dataesr/scientific-paragraphs-categorization huggingface dataset:
Both peptides and proteins were filtered with false discovery rates (FDR) of <1% to ensure identification confidence. Single-cell proteomics data analysis. SCPCompanion (Ver 15. 0, https:// www. github. com/scp-ms/SCPCompanion) was used to access the data quality by extracting the summed signal-to-noise ratio (SNR) of single-cell channels  17. The corrected reporter ion intensities from MaxQuant were imported into Perseus (Ver. 1. 6. 14. 0)  49  and were log2-transformed after filtering out the reverse and contaminant proteins. Proteins containing >70% valid values in each cell type were considered quantifiable. Missing values were imputed based on a standard distribution of the valid values (width: 0. 3, downshift: 1. 8). The summed reporter ion intensities of the quantifiable proteins were normalized using quantile normalization method.
To correct the batch effect from multiple TMT sets, we used the SVA Combat algorithm  22  , which is embedded in Perseus. Next, the data matrix was separated by cell types and grouped by TMT channel. Combat algorithm was also applied to minimize the TMT channel effect. The combined matrix was then used for statistical analysis, including principal component analysis (PCA) and hierarchical clustering analysis. ANOVA tests were performed to determine the proteins showing statistically significant abundance differences across the three cell types (Permutation-based FDR < 0. 05, S 0 = 1), and a two-way student t-test was applied to explain the significant differences between two groups (p-value < 0. 05). The processed data were visualized with Graphpad (Prism Ver 8. 3. 0) and Perseus. Protein intensities without missing values in each cell type in intra-batch or inter-batches were used to calculate the coefficient of variations (CVs) in Excel (Microsoft office 365).
Subsequently, the labeled cells were washed with ice-cold PBS followed by incubating with Alexa Flour 546 goat anti-rabbit IgG (Invitrogen Cat#A11010) at 1:1000 dilution in 1× PBS with 1% BSA for an hour at room temperature in the dark. Immunostaining images were visualized with an inverted confocal fluorescence microscope (Zeiss LSM 710) with a 63× objective (NA 0. 75). The DAPI nuclear stain was excited by a 405 nm wavelength laser. The dye conjugated antibodies (Alexa Flour 546) were excited by 561 nm wavelength laser. The antibody fluorescence channel, DAPI fluorescence channel, and bright-field channel were acquired simultaneously. The Zeiss imaging software ZEN (2. 1 SP2 version 130. 2. 518) was used to control the microscope, acquire the data, and export the images. Integrative proteomics and transcriptomics analysis.
Single-cell transcriptomics datasets containing transcript abundance of 11 C10 37 and 186 RAW 38 cells were re-analyzed. Both datasets were generated with Smart-Seq2 protocols. The data of C10 cells was obtained with unit of counts, while data of RAW264. 7 cells was obtained with unit of fragments per million (FPM). To allow comparisons, we converted the data of C10 cells with unit of FPM using a python package bioinfokit (https://github. com/reneshbedre/bioinfokit)  50. Next, we transformed scRNASeq data into log2 scale. After normalization and scaling, we selected the genes commonly captured across all transcriptomic and proteomic datasets for further correlation and other comparative analyses. Received: 23 April 2021; Accepted: 12 October 2021; Published online: 29 October 2021.
Acknowledgements We thank  Matthew Monroe  for helping with data deposition. We thank PNNL photographer  Andrea Starr  for taking the chip photo (Fig. 1a). We thank  Dr. Anupriya Agarwal  at  Oregon Health & Science University  for providing us the leukemia cell lines. This work was supported by a  Laboratory Directed Research and Development  award (Y. Z. ) from  Pacific Northwest National Laboratory  and an  Intramural program  (Y. Z. ) at EMSL (grid. 436923. 9), a  DOE Office of Science User Facility  sponsored by the  Office of Biological and Environmental Research  and operated under Contract No. DE-AC05-76RL01830. Part of this work is also supported by  NIH  grants  U01 HL122703 ,  U01 HL148860  (J. N. A. and G. C. C. ) and  P41 GM103493  (R. D. S. ).
Data availability The mass spectrometry raw data have been deposited to the ProteomeXchange Consortium via the MassIVE partner repository with the dataset identifier MSV000086809 and are available at https://doi. org/10. 25345/C5JR4K or ftp:// massive. ucsd. edu/MSV000086809/. Single-cell RNA sequencing data for control RAW cells  38  were downloaded from NCBI Gene Expression Omnibus with accession number GSE94383. Single-cell RNA sequencing data for C10 cells  37  were requested from the author and are provided in Supplementary Data 3. Source data are provided with this paper.
Gulrajani et al. (2017)  trained a WGAN on the ImageNet database  (Russakovsky et al. , 2015) , which contains over 14 million images with 1000 classes, and suc-Published by Copernicus Publications on behalf of the European Geosciences Union & the American Geophysical Union. C. Besombes et al. :  Producing realistic climate data with GANs cessfully learned to produce new realistic images. Several techniques developed for computer vision with GANs seem promising for domains in the geosciences. Notable examples of usage to date include  Yeh et al. (2017)  to do inpainting, where the objective is to recover a full image from an incomplete one,  Ledig et al. (2017)  to do super resolution, or  Isola et al. (2017)  to do image-to-image translation, where an image is generated from another one, e.
In the first step, the weights of the generator are frozen with a given set of parameters θ q and the critic neural network is trained in order to find the optimal parameter w(θ q ) solution Eq. (  9 ) (lines 3-11 in Algorithm 1). In the second step, the critic is frozen and the generator is set as trainable in order to compute θ q+1 from Eq. (  12 ) (lines 12-17 in Algorithm 1). Note that in Algorithm 1, the steepest descent is replaced by an Adam optimizer  (Kingma and Ba, 2014) , a particular implementation of stochastic gradient descent which has been shown to be efficient in deep learning. The following sections will aim to create a climate data generator from the WGAN method. The next section will describe the architecture of the network adapted to the complexity of the dataset used.
Consequently, a quality check using metrics adapted to the domain on which the GAN is applied is still necessary. Moreover, at the end of the training, a first experiment was conducted to see whether the generations are present in the dataset. The histogram of the Euclidian distance divided by the number of pixels in one sample between one generation and all of the dataset can be seen in Fig. 7. Here, one can see that the minimum is around 0. 8, which shows that the generated image is not inside the dataset. This experiment shows that the generator is able to generate samples without reproducing the dataset. It should be noted that in the WGAN framework, the generator never directly sees a sample from the dataset. There are no stopping criteria for the training, and it was stopped after 35 000 iterations in the interest of computational cost.
It should be highlighted that the performance of generative networks and especially GANs is difficult to evaluate. In the deep-learning literature, the quality of the images generated is assessed using a reference image dataset such as ImageNet  (Russakovsky et al. , 2015)  and computing the inception score (IS) or the Fréchet inception distance (FID). Both use the inception network trained on ImageNet: the IS measures the quality and diversity of the images by classifying them and measuring the entropy of the classification, while the FID computes a distance between the features extracted by the inception network and is more robust to GANmode collapse. Because our study does not apply to the ImageNet dataset, it is necessary to compute our own metrics. Section 3 proposes an approach for this kind of method in the domain of geosciences and more precisely the study of atmospheric fields.
Then the same number of generated data was considered and projected onto the five first principal components of the SVD that represent 75 % of explained variance of the dataset. In Fig. 12  the dot product is represented between SVD components derived from the dataset (u i ) i∈{0,. ,4} and another one derived from the generated data (v i ) i∈{0,. ,4}. Figure  12  represents the cross-covariance matrix defined by s ij = u i • v j. Values close to 1 or -1 show that the eigenvectors for both datasets (original and generated) are similar. This is another way of assessing whether the covariance structure of the original data is being preserved, and Fig. 12  shows that the five eigenvectors are similar. One should note that the SVD algorithm used from  Pedregosa et al. (2011)  suffers from sign indeterminacy, meaning that the signs of SVD components depend on the random state and the algorithm.
For this reason, we consider the dot product close to both 1 and -1. One should note that an inversion remains between the components with indexes 3 and 4, which could be explained by a difference of eigenvalue order (sorted in decreasing order) in each dataset that determines the order of eigenvectors. The fourth principal direction (index 3 in the figure) of the generated data represents more variation of the generated dataset than the same direction explains variation in the original dataset. Figure  13  shows clearly the inversion of the last principal components between the dataset and gen-erations. This suggests a way of improving our method in future work. Figure  15  shows the temperature (at the pressure level 1000 hPa) distribution at different pixel locations corresponding to the red dots in Fig.
2. 8. 2, p. 92). This asymptotic balance Eq. (  15 ) is verified to within 10 % of error at mid latitude, that is, u = u g + u ag , where the magnitude of the ageostrophic wind, u ag , is less than 0. 1 of the magnitude of the real wind u. Figure  19a  illustrates a particular boreal winter situation from the PLASIM dataset, focusing on the mid latitude and presenting a low area of geopotential in the southwest of Iceland. It appears that the wind is well approximated by the geostrophic wind, which is quantitatively verified in Fig. 20a  that shows the norm of the ageostrophic wind normalized by the norm of the wind (that is, the relative error when approximating the wind by the geostrophic wind): the order of magnitude of the error is around 20 %. Properties of the geostrophic flow are visible, with a counterclockwise flow around the low geopotential.
Thermal wind balance The thermal wind balance arises by combining the geostrophic wind Eq. (  15 ) and the hydrostatic approximations, ∂ ∂p = -1 ρ , where ρ is the density  (Vallis, 2006, Sect. 2. 8. 4, p. 95) : taking the derivative of Eq. (  15 ) with respect to the pressure p makes the hydrostatic approximation appear, so that the vertical derivative of the geostrophic wind can be written as ∂ ∂p = - R pf k × ∇ p T , (16) where the ideal gas equation, p = ρRT , has been used. Equation (  16 ) is the thermal wind balance that relates the vertical shear of the horizontal wind to the horizontal gradient of temperature. In particular, when the temperature falls in the poleward direction, the thermal wind balance predicts an eastward wind that increases with height. Figure  21a  and b show the vertical cross section of the zonal average of temperature and of the zonal wind for a particular weather situation in the dataset, corresponding to a boreal winter situation of the same weather situation represented in Fig.
Code availability. The code and the weights of the trained neural network are available at the following GitHub repository in v0. 1: https://github. com/Cam-B04/ Producing-realistic-climate-data-with-GANs. git (last access: January 2021). The repository is associated with the following DOI: https://doi. org/10. 5281/zenodo. 4442450  (Besombes, 2021). Data availability. The dataset used is available on demand. The GitHub repository explains how to recreate it from a PLASIM simulation.
Bounding technique using Affine Arithmetic This section describes the use of Affine Arithmetic (see  [2, 4, 9, 11, 18, 22] ) to generate a linear underestimation of function f over x= S. We add the constraint that the solution has to be inside the simplex S = conv(V), see  (1). This provides a linear program. First, we focus on the transformation of an interval vector into a vector of affine forms. Second, we describe how the computations are made using Affine Arithmetic to provide linear equations. Third, we sketch how the so-obtained linear equations are used to provide linear underestimations of f over x and then we provide the linear program to find a lower bound of f over the simplex S. Fourth, we show a simple way to solve the linear program.
2021). This was made possible thanks to the direct processing of the MSAs (i. e. , a raw-MSA method that did not rely on statistical models)  (Ju, Zhu et al. 2021) , the use of large meta-genomic databases and DL models, such as attention, brought from natural language processing (NLP)  (Vaswani, Shazeer et al. 2017) , rather than CNNs, and progress in deep geometric learning  (Bronstein, Bruna et al. 2021). Of note, was the highly accurate estimation of per residue error  (Jumper, Evans et al. 2021).
Even a single mutation can have a dramatic effect on protein structure and function. Recent studies open the possibility that protein language models, pre-trained on millions of unlabeled protein sequences, can be fine-tuned with small amount of labeled data to predict effects of mutations  (Madani, McCann et al. 2020 , Biswas, Khimulya et al. 2021 , Rives, Meier et al. 2021). Also, end-to-end deep neural networks, designed to predict inter-residue distances or protein structures, could perhaps be inverted to guide sequence design with a specific structure or function goal in mind  (Anishchenko, Chidyausiku et al. 2020 , Norn, Wicky et al. 2021). The current success of protein structure prediction is heavily grounded in the availability of large protein sequence datasets, in a relatively small fraction complemented by 3D protein structures.
What is needed for further progress? To advance the deep learning approaches in structural bioinformatics, we need large, high-quality datasets, like those found in protein sequence and structure databases. We may also need additional collaborative platforms to facilitate scientific exchange. The large-scale, blind, method performance assessment experiments, such as CASP and other CASP-modeled platforms, including Critical Assessment of Protein Interactions (CAPRI)  (Janin 2002) , Critical Assessment of Genome Interpretation (CAGI)  (Repo, Moult et al. 2012) , and others, have already proved to be a motivating factor for the academic and later even industrial teams. But in the end, this is to a considerable degree an open question and we strongly invite your input.
Evaluating significance of training data selection in machine learning Derry, Carpenter, and Altman examine performance of machine learning in three categories, (1) assessment of model accuracy, (2) design of protein sequence, and (3) catalytic residue prediction, when different types of experimental structural data are used in both evaluating and training of these techniques. Machine learning, and especially the deep learning methods, have demonstrated strong performance on these tasks, but they still very much depend on the data used in training. To assess this dependence, the authors use several datasets constructed from X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryo-EM) structural data available in the PDB. The study is timely, especially with the dramatic developments in cryo-EM and the resulting shift in structure data distribution.
Geometric pattern transferability Koehl, Jagota, Erdmann-Pham, Fung, and Song examine the geometric pattern transferability from protein self-interactions to protein-ligand interactions. Exploring the intra-protein interactions as a possible training set substitute for protein-ligand interactions has a potential to alleviate the relative scarcity of the latter type in the PDB. The authors use probabilistic models to characterize protein self-contacts and assess transferability with several statistical analyses. They then assess the results using an established protein-ligand docking dataset. In their paper, the authors strive to provide answers to the following underlying questions: Do the amino acid chemical group pairs have similar geometric distributions in protein self-contacts and protein-ligand complexes? Which ligand contact geometries are not well represented in proteins? When evaluated using a protein-ligand docking protocol, to what degree is the overall performance acceptable?.
An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NO x. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0. 55, a root-mean-squared difference of 11 ppb, and the same standard deviation and range of variability). An average difference of ∼ 8 ppb between the two observational datasets is observed, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude over land and averaging kernels reaching the middle troposphere over ocean). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for subtracting the influence of meteorological conditions in 2020 and 2019.
This adjustment is derived from the chemistry-transport model simulations using the meteorological fields of each year and identical emission inventories. Using adjustments adapted for the altitude and sensitivity of each observation, both datasets show consistent estimates of the influence of lockdown emission reduction. They both show lockdown-associated ozone enhancements in hotspots over central Europe and northern Italy, with a reduced amplitude with respect to the total changes observed between the 2 years and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally provide the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime.
traffic emissions, and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozone sonde measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistrytransport model. Whereas a general qualitative consistency of positive and negative ozone anomalies is observed with respect to observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational datasets, both for enhancements and decreases of ozone. Moreover, a significant ozone decrease observed at a large hemispheric scale is not simulated since the modelling domain is the European continent.
, 2018). The sensitivity of this satellite retrieval shows a relative maximum for ozone around 2. 2 km of altitude over land (while it is typically above 3 km for other standard satellite approaches; see details in  Cuesta et al. , 2013 Cuesta et al. , , 2018)). It offers a full cloud-free spatial coverage, which is complementary to in situ measurements. We also use the chemistry-transport model CHIMERE  (Menut et al. , 2013; Mailler et al. , 2017)  for assessing the consistency of this model with respect to the observations and to analyse the influence of meteorological conditions in 2020 as compared to 2019 (the year used as baseline or standard conditions in this study). The consistency of satellite data and simulations is analysed with respect to the Air Quality e-Reporting database providing nearreal-time air-quality measurements in Europe.
Section 2 of the paper describes these datasets and the strategy used for their analysis. Section 3 presents the results, first in terms of total differences between 2020 and 2019, secondly the influence of meteorological conditions, and finally the impact of the COVID-19 lockdown on ozone pollution. Additional discussions on anthropogenic emissions and meteorological conditions are also given in Sect. 4. A sensitivity study shown in the Supplement of the current paper considers a different setup of the CHIMERE model, in terms of emission inventories and meteorological models, for analysing possible un-certainties in the simulations. A last Sect. 5 provides the conclusions of this work.
Multi-data integrated approach Being a secondary and reactive pollutant, the analysis of tropospheric ozone is complex since it is influenced by many competing factors. Tropospheric ozone sources and sinks are affected by multiple photochemical processes and non-linear effects, which vary in time and space according to the availability of different precursors, either NO x or VOC (originating themselves from multiple anthropogenic and natural sources) and meteorological conditions. An integrated approach including multiple datasets of different kinds is therefore valuable for better understanding the evolution of this pollutant. In the current study, we use both satellite and in situ surface data to quantify the difference between the ozone pollution observed over Europe in spring 2020 with respect to that in spring 2019, which is affected by both the lockdown reduction of pollutant emissions and meteorological conditions.
First, we verify the consistency of these observations with respect to simulations from CHIMERE. This model simulates the 2 years, with a standard or business-as-usual inventory for 2019 and a modified version in 2020 accounting for the lockdown conditions, hereafter referred to as STD and COVID, respectively. Second, we use the chemistrytransport model with the same inventory (standard) for both years, 2020 and 2019, to derive a first-order adjustment for accounting for the influence on ozone pollution of the change in meteorological conditions between the 2 years. This allows us to obtain an approximate estimate of the effects of the COVID-19 lockdown conditions on ozone pollution from the two observational datasets (in situ and satellite). This is expressed by the following equation: where mod STD corresponds to simulated ozone with the standard or business-as-usual inventory.
69, a weak mean bias of -5 %, a precision of 20 %, and similar standard deviations for both datasets). Single-band approaches as those from IASI only were not able to observe such near-surface variability. The IASI+GOME2 satellite product includes vertical profiles of ozone, partial columns, averaging kernels (representing sensitivity of the retrieval to the true atmospheric state), error estimations, and quality flags. Since 2017, global-scale IASI+GOME2 retrievals have routinely been produced by the French data centre AERIS, and they are publicly available (see https://www. aeris-data. fr, last access: 1 April 2022, and https://iasi. aeris-data. fr, last access: 1 April 2022).