dataesr/scientific-paragraphs-categorization huggingface dataset:
depending on the scan position) which are compensated for by bias of the opposite sign in the case of REF. The same experiments were performed on other radiosonde data with generally the same results (not shown here). Thus, in situ chemical profiles allow us to validate the quality of our simulations essentially for channels sensitive to CO 2 and CH 4. However, it is more difficult to accurately simulate ozone-sensitive channels. Indeed, ozone differs from CO 2 and CH 4 because it has a very high spatial and temporal variability. 5. 2 Comparison between a priori, retrieval and in situ chemical profiles In situ chemical profiles can directly be used as verification data for modeled profiles or retrievals from satellite sensors. In this section, two possible usages are illustrated on APOGEE data.
Forecasts of atmospheric composition One of the possible use of these in situ profiles of CO 2 , CH 4 and O 3 is to use them as comparison data to the a priori chemical profiles from the Chemistry Transport Models (CTM). As part of our study, we compared our measured data against the profiles extracted from the CTM MOCAGE and C-IFS. These two models differ in that the global version fo the MOCAGE model does not assimilate any observation in operation, while C-IFS assimilates Level-2 products from several instruments such as MLS, OMI, SBUV-2, GOME-2A/2B, IASI, MOPITT, OMPS and PMAp  (Basart et al. , 2018). C-IFS provides analyses and 5-day forecasts of atmospheric composition at regional and global scales in near-real time. MOCAGE is an off-line global three-dimensional chemistry transport model  (Guth et al.
Conclusions This article shows the interest and possibilities of the AMULSE instrument for the fast and accurate measurement of CO 2 , CH 4 and H 2 O in the atmosphere. The APOGEE measurement campaign was an opportunity to highlight the various benefits of the data from the AMULSE instrument for different research projects: satellite validation, model comparison, verification data, etc. The measurements will continue with monthly radiosoundings on the Reims site to get more consistent data sets. The prime aim of this study was to assess the sensitivity of infrared satellite observations to chemical information. The objective was to use carbon dioxide, methane and ozone profiles measured as part of the APOGEE measurement campaign to validate 5 the quality of IASI and CrIS observation simulations.
Indeed, the latter reacts differently in the atmosphere and is highly 5 variable compared to CO 2 and CH 4. The data measured by the AMULSE instrument are valuable to validate the quality of our simulations, which is essential for NWP models. Secondly, the measured profiles in the APOGEE framework were also valuable for comparison with the chemical profiles derived from the CTMs. Thus, we extracted ozone and methane profiles from the MOCAGE and CAMS CTMs for a case study. This comparison with the measured profiles showed us that the CTMs represent atmospheric ozone rather well, especially in the stratosphere for MOCAGE and in the troposphere for CAMS. Having good chemical concentrations in the troposphere for CAMS is important as it also provides air quality forecasts. We also showed that methane is very well simulated by the CTM CAMS in the troposphere but underestimated by 0.
Code availability. Codes of the Radiative Transfer Model RTTOV and the uni-dimensional data assimilation system 1D-Var used is this study are all available on (https://www. nwpsaf. eu/site/software/rttov/download/). Data availability. IASI data are available from EUMETSAT or AERIS: (https://www. aeris-data. fr/). Copernicus Atmosphere Monitoring Service data are available from: (https://atmosphere. copernicus. eu/catalogue#/) implemented by ECMWF as part of The Copernicus Programme. Model data are available upon request.
Reaction times (RTs) plotted against the quadratic expansion of decision variable (a), relative chosen state beliefs (b, zscored within participants) and relative chosen normalised utilities (c, z-scored within participants). Error bars are s. e. m. across participants (N = 22). These analyses of RTs, which constitute a dataset independent of both fMRI activations and choice data used for model fitting validate the approach used to investigate fMRI. Indeed, our first regression analysis of fMRI activations assumes that decision-making is more demanding when the decision variable becomes more ambiguous, thereby predicting RTs to vary as a quadratic function of decision variable (i. e. , with  2  (a: T(21)=6. 6; p<0. 0001). Our second regression analysis of fMRI activations further assumes that reaching a decision requires less processing resources, when the evidence supporting actual choices increases.
Thus, participants could familiarise and learn bandits' and protocol contingencies. All experimental and training sessions were administered using the MATLAB PsychToolBox  58. Optimal model OPT. We denote 1 and 2 the two bandits. Formally, the protocol consists of a sequence of hidden statesz 1 ; z 2 ; ; z t 2 1; 2 f g, where z t = 1 (=2, resp. ) means that in trial t, bandit 1 (2, resp. ) is the rewarded bandit with probability q M and bandit 2 (1, resp. ) is the rewarded bandit with probability q m (q M and q m equal to 80% and 20%, respectively)•)  27. We denote V 1 (t) and V 2 (t) the proposed rewards (monetary values) in trial t associated with bandit 1 and 2, respectively. In this protocol, the proposed rewards are drawn from distribu- tions p V 1 t ð Þjz t ð Þand p V 2 t ð Þjz t ð Þ : p V 1 t ð Þjz t ð Þ/ exp γ V 1 t ð Þ À v ð Þ ð Þ if z t ¼ 1; exp Àγ V 1 t ð Þ À v ð Þ ð Þ otherwise; p V 2 t ð Þjz t ð Þ/ exp γ V 2 t ð Þ À v ð Þ ð Þ if z t ¼ 2; exp Àγ V 2 t ð Þ À v ð Þ ð Þ otherwise; where, v denotes the mean reward (because only ratios between bandits are meaningful, quantity v has no influence on choices: see below).
Model recovery procedure. We also verified the ability of the present protocol and the validity of our fitting procedure to discriminate between model DIST and MIX by using a model recovery procedure. We generated synthetic data by simulating the performance of models DIST and MIX, using parameters fitted on every participant for these simulations. Then we fitted models DIST and MIX on these synthetic data as if they were participants' data, using the exact same fitting procedure described above. We found that as expected, model DIST fits its own performance data better than model MIX and vice-versa (LLH: both Ts(21) > 4. 98, ps < 0. 00002). fMRI data acquisition and processing. fMRI volumes were acquired on a 3T Siemens Trio at the Centre de Neuroimagerie de Recherche (CENIR) in hospital La Pitié Salpêtrière, Paris, France.
Functional images were acquired with parameters TR = 2 s, TE = 25 ms, Nb of repetitions/run = 431, Nb slices = 39, thickness = 2 mm, flip angle 75°, voxel size = 2. 5 mm 3. Before the first trial, two TR of baseline recording were acquired for subsequent slice-timing correction. Echo planar images were 30°tilted to minimise signal drop around the orbitofrontal cortex  60. Stimuli were projected on a mirror settled on a 32-channels head coil. T1 anatomical images were also acquired before functional acquisitions. Each experimental condition (neutral, congruent and incongruent) was recorded in separate days and comprised four scanning runs. MRI data were processed and analysed using SPM8 software package (http:// www. fil. ion. ucl. ac. uk) using standard slice-timing, spatial realignment, normalisation to Montreal Neurological Institute echo planar imaging template (images resampled at 3 mm 3 ) and Gaussian spatial smoothing (isotropic 6-mm kernel).
CRISPR/Cas9 system requires short guide RNAs (sgRNAs) to direct genome modification. Most currently available tools for sgRNA design operate only with standard reference genomes, and are best suited for small-scale projects. To address these limitations, we developed Crisflash, a software tool for fast sgRNA design and potential off-target discovery, built for performance and flexibility. Crisflash can rapidly design CRISPR guides against any sequenced genome or genome sequences, and can optimize guide accuracy by incorporating user-supplied variant data. Crisflash is over an order of magnitude faster than comparable tools, even using a single CPU core, and efficiently and robustly scores the potential off-targeting of all possible candidate CRISPR guide oligonucleotides.
Results and discussion We benchmarked Crisflash against following tools: Cas-OFFinder  (Bae et al. , 2014) , CasOT  (Xiao et al. , 2014)  and CRISPRseek  (Zhu et al. , 2014) , all of which are comparable command-line CRISPR design software. All tests were run independently on the same hardware with minimal background computational activity (see Supplementary Material). Off-target search space was limited to four mismatches. In single core mode, Crisflash outperformed all other tools by an order of magnitude for inputs containing more than couple of hundred sgRNAs. The performance is improved above two orders of magnitude for inputs of few thousand candidates compared with cas-offinder while producing similar results (Fig. 1 ). Tests with higher number of sgRNAs for CasOT and CRISPRseek had to be terminated due to long running times.
Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method's likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods.
Several approaches have been designed to tackle this problem. A series of methods use zero-inflated distribution functions, to explicitly model the dropout charachteristics  [13] [14] [15]. Alternatively, other studies have proposed to infer dropouts based on expression values pooled across cells or genes 16-19. For instance, scImpute pools expression values across similar cell subpopulations in each dataset and imputes dropouts using a Gamma-Normal mixture model and population specific thresholds 18. Similarly, the MAGIC package is based on pooling gene expression values across cells using a network-based similarity metric 19. Another method is based on K-nearest neighbour smoothing, which uses Poisson distribution and aggregate information from similar cells 20. Conversely, the SAVER approach pools expression values across genes within each cell using a Gamma-Poisson Bayesian model 17.
The bayNorm rationale bayNorm is a Bayesian implementation of global scaling normalisation that simultaneously imputes missing values in scRNA-seq data. bayNorm generates for each gene (i) in each cell (j) a posterior distribution of original expression counts ( ), given the observed scRNA-seq read count for that gene ( ) (Fig. 1a ). Using the Bayes rule we have: ( | ) ( | ) ( ) Where ( | ) is the posterior distribution of true gene expression counts of a given gene in a given cell. ( | ) is a likelihood function that depends on the cell specific capture efficiency ( ). Specific capture efficiencies can be estimated using spike-in controls or directly from the data using scaling factors provided by different methods 3 and normalised to the dataset"s mean capture efficiency (see Methods). ( ) is a gene specific prior expression distribution and ( ) is the marginal likelihood.
The binomial model is an appropriate choice for the bayNorm likelihood function The bayNorm likelihood function ( | ) is at the core of the approach and describes the empirical distribution of the raw experimental scRNA-seq counts. The binomial model describes the random sampling of a fraction of a cell transcriptome with constant probability. This is a simple model of transcript capture in scRNA-seq 30 and we therefore hypothesised that it would be a good choice for bayNorm likelihood function. For the prior ( ) we assume a negative binomial model, which describes the bursty distribution of mRNAs in simple models of gene expression 31, 32. Gene specific prior parameters are estimated using an empirical Bayes approach by pooling gene expression values across multiple cells of the dataset (see Methods for details).
To validate our choice of binomial likelihood model and prior estimates, we generated simulated scRNA-seq data based on these assumptions and investigated how closely they captured statistics of several published scRNA-seq datasets (Fig. 1  b-e, Fig. S2-7 )  12, 30, 33, 34. The simulations assumed mRNA counts per cell that followed negative binomial distributions and used gene specific priors obtained with bayNorm (Fig. 1 , Binomial_bayNorm), or sampled from estimates obtained with a modified version of the Splatter package (Fig. 1 , "Binomial_Splatter", Supplementary Notes 1) 22. These were compared with simulations generated with the original Splatter package which is based on the Gamma-Poisson distribution.
22. We note that in Splatter, scaling factors are multiplicative to the Gamma distribution"s mean. In bayNorm, however, the cell specific capture efficiencies, which act as scaling factors, are set as the probability parameter of the binomial model. We found that the binomial model captures the variance-mean relationship of experimental scRNA-seq data well (Fig. 1b ). Another important feature of scRNA-seq data is their large amount of missing values, or dropouts, and several models have been proposed to explain this phenomenon 14, 15, 26, 35, 36. We therefore investigated how well the binomial model would capture dropout rates in experimental data. Our simulated dataset generated using the "Binomial_bayNorm" function reproduced accurately the dependence of dropout fractions on gene expression means performing better than Splatter (Fig.
1c-e ). Moreover, a parameter free approximation based on the binomial model predicted the dropout fraction to depend on an exponential of the negative mean expression ( ̅ , see Methods). This functions produced a very close fit to the experimental data providing additional support for our choice of the binomial model (Fig. 1c ). Notably, the Binomial_bayNorm simulation protocol using inferred gene-specific priors together with cell specific parameters ( ) was the only one that recovered the distribution of dropout rates per gene observed in experimental data (Fig. 1d ). Finally, the results presented on Fig. 1b -e could be replicated consistently using several additional experimental scRNA-seq datasets (Fig. S2-7 ). The datasets discussed so far include sequencing scores corrected for PCR amplification biases using unique molecular identifiers (UMIs).
37. Some popular protocols, however, do not include UMIs, and are therefore likely to be less well described by the binomial distribution due to technical variability arising from PCR amplification bias. Accordingly, their dependence of dropout fractions on the mean expression has been reported to be more complex than in UMI-based datasets 36. We investigated this issue further and found that a simple scaling of non-UMI raw data by a constant factor produced a reasonable match to the binomial model (Fig. S9 ; see Methods). This scaling factor can be interpreted as the average number of times original mRNA molecules were sequenced after PCR amplification. This indicates that, provided appropriate scaling, non-UMI datasets are also compatible with the bayNorm model. Importantly, as bayNorm recovered dropouts rates successfully in both UMI-based and non-UMI protocols without the need of specific assumptions, we conclude that invoking zero-inflation models is not required to describe scRNA-seq data.
Fig. 3a  shows the number of genes called differentially expressed as a function of increasing average expression levels using a series of normalisation and imputation methods 12. bayNorm normalised data show almost no differentially expressed genes, outperforming all the other methods. Moreover, log 2 gene expression ratios between cells of the two groups, were consistently close to zero, confirming bayNorm ability to correct for biases inherent to different capture efficiencies in UMI-based datasets (Fig. 3b ). Sequencing depth is another parameter affecting DE analysis especially because it impacts on the dropout rates of lowly expressed genes. Moreover, differences in sequencing depth are likely to affect levels of capture efficiencies, especially for non-UMI datasets where PCR biases are not accounted for.
To assess bayNorm"s ability to correct for this source of bias, we used a benchmark dataset published by Bacher and colleagues 12 that consists of non-UMI based scRNA-seq data for two groups of cells isolated from a single culture and sequenced to a depth of either 1 million or 4 million reads per cell. bayNorm and other imputation methods performed well in this setting (Fig. S13 ). However, a global scaling approach on its own led to poor results, unless performed independently on groups of genes with similar mRNA expression levels as in SCnorm. Finally, bayNorm corrected robustly for variability in sequencing depth when applied to a series of simulated datasets (Fig. S14 -15) 12. We have shown that bayNorm is efficient at removing spurious differential expression from scRNA-seq data caused by variability in capture efficiencies and sequencing depth.
We next explored bayNorm performance in supporting sensitive and robust detection of genes truly regulated between samples. To do this, we used two experimental scRNA-seq datasets 41, 42   and lists of benchmark DE genes derived from matched bulk RNA-seq data 40, 43. To maximise sensitivity, we used priors specific to each groups of cells in the comparison (we call this design "local priors"). With the first dataset, bayNorm normalised data generated an AUC value as high as other normalisation methods demonstrating that the approach supports sensitive DE detection (Fig. 3c ). Analysis of the second dataset (UMI-based) 42 confirmed this observation with bayNorm performing better than all other methods (Fig. 3d ). Importantly, bayNorm performance did not depend on the number of cells in each group, except for groups with very low numbers of cells (Fig.
3d , Fig. S16 ). Finally, using a series of simulated datasets, we explored situations where the compared groups have different mean capture efficiencies and found that bayNorm supported robust DE detection in all cases (Fig. S17 ). Three important parameters should be considered before bayNorm normalisation: i) the choice of priors, ii) the choice of average capture efficiencies , iii) the choice of bayNorm output format. Prior parameters can be either estimated for all cells across groups (global) or within each group (local). Since priors are gene specific, applying bayNorm across homogeneous cells (i. e. using global prior) allows for mitigating technical variations (Fig  S18a-b ). On the other hand, using priors estimated "locally" within each group amplifies differences in signals between heterogeneous groups of cells increasing sensitivity (Fig  S18c-d ).
Average capture efficiencies are specific to each scRNA-seq protocol and reflect their overall sensitivity. This value represents the ratio of the average number of mRNA molecules sequenced per cell to the total number of mRNA molecules present in an average cell. It is not always easy to determine as quantitative calibration methods such as smFISH are not widely used, and approaches based on spike-in controls have important shortcomings In summary, our analysis demonstrates that in addition to correcting for technical biases, bayNorm also supports robust and accurate DE analysis of a wide range of experimental and simulated scRNA-seq datasets.
Conclusions We introduced bayNorm, a versatile Bayesian approach for implementing global scaling that simultaneously provides imputation of missing values and true counts recovery of scRNA-seq data. Bayesian methods have been applied to different aspects of RNA-seq data analysis before  11, 15, 17, 45. The approach most related to bayNorm is taken by SAVER which uses a Poisson-Gamma model and pooling information across genes for true-count recovery.
17. In contrast, bayNorm uses a binomial model of mRNA capture as likelihood and achieves similar or improved performance relative to SAVER on real and simulated data (Fig  2 3 4 ). We showed that using the binomial model and an empirical Bayes approach to estimating gene expression priors across cells results in simulated data almost identical to experimental scRNA-seq measurements. Importantly, this suggests that zero-inflated models are not required to explain the frequency of dropout observed in scRNA-seq. Although designed initially for UMI-containing scRNA-seq protocols, a simple scaling factor makes bayNorm applicable to non-UMI data as well. This flexibility will allow using this approach with most present and future scRNA-seq datasets. We showed using datasets that combine smFISH and scRNA-seq, that bayNorm is accurately recovering true gene expression across a wide range of expression levels.
This approach could therefore be particularly useful for quantitative analysis of more difficult scRNA-seq datasets, such as those generated from small quiescent cells or microbes, for instance. In fact, we have recently used bayNorm successfully in the first scRNA-seq study of fission yeast 46. One of the most powerful features of bayNorm is its use of gene expression priors directly calculated from gene expression values across cells. We showed that by grouping cells according to experiment design or phenotypic features increased significantly the robustness and sensitivity of differential expression analysis. This allows almost complete removal of sequencing depth and capture efficiency biases, and reduced batch effects. Critically, this approach preserved accurate and sensitive detection of benchmark DE genes.
Accurate estimation of cell capture efficiencies (or scaling factors) is central to most scRNA-seq normalisation methods including bayNorm. Interestingly, we observed that the choice of cell specific capture efficiencies affect how closely simulated data recovers statistics of real data. We therefore propose that comparison of drop-out rates per cell in simulated datasets and experimental data could be used as a tool to inform appropriate choice of global scaling factors and mean capture efficiency estimates. The option to tailor bayNorm priors based on phenotypic information about cell subpopulations will be a powerful asset for discovery of gene expression programmes associated with specific phenotypic features of single cells such as cell size.
46. Finally, the concepts and mathematical framework behind bayNorm will be useful if combined with other emerging theoretical approaches such as deep learning, for instance. (e) Log 2 ratio between the CV of scRNA-seq measurements for 18 genes normalised by different methods and their matched smFISH measurements (from Grn study). (f) As in (e) using 12 genes (from Torre study). (g) Log 2 ratio between the Gini coefficients of scRNA-seq measurements for 18 genes normalised by different methods and their matched smFISH measurements (from Grn study). (h) As in (c) using 12 genes (from Torre study). For the bayNorm and SAVER normalised datasets, 20 or 5 samples were generated from posterior distributions for the Grn and the Torre studies, respectively. For bayNorm and SAVER, normalized counts across cells and samples are used.