dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples of HT workflows based on Martini. (a) Pipeline for rational drug design, taken from Souza et al. 269 (b) Protocol underlying the membrane protein database, taken from Newport et al. 374 (c) Chem-informatics workflow for predicting membrane permeability, reprinted with permission from Menichetti et al. 375 Copyright 2019 ACS. (d) Multiscale workflow "MuMMI" to couple mesoscale and CG simulations, adapted from Bhatia et al.
ACKNOWLEDGMENTS Siewert J. Marrink  acknowledges  Gerhard Hummer  for providing the image of the autophagosome,  Chen Song  for providing the image of the SARS-CoV2 viral envelope,  Helgi Ingolfsson  for providing the image of the multiscale MuMMi workflow, and both  Helgi and Bart Bruininks  for proofreading the manuscript. Riccardo Alessandri  acknowledges  Charly Empereur-Mot ,  Petteri Vainikka , and  Najmeh Delavari  for providing images for Figure 7.
F I G U R E 8 8 Examples of HT workflows based on Martini. (a) Pipeline for rational drug design, taken from Souza et al. 269 (b) Protocol underlying the membrane protein database, taken from Newport et al. 374 (c) Chem-informatics workflow for predicting membrane permeability, reprinted with permission from Menichetti et al. 375 Copyright 2019 ACS. (d) Multiscale workflow "MuMMI" to couple mesoscale and CG simulations, adapted from Bhatia et al.
Thus, the presented extensive climatology product provides an opportunity for understanding variabilities and trends in global and regional distributions of aerosol species. The climatology of the aerosol components obtained in addition to the aerosol optical properties provides additional valuable, qualitatively new insight about aerosol distributions and, therefore, demonstrates advantages of multiangular polarimetric (MAP) satellite observations as the next frontier for aerosol inversion from advanced satellite observations. The extensive satellite-based aerosol component dataset is expected to be useful for improving global aerosol emissions and component-resolved radiative forcing estimations. The GRASP/Component products are publicly available (https://www. grasp-open.
, 2018)  are proposed for inverting the Moderate Resolution Imaging Spectroradiometer (MODIS) observations. All these algorithms enforced the MODIS retrieval and made the MODIS aerosol database a reference aerosol product in the community. At the same time, MODIS is a single-view instrument and generally does not provide sufficient information to identify aerosol types. In contrast, the multi-angular intensity observations by the Multi-angle Imaging Spectroradiometer (MISR) have been used successfully to identify aerosol types in several regional-scale studies, including wildfire smoke  (Chen et al. , 2008; Guo et al. , 2013) , desert dust  (Guo et al. , 2013; Kahn et al. , 2009) , and volcanic ash  (Kahn and Limbacher, 2012; Scollo et al. , 2012; Flower and Kahn, 2017 , 2018 , 2020 , 2021).
Nonetheless, although the aerosol classification from MISR datasets has been supported by abovementioned studies, these studies mainly provided qualitative flagging of aerosol types for observed aerosol events. In addition, because of the aerosol types defined by aerosol parameters in the look-up table (LUT), the best fit to the observations for intermediate retrievals were obtained us-ing a discrete set of possible solutions. In this regard, the numerous studies suggested that multi-angle polarization measurements have very high information content and are most appropriate for providing potentially enhanced insight about aerosol microphysics and composition for advanced aerosol products  (Hasekamp and Landgraf, 2007; Knobelspiesse et al. , 2011; Mishchenko and Geogdzhayev, 2007; Mishchenko and Travis, 1997; Tanré et al.
After about a decade of development and advancement, the GRASP algorithm has been adapted from POLDER observations for operational generation-extended aerosol products. Several datasets of enhanced aerosol retrieval from POLDER observations (see Sect. 4 in  Dubovik et al. , 2021a)  are now publicly available and archived at the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille1. fr, last access: 20 July 2022) and GRASP-OPEN site (https://www. grasp-open. com, last access: 20 July 2022). All POLDER/GRASP retrieval releases include extended sets of conventional aerosol optical parameters (such as spectral AOD, spectral aerosol absorption optical depth (AAOD), spectral AODF, spectral coarsemode aerosol optical depth (AODC), particle size distribution, SSA, complex refractive index, fraction of spherical particles, etc.
(  1 ) a = (a v , a n , a k , a sph , a Vc , a h , a brdf1 , a brdf2 , a brdf3 , a bpdf ) T , (1) where the elements representing the normalized logarithms of dV (r)/d ln r, real part of complex refractive index, imaginary part of complex refractive index, spherical particles fraction, total volume concentration, mean altitude of the aerosol layer, the parameters in BRDF and BPDF model are characterized by the vectors of a v , a n , a k , a sph , a Vc , a h , a brdf1 , a brdf2 , a brdf3 , a bpdf , respectively. The GRASP aerosol optical products derived from Optimized, HP, and Models approaches are generated and released publicly as daily, monthly, seasonal, yearly, and climatological datasets in Level-2 and Level-3  (Chen et al. , 2020). Level-2 represents the quality-filtered products with an initial resolution of satellite observations.
Lastly, the complex refractive index of aerosol mixture can be obtained from the complex dielectric constant of aerosol mixture. Currently, the complete POLDER-3 data archive has been processed by the GRASP/Component approach to provide extensive retrievals of aerosol component concentrations and aerosol optical properties. Similar to previous products described in  Chen et al. (2020) , extensive GRASP/Component products generated as daily, monthly, seasonal, yearly, and climatological datasets in Level-2 and Level-3 are publicly accessible via GRASP-open (https://www. grasp-open. com/, last access: 20 July 2022). Although several regional applications of this GRASP/Component have been discussed in several studies  (Li et al. , 2020a, b; Zhang et al. , 2021) , the climatology of derived aerosol component distribution has not yet been fully analyzed in details, and therefore the full potential of this new satellite-derived aerosol composition was not fully clarified for the broad aerosol community.
MERRA-2 data The new version of the atmospheric reanalysis, MERRA-2, is publicly distributed by the NASA Global Modeling and Assimilation Office (GMAO)  (Gelaro et al. , 2017; Randles et al. , 2017)  and the detailed description can be found in  (Buchard et al. , 2017; Gelaro et al. , 2017; Randles et al. , 2017). The AOD products derived from the groundbased AERONET measurements and satellite observations (e. g. , MODIS, MISR) are assimilated into MERRA-2 by the Goddard Earth Observing System (GEOS-5). The Goddard Chemistry, Aerosol, Radiation and Transport (GOCART) model is employed for the simulation of aerosol compositions such as BC, organic carbon, dust, etc. Up to now, numerous studies presented quantificational validations and assessments of different MERRA-2 products on a global or regional scale using measurements, such as AOD  (Randles et al.
Comparisons of BC and dust concentration derived from GRASP/Component with MERRA-2 Although applications of GRASP/Component to different instruments (sun-photometer, POLDER-3, DPC/GF-5) have presented good consistency with the available AERONET aerosol optical products such as AOD, AAOD, AE, and also agree with the physical expectations about aerosol composition in different locations, additional reference data are still desirable for the validation. Because the in situ measurement data are extremely scarce and difficult for global validation, we employ an intercomparison of BC and dust column concentration between the GRASP/Component climatological dataset and the corresponding MERRA-2 prod-  ucts. Indeed, this is not a pure validation experiment, but can still provide a better indicator of a global consistency.
Firstly, MERRA-2 includes the estimations of several externally mixed aerosol components (BC, OC, dust, etc. ) as defined in GOCART  (Chin et al. , 2002). The emissions for carbonaceous aerosol species in GOCART include fossil fuel combustion, biomass burning, biofuel consumption, and biogenic sources. Both natural and anthropogenic sources make contributions to BC emissions in MERRA-2. Various inventories during the different time periods were considered to produce biomass burning emissions of carbonaceous aerosols in MERRA-2. Specifically, the Quick Fire Emissions Dataset (QFED) version 2. 4-r6  (Darmenov and da Silva, 2015)  has been employed for daily biomass burning BC emissions since 2010. The monthly biomass burning emissions in the Global Fire Emission Dataset (GFED) version 3. 1  (Randerson et al.
, 2021). The comparisons of biomass burning emission datasets, including GFED version 3. 1 (GFED3. 1), GFED version 4 with small fires (GFED4s), FIre INventory from NCAR version 1. 5 (FINN1. 5), Global Fire Assimilation System version 1. 2 (GFAS1. 2), Fire Energetics and Emissions Research version 1. 0 (FEER1. 0), and Quick Fire Emissions Dataset version 2. 4 (QFED2. 4) for 2008 globally have indicated that a factor of 3. 4 can be obtained on an annual average for the differences of BC emissions  (Pan et al. , 2020). At the same time, the knowledge on the complexity and variability of different fire features are still required to be improved  (Hyer et al. , 2011). Continuous measurements are required to contribute toward advancing our understanding of the fire-generated BC globally, at least in the major biomass burning regions.
Data availability The GRASP/Component products derived from POLDER-3 are publicly available on the GRASP algorithm website (https://www. grasp-open. com/products, Dubovik, 2022). The monthly MERRA-2 BC and dust column concentration products are available on the NASA website (https://daac. gsfc. nasa. gov, MERRA-2, 2022). The dataset used in the current study is registered under https://doi. org/10. 5281/zenodo. 6395384 (Li et al. , 2022b).
More importantly, the data assimilation of this extensive satellite-based aerosol component dataset can importantly contribute to improving the emissions of global aerosol estimation, and the further improvement of accuracy of the estimated aerosol radiative forcing in general and per aerosol component in particular. For instance, the AOD and AAOD products de- rived from POLDER-3 observations have already been used to constrain GEOS-Chem inverse modeling for the improvement of global BC, organic carbon and desert dust aerosol emissions  (Chen et al. , 2018 (Chen et al. , , 2019)). Using the additional satellite-based aerosol component products presented in this study, the further improvement of global aerosol emissions estimation is expected. The presented efforts are also in line with the studies suggesting that the employment of satelliteconstrained anthropogenic and natural aerosol emissions by the climate models is required to improve the accuracy of aerosol radiative forcing estimations (e.
10 5 with maximum injection time set to 120 ms; MS scan range was from 400 to 2000 m/z. Dynamic exclusion was set to 30 s. The MS files corresponding to the interactome analysis and to the phosphoproteome and the total proteome analysis were processed differently. For interactome analysis, MS files were searched with the MaxQuant software v. 1. 6. 6 using Andromeda search engine against Andromeda search engine against the database of Mus musculus from swissprot 07/2017 and Plasmodium berghei ANKA from PlasmoDB (v. 37). To search for parent mass and fragment ions, a mass deviation was set at 3 ppm and 20 ppm respectively. The minimum peptide length was set to 7 amino acids and strict specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as fixed modification, whereas oxidation (Met) and N-term acetylation were set as variable modifications.
The false discovery rates (FDRs) at the protein and peptide level were set to 1%. Scores were calculated in Max-Quant as described previously  [81]. Match between runs was allowed. Proteins were quantified according to the MaxQuant label-free algorithm using LFQ intensities; protein quantification was obtained using at least 2 peptides per protein. The reverse and common contaminants hits were removed from MaxQuant output. Statistical and bioinformatic analysis were performed with Perseus software (v. 1. 6. 14) , freely available at https:// maxquant. net/perseus/ [82]. For statistical comparison we set two groups: Plasmodium berghei parental (WT PBANKA GFP) and KO. Each group contained three biological replicates. We then filtered the data to keep only proteins with at least three valid values in at least one group.
Next, the data were imputed to fill missing data points by creating a Gaussian distribution of random numbers with a standard deviation of 33% relative to the standard deviation of the measured values and 1. 8 standard deviation downshift of the mean to simulate the distribution of low signal values. We performed a t-test represented on a volcano plot, FDR < 0. 01, S0 = 1. For proteomic and phosphoproteomic analysis, the MS files were processed with the MaxQuant software v. 1. 5. 8. 3 and searched against the same database and the same settings as above, except for the following. Two distinct parameter groups were used for the search: phosphorylation (Ser, Thr, Tyr) were set as additional variable modifications only for phosphoproteomics data  [81]. Statistical and bioinformatic analysis, including heatmaps, profile plots and clustering, were performed with Perseus software (v.
The significantly altered phosphosites (t-test S0 = 0. 1, FDR = 0. 05) were represented in a volcano plot. The protein groups output table was used for total proteome analysis, murine proteins were also excluded. We kept only proteins three replicates out of four in at least on group (WT and KO). Missing values were imputed as above. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [83]  partner repository with the dataset identifier PXD030389. 1a, b ; electronic supplementary material, figure  S1A ) LRR domains are known to be involved in various processes such as bacterial pathogenesis or plant immune response through macromolecular interactions  [47, 84, 85]. As seen with other LRR domain-containing proteins, PbLRR1 sequence examination revealed the presence of a carboxy-terminal cap domain following the consensus sequence: YRxxwxxxwPxwxxLD (amino acids 281 to 297, w: hydrophobic residues) (figure  1a ; electronic supplementary material, figure S1A  [86] ) crucial for the folding of those proteins.
A total of 96 sites were changed by greater than 2-fold, with 90 exhibiting a greater than 2-fold decrease but only 6 exhibiting a greater than 2-fold increase in the PbLRR1 KO (figure  7a, b ). We checked that these proteins, when detected, did not vary in the global proteome analysis. Indeed, integration of the proteomics and phosphoproteomics data allowed us to verify that the modulation observed in the phosphoproteome was linked to the phosphorylation itself rather than variations in protein abundance. Likewise, PbPP1c expression remains unchanged in the KO lines suggesting that the observed variations of phosphorylation levels must be related to a mislocalization and/or inadequate control of PP1c activity rather than to its absence. When possible, the proteins whose phosphorylation significantly varied were classified based on their putative biological processes (COG database,  [97] ).
For example, we identified PbGAK, a cyclin G-associated kinase, the knock-out of which has been shown to induce a strong reduction in oocyst production due to the absence of sporoblast formation and the development of unusually large oocysts  [6]. Likewise, PbLAP5, a lectin-adhesive like protein localized in the crystalloids, is involved in oocyst development, with its deletion blocking The proteins highlighted in blue were identified as PbLRR1 interacting partners. The protein highlighted in green was specifically identified at that stage as a PbLRR1 interacting partners (t-test, FDR < 0. 01, S0 = 1). The proteins highlighted in brown were identified as partners in the activated gametocytes analysis but not in zygotes. (c) List of PbLRR1 interacting partners with names and PlasmoDB accession numbers.
Finally, the phosphoproteome analysis strongly supports the involvement of PP1c in the regulation of I3 phosphorylation status in PbLRR1 KO zygotes. Indeed, to the best of our knowledge, it is the first time that this protein, previously described in Plasmodium as a regulator of PP1  [25]  has been found to be hypophosphorylated in the absence of LRR1. This strongly indicates that I3 may be a substrate of PP1c, and its correct phosphorylation level could be regulated by the LRR1/PP1c complex. Thus, I3 phosphorylation status may determine its ability to both interact with the complex and to regulate its activity as previously described in other organisms  [45]. Proteomic and Phospho-proteomic analysis Proteomic and phospho-proteomic analysis that support the findings of this study have been deposited in the ProteomeXchange Consortium via the PRIDE  [83]  partner repository with the dataset identifier PXD030389.
Funding. This work was supported by la  Région Hauts-de-France  (' Accueil de Jeunes Chercheurs' award ,  2016_06706 (A. F. ) ), the  French National Research Agency (ANR)  (' Laboratoire d'Excellence ParaFrap '  ANR-11-LABX-0024  (O. S. and P. F. )), and by the  UK Medical Research Council  ( MR/P021611 ) (J. T. D. ) and the  UK Biotechnology and Biological Sciences Research Council  ( BB/V006428 ) (J. T. D. ).
Phenotypic alterations in COVID-19 monocytes Global alterations in innate immune cell phenotypes have been identified in severe COVID-19  11, [20] [21] [22]. As the main human monocyte population, we focused on deeply characterizing the ex vivo phenotype of classical CD14 + monocytes in uninfected healthy individuals and patients with COVID-19 presenting with mild or moderate symptoms (1-2 or 3-4 WHO ordinal scale for COVID-19 severity, respectively) during the acute phase of disease (Dataset 1). The battery of markers examined by high dimensional flow cytometry included MHC molecules and costimulatory and coinhibitory receptors (Fig. 1 ). Dimensionality reduction tools demonstrated that while some overlap in the global phenotypes was observed among the three study groups, monocytes from healthy individuals were clearly distinct from both mild and moderate COVID-19 on a tSNE plot (Fig.
2 ). Thus, CD14 + monocytes from both mild and moderate COVID-19 patients were able to efficiently activate SARS-CoV-2-specific CD8 + T cells upon UV-inactivated SARS-CoV-2 stimulation. However, only CD14 + monocytes from healthy individuals were able to trigger the activation of SARS-CoV-2-specific CD4 + T cells. To further define and quantify the phenotypic differences observed between healthy individuals and COVID-19 patients, we applied clustering algorithms using the 12 phenotypic markers previously examined. Cell clustering identified 16 different subpopulations of monocytes that were distinctively distributed in healthy and COVID-19 monocytes (Fig. 1c, d , Dataset 2), with 11 clusters containing more than 88% of the total cells analyzed (Supplementary Fig. 3 ). Interestingly, expansions of specific monocyte subpopulations were different in mild and moderate COVID-19 monocytes.
Differential gene expression analysis yielded 422 upregulated and 187 downregulated genes (≥1. 5fold change, FDR < 0. 05) in COVID-19 monocytes compared to healthy controls (Fig. 2b ). We used these genes to perform a pathway enrichment analysis with XGR  25  and pathway annotations from Reactome to gain insight on potential pathways differentially expressed in COVID-19 monocytes (Supplementary Fig. 6  and Dataset 3). Interestingly, pathway enrichment identified glycolysis as the most enriched pathway in COVID-19 monocytes, and also included metabolism of lipids and lipoproteins among the statistically significant pathways (Supplementary Fig. 6 ). Other significantly enriched pathways included interferon signaling and cytokine signaling, results that are in agreement with previous reports on the role of these two pathways in COVID-19 pathogenesis  6, 16, 22  (Supplementary Fig.
6  and Dataset 3). We subsequently examined the directionality of expression of the enriched pathways by analyzing downregulated genes and upregulated genes separately (Fig. 2c ). Pathway enrichment analysis of genes significantly upregulated (≥1. 5-fold change, FDR < 0. 05) in COVID-19 compared to healthy individuals demonstrated a significant increase in the metabolism of a number of lipids, including sphingolipids, phospholipids, and lipoproteins. Other upregulated pathways in COVID-19 monocytes included interferon signaling, cytokine signaling, and transmembrane transport of small molecules (Fig. 2c ). Heatmap showing the top 40 upregulated genes from the enriched pathways demonstrated a somewhat variable expression patterns among COVID-19 monocytes and included a number of type I interferonstimulated genes (IFI27, IFITM2, IFI6, IFITM3, MX1), metabolic enzymes (ASAH1, CYP27A1, SGPP2, SPHK1) and others (Fig.
2h, i ) and directly by phosphorylation of the p65 NFκB subunit (Fig. 2j, k ), as a readout for cytokine signaling  27, 28. While monocytes from mild COVID-19 patients displayed a decrease in the expression of IκBα compared to that of healthy individuals (Fig. 2i ), monocytes from moderate COVID-19 patients did not. Furthermore, neither mild nor moderate COVID-19 monocytes displayed an increased expression of phospho-p65 NFκB (Fig. 2k ). This observation suggests that other additional mechanisms may be regulating the activation of NFκB, and that NFκB-driven cytokine responses may be altered in patients with COVID-19, in agreement with the lack of increased pro-inflammatory cytokine expression by COVID-19 monocytes (Fig. 2c , Dataset 4) and with previous single cell transcriptomic data of acute COVID-19 PBMC  29.
Moreover, several of the genes contributing to the "Cytokine signaling" pathway enrichment (Fig. 2c ) were interferonstimulated genes (Dataset 4). We subsequently selected the set of significantly downregulated genes in COVID-19 monocytes (≥1. 5 fold decrease, FDR < 0. 05) to perform pathway enrichment (Fig. 3 ). The only pathway that was  significantly downregulated in COVID-19 monocytes was glycolysis (Fig. 3a  and Dataset 5), with decreased expression of a number of enzymes involved in glucose degradation, including PFKP, ENO1, PFKB4 and others (Fig. 3b ). This metabolic profile with increased metabolism of lipids (Fig. 2c ) and decreased glycolysis was unexpected, as glycolysis is an important driver of innate immune cell function during the recognition of pathogens  30. To confirm these gene expression data, we used SCENITH TM31 to metabolically profile CD14 + monocytes from COVID-19 patients and healthy controls ex vivo.
healthy monocytes, FDR < 0. 05) using XGR software and the Reactome pathway database demonstrated a number of expected pathways involved in the innate immune response to pathogens, including type I IFN signaling, cytokine signaling, interactions between lymphoid and non-lymphoid cells, NLR sensing, etc. (Supplementary Fig. 11  and Dataset 6). However, when we focused our analysis on pathways enriched in upregulated genes in activated COVID-19 monocytes compared to activated healthy monocytes, the most significantly enriched pathways were involved in or closely related to hemostasis and coagulation, including integrin signaling, extracellular matrix organization, signaling by PDGF, interactions with activated platelets and general hemostasis (Fig. 5c  and Dataset 7). Integrin receptors are used by cells to interact with other cells and with the extracellular matrix, by binding numerous matrix proteins including collagen, actin and laminin, being also involved in hemostasis and platelet aggregation  36.