dataesr/scientific-paragraphs-categorization huggingface dataset:
Blood gene expression RNA was extracted from whole blood collected in Tempus tubes. Gene expression was assessed using the Gen-eChip® Human Transcriptome Array 2. 0 (HTA 2. 0) (Affymetrix, USA) at the University of Santiago de Compostela (USC, Spain). Samples were randomized and balanced by sex and cohort within each batch. Data was normalized at the gene level with the GCCN (SST-RMA) algorithm, and batch effects and blood cell type composition were controlled with two surrogate variable analysis (SVA) methods, isva  [38]  and SmartSVA  [39] , during the differential expression analyses. Gene expression values were log2 transformed, and annotation of transcript clusters (TCs) to genes was done with the Affymetrix Expression Console software using the HTA-2_0 Transcript Cluster Annotations Release na36 (hg19).
Serum metabolites Serum metabolites were quantified using the targeted metabolomics Absolute-IDQ TM p180 Kit (Biocrates Life Sciences AG, USA). Serum metabolic profiles were acquired following the manufacturer's protocol using LC-MS/MS on a Sciex QTrap 6500 equipped with an Agilent 1100 series HPLC (Agilent Technologies, USA), at Imperial College London (ICL, UK); a full description of the HELIX metabolomics methods and data can be found elsewhere  [47]. Samples were fully randomized. Metabolites were quantified (mM) following the manufacturer's protocol (appendix), and then log2 transformed. Metabolite exclusion was based on a metabolite variable meeting two conditions: (1) CV of over 30% and (2) over 30% of the data are below LOD. Eleven out of the 188 serum metabolites detected were excluded as a result, leaving 177 serum metabolites to be used for further statistical analysis.
Urinary metabolites Urinary metabolic profiles were analyzed on a 14. 1-T (600 MHz 1 H) NMR spectrometer (Bruker, USA) at Imperial College London (ICL, UK)  [47]. Urine samples were fully randomized. In addition, 60 identical study quality control urine samples were included at regular intervals during the runs. While data acquisitions were untargeted, data processing workflow followed a targeted strategy to identify and quantify the 44 most abundant metabolites in urine  [47, 49]. Sample concentration of a given metabolite was estimated from the signal of the internal standard trimethylsilylpropanoic (TSP). Data was normalized using median fold change normalization method which takes into account the distribution of relative levels from all 44 metabolites compared to the reference sample in determining the most probable dilution factor.
Association between exposure to smoking and molecular phenotypes To test the relationship between exposure to tobacco smoke versus child molecular marks, we fitted linear regressions between each tobacco smoking variable (predictor) and each molecular mark (outcome) adjusting for covariates, using limma  [44]  within the implementation of omicRexposome R package  [41]. Children unexposed to tobacco smoke, in each definition and period, were considered the reference group. Since we wanted to assess period-specific smoking effects, models were mutually adjusted: any and sustained maternal smoking in pregnancy were adjusted for childhood global-SHS, while childhood global-SHS and urinary cotinine detection were adjusted for sustained maternal smoking in pregnancy. Other covariates were selected through directed acyclic graphs (DAGs) with the DAGitty tool  [50]  (Additional file 3: Fig.
S1 ; and Additional file 3: Fig. S2 ). The following variables were selected for both periods: (i) cohort, (ii) self-reported ancestry (European ancestry, Pakistani or Asian, and others), (iii) maternal age, and (iv) self-reported maternal education (low (primary school), medium (secondary school), and high (university degree or higher)). In addition, pregnancy models were adjusted for maternal obesity status, and childhood models for child body mass index (zBMI). Maternal BMI was calculated from pre-or early-pregnancy weight and height and divided in four categories derived from World Health Organization (WHO) definitions. Child zBMI is a sex and age z-score calculated according to WHO reference curves  [51, 52]. Single imputation of missing data for covariates was done using a chained equations method  [53]  with the mice R package  [54].
The proportion of missing data in the selected covariates was minimal: 0. 17% in maternal age, 0. 75% in maternal BMI, and 2. 16% in maternal education. Apart from covariates resulting from DAG diagrams, models were also adjusted for sex and child age, as well as for specific covariates in models of each of the omics. Methylation models were adjusted for blood cell type composition (CD4+ and CD8+ T cells, natural killer cells (NK), monocytes, granulocytes, and B cells), which was estimated from raw methylation data  [55, 56]. Models of plasma proteins and serum metabolites were adjusted for time to last meal and hour of blood collection, and models for urine were adjusted for sample type (morning, night, or the pool of both). Serum and urinary metabolites were additionally corrected for technical batch.
Technical batch effects of the other omics datasets were eliminated during the quality control process, as described in previous sections. Multiple-testing correction was performed within each molecular layer following different approaches. For omics with more than 1000 features (methylation, gene expression, and miRNAs), we used false discovery rate (FDR)-Benjamini-Hochberg correction  [57]. For the remaining omics, we divided the nominal p value by the effective number of tests  [58]  (proteins-18. 4, p value = 2. 72E-03; serum metabolites-60. 3, p value = 8. 29E-04; urinary metabolites-33. 3, p value = 1. 50E-03). The effect of smoking on DNA methylation is reported as a difference in methylation levels between exposed and unexposed children, while for the other omics, the effect is reported as a log2 fold change (log2FC).
Expression quantitative trait methylation For CpGs significantly associated with exposure to tobacco smoke, we searched for cis eQTMs using methylation and gene expression data from 874 HELIX children. Cis effects were defined in a window of 1 Mb from the transcription start site (TSS) of each TC (each gene). The association between DNA methylation and gene expression levels was assessed via 1420 linear regressions. Models were adjusted for cohort, child's age, sex, zBMI, ancestry, and blood cell type composition. To control for gene expression batch effect, we estimated surrogate variables (SVs) with the SVA R package  [45] , protecting covariates (cohort, child's age, sex, zBMI, ancestry, and blood cell type composition), and the effect of these SVs was eliminated from the gene expression matrix by calculating the residuals.
Direct comparison with the literature and enrichment analyses We compared findings in our study with previous literature on the molecular effects of exposure to tobacco smoke in pregnancy (DNA methylation  [15] ) and of own smoking in adults (DNA methylation  [21] , gene expression  [20] , miRNA expression  [22] , serum metabolites  [19] ). When possible, we did a detailed comparison with previous findings; for genome-wide omics without significant marks in our study, we conducted enrichment analyses. To explore enrichment of our results for molecular marks (CpGs/genes) identified previously for current smoking in adults, we tested whether the distribution of p values at these marks in our data deviates from a null distribution using one-sided Kolmogorov-Smirnov tests, and compared the direction of effect estimate to what was reported previously.
Sensitivity analyses On the molecular marks that survived multiple-testing correction, we performed sensitivity analyses to test the robustness of results under the following scenarios: (i) for models of any and sustained maternal smoking in pregnancy, we tested the effect of adjusting for home-SHS, instead of global-SHS; (ii) we compared main models, which are mutually adjusted for the two exposure periods (i. e. , maternal smoking in pregnancy adjusted for childhood SHS), versus unadjusted models (i. e. , maternal smoking in pregnancy without adjustment for childhood SHS); and (iii) finally, we restricted the analysis to children of European ancestry (N = 1083). All the analyses were done in R environment. The R packages MultiDataSet  [59] , rexposome, and omicRexposome  [41]  were used to manage and analyze the omics and exposure data.
In contrast, childhood SHS was related to higher plasma levels of PAI1, a protein that inhibits fibrinolysis, and to certain metabolites. The final clinical impact of sustained increased levels of PAI1 in children is unknown, but this finding highlights the importance of the analysis of molecular traits to capture subtler effects at earlier timepoints. control of the methylation and transcriptomics data under the supervision of JRG. CT measured the cotinine levels. LM coordinated the cohorts, and OR developed the questionnaires and the clinical and sample collection protocols. SA, MC, LCh, RG, KBG, JL, DM, GS, PS, RS, CT, and MVa are the PIs or representatives of the cohorts, and obtained the smoking information and biological samples. All approved the manuscript.
Funding The study has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 ) under grant agreement no. 308333  (the  HELIX  project); and the  H2020-EU. 3. 1. 2. - Preventing Disease Programme  under grant agreement no  874583  (the  ATHLETE  project). BiB received core infrastructure funding from the  Wellcome Trust  ( WT101597MA ) and a joint grant from the  UK Medical Research Council (MRC)  and  Economic and Social Science Research Council (ESRC)  ( MR/N024397/1 ). INMA data collections were supported by grants from the  Instituto de Salud Carlos III, CIBERESP , and the  Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the  Lithuanian Agency for Science Innovation and Technology  ( 6-04-2014_31V-66 ). The Norwegian Mother, Father and Child Cohort Study is supported by the  Norwegian Ministry of Health and Care Services  and the  Ministry of Education and Research.
A lock-in-like detection scheme allowed for the measurement of the magnetization dynamicsand in particular spin waves-excited at microwave frequencies with a time resolution of 50 ps using all photons emitted by the synchrotron. Input signals were provided by an arbitrary waveform generator, allowing for the independent control of both amplitude and phase of multiple microwave excitation channels. Further details on the detection and data analysis scheme can be found in  (32). All-electrical microwave measurements. All-electrical measurements of SWMGs were performed using a two-port Vector Network Analyzer (VNA). The signal from port 1 of the VNA was split into three separated lines connected to three ground-signal-ground microwave probes connected to the three input coplanar waveguides attached to the input antennas, P1, P2, and P3, of the SWMG.
Two power splitters were used as schematically depicted in Fig. S1. In all lines, variable attenuators and phase shifters were inserted such that it was possible to control the amplitude and phase of each input independently, especially to match amplitudes of the spin waves at port O and create the two logic phase states, 0 and π. The output O was connected to port 2 of the VNA, allowing for a phase-sensitive measurement of the transmitted power. The bandwidth (frequency cutoff) of the entire circuit was 18 GHz. During all-electrical measurements, the power delivered to individual antennas was -6 dBm (~0. 25 mW). Considering the measured antenna impedances, this corresponded to an input voltage of about 120 mV. Micromagnetic simulations. Micromagnetic simulations have been performed using the objectoriented micromagnetic framework (OOMMF) software package  (33).
Acknowledgments General: The authors would like to thank the  Helmholtz-Zentrum Berlin (HZB)  for the allocation of synchrotron radiation beamtime and  Sina Mayr  ( PSI ) for her support of the time-resolved scanning transmission x-ray microscopy measurements. Odysseas Zografos (imec) and  Sorin Cotofana  ( TU Delft ) are acknowledged for many valuable discussions on spin-wave circuits. Funding: This work has been supported by imec's industrial affiliate program on beyond-CMOS logic. It has also received funding from the  European Union 's  Horizon 2020 research and innovation program  within the  FET- OPEN  project  CHIRON  under grant agreement No. 801055  as well as under grant agreement No. 730872. Author.
Mycobacterium abscessus complex (MABC) is a taxonomic group of rapidly growing, nontuberculous mycobacteria that are found as etiologic agents of various types of infections. They are considered as emerging human pathogens. MABC consists of 3 subspecies-M. abscessus subsp. bolletti, M. abscessus subsp. massiliense and M. abscessus subsp. abscessus. Here we present a novel method for subspecies differentiation of M. abscessus named Subspecies-Specific Sequence Detection (SSSD). This method is based on the presence of signature sequences present within the genomes of each subspecies of MABC. We tested this method against a virtual database of 1505 genome sequences of MABC. Further, we detected signature sequences of MABC in 45 microbiological samples through DNA hybridization. SSSD showed high levels of sensitivity and specificity for differentiation of subspecies of MABC, comparable to those obtained by rpoB sequence typing.
Most commonly, the detection of deletion is used to differentiate M. abscessus subsp. massiliense from M. abscessus subsp. bolletii and M. abscessus subsp. abscessus by sequencing of the erm(41) gene, as it also serves for clarithromycin resistance prediction. Here we present a novel method for subspecies differentiation of M. abscessus named SubSpecies-Specific Sequence Detection (SSSD). This method is based on the presence of signature sequences within the genomes of all MABC subspecies. The method was validated against a virtual database of 1505 MABC genome sequences from across the world, and it was highly effective for subspecies-level discrimination. Furthermore, the method showed to be valid for discrimination of subspecies of MABC in laboratory conditions through DNA hybridization.
Results Differentiation of genome sequences included in the virtual database. A total of 1505 genomic sequences of M. abscessus included in our virtual database, were differentiated by MLST typing coupled with gANI differentiation and SSSD, rpoB sequence typing and erm(41) sequence typing. First, we performed virtual MLST typing on all MABC genomes. We used PasteurMLST database of Institute Pasteur to identify alleles and sequence types (STs). We identified 181 distinct MLST patterns, 50 of which could be assigned to already known STs. The remaining 131 patterns had previously been unreported. We named new patterns of MLST with the alphabetic scheme. The three most prevalent STs within our database were ST1, representing M. abscessus subsp. abscessus (18. 5%, n = 279), ST23, representing M.
abscessus subsp. massiliense (16. 6%, n = 250), and ST26, representing M. abscessus subsp. abscessus (10. 2%, n = 154). We differentiated a total of 181 M. abscessus strains showing unique MLST patterns into individual subspecies based on gANI values. Our reference strains were NC_010397 (ATCC 19977) for M. abscessus subsp. abscessus, NC_018150 for M. abscessus subsp. massiliense and NZ_CP014950 for M. abscessus subsp. bolletti. The strains included in the database were clustered into three separate groups (Figs. S6, S7), corresponding to individual subspecies. Similar to previous reports  11  , the intrasubspecies ANI values exceeded 98%, while intersubspecies ANI values ranged from 96 to 98% (Table  S2 ). The ANI values for the most closely related species of mycobacteria, Mycobacterium chelonae, Mycobacterium porcinum, Mycobacterium farcinogenes, Mycobacterium fortuitum, and Mycobacterium immunogenum ranged between 74 and 88%.
Based on combined MLST and ANI score differentiation, we identified 63% (n = 941) strains in the database as M. abscessus. subsp. abscessus, 30% (n = 454) as M. abscessus subsp. massiliense and 7% (n = 110) as M. abscessus. abscessus subsp. bolletii (Fig. 1 ). We used ANI identification as the gold standard, which allowed us to estimate the sensitivity and specificity of the SSSDs and other methods used for subspecies identification. Upon full-length rpoB sequence typing of genomes included in the virtual MABC genome database, we observed 97 distinct rpoB alleles. There were 213 variable sites within this gene, with a total of 220 mutations. The average number of nucleotide differences was 38. 118. All rpoB gene sequevars, including those of three reference strains of MABC subspecies, grouped into three branches on the phylogenetic tree (Fig.
abscessus subsp. abscessus/bolletii. Overall, erm(41) typing identified properly 98. 1% (n = 1476) of MABC strains at the subspecies level. For SSSD differentiation, our custom virtual database of MABC genome sequences was BLAST-searched to identify sequences specific for each of the MABC subspecies. We typed 1489 (98. 9%) out of 1505 genomes at the subspecies level. We obtained ambiguous results for 16 genomes. These genomes represented four MLST patterns. For two MLST patterns, ST109 type (n = 1), and new MLST pattern (DF) (n = 13), the ambiguities resulted from simultaneous detection of bol-s-s and mas1-s-s or mas2-s-s. gANI analysis identified these strains as M. abscessus subsp. bolletii. One strain, new MLST pattern (BD), was recognized by mas1-s-s and abs-s-s, and one strain, new MLST pattern (CL), was recognized by mas2-s-s and abs-s-s.
Discussion The population of strains included within the virtual database reflects the population of infecting strains, with the highest prevalence of strains of M. abscessus subsp. abscessus, followed by M. abscessus subsp. massiliense and M. abscessus subsp. bolletii. Among 1505 genome sequences, we identified 131 MLST patterns that were previously unreported, which shows that there is still a significant level of variability to be discovered within the genus of M. abscessus. The SSSD developed in this study reached satisfactory levels of sensitivity and specificity upon in silico, genome-based analysis, and validation procedure on DNA from 45 clinical isolates. When we validated SSSD on clinical strains, we found that the probes detecting bol-s-s and mas1-s-s detected specific sequences in all respective strains and one strain of M.
The entire experiment took approximately a week. However, this method is amenable to more time-and cost-efficient protocols with other molecular biology techniques, for example, microarrays  29  or line probe assays based on reverse hybridization  30. Subspecies specific sequences can easily be detected using the simplest PCR. Importantly, as showed by the results of this study, the method of SSSD can be exploited for quick and efficient analysis of data obtained by whole-genome sequencing. The bioinformatic methods based on the comparison of entire genomes, like gANI, still require large amounts of computer power, which is neither practical nor accessible for many laboratories. In turn, the BLAST search of the sequence of interest takes seconds. Finally, the use of coding sequences for differentiation allows the possibility to identify M.
Materials and methods Construction of virtual database. Genome sequences of 1505 M. abscessus strains were downloaded from Genome Database at the NCBI. Information on the geographic origin of the strains was retrieved from the Genome Database (Table  S1 ). The strains included in the database were isolated in 14 countries from across the globe (Fig. S1 , Table  S1 ). All genome sequences consisted of less than 145 contigs in order to ensure good quality of extracted sequences. The cut-off for the number of contigs, for each strain, was linked to the N50 value, typically used to describe the quality of draft assembly. The N50 value is defined as the shortest sequence length at 50% of the genome. The lowest probable N50 value in our dataset was 35 kbp, with an average N50 of 384. 13 ± 18. 06 kbp.
Virtual database strain differentiation. The sequences were processed and analyzed with Geneious R11 (Biomatters, Auckland, New Zealand)  31. We compiled genomic sequences of M. abscessus strains into a custom database. We searched the database with the BLAST tool implemented in Geneious R11 (Megablast, max e-value 1e-4) for the presence of the rpoB, erm(41) gene sequences and another seven gene sequences (argH, cya, glpK, gnd, murC, pta , and purH. ) used in the MLST typing. Reference sequences for BLAST search were those of strains NC_010397 (reference strain M. abscessus ATCC 19977) for M. abscessus subsp. abscessus, NZ_ CP014950 for M. abscessus subsp. bolletii, and NC_018150 for M. abscessus subsp. massiliense. Sequences found through BLAST search were aligned with Geneious R11. The strains were differentiated to the subspecies level based on rpoB and erm(41) sequence typing and MLST typing, as previously described  32.
For rpoB sequences, a phylogenetic tree was built with RAxML implemented within Geneious R11. Sequence variability was analyzed with DnaSP 33. For erm(41), strains were assigned to individual subspecies upon analysis of length of the sequences found during BLAST search, as erm(41) sequence typing differentiates M. abscessus subs. massiliense from other members of the complex based on the absence of the C-terminal end of the gene. For SSSD, strains were categorized into individual subspecies, based on the presence of particular sequences of a given strain (Table  3 ). These sequences were identified on a hit list after the BLAST search of the custom virtual M. abscessus database. The reference sequence used to identify M. abscessus subsp. abscessus (M. abscessus subsp. abscessusspecific sequence, abs-s-s) was a gene sequence encoding hypothetical protein MAB_3505c.
For identification of M. abscessus subsp. bolletii we used sequence of aac1 gene encoding ADP/ATP carrier protein (bol-s-s), located between homologs of MAB_1240 and MAB_1241c. For identification of M. abscessus subsp. massiliense we used two sequences. The first sequence (mas1-s-s) was annotated as a steroid dehydrogenase and was located between homologs of MAB_1115 and MAB_1116. The second sequence (mas2-s-s) was annotated as tetR gene and located within homolog of MAB_2150c. MLST patterns were assigned sequence type (ST) numbers from publically available database PubMLST. org 34 (Table  S1 ). In order to designate the subspecies for each MLST pattern, strains showing distinct MLST patterns were chosen for subspecies delineation by genome average nucleotide identity estimation (gANI)  11, 35.
gANI is a similarity index between a given pair of genomes. Briefly, 181 genomes of M. abscessus showing distinct MLST patterns were annotated with DFAST  36. Predicted coding sequences were used for calculating pairwise gANI with ANIcalculator 37 (Fig. S2 , Table  S2 ). Data processing and visualization were performed with Python 3 scripts using pandas and seaborn libraries. Additional ANI analysis was performed for 47 genomes of MABC, where subspecies delineation results were not concordant for all methods (Fig. S3 , Table  S3 ). In the case of discrepancies, the delineation of species by ANI was considered as correct. Bacterial strains. All strains were isolated from pulmonary patients from hospitals in South Korea, Canada, and Slovenia (Table  S4 ). Each strain was assigned to adequate subspecies by either hsp65, rpoB gene sequencing, 16S rRNA gene, and internal transcribed spacer sequence, or GenoType NTM-DR Hain's test.