dataesr/scientific-paragraphs-categorization huggingface dataset:
Although heterogeneous (Additional file 1: Figure  S7b ), the cytokine production capacity of CD8 + TILs was consistently suppressed by Ado (Additional file 1: Figure  S7c ). In line with the effects of Ado on peripheral T cells, inhibitors of A2AR prevented the Ado-mediated suppression of CD8 + TILs (Fig. 4a  and Additional file 1: Figure  S7c ). Surprisingly, in prostate-derived samples, A2BR blockade was also able to prevent the Ado effect (Fig. 4a  and Additional file 1: Figure  S7c ). However, we did not find any impact of A2AR or A2BR inhibitors on the TIL expansion capacity (data not shown). We also evaluated the sensitivity of CD8 + TILs to Ado and compared it to circulating CD8 + T cells from healthy donors and cancer patients in resting conditions (ex vivo) or after in vitro expansion.
b Cumulative data of p-S6 expression in CD8 + T cells treated with the indicated combinations of Ado, A2AR agonist (CGS 21680), A2AR/A2BR antagonists (ZM 241385 and PSB 1115, respectively), the PKA inhibitor KT570 or the mTOR inhibitor rapamycin (RAPA) and stimulated for 3 h by anti-CD3/anti-CD28-coated beads. c Representative example of 3 independent experiments depicting the OXPHOS (measured as the oxygen consumption rate; OCR) and the glycolytic (measured as the extracellular acidification rate; ECAR) metabolism in CD8 + T cells stimulated overnight by anti-CD3/CD28-coated beads in the presence of the indicated combinations of Ado and the A2AR antagonist ZM 241385. d Cumulative data of CD71 and CD98 expression or 2-NBDG uptake in CD8 + T cells treated with the indicated combinations of Ado and A2AR/A2BR antagonists (ZM 241385 and PSB 1115, respectively) or the PKA inhibitor KT570 and stimulated overnight by anti-CD3/anti-CD28-coated beads (n = 6, n = 7 and n = 8 from left to right).
Funding The research leading to these results has received funding from the following: The European Community's  Seventh Framework Program  ( [FP7/20013-2018 ] under grant agreement  n°602200 ). The  SNSF  project grant ( 31003A_163204 ), the  Institute for Experimental Cancer Research  ( 26075483 ), and the  Swiss Cancer Foundation  ( KFS-3949-08-2016 ) to P. -C. H. The Swisslife foundation project grant ( 2607011902 ) to S. V. The  SNSF   Ambizione grant  ( PZ00P3_161459 ) and the  Swiss Cancer Foundation  grant ( KFS-3710-08-2015 ) to C. J. and  SNSF  grant  156469  to P. R.
Genetics and functional analysis At this time, 28 different mutations from 42 cases have been described, located mostly in the DNA binding domain (Figure  2  and Table  E2 ). All patients were heterozygous for the mutation, 21 were de novo mutations and 7 were inherited in an autosomal dominant manner within 5 different families. Six unaffected members were described within 4 out of these 5 families. Penetrance is thus incomplete. Analysis of described STAT3 variants in the gnomAD database showed only one mutation as already reported (p. R246Q with a frequency of 1/246408 alleles), all the others are not reported. All mutations reported but one (an in frame amino acid deletion) are missense. All variants were predicted pathogenic or probably pathogenic by UMD-predictor and disease causing by Mutation Taster.
A HTLV-1a strain deleted for p12 has been described in 3 siblings, suggesting that this virus was transmitted by their mother and is therefore capable of transmission, replication, and persistence in vivo  [61]. Similarly, in closely related BLV, the mutation of accessory proteins (R3 and G4) attenuates the virus; the attenuated virus can still replicate and, in the long term, cancers can still appear (although rarely)  [65]. Finally, a recent in silico study comparing the complete PTLV-1 genomes available on GenBank confirmed, as expected, that each complete HTLV-1a strain has accessory ORFs and encodes for the 4 proteins. In contrast, strains of the HTLV-1c and -1b subtypes lack some accessory genes  [66]. The start codon of ORF I is missing from the complete HTLV-1c and HTLV-1b sequences.
As a result, mutation rates were estimated at 3. 5E-6 and 7. 3E-6 substitutions/site/year for LTR and env, respectively. In a similar study in Brazil, the estimation was found surprisingly high (2E-5 substitutions/site/year for LTR), supposedly because it was calculated on the basis of 1 mutation on a single mother-child pair  [30]. This value may be largely overestimated. Indeed, in Melanesia, the intra-familial genetic heterogeneity is as low as 0-0. 2% over 931 nt  [99]. This method focuses mainly on vertical transmission of the virus and generates an estimation of the mutation rate in the short time scale. On the other hand, the mutation rate can be estimated using phylogeny and an anthropological approach, using a dating anchor point for a given clade. Such analyses are based upon several assumptions: (1) the data set is informative, i.
The study was approved by the local ethics committee of the IHU (Institut Hospitalo-Universitaire)-Méditerranée Infection under the registration number 12-016  [4]. Statistical analyses were performed using Prism 6. 0 (Graphpad Software Inc. ) and SPSS 22 Statistics Software. Multivariate models were adjusted for sex and age at baseline  [4]. P<0. 05 was considered as significant.
Pairing multiple chains in the same clone We analysed previously published data on sequenced T-cell CDR3 regions obtained from two human subjects (PairSEQ), as described by Howie and collaborators  [9]. In the original study, sequences of α and β chain pairs associated to the same clone were isolated using a combination of high-throughput sequencing and combinatorial statistics. Briefly, T cell samples were deposited into wells of a 96-well plate, their RNA extracted, reversetranscribed into cDNA with the addition of a well-specific barcode, amplified by PCR, and sequenced. αβ pairs appearing together in many wells were assumed to be associated with the same T-cell clone, and thus expressed together in the same cells. Because the method relies on the presence of cells of the same clone in many wells, the method can only capture large memory T cell clones present in multiple copies in the same blood sample.
Correlations between chains of the same cell Correlations between the features of the recombination events of the chains present in the same cells are informative about the rules governing the formation of a mature αβ TCR in the case of α-β pairings, and also about the mechanisms and temporal organization of recombination on the two chromosomes in the case of α-α and β-β pairings. We computed the mutual information, a non-parametric measure of correlations (see Methods), between pairs of recombination features for each chain: V, D, and J segment choices, and the numbers of deletions and insertions at each junction (Fig. 2 ). Because recombination events cannot be assigned with certainty to a given sequence, we used the IGoR software  [28]  to associate recombination events to each sequence with a probabilistic weight reflecting the confidence we have in this assignment (see Methods).
For a statistical anlaysis of the significance of the reported mutual informations, see Fig. S2. lations would require analysing pairs of non-productive sequences, which are not subjected to selection, but the number of such pairs in the dataset was too small to extract statistically significant results. An analysis of the correlations between gene segments (Fig. S3 ) does not show any particular structure. Pairs of β chains show almost no correlations (Fig. 2C  and Fig. S2 for an analysis of statistical significance). Looking in detail at the correlations between gene segments reveals a strongly negative correlation of TCRBV21-01 and TCRBV23-01 (both pseudogenes) with themselves (Fig. S4 ), which is expected because at least one of the two β chain must have a non-pseudogene V. More generally, correlations are likely to arise from selection effects, since the two recombination events of the two β chains are believed to happen sequentially and independently.
Probability of recombination of the second chromosome We wondered if the paired data could be used to estimate the percentage of cells with two recombined chains of the same type. However, since pairing was done based on mRNA transcripts through cDNA sequencing, silenced or suppressed genes are not expected to be among the identified pairs, leading to a systematic underestimation of double recombinations. While the authors of  [9]  also provided a genomic DNA (gDNA) dataset that does not have this issue, the number of sequences was too small to resolve statistically significant pairings. Nonetheless, we can derive strict bounds from the proportion of productive sequences found in this (unpaired) gDNA dataset. Following recombination, using IGoR we estimate p α nc = 69. 5% of the α sequences, and p β nc = 73.
Only the third experiment (the most data rich) is considered to avoid pooling together datasets with different sequencing depths. Sequences of chains paired with a non-coding chain of the same type must be functional and expressed on the surface of the cell. Those sequences have a markedly different distribution of read counts than non-coding sequences (Fig. S7A  and B ). Coding sequences that are coupled with another coding sequence can be either expressed or silenced, depending on their own functionality and the status of the other chain. Thus, their read count should follow a mixture distribution of both expressed and silenced sequences, the latter being assumed to follow the same distribution as noncoding sequences. Fitting the parameters of this mixture to the read counts of paired coding sequences (Fig.
Model predicts very rare αβ TCR sharing Ignoring small correlations between features of the α and β chains reported in Fig. 2 , we can assume that the probability of generating a αβ pair is given by the product of the probabilities of generating each chain independently. These probabilities can be calculated using the IGoR software  [28]  for each paired chain in our datasets. The distribution of the pair generation probabilities obtained in this way (Fig. 5 A ) shows an enormous breadth, spanning more than 20 orders of magnitude. We selfconsistently validated the assumption of independence by showing that random assortments of α and β chains yielded an identical distribution of generation probabilities (green curve). The maximum TCR generation probability is < 10 -12 , meaning that generating the same pair twice independently is extremely unlikely.
Generation model The generation model was obtained and used through the IGoR software  [28]. The IGoR software is able to learn, from out-of-frame receptor sequences, the statistics of a V(D)J recombination process. We don't use IGoR in its inference capacity here, but rather rely on the pre-inferred recombination model for TRA and TRB chains in humans supplied with IGoR, as the recombination process is widely shared between individuals  [12]. Briefly, the probabilities of recombination of α and β chains factorize as: P α recomb = P (V, J) P (delV |V ) P (delJ|J) P (insV J) × insV J i PV J (ni|ni-1) , (2) P β recomb = P (V, D, J) P (delV |V ) P (insV J) × P delD5 delD3 D P (insDJ) × P (delJ|J) insV D i PV D (mi|mi-1) × insDJ i PDJ (ri|ri-1) , (3) where (ni), (mi), (ri) are the inserted nucleotides at the VJ, VD, and DJ junctions.
RNA Extraction and Sequencing Analysis RNA was isolated from human CD and healthy HILEC with the RNeasy Mini Kit ® (Qiagen, Hilden, Germany) according to the manufacturer's instructions and stored at -80 °C. Library preparation and RNA-sequencing were performed at Galseq NGS facility (Milan). Read alignments to the hg38 reference genome and gene counts were performed with Spliced Transcripts Alignment to a Reference (STAR) v2. 5  [14]. Differential gene expression was carried out with DESeq2  [15]. Functional enrichment for gene ontology (GO) biological processes was performed with the Gene Set Enrichment Analysis (GSEA) software  [16]  (OSF submission ID an8w3, https://osf. io/an8w3/).
RNA Extraction and Sequencing Analysis RNA was isolated from human CD and healthy HILEC with the RNeasy Mini Kit ® (Qiagen, Hilden, Germany) according to the manufacturer's instructions and stored at -80 • C. Library preparation and RNA-sequencing were performed at Galseq NGS facility (Milan). Read alignments to the hg38 reference genome and gene counts were performed with Spliced Transcripts Alignment to a Reference (STAR) v2. 5  [14]. Differential gene expression was carried out with DESeq2  [15]. Functional enrichment for gene ontology (GO) biological processes was performed with the Gene Set Enrichment Analysis (GSEA) software  [16]  (OSF submission ID an8w3, https://osf. io/an8w3/).
Statistical Analysis and Data Visualization Statistical analysis was performed with Microsoft Excel, IBM SPSS and Graph Pad 7 Prism statistical packages. Data were presented as means ± s. e. m. Mean comparisons were analyzed with two-tailed unpaired Student's t-test or a two-way ANOVA with a two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli for multiple comparisons. P-values lower than or equal to 0. 05 were considered statistically significant. Genes shared among the enriched pathways were obtained by Venn diagrams created using JVenn software (http://jvenn. toulouse. inra. fr/).
Among the upregulated, we found CREB, IL7, IGF1R, and mTOR-related pathways harbored the highest number of differentially expressed genes (Figure  1B-F ). In order to find a possible target to be modulated, we extrapolated the list of genes belonging to CREB, IL7, IGF1R and mTOR data sets. Using Venn diagrams, these pathways resulted to be linked with each other, as they shared two genes encoding the regulatory subunits of PI3K, PIK3CA, and PIK3R1 (Figure  1G ). It has been largely reported that both IGF1/IGF1R and IL7/IL7R signaling contribute to mTOR activation  [17, 18] , and that mTOR is associated with the activation of the CREB pathway via AKT signaling  [19]. Therefore, in this scenario, we reasoned that, by targeting mTOR, all the other pathways might be simultaneously affected in HILEC, independently from their inflammatory state (Figure  1H ).
agalactiae, which is genetically close to M. bovis with a 99% nucleotide similarity between their 16S rRNA sequences  [29]  but usually not isolated from the bovine host with a few (See figure on previous page. ) Fig. 2  Determination of the analytical sensitivity of various M. bovis PCR assays in use by six laboratories (Lab 1 to Lab 6) on spiked bronchoalveolar lavage fluid (BALF) samples. Ten-fold serial dilutions (n = 7) of a mixture of 1 × 10 8 M. bovis CFU/ml were prepared in BALF resulting in a range of 1 × 10 7 to 1 × 10 1 M. bovis CFU/ml and tested by the various PCR methods. Viability of M. bovis in the BALF samples was verified with culture. Raw data to this figure are provided in Additional file 1 exceptions  [30]. Moreover, weak positive results with Ct values between 37 and 40 were obtained on non-target Mycoplasma strains which could result in misinterpretation of results and highlights the requirement for thorough assessment of cut-off during validation at individual laboratory level.
e. , hotspots); therefore, treating and prophylactically protecting these individuals could prevent future morbidity and onwards transmission as these individuals are more likely to develop future symptomatic infections  18. In this article we analyse a series of datasets, harnessing the increasing quantities of molecular data generated in recent years, to investigate the density, temporal dynamics and infectiousness of low-density P. falciparum infections. Firstly, we examine the parasite density distributions of asymptomatically infected populations in a range of transmission settings measured by quantitative molecular methods, with implications for the required sensitivity of new diagnostics. Next, longitudinal studies are analysed to elucidate the temporal dynamics of parasite densities in endemic populations, with particular reference to the risk of subpatently infected individuals developing future higher density infections.
Results Density and detectability of P. falciparum infections. A literature search was conducted to identify cross-sectional data on the parasite densities of individuals with Plasmodium falciparum infections in endemic areas measured using a quantitative molecular method. Fifteen articles were identified, consisting of data from 22 locations in a wide range of transmission intensities (prevalence by molecular method ranging from 0. 4% to 90. 6%)  3, 4, 12, 13, [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. Details of the literature search criteria and more information on the diagnostic methods and study settings are in Methods and Supplementary Table  1. The median parasite density in identified dataset was between 1 and 1336 parasites per µl (Fig. 1a ). The range of parasite densities (parasites per µl) in infected individuals spanned at least two orders of magnitude, and in several cases, more than six orders of magnitude (Fig.
1a ). Individuals in each dataset were split into two groups: those with infections that were detectable by PCR and microscopy or RDT, and those with infections that were detectable by PCR only (Fig. 1b ). Median parasite density is lower in the subpatently infected individuals in all datasets compared with the microscopy/RDT-positive group. However, the interquartile range of the two groups overlaps in 3/18 of the datasets and the total range overlaps in all datasets. The limit of detection for microscopy in routine diagnostic settings is thought to be around 100 parasites per µl  31  and around 10 parasites per µl by expert microscopists, yet 12/18 datasets have subpatent infected individuals with parasite densities >100 parasites per µl and all have subpatent-infected individuals with parasite densities >10 parasites per µl.
Infected individuals that are patent but have very low-parasite densities by PCR typically have asexual parasite densities by microscopy in the traditional range of this method (> 50 parasites per µl), indicating a disagreement in density measures between the two tests. Figure  1c  shows that median parasite density (from each of the studies in Fig. 1a ) increases in higher transmission settings; a trendline fitted to the data indicates that the median parasite density increases from below five parasites per µl in the datasets with the lowest prevalence to 100 parasites per µl in the highest transmission datasets. As has been shown previously  1  , the proportion of infected individuals that are detectable by microscopy/RDT is lowest in low transmission settings (Fig. 1d ). We did not standardise the age distribution of data across the studies (as several studies did not include age data), but results from an age-standardised analysis were consistent with those in Fig.
2k  and a pooled prediction of all data is presented in Fig. 2l. There is large variation between studies on the probability of detection by microscopy; at a qPCR-detected parasite density of 100 parasites per µl, the median probability of being detected by microscopy is 29. 7%, with a range of 3. 8-69. 7%. The proportion of infected individuals in each dataset with parasite densities above certain thresholds (1, 10 and 100 parasites per µl) is shown in Fig. 3. Across all datasets, a mean of 42% (range 1-97%) of infected individuals had densities above 100 parasites per µl, and the proportion of infected individuals detected at this threshold increased in higher prevalence settings. This is consistent with Fig. 1d  showing that a lower proportion of infected individuals have subpatent infections in higher transmission settings.
By contrast, an average of 56. 5% infected individuals (11-100%) had densities above 10 parasites per µl, and the parasite density distributions suggest that a diagnostic tool with this sensitivity would detect >55% of infections in the majority of settings. The proportion of infections that would be detected with a diagnostic with this sensitivity was lower in low transmission settings. For simplicity, this estimate assumes absolute detection thresholds rather than a gradual decrease in sensitivity as parasite density decreases. More than 80% of infected individuals (average of 89. 2%) had densities >1 parasite per µl in all settings except those in very low transmission settings (6 out of 10 of the datasets with PCR prevalence <4%). There is a clearer decline in diagnostic performance (i.
Sensitivity of diagnostics over the course of an infection. A systematic review was conducted to identify studies which had information on how the detectability by microscopy/RDT of naturally acquired P. falciparum parasites changes over time (see the Methods section). We analysed two types of studies that are informative for this question: cohort studies testing individuals by both microscopy and PCR, and studies that monitored population prevalence during a change in transmission intensity. We identified and requested access to 16 cohort datasets in endemic areas that used both microscopy and PCR. We analysed seven cohorts to which we were granted access within the time frame of the study or which contained the full information in the publication (Table  1 ). These were all conducted in moderate-tohigh transmission areas and the total duration of follow-up was from 3 days to 16 months.
A previous analysis found a non-significant increase in detectability in the rainy season  36. Here, we analyse the seasonal patterns in more detail with the increased number of datasets. If more than one survey was conducted during the same season and same year in a location, these were pooled. We discarded data from three locations, where the relative difference in slide prevalence between seasons was <5%. Surveys do not necessarily include the same individuals at each time point, therefore this is an ecological analysis. The sensitivity of microscopy compared with PCR as gold standard was not significantly different between seasons in 17/20 locations (Supplementary Figure  1 ). When all the seasonal survey results were pooled by meta-regression as previously,  36  the ratio of microscopy sensitivity in the high season relative to the low season was 1.
Our literature search also identified five studies in which Pf prevalence was measured multiple times by slide/RDT and   3  The proportion of PCR-positive infections with parasite densities above three thresholds. a all datasets-the triangle, square and circle represent the proportion of individuals in each dataset with parasites densities greater than 1, 10 and 100 parasites per µl, respectively (assuming 100% detection below the threshold and 0% detection above, for simplicity). b Zoom-in of the 0-4% prevalence area. The black stars indicate the actual proportion of infections detected using the ultra-sensitive RDT in Myanmar (left) and Uganda (right)  12. The dashed grey lines show the best-fit line (of the form: proportion detected = a-b * exp(m * PCR prevalence) for the estimated proportion of PCR-positive individuals that would be detected using the three limits of detection.
Source data are provided as a Source Data file Methods Systematic review. There are three main topics within our review (see below) and we designed specific search strategies for each. Abstracts of publications within PubMed were searched for the specific terms below. The authors also sought relevant unpublished data as far as possible within the time frame of the review. Statistical analysis. The relationship between PCR parasite density and detectability by microscopy was modelled using a logistic regression model with a studylevel random effect fitted using a Bayesian framework and Stan  61. For the analysis of cohort studies, to test whether individuals with submicroscopic infections are more likely than PCR-negative individuals to become slide positive in the future, we computed the probability of slide-positive infection at any follow-up time, by initial infection status.