing data for each (SRA accession #: PRJNA836783). TMC102 appears to be used more commonly used by the global scientific community than NR123, being cited >1000 times versus 246  40  , respectively at the time of this writing, suggesting that TMC102 should perhaps become the single accepted laboratory strain for M. tuberculosis.
Artificial in silico read generation Bact-Builder development and testing made use of artificial reads generated using the H37Rv1998 reference sequence. A published H37Rv fasta obtained from NCBI (NC_000962. 3) was run through BadRead (v0. 1. 5; -quantity 100x -length 1000, 1000) (https:// github. com/rrwick/Badread) in order to generate simulated ONT reads  23. In silico paired end Illumina reads were generated with ART  24  using the same published H37Rv fasta and the default ART parameters to generate Illumina 150 bp paired-end reads with a fold coverage of 30x.
Basecalling and demultiplexing Raw nanopore sequencing reads were base-called and demultiplexed with the latest version of the Guppy basecaller available at the point of sequencing (Guppy v. 4. 2. 2). Sequencing quality and output was evaluated using Nanoplot (v1. 33. 0) (https://github. com/wdecoster/ NanoPlot)  45  and pycoQC (v2. 5. 0. 23) (https://github. com/tleonardi/ pycoQC)  46.
Quality control and filtering Basecalled and demultiplexed files were trimmed using Filtlong (v0. 2. 0; -min_length 1000 -min_mean_q 70) (https://github. com/ rrwick/Filtlong). Sample contamination was evaluated via taxonomic classification using Kraken2 (v2. 1. 1) (https://github. com/DerrickWood/ kraken2)  47, 48. Illumina data was trimmed using trim-galore (v0. 6. 6) (https://github. com/FelixKrueger/TrimGalore).
Assembly Nanopore reads for each sample were de novo assembled using the following four assemblies in triplicate: Canu (v1. 5; genomeSize= 5 m)  11  , Flye (v2. 8. 1-b1676; genome-size 5 m -plasmids)  12  , Miniasm (v0. 3-r179)  49  , and Raven (v1. 3. 0)  13. Gap-closed sequences were evaluated in Bandage (v0. 8. 1) (https://github. com/rrwick/Bandage)  50.
Consensus assembly Trycycler (v0. 3. 0) (https://github. com/rrwick/Trycycler) was used to generate a consensus long-read assembly using the twelve individual assemblies. The cluster step was run with the following modifications: trycycler cluster -min_contig_depth 0. 5. Within Bact-Builder, cluster001 is used by default for downstream processes. Trycycler can be run manually for bacteria with extra-chromosomal plasmids or if assemblers need to be removed. Trycycler reconcile was run with the following modifications: trycycler reconcile -cluster_dir trycycler/cluster_001 --max_length_diff 1. 3 -max_add_seq 10000 -min_identity 95 -max_indel_size 1000. All other steps were run with default parameters. Following the reconcile step, contigs that could not be reconciled or had a high degree of dissimilarity from the majority of assemblies were removed and the reconcile step was run again.
Polishing Following Trycycler, the consensus assemblies were further polished using both long and short read polishers. Racon (v1. 4. 20; -m 8 -x -6 -g -8 -w 500) (https://github. com/isovic/racon) was run three times on each assembly using ONT reads. Following racon, medaka (v1. 0. 1; -m r941_min_high_g360) (https://github. com/nanoporetech/medaka) was used for long read polishing. Finally, Pilon (v1. 24; -fix all -changes) (https://github. com/broadinstitute/pilon) was run three times on the medaka consensus using Illumina reads  51.
Evaluating outputs Final assemblies were assessed for SNPs, indels and regions of difference using DNAdiff (v1. 3) from the MUMmer program (https://github. com/mummer4/mummer)  52, 53 and quast (v5. 0. 2). Complete, polished assemblies were also compared to each other using a pan-genomics approach implemented in anvi'o  54  (https:// anvio. org) to investigate large structural changes and differences in the accessory gene pool as a function of the assembly and curation steps. Annotation of the consensus genome sequence was performed using the NCBI's prokaryotic genome annotation pipeline (PGAP)  55.
PCR and sanger sequencing Following DNAdiff analysis, identified regions were PCR validated by designing PCR primers both within the identified region and flanking regions. PCR primers were designed using Primer Quest (IDT, Coralville, USA) (Supplementary Data 4). GoTaq Green Master Mix (Promega) was used to run PCR for 30 cycles using manufacturers suggested parameters  56. PCR products were run on a 1% Agarose gel containing. 08% ethidium bromide and visualized using the ChemiDoc imaging system (BioRad, Hercules, USA). PCR amplicons were sanger sequenced (Psomagen, Rockville, USA) and aligned to the reference to validate the region.
Expression analysis Accession numbers for all M. tuberculosis H37Rv RNA-sequencing experiments publicly available on the NCBI Sequence Read Archive (SRA) as of October 1, 2021 were downloaded using the NCBI's esearch and efetch utilities, similar to Yoo et al. 57  Sequencing runs were filtered to include only those performed with Illumina short read sequencing, and runs were processed as described in Ma et al. 2021  58. Sequences were downloaded using their SRA accession numbers using fasterqdump  59. Raw sequence quality was assessed using fastqc  60  and adapter trimming was performed using bbduk  61. Trimming and sequence removal based on quality was performed using trimmomatic  62. Sequences were aligned to either the existing H37Rv reference (NC_000962. 3) or to H37Rv(new) using Bowtie2  63.
Read counts were compiled using featureCounts  64. Quality data, adapter and quality trimming statistics, and alignment and counts metrics were compiled and assessed using multiqc  65. Of 908 RNA-sequencing runs available from the strain H37Rv, 905 used Illumina sequencing chemistry and passed quality control metrics and were included in the expression analyses reported here. Batch correction was completed by grouping samples by study, identified by the BioSample ID corresponding to a given run's SRA accession, and performing quantile normalization on raw counts of reads using qsmooth  66. Visualization of data was performed in R 67. To evaluate whether expression reads mapping to the novel paralogs were incorrectly mapping to esxN. 1 and esxJ. 1, Bowtie2 aligner with the "-very-sensitive-local" option was used to align triplicate H37Rv RNAseq data (SRA accession: PRJNA507615) against both the esxN.
Acknowledgements Research reported in this publication was supported by the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under award numbers  U19AI11276  and  U19AI162598. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additionally, JHY is supported by the following National Institutes of Health awards:  R00-GM118907  and  R01-AI146198  and an  Agilent Early Career Professor Award. The authors acknowledge the  Genomics Center Rutgers New Jersey Medical School  (https://research. njms. rutgers. edu/genomics/) and the  Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey  (http://oarc. rutgers. edu)for providing access to the Amarel cluster and associated research computing resources that have contributed to the results reported here.
Data availability Data Collection: Whole Genome Sequencing data for the strains used in this study have been deposited in the NCBI Sequence Read Archive via BioProject accession number PRJNA836783. Updated sequence files have been deposited in GenBank, with primary accession code: CP110619. The Fasta sequence and annotated file for H37Rv(new) can also be found in the Supplementary files as Supplementary Data 2 and Supplementary Data 3 respectively.
Introduction The ENSEMBLE trial (NCT04505722) was conducted in Argentina, Brazil, Chile, Colombia, Mexico, Peru, South Africa, and the United States to test the ability of a single dose of the replication-incompetent human adenovirus type 26 (Ad26)-vectored Ad26. COV2. S vaccine vs. placebo to prevent moderate to severe-critical COVID-19. 1, 2  Estimated vaccine efficacy against COVID-19 with onset at least 28 days post-injection was 66. 1% (95% confidence interval (CI): 55. 0 to 74. 8) in the primary analysis (median follow-up two months). 1  The US Food and Drug Administration (FDA) granted an Emergency Use Authorization to the Ad26. COV2. S vaccine as a single primary vaccination dose for individuals aged ≥18 years and, more recently, as a single homologous or heterologous booster dose for individuals aged ≥18 years.
COVID-19 endpoint Correlates analyses were performed based on adjudicated moderate to severe-critical COVID-19 with onset that was both ≥ 28 days post-vaccination and ≥ 1 day post-D29, through to January 22, 2021, the data cut date of the primary analysis. 1  This COVID-19 endpoint was selected to be as close as possible to the COVID-19 endpoint used in the primary analysis  1  (efficacies against the primary 1 vs. correlates analysis "moderate to severe-critical COVID-19" endpoints were very similar), while also seeking inclusiveness of endpoints to aid statistical precision. See Online Methods for details on the analysis databases and exact differences between the two endpoints. The last COVID-19 endpoint included in the correlates analysis occurred 48 days post-D29 (Extended Data Figure  1E ).
Discussion Each evaluated D29 antibody marker was an inverse correlate of risk of moderate to severecritical COVID-19 over 83 days post Ad26. COV2. S vaccination, with strongest evidence for ID50 titer, passing the pre-specified multiple testing correction bar. In addition, vaccine efficacy increased with higher D29 antibody marker levels, with results supporting the importance of achieving quantifiable antibodies, as negative binding antibody response and nonquantifiable neutralization corresponded to marginal vaccine efficacy of about 50%. Overall, these findings constitute a step towards establishing an immune marker surrogate endpoint for adenovirusvectored COVID-19 vaccines, and possibly even a surrogate endpoint that is transportable across vaccine platforms. As discussed further below, it remains to be seen how well each evaluated D29 antibody marker predicts vaccine efficacy against SARS-CoV-2 strains other than those circulating during the trial period, as well as over longer follow-up periods; such data would be important for informing the use of any of these biomarkers as a surrogate endpoint in practice.
Trial design, study cohort, and COVID endpoints Enrollment for the ENSEMBLE trial began on September 21, 2020. A total of 44,325 participants were randomized (1:1 ratio) to receive a single injection of Ad26. COV2. S or placebo on Day 1. Serum samples were taken on D1 and on D29 for potential antibody measurements. Antibody measurements were evaluated as correlates against the moderate to severe-critical COVID-19 endpoint defined in the main text. While the correlates analysis only included cases up to Jan 22 nd , 2021 (the cut-off date of the primary analysis 1 ), the correlates analysis was performed using the analysis database of the final analysis. 2  Compared to the analysis database of the primary analysis, the analysis database of the final analysis includes changes to the SAP and protocol, as well as information that became available only after the database lock date on cases up to Jan 22 nd , 2021.
Cross-trial comparisons Calibration of ID50 nAb titers between the Duke neutralization assay (COVE trial samples) and the Monogram PhenoSense neutralization assay (COV002 and ENSEMBLE trial samples), performed using the WHO Anti-SARS CoV-2 Immunoglobulin International Standard (20/136)   and Approach 1 of Huang et al. 28  (with arithmetic mean as the calibration factor) is described in the supplementary material of Gilbert, Montefiori, McDermott et al. 24  Software and data quality assurance The analysis was implemented in R version 4. 0. 3  38  ; code was verified using mock data.
Acknowledgments This work was partially funded by the  Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority , under Government Contract Nos. HHSO100201700018C  with  Janssen  and  75A50122C00008  with  Labcorp -Monogram Biosciences ; by the  National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID)  under  Public Health Service  Grants  UM1 AI068635  ( HVTN  SDMC),  UM1 AI068614  (HVTN LOC), and  R37AI054165 ; and by the  Intramural Research Program  of the  NIAID Scientific Computing Infrastructure at   Fred Hutch , under  ORIP  grant  S10OD028685. This work was also supported by  Janssen Research and Development , an affiliate of  Janssen Vaccines and Prevention  and part of the  Janssen  pharmaceutical companies of  Johnson & Johnson.
S4A )  (41, 42). In addition, PPARδ protein levels in human intestinal organoids are comparable to mouse tissues where PPARδ is highly expressed (Fig. S4B ). Treatment with the PPARδ agonist GW1516 upregulated the mRNA expression of target genes in the organoids (i. e. , CPT1A, HMGCS2, PDK4, and ASCL1; Fig. 5A ). In agreement with the GW1516-based results, the Figure  5. GLA-derived fatty acids increase the expression of PPARδ target genes in human intestinal organoids. A and B, human intestinal organoids were treated with 1 μM GW1516 (PPARδ agonist) or 30 μM GLA-derived fatty acids for 24 h. Relative mRNA levels were determined using RT-qPCR (A). CPT1A, HMGCS2, and β-ACTIN protein levels were measured using Western blotting (B). Data represent means ± SEM (n = 3 or 4), and Tukey's test was used for analysis.
Docking simulation A docking simulation was performed using the AutoDock Vina computational program  (38)  to predict the binding models of human PPARδ LBD protein with GLA-derived fatty acids. The PPARδ LBD segment of the PPARδ X-ray crystallography structure (PDB: 3GWX, 2AWH, 2BAW, and 2B50) was obtained and used as a receptor.
Acknowledgments-We thank  Dr Makoto Makishima  ( Nihon University School of Medicine ),  Dr David Mangelsdorf  and  Dr Steven Kliewer  (both  University of Texas Southwestern Medical Center ) for providing plasmids, and  Dr Michihisa Umetani  ( University of Houston ) for helpful advice related to the ligand binding assay. Author contributions-M. N. , M. S. , and R. S. conceptualization; M. N. , M. S. , P. L. , Y. T. , and K. N. investigation; M. N. , M. S. , Y. T. , L. P. , Y. Y. , K. N. , and R. S. methodology; M. N. , M. S. , P. L. , Y. T. , Y. Y. , S. S. , H. K. , S. K. , J. O. , K. N. , and R. S. resources; M. N. , M. S. , P. L. , and K. N. formal analysis; M. N. , M. S. , and R. S. writing-original draft; M. N. , M. S. , P. L. , Y. T. , Y. Y. , S. S. , H. K. , S. K. , J. O. , K. N. , and R.
Evidence before this study We searched PubMed for genomic epidemiological studies of Mycobacterium tuberculosis published in English before April 2022 employing whole-genome sequencing, using the search terms "tuberculosis", "transmission", "population based", and "whole-genome sequencing". We identified only 11 studies in which whole-genome sequencing was used to investigate transmission of M tuberculosis at the population level. We also searched the China national knowledge infrastructure (CNKI) and WANFANG databases with the same search terms for papers published in Chinese, but did not identify any studies. The duration of most of the 11 studies we identified was less than 5 years. Seven studies conducted epidemiological investigations of genomic-clustered cases, but the proportion of cases with confirmed epidemiological links was very low.
11  Sequencing data were deposited in the Genome Sequence Archive (https://bigd. big. ac. cn/gsa) under BioProject PRJCA008815 and PRJCA008816. Based on the identified SNPs, a phylogeny tree was constructed with RAxML-NG (version 1•0•2) software, using the maximum likelihood method with 100 bootstraps and visualized with Interactive Tree of Life (https://itol. embl. de/). Strains were classified into different lineages according to Coll et al. 12  Strains belonging to lineage 2 (L2), also termed the Beijing family, were divided into L2•3, representing "modern" Beijing, and other sub-lineages considered "ancient" Beijing. 13  Drug-resistance profiles were predicted for 14 anti-TB drugs based on the mutations reported to be associated with resistance,  14  using Whole-genome sequencing (WGS) data as described previously.
Epidemiological investigation We conducted epidemiological investigations twice. First, at the time of TB diagnosis we collected demographic, clinical and laboratory information on each patient and also collected data on their close contacts. We then conducted in-depth epidemiological investigation of patients whose strains belonged to genomic clusters, including their residences, workplaces, and public community centers and facilities frequented in the three years prior to their TB diagnosis. Putative transmission networks were constructed based on the structure of the genomic phylogeny and the epidemiologic links. The epidemiologic links were defined as confirmed --patients who knew each other and had a history of contact before diagnosis, or probable -patients who did not know each other but lived in the same village.
Acknowledgments This study was funded by the  National Science and Technology Major Project of China  (grants  2017ZX10201302-006 ). It was also funded by  Natural Science Foundation of China  ( 81661128043 ) and  Science and Technology Major Project of Shanghai. We thank the tuberculosis public health teams in the  Wusheng County Center for Disease Control and Prevention (Wusheng CDC)  and the  Wuchang City Center for Tuberculosis Control and Prevention (Wuchang CTC).
V. SIMULATIONS AND DISCUSSION This section presents the simulations results, the complexity analysis, and a discussion of the suggested method and results. Simulations have been performed using Python 3 including numpy library, and the source code can be accessed at https: //github. com/b-com/synchro_LoRa. All results of Monte-Carlo simulations have been averaged over 10 4 runs. We consider -15 -10 -5 SD(nτ ) versus SNR (dB). LoRa signals with SF7 and SF9, and with delays (in sampled version) and frequency offsets randomly chosen within the sets n τ ∈ [[5, 5 + N R ]] and θ f ∈ [-10, 10] kHz, respectively. Moreover, we arbitrarily set K = 8, R = 8, R 0 = 2, and G = 64, as we empirically found that this provides a good trade-off between performance and complexity.
Population and statistical methods We included all patients admitted to the hospital group between 30 December 2017 and 31 July 2020 who tested positive for vanB VRE (asymptomatic carrier or infected) and their contact patients. Table  1  describes the definitions used for outbreak characterisation. The relevant demographic information (age, sex), clinical (department, presence of infection) and microbiological details were prospectively extracted from the electronic medical records and from infection prevention databases. Continuous variables are summarised as medians and interquartile ranges (IQR), categorical variables as percentages. All statistical analyses were performed using R (version 4. 0. 0).
Molecular typing To describe the clonal relationship for the samples, we performed whole genome sequencing (WGS). As previously described  [24] , WGS was performed using a MiSeq Illumina platform (accredited with ISO 17025 norm at the Division of Clinical Microbiology, University Hospital Basel) with 2 × 300 nt paired-end sequencing after Nextera XT library preparation. After sequencing, the resulting reads were de novo assembled and analysed by core genome multilocus sequence typing (cgMLST) using Ridom SeqSphere Software (version 4. 1. 6). With the same software, the clonal relationship was illustrated with a minimum spanning tree. All read data have been deposited with the European Nucleotide Archive (ENA) under the project number PRJEB27159.
It was developed using Solidity, and compiled and deployed using Python's library web3. py. Each battery is associated with an account, similar to the aggregator, who deploys and manages the smart contract and associated operating fees, as shown in Fig. 4. The functions implemented in the smart contract are • Constructor, to initialize the contract. • Register a new household. • Update the time series sent by the aggregator (SoC t,A , E t c , S t ). • Update a household information: SoC tsc,k the battery SoC at the current time, E tsc,k e the energy exported/imported by household k since the last communication between the battery k and the smart contract,  and E t,k  e a new variable equal to the net exported energy of household k since the beginning of time interval t. It is computed as the sum of all previous quantities E tsc,k e.
The pricing data for the wholesale market price have been extracted from Nordpool's day-ahead market  [26]. Retail import prices follow dynamic Time of Use (ToU) pricing scheme from  [27]. Market time interval is 30 minutes. Hence, the day-ahead optimisation uses time steps of 30 minutes. We do not consider export tariff for households (such as FiT), as these incentives have been removed in the UK. In real-life applications, forecasts errors and communication latency can lead RT operations to achieve different export energy quantities than the contractual agreement. In this case a penalty applies to the aggregator, that is equal to the product of the energy quantity difference and an imbalance price. Imbalance prices are computed by the system operator and depend on the system's frequency.
B. Experimental Results The results presented in this section were obtained with a communication time interval of 5 minutes between the assets and the smart contract. Day-ahead forecasts for the aggregated demand and production were generated using the real data multiplied by a random variable uniformly distributed to simulate an uncertainty of 25% around the real value. The time interval between two forecast values was 2 hours. Fig. 5. a shows the wholesale market price. Fig. 5. b displays the aggregated demand and production forecasts, along with the fleet net energy export contracted in the wholesale market. Finally, Fig. 5. c compares this contractual energy export quantities with the realised export of the aggregated fleet. The achieved export match the market commitments with an average error below 10%.