dataesr/scientific-paragraphs-categorization huggingface dataset:
Each of the following signatures was applied in each data set separately: three Palmer's B-cell, T-cell, and CD8+ T-cell signatures  [73] , four signatures reflecting the cytotoxic T-cell response including the "LCK signature"  [74] , the "medullary breast cancer" signature  [75] , the "28-kinase immune" signature  [76] , and the "immune response" signature  [77] , and two Gatza's signatures of IFNα and IFNγ biological pathway activity  [78]. Finally, to explore more-in-depth the biological pathways linked to PDL1 expression in pancreatic cancer, we applied a supervised analysis by using the largest data set (TCGA: 178 samples) as learning set, and the other data sets as independent validation sets (275 samples). In the learning set, we compared the expression profiles of 20,531 genes between tumors with (N = 46) versus without (N = 132) PDL1 upregulation using a moderated t-test with empirical Bayes statistic  [79]  included in the limma R packages.
False discovery rate (FDR)  [80]  was applied to correct the multiple testing hypothesis and significant genes were defined by the following thresholds: p < 5%, q < 25% and fold change (FC) superior to |1. 5x|. Ontology analysis of the resulting gene list was based on GO biological processes and Biocarta ontologies of the Database for Annotation, Visualization and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/). We verified the robustness of the resulting gene list in each validation set (a total of 41 tumors with and 234 without PDL1 upregulation were represented) separately by computing for each tumor a metagene-based prediction score defined by the difference between the "metagene PDL1-up" (mean expression of all genes upregulated in the "PDL1-up" group) and the metagene PDL1-not-up" (mean expression of all genes upregulated in the "PDL1-not-up" group).
Variables with a p-value < 0. 10 were tested in multivariate analysis. All statistical tests were two-sided at the 5% level of significance. Statistical analysis was done using the survival package (version 2. 30) in the R software (version 2. 9. 1; http://www. cran. r-project. org/). We followed the reporting REcommendations for tumor MARKer prognostic studies (REMARK criteria)  [81].
Single molecule FISH (smFISH) allows studying transcription and RNA localization by imaging individual mRNAs in single cells. We present smiFISH (single molecule inexpensive FISH), an easy to use and flexible RNA visualization and quantification approach that uses unlabelled primary probes and a fluorescently labelled secondary detector oligonucleotide. The gene-specific probes are unlabelled and can therefore be synthesized at low cost, thus allowing to use more probes per mRNA resulting in a substantial increase in detection efficiency. smiFISH is also flexible since differently labelled secondary detector probes can be used with the same primary probes. We demonstrate that this flexibility allows multicolor labelling without the need to synthesize new probe sets. We further demonstrate that the use of a specific acrydite detector oligonucleotide allows smiFISH to be combined with expansion microscopy, enabling the resolution of transcripts in 3D below the diffraction limit on a standard microscope.
Segmentation is routinely performed on 2D maximum intensity projections, even if images are acquired in 3D. We found that such projections can lead to blurry cell boundaries, reducing the segmentation quality and result in the loss of thin cellular extensions such as pseudopods. Here, we present a new focus-based projection approach that allows a better determination of cell boundaries. This approach can use the non-specific smFISH signal and can be combined with traditional 2D segmentation software  (10, 11)  based on mathematical morphology and traditional filtering and thresholding techniques. We integrated this approach in the existing Matlab toolbox to analyse smFISH data (FISH-quant; (5)). FISH-quant can be fully controlled via graphical user interfaces and is hence easily accessible for non-specialist.
In summary, we present a complete and validated workflow for single molecule FISH. This workflow comes with several key advantages: (i) a reduced probe cost allowing the generation of more efficient probe sets, (ii) an optimized approach to determine good hybridizing sequences, (iii) a simple experimental protocol -including probe synthesis (Supplementary Protocol), (iv) a flexible probe design, allowing multi-colour smiFISH or super-resolution imaging with expansion microscopy  (12) , (v) software tools going from probe design to image analysis.
M. Escudier (SPCMIB, Toulouse, France) and labelled with Cy3 mono-reactive dye pack (GE Healthcare). All primary probes sequences are available online at https://bitbucket. org/muellerflorian/fish quant in the Oligostan folder. FLAP sequences are listed in Supplementary Note 1. smiFISH and image analysis smiFISH was performed according to the Supplementary Protocol. Three-dimensional image stacks were captured on a wide-field microscope (Zeiss Axioimager Z1/Apotome) equipped with a 100 × 1. 4 NA objective and a CCD camera (Axiocam MRm 4) and controlled with Metamorph (Molecular Devices). For MS2 experiments, spots in the GFP and Cy3 channels were detected in 3D stacks with Imaris (Bitplane) and categorized as being labelled either with MS2-GFP, smiFISH probes or both. Because the MS2-GFP protein is nuclear, DAPI staining was used to de-PAGE 3  OF 11 Nucleic Acids Research, 2016, Vol.
We therefore applied smiFISH with ExM to GAPDH -a housekeeping gene routinely used as an expression control (Supplementary Figure  S2 ). Before expansion, individual GAPDH molecules were often too close to be readily distinguished (Figure  4E , left). After expansion, individual mRNA could be distinguished also in dense regions (Figure  4E , right and Figure  4F ). As a consequence, mRNA counts after expansion were substantially higher (Figure  4G ) reflecting the improved ability to spatially resolve transcripts. In summary, combining smiFISH with ExM provides better SNR and allows resolving spatially dense transcripts. 5 ) and co-localization determined with the Matlab function munkres using the Hungarian Algorithm for linear assignment problems, which is available on Matlab File Exchange.
(E) Co-localization analysis for dual-colour smiFISH experiment in (C). mRNAs were detected in 3D images with FISHquant (5) and co-localization determined with the Matlab function munkres using the Hungarian Algorithm for linear assignment problems, which is available on Matlab File Exchange. Plots show the co-localization percentage for TPX2 (blue, 27 178 mRNAs) and CTNNA1 (red, 35 623 mRNAs) as a function of the maximum allowed distance between spots to be still considered to be co-localized. For distances smaller than 500 nm, co-localization is smaller than 5%. Larger allowed distances lead to a significant increase of co-localization percentage, since neighbouring spots are erroneously considered to co-localize (especially in denser area such as zoom-in 4).
FUNDING Scholarship from  Lebanese National Council for Scientific Research (LNCSR)  [to R. C. ];  Agence Nationale de la Recherche  [ ANR-11-BSV8-018-02 ,  ANR-14-CE10-0018-01 ];  Institut Pasteur ,  Fondation pour la Recherche Médicale (FRM) ;  Agence Nationale de Recherche sur le Sida (ANRS). Funding for open access charge:  Institut Pasteur.
DISCUSSION We present smiFISH -an easy-to-use, cost-effective and reliable smFISH workflow going from probe design to image analysis. A key advantage of smiFISH is an optimized probe design that combines unlabelled specific primary probes with labelled secondary probes. This design has several advantages. First, probes are substantially cheaper, and this allows to use more probes per mRNA and thus to obtain a higher SNR and a better efficiency of RNA detection. Second, it provides large flexibility in labelling primary probes, to perform for instance dye-swap, multi-colour experiments and ExM without the need of generating new probes sets. We further provide fully automated software solution from probe design to image analysis. In particular, we implemented a focus-based projection method providing sharper 2D projections from 3D stacks.
ACKNOWLEDGEMENT The authors thank Benjamin King, Robert Feil, David Llères, Michael Girardot and Thierry Forné (IGMM, Montpellier, France) for helpful discussions, Stéphane Emiliani (Institut Cochin, Paris, France) for the HeLa-HIV-MS2-GFP cell line, Anne Debant and Susanne Schmidt (CRBM, Montpellier, France) for neuron samples. Conflict of interest statement. None declared.
Abstract. Within the framework of air quality studies at the megacity scale, highly time-resolved volatile organic compound (C 2 -C 8 ) measurements were performed in downtown Paris (urban background sites) from January to November 2010. This unique dataset included non-methane hydrocarbons (NMHCs) and aromatic/oxygenated species (OVOCs) measured by a GC-FID (gas chromatograph with a flame ionization detector) and a PTR-MS (proton transfer reaction -mass spectrometer), respectively. This study presents the seasonal variability of atmospheric VOCs being monitored in the French megacity and their various associated emission sources. Clear seasonal and diurnal patterns differed from one VOC to another as the result of their different origins and the influence of environmental parameters (solar radiation, temperature).
To consider a station as an urban background site, the ratio R between annual average NO and NO 2 concentrations (NO / NO 2 ) should be less than 1. 5 ppb ppb -1 , as indicated in a report  (Mathé, 2010)  written at the national level for regulatory purposes. We observed a very close NO : NO 2 ratio (expressed as ppb ppb -1 ) between both sites in 2010: 0. 40 (LHVP) vs. 0. 38 (Les Halles). The same conclusion can be made for the years 2008 and 2009 (0. 45 ± 0. 01 for LHVP and 0. 48 ± 0. 01 ppb ppb -1 for Les Halles). With NO : NO 2 ratios very similar and less than 1. 5, this confirms that these two locations have the same site typology and can be considered as urban background stations. To prove that it was valid to merge these two specific datasets from different locations, we opted for a comparison of PMF results derived from MEGAPOLI-FRANCIPOL (as performed in the present paper) and FRANCIPOL data files, respectively.
PMF modeling simulations were performed using only the FRANCIPOL dataset. A good agreement was found for the majority of the emission sources. Within the MEGAPOLI and FRANCIPOL projects, on-line high-sensitivity proton transfer reaction -mass spectrometers (PTR-MS, Ionicon Analytik GmbH, Innsbruck, Austria) were used for real-time (O)VOC measurements. As this instrument has widely been described in recent reviews  (Blake et al. , 2009, and references therein) , only a description of analytical conditions relating to ambient air observations is given here. During these two intensive field experiments, a PTR-MS was installed in a small room located on the roof of the LHVP site (15 m a. g. l. ). For the MEGAPOLI winter campaign, PTR-MS measurements performed by the Laboratoire de Chimie Physique (LCP, Marseille, France) have already been described in  Dolgorouky et al.
(2009). As the measurement frequency was different for each analyzer, a common average time was defined to get all datasets on a similar time step of 1 h. Standard meteorological parameters (such as temperature, relative humidity as well as wind speed and direction) were provided by the French national meteorological service Météo-France from continuous measurements recorded at the Paris-Montsouris monitoring station (14th district -48 • 49 N, 02 • 20 E), located about 2 km away from the LHVP site. In order to determine the air masses' origin, 5-day back trajectories were calculated every 3 h from the PC based version of the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model  (Stein et al. , 2015)  with Global Data Assimilation System (GDAS) meteorological field data. Back trajectories were set to end at Paris coordinates (49 • 02 N, 02 • 53 E) at 500 m a.
Bilinear receptor modeling: PMF tool Developed about 20 years ago, PMF is an advanced multivariate factor analysis tool widely used to identify and quantify the main sources of atmospheric pollutants. Concerning VOCs, PMF studies have been conducted in urban (e. g. ,  Brown et al. , 2007 -Los Angeles, California, USA; Lanz et al. , 2008 -Zürich, Switzerland; Morino et al. , 2011 -Tokyo, Japan; Yurdakul et al. , 2013 -Ankara, Turkey)  and rural areas (e. g. ,  Sauvage et al. , 2009 -France; Lanz et al. , 2009 -Jungfraujoch, Switzerland; Leuchner et al. , 2015 -Hohenpeissenberg, Germany). For this current study, the PMF 5. 0 software developed by the EPA (Environmental Protection Agency) was used in the robust mode from ambient air VOC measurements from January to November 2010. A more detailed description of this PMF analysis is given in Appendix A.
PMF mathematical theory was extensively described in  Paatero and Tapper (1994). Briefly, this statistical method consists in decomposing an initial chemically speciated dataset into factor profiles and contributions. Equation (  1  summarizes this principle in its matrix form: X = G F + E, (1) where X is the input chemical dataset matrix, G is the source contribution matrix, F is the source profiles matrix and E is the so-called residual matrix. The initial chemical database used for this statistical study contains a selection of 19 hydrocarbon species and masses divided into 10 compound families: alkanes (ethane, propane, iso-butane, n-butane, iso-pentane, n-pentane and nhexane), alkenes (ethylene and propene), alkyne (acetylene), diene (isoprene), aromatics (benzene, toluene, xylenes plus C 8 species), alcohol (methanol), nitrile (acetonitrile), aldehyde (acetaldehyde), ketone (acetone) and enones (methyl vinyl ketone, methacrolein and isoprene hydroxy hydroperoxides), which have been measured from 15 January to 22 November 2010 (n = 6445 with a 1 h time resolution).
2, "weak" if it was between 0. 2 and 2 and "strong" if it was greater than 2. Bad variables are excluded from the dataset, weak variables get their uncertainties tripled while uncertainties of strong variables stay unchanged. Here, all species exhibited a S/N ratio greater than 3, except for isoprene which had a ratio of 1. 7 due to its 41 % missing values recorded. To address this lack of isoprene data, several empirical tests (e. g. , simulating an averaged seasonal/diurnal cycle of isoprene or increasing the analytical uncertainty of raw data from 15 to 30 %) were conducted within PMF simulations with the aim of better modeling the variability of this compound. As a consequence of these tests, no significant improvement on the quality of modeling isoprene was observed. Finally, isoprene is still categorizing as strong here.
In addition to isoprene, acetonitrile exhibited a S/N ratio less than 3 (S/N ratio of 2. 7). It was the only VOC to be defined as weak because it may be eventually contaminated with local emissions from laboratory exhausts (although visible spikes of acetonitrile were excluded from the initial dataset). Keeping in mind these limitations for isoprene and acetonitrile, it was decided to include these two compounds into the PMF model because they are considered relevant tracers for biogenic and wood-burning activities, respectively. VOC was defined as "total variable" and automatically categorized as weak to lower its influence in the final PMF results. No optional extra modeling uncertainty was applied here. All the Q values (Q true , Q robust , Q expected ), scaled residuals, predicted vs. observed concentrations interpretation and the physical meaning of factor profiles were investigated to determine the optimum number of factors.
9 µg m -3 (2. 0 ppb)) are considered to be the two major oxygenated compounds measured in this study. This conclusion is in agreement with previous VOC measurements performed in downtown Paris in 2007  (Gros et al. , 2011). The comparison between these average ambient levels and VOC measurements reported in the literature for different urban areas is restricted here to PTR-MS data as they constitute the most original dataset of this study. Most atmospheric studies were indeed conducted in urban metropolitan areas by investigating only NMHC measurements. Table  2  summarizes PTR-MS data collected during the intensive experiment together with average VOC levels reported in ppb from other cities around the world. For the Paris megacity, a significant decrease in VOC concentrations was observed between spring 2007 and spring 2010 (from -53.
Seasonal and diurnal variations The variability in VOC concentration levels is controlled by a combination of factors including source strengths (e. g. , emissions), dispersion and dilution processes as well as photochemical reaction rates with OH radicals and other oxidants  (Filella and Peñuelas, 2006). Variations of selected trace gases (nitrogen / carbon monoxide, NO / CO -Fig. 6 ) and VOCs illustrating contrasting emission sources and atmospheric lifetimes were analyzed at different timescales. As (O)VOCs measured by PTR-MS constitute the most original data of this study (and represent ∼ 37 % of the TVOC mass); a discussion on their variations (Fig. 7 ) and their respective sources is given here. For information, an overview of seasonal and diurnal profiles of lighter hydrocarbons (C 2 -C 6 ) measured by GC-FID is reported in Sect.
Data availability All the data presented in this paper are available upon request. Please contact Valérie Gros (valerie. gros@lsce. ipsl. fr) for further information. A. Baudic et al. : Seasonal variability and source apportionment of VOCs Appendix A: Application of the PMF approach in source apportionment of VOCs in Paris A1 Data preparation Initially, the EPA PMF 5. 0 model requires two input datasets: one with the chemical species atmospheric concentrations for each observation point and another with either uncertainties values or parameters for calculating the associated uncertainty. The initial chemical dataset contains a selection of 19 hydrocarbon species and masses (for a detailed overview, see list of compounds in the Sect. 2. 4. 1) measured from 15 January to 22 November 2010. NMHCs and OVOCs were, respectively, monitored with GC-FID and PTR-MS instruments belonging to different partners involved during the MEGAPOLI and FRANCIPOL intensive field campaigns.
Unfortunately, no intercomparison between these instruments was possible because there was approximately a 1-month delay between both experiments. However, preliminary PMF modeling simulations were performed using only the FRANCIPOL dataset (24 March-22 November). The results have shown similar source profiles (see Sect. S1), as those already described in this paper. Consequently, two datasets (corresponding to MEGAPOLI and FRANCIPOL ones, respectively) were considered to form a single one and use it as an input unified database for the final PMF analysis. The uncertainty dataset was built upon the equation-based method described by  Norris et al. (2014). It requires both MDL (here in µg m -3 ) and the analytical uncertainty (u, here in percent) for each considered species. Two sets of MDLs were used, one for each measurement campaign.
Slight differences among species MDLs were found for n-hexane, aromatics, acetaldehyde and MVK + MACR + ISOPOOHs between both experiments. Of these VOCs, MDLs from the FRANCIPOL campaign were chosen for representativity reasons (as the corresponding dataset represents ∼ 88 % of the total data matrix) to keep consistency in uncertainty calculations. The analytical uncertainties were, respectively, estimated at 15 and 20 % and kept constant over the experiments. The PMF uncertainty (σ ) is therefore calculated as follows. If X ij ≤ MDL, ∀j ; X ij = MDL 2 and σ ij = 5 6 × MDL (A1) If X ij ≥ MDL, ∀j ; X ij does not change and σ ij = (Error Fraction u × X ij ) 2 + (0. 5 × MDL) 2 (A2) MDLs and analytical uncertainties (u) for each VOC are reported in Table  A1.
Acknowledgements. The authors would like to thank  B. Temime-Roussel  and  N. Marchand  from the  Laboratoire Chimie Provence  ( LCP, University of Provence, Marseille, France ) for PTR-MS measurements performed at the LHVP site during the MEGAPOLI winter campaign. We would like to thank also  M. Beekmann  for the coordination of the EU-F7  MEGAPOLI  project. We gratefully acknowledge  M. M. Squinazzi  and  Y. Le Moullec  for having hosted MEGAPOLI and FRANCIPOL intensive campaigns as well as all colleagues involved in the monitoring process of ambient air measurements, especially  Hanitriniala Ravelomanantsoa ,  Thomas Chaigneau  (LHVP) and  Laurent Martinon (Laboratoire d'Etude des Particules Inhalées, LEPI). T. Le Priol  and  J. -F. Petit  from the  Centre d'Etudes et d'expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement (CEREMA ) are also acknowledged for the logistical assistance in the road tunnel experiment.
Introduction The genetic sequences of pathogens are a rich data source for studying their epidemiology and evolution, and provide information for vaccine and therapeutic design. In the past decade, next-generation sequencing (NGS) has transformed genomics, with decreasing costs and enormous increases in the amount of data available. Despite the success of NGS in other fields, sequencing of human immunodeficiency virus (HIV) is still largely based on the older method of Sanger sequencing. For example, on the comprehensive Los Alamos HIV database [1], of the 119,237 samples with platform information, 91. 6% were generated by Sanger sequencing, 7. 0% with the Roche 454 platform, 1. 4% with Illumina platforms, and 0. 02% with the IonTorrent platform. Restricting to the 38,635 samples dating from 2010 or later, these numbers change only to 94.
6% Sanger sequencing, 2. 0% 454, 3. 4% Illumina and 0. 02% IonTorrent. More broadly, NGS has been hugely successful both for sequencing samples with no within-sample diversity, and at the opposite end of the spectrum, for metagenomic studies. In the first case, any apparent withinsample diversity is attributable to sequencing error; in the latter case, there is no presumption that different fragments of DNA have the same origin, and so each fragment is checked against large databases to catalogue withinsample diversity  [2, 3]. HIV is an intermediate case: the long duration of chronic infection coupled with high rates of replication and mutation mean that a single infection, and hence a single sample, will contain a diverse collection of related viral particles, frequently called a quasispecies.
Indeed insertions and deletions (indels) are very common in HIV  [15, 16] , especially in the env gene  [17] , and reads from indels are particularly difficult to map correctly  [18] [19] [20] [21]. The loss of reads during mapping is roughly proportional to the divergence between the true consensus and the reference used, with for example 10% divergence giving a 25% loss of reads  [11]. In the env gene, divergence of greater than 10% can arise in the course of a single infection  [22] [23] [24]. Crucially, the loss of data is also biased, with the bias occurring at different scales. Data is more likely to be lost in (i) those samples in a dataset that differ more greatly from the reference used for their mapping; (ii) those parts of the genome, in a single sample, where the sample and reference are most different; and (iii) a subset of genotypes, in a single diverse sample, that are more different from the reference than the other genotypes.