dataesr/scientific-paragraphs-categorization huggingface dataset:
The AERONET data The modelled aerosol optical properties are compared to observations using level 2 AERONET (AErosol RObotic  NETwork; Holben et al. , 2001)  photometer data, namely (i) AOD at a wavelength of λ = 550 nm and (ii) the Ångström coefficient calculated using the AOD measured at λ = 470 and 870 nm. The stations used for the model validation are listed in Table  1  and their location is shown in Fig. 1. Note that, except for Lope, most of the AERONET stations are located in the northern part of the studied region, mainly under the influence of mineral dust emissions. Comparisons are performed using statistical scores calculated with an hourly time step and are presented for a given AERONET station only if data are acquired on a regular basis over a period of 3 months (i. e. 2280 h) and if more than 30 values can be used to compute them (only scores for which at least 1.
The satellite data Three different satellite datasets are used in this study: (i) the Moderate Resolution Imaging Spectroradiometer (MODIS) for AOD, (ii) the Infrared Atmospheric Sounding Interferometer (IASI) for CO, and (iii) the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) for aerosol classification. The first two correspond to vertically integrated data when CALIOP provides vertical profiles. The MODIS AOD product at 550 nm (from the MODIS-Terra aerosol 5 min L2 swath 10 km data collection 5. 2) is used to quantify the increase in aerosol due to biomass burning  (Levy et al. , 2010). The model outputs and observations are collocated in space and time in order to exactly compare the model to the available observations. The IASI CO total column retrievals by the FORLI algorithm  (Hurtmans et al.
CHIMERE vs. CALIOP aerosol sub-types The vertical cross sections of aerosol types derived from CALIOP observations and CHIMERE simulations along the CALIPSO track for 26 and 27 July 2014 are displayed in Fig. 8. We focus on these two days because (i) CALIOP data are available above the studied region, and (ii) the long-range transport of biomass burning is at a maximum at the end of the studied period. The two CALIOP ground tracks are shown in Fig. 1. The first result with this comparison is that the aerosol characteristics of the main air masses are well reproduced by the model: over land, the main aerosol is mineral dust, while over sea, sea-salt aerosols dominate the composition near the surface. Over sea at altitude, the main aerosol type is related to biomass burning (denoted as smoke).
Impact on the coastal urbanized area pollution In this section, we focus on the atmospheric composition in coastal urbanized areas. The analysis is carried out with the model only, as no data are available in the region and for the studied period. Results are presented for the sites Lagos (Nigeria) and Abidjan (Ivory Coast), which are representative of strongly urbanized coastal areas in the Gulf of Guinea. The surface concentrations of three chemical species are presented: (i) O 3 , a secondary species produced by anthropogenic, biogenic, and fire emissions, (ii) CO, a gaseous species primarily emitted by anthropogenic and fire emissions, and PM 10 , which is representative of the sum of aerosol produced by anthropogenic and natural sources. Time series of surface concentrations of CO, O 3 , and PM 10 are presented in Fig.
The reason is that, during a fire, the pyroconvection induces a strong and fast mixing of the surface flux. Whatever the shape of the injection profile, the pollutants are quickly mixed in the vertical before being transported over long distances. The simulations with realistic biomass burning emissions were analysed by comparison to numerous datasets: CO from IASI, AOD from MODIS and AERONET, surface concentrations of PM 10 from the Sahelian Dust Transect data, and aerosol sub-type classification from CALIOP. It was shown that the model is able to reproduce the physical and chemical characteristics of the emitted gas and aerosol species due to biomass burning. In addition, and using the vertical information provided by CALIOP, it was shown that the location and altitude of the several aerosol plumes (mineral dust and biomass burning) are correctly modelled.
SO 4 , NO 3 , and NH 4 are equivalents of sulfate, nitrate, and ammonium, respectively. WATER is for water. More details are provided in  Menut et al. (2013a Menut et al. ( , 2016)). The modelled AOD is calculated by FastJX for several wavelengths over the whole atmospheric column, as detailed in  Menut et al. (2016). At the boundaries of the domain, climatologies from global model simulations are used. In this study, outputs from LMDz-INCA  (Hauglustaine et al. , 2014)  are used for all gaseous and aerosols species, except for mineral dust for which the simulations from the GOCART model are used  (Ginoux et al. , 2001). The anthropogenic emissions are issued from the Hemispheric Transport of Air Pollution (HTAP) global database  (Janssens-Maenhout et al. , 2015). These emissions are provided as gridded maps for each month of the year.
For the simulation, weekly profiles are applied to include weekdays, Saturdays, and Sundays. In addition, hourly profiles are applied to have an hourly variability also depending on the activity sector. The complete calculation of these fluxes is detailed in  Menut et al. (2012)  and  Mailler et al. (2017). The mineral dust emissions are calculated using the Alfaro and Gomes (2001) scheme, optimized following  Menut et al. (2005) , and use the soil and surface databases presented in  Menut et al. (2013b). Since this latter article, several changes have been made in the emissions scheme. They are all related to the spatial extent of the emissions flux calculations: from the Sahara only to any arid or semi-arid areas in the world. The surface and soil databases being global, the fluxes are now systematically calculated over the whole domain, including non-desert areas such as Europe.
In order to keep realistic fluxes under a variety of meteorological conditions, the emissions scheme was adapted. These changes are active for all model cells including the desert ones. These changes are briefly described below. The erodibility is diagnosed using the United States Geological Survey (USGS) land use and an additional database, which was built using MODIS surface reflectance  (Beegum et al. , 2016). For all model cells considered as "desert", the MODIS erodibility is used, while for all other cells, a constant erodibility factor is applied depending on the USGS land use, as in  Menut et al. (2013b). To take into account the rain effect on mineral dust emissions limitation, a "memory" function is added. During a precipitation event, the surface emissions fluxes are set to zero.
Acknowledgements. The research leading to these results received funding from the  European Union   7th Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no. 603502  ( EU  project  DACCIWA: Dynamics-Aerosol-Chemistry-Cloud interactions in West Africa ). Thanks to the  British Atmospheric Data Centre , which is part of the  NERC National Centre for Atmospheric Science (NCAS) , for the meteorological surface data used in this paper. Solène Turquety acknowledges the  French space agency (CNES)  for financial support. The IASI CO data were provided by  LATMOS/CNRS  and  ULB. The MODIS AOD datasets were acquired from the Level-1 and  Atmosphere Archive  and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the  Goddard Space Flight Center in Greenbelt, Maryland  (https://ladsweb.
We show that our likelihood ratio statistic can be computed in O(nq) nevertheless, making it compatible with proteomic platform throughput. 3. We empirically observe that regularized estimators of the variance parameter lead to more powerful tests. We show that under the null hypothesis of homogeneity, the regularized log-likelihood ratio statistic is still asymptotically χ 2 distributed up to some normalization. 4. We provide R code for all our methods in the DAPAR Bioconductor R package, so they can be routinely used by proteomic practitioners. 2 State-of-the-art Leaving aside the various preprocessing steps that are necessary to account for e. g. batch effects or missing values (see for instance  Wieczorek and others (2016) ), methods for differential analysis of proteomic datasets can be divided in two main families: peptide-based and aggregation-based methods, also referred to as summarization-based in  Goeminne and others (2015).
The use of this distribution corresponds to a common assumption on observed peptide distribution  (Podwojski and others, 2010). Unfortunately, maximizing the corresponding likelihood is more computationally demanding than that of the normal distribution we use, as no closed form maximizer is available. As reported in its original article, a synthetic datasets with 100 proteins requires 3 days of computation and for such a dataset the algorithm does not converge in 18% of the cases. As a result, it was impossible for us to apply AllP on the much larger (real or simulated) datasets that are considered in this work.
Experimental setting We use both simulated and real data to evaluate the performance of our test procedure. Simulated datasets allow us to control key parameters such as the proportion of shared peptides, but the conclusions we draw only hold for data which behave like the simulations. On the other hand, because of the difficulties that are inherent to the wet-lab procedures (see Section 4. 2), it is not possible to prepare real samples with an indisputable ground truth and which contain an important proportion of shared peptides. As a result, real and simulated datasets provide complementary views on the performance evaluation.
Simulated datasets We simulate peptide intensities for each of n 1 = n 2 = 3 samples under two biological conditions, for q = 5000 peptides belonging to p = 1000 proteins. We purposely use a generative model which differs from the normal model (3) used in our testing procedure. This allows us to obtain more realistic data, and to assess the robustness of our method to deviations from the model. We first generate a q × p peptide-protein membership matrix X. To do so, we assign to each protein a number of peptides drawn from a P(q/p) Poisson distribution. We make sure that each protein is assigned at least one peptide, and that all peptides are assigned to one protein. We then randomly select a subset of peptides to be shared across proteins (we show results for proportions 0%, 5%, 20% and 50%).
For each of them, a Poisson draw with parameter 0. 5 is used to determine the number of additional protein the peptide should connect to. As explained in the introduction, the relationship between the measured peptide intensity and its actual abundance is peptide specific, so that the observed intensity of two protein-specific peptides from a same protein can be rather different (up to 3 orders of magnitude, according to  Silva and others (2005) ). To account for this fact, we multiply each positive entry of X by a draw from a 0. 001 + β(1. 1, 3) distribution. In real cases, only the binary X membership matrix is known thanks to the protein sequence database: accordingly, the modified matrix is only used to simulate the intensities but only its binary counterpart is involved in our test. We draw a θ (i) ∈ R p + abundance vector for each of the n = n 1 + n 2 samples, in contrast to the model underlying our testing procedure where all samples under the same condition share the same θ parameter.
More precisely, we draw one mean θ parameter for each condition from a log-normal distribution, then add independent normal perturbations around this mean for each sample (thresholding at 0 to make sure each θ (i) ∈ R p + ). The mean and variance of protein-level log-normal distribution are chosen to fit classically observed datasets -that is peptide-level distributions that follow a Gaussian model centered on 23. 5 and with a standard deviation of 2 on the log2 scale. The individual normal perturbations on the average abundance θ j of each protein j have mean 0 and standard deviation 0. 005θ j. Under H 1 , the first 50 proteins are considered to be differentially abundant. The ratio between conditions is sampled from a normal distribution with mean R = 15 and of standard deviation 0. 05R.
Real datasets The true set of differentially expressed proteins is generally not known in real data, making it difficult to compare differential analysis methods. We resort to using spike-in samples prepared according to the protocol of  Ramus and others (2016). First, a lysate of yeast is split into n = 2m, so as to form an equal background for n samples. Then, a volume V of a mixture containing a series of precisely known human proteins is spiked in the first m samples so as to form the first biological condition. The other m samples receive a R × V volume of the same mixture so as to form the second biological condition with an abundance ratio R. As yeast and human proteins are different, any identified protein within each sample can uniquely be associated to its parent organism (i. e. yeast or human).
During the relative quantification step, one should find that all and only the human proteins are differentially abundant. We use the two datasets that are described in (Giai Gianetto and others, 2016), and which are available in the DAPARdata R package  (Wieczorek and others, 2016). These two datasets contain 6 samples (m = 3) that have been prepared using the equimolar human protein mixture Sigma UPS1, including 48 human proteins. Their differential abundance ratios R are 2 and 2. 5 for the first and second datasets, that are respectively referred to as Exp1 R2 pept and Exp1 R25 pept. Both datasets have been preprocessed with ProStaR software  (Wieczorek and others, 2016) , so as to make them compliant with the constraints of the reported experiments. First, all the peptides that corresponded to contaminant proteins or reversed proteins (i.
e. peptides that were mistakenly identified as part of inexistent proteins) were removed, as well as those with more than one missing values out of three, in any of the two conditions. Second, the peptide intensities were normalized to account for replicate variability (within-condition median centering for Exp1 R25 pept, and global median centering for Exp1 R2 pept). Finally, the remaining missing values were imputed with an algorithm based on maximum likelihood estimation. A limitation of this dataset is that it contains few shared peptides contrarily to real human samples, for a combination of reasons. First, the 48 human proteins available in the Sigma UPS1 equimolar mixture do not share peptides that are easily identified by mass spectrometry. Second, yeast is a simple organism that does not have a lot of homology sequences in its genome, so that there are very few shared peptides among its proteins.
Finally, human and yeast proteins do not share many peptides, because they are rather different organisms. To cope with this issue, we artificially add shared peptides in these two real datasets, by merging pairs of peptides. Merging pairs of yeast peptides had no influence, as the resulting peptides (from non-differentially abundant yeast proteins) were even less prone to create error by introducing confusion between differentially and non-differentially abundant proteins. It was not possible to merge pairs of human peptides, because of their too limited number in the original datasets. We therefore resort to merging pairs of peptides where one is differentially abundant (human) and the other is not (yeast). To concretely implement such a merging procedure, a yeast peptide and a human one are randomly chosen, their identifications are merged, their abundances are summed, and their list of parent proteins are concatenated.
Compared methods We evaluate two versions of PEPA test, both using the likelihood ratio statistic (9): PEPA-ML relies on the ML estimator of the variance (7), and PEPA-MAP on the MAP estimator (8) of the variance with an inverse-gamma prior. Comparing these two procedures provides insights on the respective interests of the likelihood ratio test itself and the variance regularization. These two methods are compared to several other reference methods. The only other peptide-based model that accounts for all shared peptides is AllP (Blein-Nicolas and others, 2012). However as explained in Section 2. 2, it cannot cope with large scale datasets that we consider in our experiments and we compare to methods that only account for protein-specific peptides. The first one, referred to as PeptideModel, performs a two-sample t-test for each protein, where each group is formed by pooling all protein-specific peptides across all biological samples in one condition.
Performances on simulated datasets The performances on simulated datasets with 0%, 5%, 20% and 50% of shared peptides are displayed on Figure  1. Additional figures representing 1%, 10%, 33% and 67% of shared peptides are provided in Section D of supplementary material. From these results, one draws several conclusions: Peptide-based dominate aggregation-based methods In all settings, the baseline method Top3Spec-TT is by far the least accurate. Overall, as noticed by  Goeminne and others (2015) , aggregation-based methods (Top3Spec-TT and AllSpec-SAM, depicted by lighter or darker green dotted curves respectively) are less accurate than other methods, whether or not they exploit shared peptides. Benefit of using shared peptides In the absence of shared peptides, both PeptideModel and MSqRob are very similar to PEPA-ML and PEPA-MAP, respectively, as discussed in Section 4.
Performances on real datasets We build PR curves to compare all methods on Exp1 R2 pept (Figure  2 ) and Exp1 R25 pept datasets (Figure  3 ) with 0, 120, 200 and 280 shared peptides. Additional figures representing the cases with 40, 80, 160 and 240 shared peptides are provided in Section D of supplementary material. Exp1 R2 pept and Exp1 R25 pept originally contain 10722 (resp. 10601) peptides, among which 211 (resp. 290) come from human proteins, so that the proportion of shared peptides is rather small: for either datasets, the proportion of introduced shared peptides is smaller than 2. 65% (to be compared with a proportion up to 50% of shared peptides as recalled in the introduction). We notice a large difference of performances and of behavior between the two datasets. Exp1 R25 pept derives from a series of LC-MS/MS experiments that did not undergo any malfunction, so that the data is of rather high quality.
Peptide-based methods not always dominate aggregation-based methods The domination of peptide-based over aggregation-based models that was clearly illustrated on simulated dataset does not hold on our real datasets. This is especially true on Exp1 R2 pept, where AllSpec-SAM outperforms all other methods (including ours) when there are no shared peptides. In the presence of shared peptides, it is either competitive with or dominated by our method. All other methods (PeptideModel, Top3Spec-TT and MSqRob) are dominated by AllSpec-SAM, PEPA-ML and PEPA-MAP regardless of the number of shared peptides. A possible explanation is that when peptide-level intensity values are unreliable, the aggregation process somehow regularize the resulting protein-level intensity values. The same conclusions hold on the Exp1 R25 pept dataset, yet with dimer magnitude: when the number of shared peptides increases, MSqRob keeps up but remains dominated by aggregation-based methods.
Benefit of shared peptides and regularization PEPA-ML and PEPA-MAP outperform all other methods as soon as enough shared peptides are introduced. In all cases, the regularized methods AllSpec-SAM and PEPA-MAP outperform their unregularized counterparts Top3Spec-TT and PEPA-MAP. MSqRob dominates PeptideModel on Exp1 R25 pept, but is outperformed for small recall values on Exp1 R2 pept, suggesting that on this dataset MSqRob assigns the lowest p-values to a few non differentially abundant proteins. Our methods handle proteins with no specific peptide In both experiments, some proteins are lost by methods that only rely on protein-specific because as the number of shared peptides increases, these proteins end up with only shared peptides. On Figures  2  and 3 , this leads to the noticeable dropouts on the lower end of the curves for these methods.
Calibration of PEPA-ML The left panel of Figure  4  shows the plots obtained on simulated data from Section 4. 1 with no shared peptide. All methods except PEPA-MAP are reasonably well calibrated. The small deviation observed for PEPA-ML can be explained by a combination of two factors. First, the χ 2 distribution of PEPA-ML is an asymptotic result, and we only have n 1 = n 2 = 3 observations for each group in this case. Second, the null hypothesis that we are testing is θ = θ , i. e. , that no protein is differentially abundant. This model is misspecified as soon as the protein j that we are testing is in the same connected component as a differentially abundant protein j , i. e. θ j = 0, even if indeed θ j = 0. This may happen in our simulations, as our dataset contains 50 differentially abundant proteins.
We observe nonetheless that using the same simulation setting with 10 samples per group instead of 3 leads to a perfectly calibrated PEPA-ML (not shown), suggesting that the main issue is the low sample size. The right panel of Figure  4  shows the plots obtained on the Exp1 R2 pept data. PEPA-ML is more severely decalibrated, leading to a false positive rate of 7. 3% when thresholding at 0. 01 when AllSpec-SAM leads to a 1. 9% rate, PeptideModel to 0. 4% and MSqRob to 4. 3%. The deviation is likely caused by the low sample size, as the number of differentially abundant proteins in the dataset is very similar to the one used in our simulation -where we recover a correct calibration by increasing the sample size. Calibration of PEPA-MAP As predicted by Proposition 2, the PEPA-MAP statistics are not χ 2 1 distributed under H 0 which is illustrated by the strong deviation of the grey curve from the y = x axis -the selected regularization parameter s is large.
MSqRob however is two orders of magnitude slower as it involves more observations and an iterated reweighted least square procedure, but it remains fast enough to be applicable to proteomics datasets with hundreds of proteins and thousands of peptides. Our methods run in one minute, most of which is spent computing the connected components of the peptide-protein graph. The computation of our test statistic using Proposition 1 takes less than 10 seconds, even though it requires the residuals of a linear regression problem with an nq × (p + q) design matrix.
Discussion and conclusions We have proposed a linear model that accounts for shared peptides in relative quantification proteomic experiments based on mass-spectrometry analysis of peptides. This model can be used to build likelihood ratio tests relying either on the maximum likelihood or maximum a posteriori estimator of its variance parameter. We have also introduced a faster way to compute the test statistic, making it amenable to datasets with thousands of peptides and proteins. The faster form relies on the fact that the likelihood ratio statistics only requires the regression residuals as opposed to estimates of the regression parameters, in particular of protein abundances. Using our model to estimate abundances -a task which is out of the scope of this paper and which we did not include in our experiments -would require to actually estimate the regression parameters, e.