dataesr/scientific-paragraphs-categorization huggingface dataset:
The search was repeated in Pubmed, Google Scholar, and the Global Health database with "AND pregnan* AND malaria" added to the search terms to ensure completeness. Studies were eligible if they contained data from both a NAATbased diagnostic (either PCR or LAMP) and microscopy from the same women during pregnancy or at delivery. Studies (or subgroups or study groups) that selected participants on the basis of the presence of fever or a positive blood smear were excluded to avoid selection bias. Two authors (AmvE and AM) reviewed the potential studies. Any discrepancies were resolved after further discussion until consensus was reached. The protocol is available through the WorldWide Antimalarial Resistance Network.
Data analysis AmvE, AM, JH, and FotK contacted investigators of source studies to request primary data. Prespecified variables (appendix p 7) were obtained and standardised. For studies for which IPD were unavailable, data about number of infections and number of women examined were extracted from the published articles as aggregated data when there was sufficient information. Estimates of malaria transmission intensity and sulfadoxinepyrimethamine resistance, matched by study location and year, were extracted from publicly available data (appendix p 8). [16] [17] [18]  The risk of bias in primary studies was assessed using a modified version of the Newcastle Ottawa quality assessment tool for observational studies (appendix pp 7-8). Two authors (AmvE and AM) independently assessed the risk of bias across six domains.
Because of the observed interactions between moderatetolow and high transmission areas for gravidity, results in Africa were presented stratified by high (P falciparum prevalence for children aged 2-10 years [PfPR 2-10 ] of at least 35%) and moderatetolow transmission areas (PfPR 2-10 lower than 35%). Potential predictors and confounders included age, gravidity, season, year of study, gestational age, bednet or insecticidetreated net (ITN) use, HIV status, residence (rural or urban), antimalarial use for treatment or prevention, and study design (survey, cohort study or trial). The risk of microscopic and submicroscopic malaria was evaluated in peripheral maternal blood during pregnancy and delivery and in placental blood. Studies were split by location when possible. Weighted pooled prevalence estimates for submicroscopic and microscopic malaria and weighted pooled odds ratios (ORs) were obtained using randomeffects twostage models using IPD and aggregated data (metaprop and meta, Stata 17).
High sulfadoxine-pyrimethamine resistance was defined as a prevalence of Lys540Glu of at least 30% in east and southern Africa and Ala437Gly of at least 90% in central and west Africa, whereas low resistance was defined as Lys540Glu of less than 30% in east and southern Africa, and Ala437Gly of less than 90% in central and west Africa. Factors with a p value of at least 0•1 (Wald test) in the multivariate model were removed. To explore the robustness of findings, we used alternative measures of transmission intensity based on malaria infection prevalence by PCR assessed in the first trimester among all gravidae and established whether the data source (IPD vs aggregated) affected prevalence or whether study quality or quality of blood slide reading affected the risk factor analysis. All analyses were conducted using Stata (version 17).
Results The search identified 87 eligible studies, 68 (78%) of which contributed to the analyses. Of these 68 studies, 45 (66%) studies had available IPD (48 869 participants) and 23 (34%) had aggregated data (11 683 participants; figure  1 ). The included studies were conducted between 1995 and 2017 in 27 countries and included 15 trials, 39 surveys (ie, single observations), and 14 cohort studies (appendix pp 15-21). 54 (79%) studies (35 with IPD) were from Africa, eight (12%) studies (six with IPD) were from Asia, and seven (10%) studies (five with IPD) were from the Americas. One (1%) study was conducted in both Asia and the Americas. The 68 studies provided 83 datapoints by location (appendix p 58). Over half of the included participants (54-63%, depending on test and outcome) were enrolled in clinical trials.
Discussion In this systematic review and metaanalysis of 68 studies, including 45 IPD and 23 aggregated datasets, most NAATdetected infections were submicroscopic, ranging from 59% during pregnancy to 71-74% at delivery. Infections were more likely to be submicroscopic in Asia and the Americas than in Africa. Submicroscopic infections were predictive of future episodes of submicroscopic or microscopic infections in areas of high malaria transmission and high sulfadoxinepyrimethamine resistance. Submicroscopic infections were also associated with fever in Africa, albeit less than women with microscopic malaria. In Africa, consistent with previous observations among nonpregnant populations,  20  the proportion of submicroscopic malaria in pregnancy decreased with increasing transmission intensity.
Available data refer to proportion of participants with an outcome on submicroscopic, microscopic, and no malaria. Studies in Asia and the Pacific were included under Asia; studies in central or South America were included under the Americas. In the multinomial model, the parameter estimates are relative to the referent group; for a unit change in the covariate the logit of the outcome is expected to change by its respective parameter estimate given the variables in the model are held constant (eg, for the probability of having microscopic malaria during pregnancy in high transmission areas in Africa, one increase in unit of PfPR 2-10 resulted in an increase of 0•029 logit of microscopic malaria, which translates to an OR of 1 ). ‡Comparison group for first antenatal clinic visit was not first antenatal clinic visit or unknown whether first visit or not.
Acknowledgments The WWARN is funded by the  Bill & Melinda Gates Foundation  (Grant Agreement Investment ID INV004713). We thank all the women who participated in and all the people who worked on the original studies, and the  Malaria Atlas Project  for transmission estimates and the WWARN for maintaining their maps for sulfadoxine-pyrimethamine resistance markers and funding. We would like to thank  Ric Price  for data in one of the contributing articles. The findings and conclusions presented in this manuscript are those of the authors and do not necessarily reflect the official position of the  US Centers for Disease Control and Prevention.
The authors acknowledge inspiring discussions with  H. Liu ,  C. M. Ko ,  I. Vasiliev. This study is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  STRONG -2020 -No 824093. Furthermore, we acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ): grants  BR 4000/7-1  and  BL 982/3-1  and by the  GSI-IN2P3  agreement under contract number  13-70. JS acknowledges support from the  BMBF  through the  ErUM-datapilot  project as well as the  Samson AG. Further support was provided by the  PPP Program  of the DAAD. Computational resources were provided by the  Center for Scientific Computing (CSC)  of the Goethe University, by the " Green Cube" at GSI, Darmstadt.
Targeted sequencing of a multigene panel for myofibrillar myopathies revealed the missense mutation c. 734A>G,p. (Glu245Gly) in the DES gene (NM_001927. 4) in the heterozygous state. This missense variant localized before the last nucleotide of exon 3 in the coiled 1B domain of the protein has already been detected in an Indian family with a dominant inheritance but with an unknown effect  [28]. Its allelic frequency is not reported in GnomAD (v1. 3). The LOVD database reports it as of uncertain significance. Bioinformatics prediction tools predicted a splice effect. We performed RNAseq on an RNA sample from a quadriceps muscle biopsy. A defect in exon 3 splicing was observed in the heterozygous state with 3489 reads of the exon 2 to exon 4 junction, compared to 3829 reads for junction 2-3 and 3964 for junction 3-4.
In comparison, we did not observe the 2-4 junction among controls consisting of RNAs from another muscle biopsy used in the same run (Figure  3 ). Targeted sequencing of a multigene panel for myofibrillar myopathies revealed the missense mutation c. 734A>G,p. (Glu245Gly) in the DES gene (NM_001927. 4) in the heterozygous state. This missense variant localized before the last nucleotide of exon 3 in the coiled 1B domain of the protein has already been detected in an Indian family with a dominant inheritance but with an unknown effect  [28]. Its allelic frequency is not reported in GnomAD (v1. 3). The LOVD database reports it as of uncertain significance. Bioinformatics prediction tools predicted a splice effect. We performed RNAseq on an RNA sample from a quadriceps muscle biopsy. A defect in exon 3 splicing was observed in the heterozygous state with 3489 reads of the exon 2 to exon 4 junction, compared to 3829 reads for junction 2-3 and 3964 for junction 3-4.
In comparison, we did not observe the 2-4 junction among controls consisting of RNAs from another muscle biopsy used in the same run (Figure  3 ). The precise deletion breakpoints determined by BAM data from RNAseq showed the deletion of exon 3 in the reading phase (r. 640_735, hg38) in IGV software (Integrative Genomic Browser, version 2. 11. 1). The precise deletion breakpoints determined by BAM data from RNAseq showed the deletion of exon 3 in the reading phase (r. 640_735, hg38) in IGV software (Integrative Genomic Browser, version 2. 11. 1).
Bioinformatics Analysis MiSeq Software (MiSeq Control Software v. 2. 6. 2. 1) generates FASTQ format files after demultiplexing patients' sequences. Sequence alignment against the human reference genome (Hg19) was performed using BWA-MEM. Variant calling was performed using the GATK Haplotype Caller program. Detected variants were then annotated using ANNOVAR and CADD tools. Detected variants with sequencing depth greater than 30× and with at least 20% of reads supporting the alternative allele were kept for analysis. Detection of copy number variation (CNV) was performed after coverage normalization, by computing the ratio of a target's coverage of a given individual over the mean coverage of this target across all patients of the same sequencing run.
Variants Interpretations Pathogenicity of variants was determined according to current ACMG guidelines  [40]. Variants were filtered out according to their allele frequency (≤1%) as reported in the GnomAD database (http://gnomad. broadinstitute. org/, accessed on 1 April 2020) We then evaluated each variant considering a review of the literature, the location of the variant in the gene and the resulting corresponding protein, the in silico prediction tools (Polyphen2, SIFT, GVGD and CADD for missense variants and SpliceSiteFinder like, MaxEntScan, NNSPLICE, GeneSplicer and Human Splicing Finder for splicing variants) and functional studies when available. The SuSPect method (http://www. sbg. bio. ic. ac. uk/suspect, accessed on 1 April 2020) was also used for prediction. In addition, we looked at a local database of pathogenic variants related to our experience on the molecular diagnosis of myopathies.
Raw data from the Illumina NextSeq 500 sequencer (Illumina) was converted to FASTQ and demultiplexed with bcl2fastq. Reads from each sample were aligned to the human hg38 reference genome using the STAR tool. The quality metrics of the two previous software were supplemented by those of FastQC, Picard RnaSeqMetrics and RNA-SeQC and brought together in an HTML file thanks to MultiQC. The analysis of aberrant transcripts was carried out with visualization of Sashimi plots in IGV (Integrative Genomic Browser, version 2. 11. 1) on the regions of interest previously identified after DNA sequencing.
Frequency calculations were carried out for the verification of the nature of the stationary state and to confirm the absence of any imaginary frequency, which eventually confirmed the minima on the potential energy hypersurface for all structures. NMR chemical shifts were computed by employing the gauge-including atomic orbitals (GIAOs) method  [37]  using the BP86/def2-TZVP optimized geometries. Chemical shifts corresponding to 11  to the computed values  [38]. Wiberg bond indices (WBI)  [39]  were generated from natural bond orbital analysis (NBO)  [40]. All the optimized geometries and orbital pictures were drawn by Chemcraft [41] visualization programs. Laplacian electronic distribution plots and two-dimensional electron density were generated using the Multiwfn package  [42]. X-ray structure determination analysis details: Suitable X-ray quality crystals for 5 and 6 were grown by slow diffusion of a hexane-CH 2 Cl 2 solution at -5 °C.
The X-ray data were collected and integrated using a APEXII Bruker-AXS diffractometer for 5 , Bruker D8 VENTURE for 6, and with graphite monochromated Mo-K α ( λ = 0. 71073 Å) radiation at 150(2) K (for 5 ), 296(2) K (for 6 ). The structures were solved using SHELXT-2014, SHELXS-97 [43] , and refined using SHELXL-2017, SHELXL-2018, SHELXL-2014  [44]. X-ray structures were drawn using Olex2  [45]. CCDC-2150288 ( 5 ) and 2150289 ( 6 ) contain crystallographic data. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data _ request/cif. Crystal data of 5 : C 20 H 32 B 2 BrCuCo 2 Se 2 ; formula weight, M r = 713. 30; monoclinic; P.
In this scenario, our ongoing randomized phase II NIBIT-ML1 study (NCT04250246) in PD-1 resistant melanoma patients will help to address the clinical and immunobiologic contribution of the addition of the DHA ASTX727 to ICI therapy. However, further support the notion that guadecitabine is a promising way forward to improve the efficacy of ICI therapy in solid tumors derives from two most recently published trials in platinum-resistant ovarian cancer  16  , and in different tumor types  17. Indeed, the combination of guadecitabine with the anti-PD-1 pembrolizumab led to encouraging response rates, immunomodulation in the tumor tissue and/or in periphery, evidence of demethylation in on-treatment lesions, with manageable toxicity  [16] [17]. Our aim here was to exploit the complete longitudinal multi-omics profiling of the whole NIBIT-M4 cohort in conjunction with the five years follow-up to shed light on the effect of the combination during treatment and to identify early biomarkers of response.
The prognostic significance of the ICR/GIE index was confirmed by the analysis of independent datasets from ICI-treated patients, suggesting that this is a robust classifier that captures crucial immunological processes acting in the context of different immunotherapy regimens. The activation of T cell-mediated immunity is dependent upon the recognition of tumor antigens on major histocompatibility complexes (MHC) of antigen-presenting cells  46. Tumor antigen presentation by MHC class I is mediated by the coordinated expression of multiple genes. The differences between the High-ICR/GIE and High-ICR/Non-GIE, observed in our cohort and other independent cohorts, confirm that even in the presence of an adaptive immune response, tumor cells that develop defects in antigen processing or presentation can escape immune surveillance  37, 42.
Data collection, Library preparation, and sequencing Isolation of total DNA/RNA and library preparation for RNA Sequencing and Reduced Representation Bisulfite Sequencing (RRBS) were performed as previously described  13  at different time points of treatments (week0, week4, week12) for N=14 patients, including eight additional patients not available in the previous study. For Whole exome sequencing (WES), Nextera Flex for Enrichment solution (Illumina, San Diego, CA) in combination with SureSelect Human All Exon V7 probes (Agilent, Santa Clara, CA) was used for library preparation and generated libraries were sequenced on NovaSeq 6000 (Illumina, San Diego, CA) in 150 pair-end mode for biopsies of patients from 1 to 8; TruSeq Rapid Exome (Illumina, San Diego, CA, USA) was used for library preparation and generated libraries were sequenced on HiSeq 3000/4000 (Illumina, San Diego, CA) in 150 pair-end mode for biopsies of patients from 9 to 14.
RNA Sequencing Fastq quality was assessed using (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and low quality reads were discarded. Sequence reads were aligned to Human reference genome (UCSC genome assembly GRCh38/hg38) using STAR (v. 2. 7. 0b)  58  , and the expression was quantified at gene level using featureCounts (v. 1. 6. 3), a countbased estimation algorithm  59. Downstream analysis was performed in the R statistical environment as described below. Raw data were normalized according to sample-specific GC-content differences as described in EDAseq R package (v. 2. 22. 0)  60. Differential expression analysis was performed using EdgeR R package (v. 3. 30. 3)  61. Genes sorted according to log2 fold-change (log2FC) were used for performing Gene Set Enrichment Analysis (GSEA) of Gene Ontology (GO) Biological Processes (BP)  62  , as implemented in the clusterProfiler R package (v.
RRB Sequencing Reduced Representation Bisulfite Sequencing (RRBS) raw reads were trimmed for adaptor sequences using trim galore (v. 0. 6. 5) (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) and filtered for lowquality sequences using fastQC (v. 0. 11. 8) (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). High quality trimmed reads were mapped to the Human reference genome (UCSC genome assembly GRCh38/hg38) using Bismark (v. 0. 22. 3) 64 with default parameters. Methylation data as β values were retrieved from bismark coverage outputs using R package RnBeads 2. 0 (v. 2. 6. 0) 65 with default parameters. Then, human GRCh38 annotated genes and promoters exhibiting differential DNA methylation between predefined groups of patient samples were identified using R package limma (v.
3. 44. 3) 66. Prediction of immune response and tumor microenvironment deconvolution ICR scores were computed for each sample using a single sample GSEA (ssGSEA) based on the Mann-Whitney-Wilcoxon Gene Set Test (MWW-GST)  67  and the ICR signature (IFNG, IRF1, STAT1,IL12B, TBX21, CD8A, CD8B, CXCL9, CXCL10, CCL5, GZMB, GNLY, PRF1, GZMH, GZMA, CD274/PD-L1, PDCD1, CTLA4, FOXP3, and IDO1)  24. MIRACLE scores were computed using the MIRACLE R package as described in Turan et al. (  2021 )  9. TIDE score was computed using TIDE command-line interface (https://github. com/jingxinfu/TIDEpy)  26, 27. IMPRES score was computed using calc_impres R function (https://github. com/Benjamin-Vincent-Lab/binfotron/)  68. Estimation of immune and stromal subpopulation abundances was computed using MCP-counter  36.
TCR repertoire analysis from RNA Sequencing data The docker implementation of MiXCR software (v. 3. 0. 13)  69  was used to retrieve the VDJ repertoire from RNA Sequencing data. For the T cell receptor Beta locus (TRB), the clonality was calculated as 𝐶𝑙𝑜𝑛𝑎𝑙𝑖𝑡𝑦 !"# = 1 - $ %&' ! ( 𝐻(𝑥) where 𝐻(𝑥) is the Entropy computed as standard Shannon entropy as follow: 𝐻(𝑥) = -( ! "#$ 𝑃(𝑥 " ) 𝑙𝑜𝑔 % 𝑃(𝑥 " ) For a productive (in-frame) sequence 𝑥 ) , 𝑃(𝑥 ) ), is the ratio between the sequence count and total productive count and 𝑁 is the number of productive unique in-frame sequences.
Integrative analysis of RNASeq and RRBS data Integration of gene expression and methylation data was performed as follows, according to the type of region level methylation data. 1. Gene level methylation. For each matched RNAseq-RRBS profiling sample, gene expression and methylation ranks were aggregated using robust rank aggregation (RRA) method implemented in the R package RobustRankAggreg (v. 1. 2)  70. A Single sample GSEA (ssGSEA) was computed using the Mann-Whitney-Wilcoxon Gene Set Test (MWW-GST) as previously described in Frattini et al. , 2018  67  , starting from the expression-methylation aggregated rank and a manually curated collection of signatures downloaded from MSigDB 71 , including GO BP, REACTOME-, BIOCARTA-and HALLMARK-pathways. Molecular pathway changes that occurred at different treatment time points in R and NR groups and were simultaneously mediated by expression and methylation were summarized as heatmap using R package complexHeatmap (v.
2. 11. 1)  72. For visualization purposes, the resulting heatmap was obtained using the scaled Normalized Enrichment Score (NES) averaged in each time-response subgroup of the top differentially enriched and most statistically significant (FDR <0. 01) signatures in R vs. NR patient comparison. 2. Promoter level methylation. R package SMITE (v. 1. 16. 0)  73  was applied to identify functionally related genes with altered DNA methylation on promoters. Briefly, each differentially expressed gene (R vs. NR patients at each treatment time point week0, week4 and week12) previously computed using the EdgeR R package (v. 3. 30. 3) 61 was associated with a promoter region [TSS -1 kb, TSS + 500 bp] using UCSC GRCh38/hg38 refSeq transcripts coordinates. Each promoter was then associated with a set of overlapping regions from the differential methylation analysis previously computed on the same comparison using R package limma (v.
3. 44. 3) 66. To identify genes whose expression is inversely correlated with promoter methylation, a score based on a weighted significance value (0. 5 for expression and 0. 3 for promoter methylation) was computed. Hypomethylated/up-regulated and hypermethylated/down-regulated genes for each comparison were then visualized as scatterplot using R package ggplot2 (v. 3. 3. 6). These modules of expression/methylation concordat genes were functionally analyzed through a GO BP pathway enrichment analysis within the R package SMITE. Significant categories (p-value < 0. 05) were visualized as barplot using R package ggplot2. HLA typing and neoantigen prediction HLA typing was performed from WES data using the docker implementation of Polysolver (v. 4)  74. The neoantigen prediction tool pVACseq from pVACtools  75  was run using the following predictors: MHCnuggetsI, NNalign, NetMHC, SMM, SMMPMBEC, and SMMalign.
𝐺𝐼𝐸 = 𝑂 𝐸 The observed number of neoantigens O was obtained from the output of pVACtools  75  , filtered according to the criteria described above. The expected number of neoantigens E was computed as a function of the number of nonsynonymous mutations by fitting a linear regression model using the lm function of R package stats (v. 4. 0. 2) trained with the data of our cohort, using the number of neoantigens as dependent variable. We assumed that samples that show a frequency of neoantigens lower than expected (i. e. , lower GIE values) have evidence of immunoediting. Based on GIE and ICR scores, we classified samples as "High-ICR/GIE" (ICR > 0 and GIE < 1), "HighICR/Non-GIE" (ICR > 0 and GIE > 1), "Low-ICR/GIE" (ICR < 0 and GIE < 1), "Low-ICR/Non-GIE" (ICR < 0 and GIE > 1).
IHC analysis Serial 3 µm formalin-fixed paraffin-embedded tissue sections were stained using an AutostainerPlus (Dako). Antigen retrieval and deparaffinization were carried out on a PT-Link (Dako) using the EnVision FLEX Target Retrieval Solutions (Dako). Endogenous peroxidase and non-specific staining were blocked with H202 3% ICR and Genetic Immunoediting scores were computed as previously described. Differential expression analysis was performed between "High ICR/GIE" and "High ICR/Non-GIE" classes using a wilcoxon test. Genes sorted according to log2 fold-change (log2FC) were used for performing Gene Set Enrichment Analysis (GSEA) of Gene Ontology (GO) Biological Processes (BP)  62  , as implemented in the clusterProfiler R package (v. 3. 3. 6)  63. Selected enriched GO:BP categories (FDR < 0.