dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  Agence Nationale de la Recherche  funding ( G4PLAST ,  ANR-17-CE13-0005 ). MM was supported by an  Erasmus international mobility  grant and a mobility grant of the  University of Tunis El Manar. Fluoresence microscopy experiments were performed on the  PiCSL-FBI core facility , member of the  France-BioImaging national research infrastructure  ( ANR-10-INBS-04 ). We thank  Pascaline Auroy-Tarrago  for facilitating the development of synthetic biology initiatives within the BIAM.
This MCD-induced downregulation of CPT1 was observed at the protein and mRNA levels and was reversed by α-DBI (Fig. 4 B  and C ). MCD also caused the α-DBI-repressible downregulation of Ppara (a transcription factor essential for β-oxidation) (51, 52) at the mRNA (Fig. 4D ) and protein levels (SI Appendix, Fig. S5E ). In sum, ACBP/DBI inhibition close-to-fully reverses the transcriptional alterations associated with MCD-induced NASH. Of note, the genes that were significantly downregulated by ACBP/ DBI neutralization in the context of MCD-triggered murine NASH significantly overlapped with genes that are upregulated in human NAFLD or NASH compared with normal livers across 10 different datasets (SI Appendix, Table  S1 ), pointing to the translational relevance of these results.
In sharp contrast, direct immunostimulators acting on pattern-recognition receptors have largely failed in the field of immuno-oncology  (65, 66). By analogy to ICIs, α-DBI, an antibody that blocks an inhibitory circuitry normally restraining autophagy, might be considered as an autophagy checkpoint inhibitor (ACI). It remains to be seen, however, whether such ACIs targeting ACBP/DBI will be developed for clinical applications. Data, Materials, and Software Availability. [RNA-seq data] data have been deposited in [Gene Expression Omnibus (GEO)](GSE 194346). All study data are included in the article and/or supporting information. All data are included in the manuscript and SI Appendix. Additional data are available at the Gene Expression Omnibus at accession No. GSE194346  (67).
ACKNOWLEDGMENTS. We thank  Dr. Paule Opolon  ( Department of Pathology, Gustave Roussy, Villejuif, France ) for histopathological analysis. We thank the core facilities of  Centre de Recherche des Cordeliers and Gustave Roussy  for technical support. G. K. is supported by the  Ligue contre le Cancer  ( equipe labellis ee);  Agence National de la Recherche (ANR)  -Projets blancs; AMMICa  US23/CNRS UMS3655 ;  Association pour la Recherche sur le Cancer; Association "Ruban Rose"; Canc eropôle Ile-de-France ;  Fondation pour la Recherche M edicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ; European Joint Programme on Rare Diseases;  Gustave Roussy Odyssea , the  European Union Horizon 2020 Projects Oncobiome and Crimson  (No. 101016923 );  Fondation Carrefour ;  Institut National du Cancer; Inserm  (H et erog en eit e des tumeurs dans leur microenvironnement);  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  Leducq Foundation ; a  Cancer Research Accelerating Scientific Platforms and Innovative Research Award  from the  Mark Foundation ;, the  Recherche Hospitalo-Universitaire Torino Lumi ere; Seerave Foundation ; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination; and  SIRIC Cancer Research and Personalized Medicine.
This study contributes to the  IdEx Universit e de Paris   ANR-18-IDEX-0001. G. A. is supported by the  FRM. L. S. is supported by  Beatriz Galindo  senior program of the  Spanish Ministry of Universities ;  Strategic Program  " Instituto de Biolog ıa y Gen etica Molecular (IBGM) ,  Junta de Castilla y Le on" (Ref. CCVC8485 ); and  Internationalisation Project of the "Unidad de Excelencia IBGM of Valladolid" (Ref. CL-EI-2021 ).
***Currently mortality statistics are taken from the existing literature but can be obtained from available local data. In the third step, positive and negative environmental impacts are monetised to calculate externalities by making use of value transfer methods. These methods rely on monetary values from other studies that are initially obtained through primary valuation methods. Values are then transferred and adapted to new case studies for which conditions are considered equivalent. Value transfer is common practice in ES monetary valuation (e. g. ,  [47] [48] [49] ). Moreover, a number of platforms and inventories/databases collect economic values on ES and life cycle assessment (LCA) to provide data for value transfer (e. g. ,  [50] [51] [52] ).
At early planning/design stages, e. g. , strategic definition and concept design (see  [56]  for an example of design stages), archetypes are similar to land cover classes, such as deciduous forests (First and Second levels in Table  2 ). This level of definition of the archetypes is coherent with data availability on site condition and with how professionals plan/design NBS in early stages, i. e. , mainly as zoning diagrams equivalent to land-use/cover maps. For intermediate and advanced stages, NBenefit$ offers urban forest archetypes differentiated for vegetation species, plant size/age at planting, soil texture, and management actions. This is to align with the high granularity of data (e. g. , tree species, size of trees to be planted, management actions) needed for the design stages.
Offline Archetype Model and Archetype Database In the offline archetype model, the urban forest archetypes defined in the design step are pre-calculated. First, their environmental performance (i. e. , supplied ES and applied management actions) during the NBS life cycle operational phase is estimated in biophysical units. Such a performance is calculated using the system dynamics model for urban forests (the foreground level) built in Simile, which is compiled and run via the open statistical software R  [58]. Outputs are calculated at daily and monthly temporal resolutions and then aggregated into yearly time steps. Yearly aggregations are used because the scope of NBenefit$ is to inform about the performance of NBS over their entire life cycle. However, modelling at a detailed temporal resolution is necessary because most of the socio-ecological processes are scale-sensitive due to their non-linearity (e.
These impacts are assessed in the background level (steady state model), applying a life cycle impact assessment (LCIA) approach  [61]  with the support of the LCA software SimaPro, making use of the ReCiPe 2016 method at mid-point level  [62]. To characterise midpoint level impacts, substance flows that have the potential to contribute to the same environmental effect are grouped in single impact categories (e. g. , eutrophication, climate change). Such an operation of aggregation is similar to the quantification of positive environmental effects that occur in the ES biophysical assessment. Once the negative and positive environmental impacts (i. e. , ES classes and life cycle impact categories) are calculated in biophysical units for all the life cycle phases, their values are monetised (i.
e. , estimation of externalities) and stored in the archetype database. Lower and upper monetary values are also computed to provide the level of uncertainty in the monetisation of each environmental impact. The lower and upper boundaries depend on two factors: (i) on the range of values found in different source studies computing the total economic value of a specific ES; (ii) on the standard deviation of each type of environmental impact (in biophysical units) modelled. This helps to showcase that monetary values should be understood as indicative ranges and not as exact outputs and to show how the variability (uncertainty) from the environmental assessment propagates in the calculation of externality values. On top of the externality values, the financial values (representing expected cash flows related to the NBS project) are added based on publicly available national cost databases and local datasets on expenses.
Similarly, for financial values, central, lower, and upper monetary values are also provided. Accordingly, the valuation of economic impacts combining externality and financial values provides a better understanding of the net economic value of an NBS archetype (by year and for its entire life cycle). Once all environmental impacts, externalities, and financial values are calculated for an archetype, values are stored in the archetype database of NBenefit$. End users of NBenefit$ do not need to re-calculate archetypes, as it is a time-consuming task on the side of the NBenefit$ developers. In this sense, the archetype database acts as a repository of disaggregated values for all the pre-calculated archetypes. The database is linked to the web user interface through a RESTful API, i. e. , the calculation service (Figure  5 ).
Application of NBenefit$ to Evaluate Urban Forests To illustrate the functionality of NBenefit$, 48 urban forest archetypes were prepared for the environmental conditions of Barajas, in the northeast of Madrid (Spain). Section 3. 1 provides an overview of these archetypes. Detailed output data for the 48 archetypes are provided in SM8. In Section 3. 2, three alternatives using three archetypes are tested for a hypothetical project of a small urban forest (0. 1 Ha, 10 cells).
Introduction of GRASP algorithm GRASP is an open-source software package (https://www. grasp-open. com/, last access: 15 March 2020) for calculating and retrieving various optical and microphysical properties of aerosol and surface from observations of different remote sensing instruments, such as satellite, lidar, radiometer, and radiosonde  (Dubovik et al. , 2021). It was originally designed to solve the problem of aerosol retrieval from the PARASOL observations  (Dubovik et al. , 2014) , but it has now become a scientifically rigorous and versatile algorithm based on generalization principles that works with diverse remote sensing applications in the community after continuous development  (Dubovik et al. , 2021). The GRASP algorithm contains two pivotal and independent modules. One is used to calculate the scattering, absorption, and extinction of light between different media from the physical level, simulating theoretical observational radiation signal, called the "Forward Model".
Settings of retrieval parameters The settings of initial value and spatial-temporal constraint can significantly impact results of the statistically optimized strategy in the GRASP algorithm  (Dubovik et al. , 2011). The GRASP allows several strategies to fit observations. For instance, the GRASP software gives two retrieval schemes for POLDER observations. The configurations of the two schemes differ only in settings of aerosol size distribution in the forward model. One fits the aerosol size distribution with 16 triangle bins from the range of 0. 05 to 15. 0 µm, while the other uses five lognormal bins at 0. 1, 0. 1732, 0. 3, 1. 0, and 2. 9 µm based on pre-calculated optimized kernels of the POLDER-3. The scheme with five lognormal bins increases the speed by ∼ 9 times (2. 5GHz CPU) without any graphical acceleration compared to the scheme with 16 triangle bins, and it has been used to generate the operational PARA-SOL/GRASP aerosol products  (Chen et al.
, 2020). In addition, there is a scheme that is being tested called "GRASP/-Model". The GRASP/Model approach assumes an external mixture of several aerosol types with fixed optical parameters, which is a faster and more stable AOD retrieval, especially when aerosol loading is low  (Chen et al. , 2020). A tolerable absolute error in radiative transfer calculations is set to 0. 0005, and multiple scattering effects have been considered. The number of atmospheric layers is set to 10 with an exponential distribution. The input data of the GRASP algorithm were the normalized radiative measurements at 443, 490, 565, and 670 nm and the DOLP values of 490 and 670 nm. The initial guess of aerosol and surface properties are default in the GRASP software. They are applied to calculate AOD at a global scale.
The high values of AOD in the South China Sea and estuaries of the Yangtze River can be clearly captured. To summarize, the DPC showed the spa-  tial ability of AOD retrieval based on the GRASP algorithm in the Chinese region, and similar results have also been reported recently by using the GRASP component module  (Li et al. , 2022). Another case was selected in Western Europe where the air is clean and aerosol loading is low (< 0. 2) most of the year. As shown in Fig. 6b , different satellites and aerosol retrieval methods showed slightly different distributions of AOD. In addition to the different transit times between DPC and MODIS, this phenomenon is also probably because the aerosol signal is difficult to separate from the remainder of signal under low aerosol loading conditions and thus results in relative larger uncertainties of retrieval.
Comparison of DPC AOD with MODIS products at a temporal scale In this section, time series of AOD were evaluated against MODIS aerosol products based on the observations of AERONET site. The time series of daily RMSE is calculated for the global AERONET data set, as shown in Fig. 8. Lower values of RMSE mean smaller actual errors, indicating a good match with the AERONET. The AERONET stations had relatively continuous observations during the study period to avoid errors relating to how global validation statistics shift with the spatial distribution of observations  (Gupta et al. , 2018). As shown in Fig. 8 , it was found that the time series of AOD from the DPC GRASP/Model scheme had a matched well with the AERONET AOD. The values of RMSE were ∼ 0. 06 and stable before 87th day. The reason for the relatively large RMSE (∼ 0.
The optimization of the region is expected to improve the retrieval effect, and further evaluation also requires the use of longer sequences of DPC data in the future. Figure  9  shows three cases at different underlying surfaces to display the time series of AOD retrieved from the DPC GRASP/Model scheme on the basis of AERONET observations. The DT AOD was also compared as a reference due to its stable performance. It was found that the behaviour of AOD from DPC/GRASP and MODIS DT was generally consistent with AERONET at the three sites. From the scatterplots, the values of R were 0. 947 and 0. 949, 0. 943 and 0. 959, and 0. 967 and 0. 859 for MODIS DT and DPC GRASP/Models AOD at Raciborz, Magurele_Inoe, and FZJ-JOYCE, respectively. The GRASP/Model AOD values retrieved from DPC were slightly higher than the AERONET values at the FZJ-JOYCE site, resulting in a relatively low R.
Conclusion and summary DPC/GaoFen-5 is the first multi-angle polarized sensor launched by China and thus it occupies an important position in the development of satellite sensors. In this study, AOD was retrieved from the DPC images by using the GRASP algorithm and compared with AERONET and MODIS observations. The main purpose was to evaluate the performance of the DPC to monitor global aerosols. On a global basis, a uniform parameterization scheme, which defined the variation ranges and start values of the optical and microphysical properties (realized by aerosol type) of the aerosol, was applied in the "Model" module of GRASP. Validations against AERONET showed that the R and EE% of the DPC GRASP/Model scheme were 0. 8511 % and 79. 30 %, respectively, in the first attempt. The SCA, number of averaged pixels in retrieval units, and fitting residual showed an impact on the results of AOD.
Acknowledgements. We are grateful to the  Moderate Resolution Imaging Spectroradiometer (MODIS)  team, the  Aerosol Robotic Network (AERONET)  organization and the  GaoFen-5 Directional Polarimetric Camera (DPC) development  team for freely distributing their aerosol products and measurements. The numerical calculations in this paper have been done on the supercomputing system in the  Supercomputing Center of Wuhan University. Finally, we would also like to thank all reviewers for their constructive and valuable comments. Financial support. This research has been supported by the  National Key Research and Development Program of China  (grant no. 2018YFB0504500 ) and the  National Natural Science Foundation of China  (grant nos. 41875038 ,  42071348 , and  42001291 ). Review statement. This paper was edited by  Linlu Mei  and reviewed by  Stefan Kinne  and two anonymous referees.
Data availability. The AERONET Level 2. 0 AOD dataset can be downloaded from https://aeronet. gsfc. nasa. gov/ (last access: 15 March 2022;  Holben et al. , 2006)  and a request to download the GRASP model can be made at https://www. grasp-open. com/ (last access: 15 March 2022; Dubovik et al. , 2022). The MODIS Dark Target and Deep Blue aerosol products can be accessed and downloaded at https://ladsweb. modaps. eosdis. nasa. gov/ (last access: 15 March 2022).
Synthetic amorphous silica (SAS) is a nanomaterial used in a wide variety of applications, including the use as a food additive. Two types of SAS are commonly employed as a powder additive, precipitated silica and fumed silica. Numerous studies have investigated the effects of synthetic amorphous silica on mammalian cells. However, most of them have used an exposure scheme based on a single dose of SAS. In this study, we have used instead a repeated 10-day exposure scheme in an effort to better simulate the occupational exposure encountered in daily life by consumers and workers. As a biological model, we have used the murine macrophage cell line J774A. 1, as macrophages are very important innate immune cells in the response to particulate materials. In order to obtain a better appraisal of the macrophage responses to this repeated exposure to SAS, we have used proteomics as a wide-scale approach.
Furthermore, some of the biological pathways detected as modulated by the exposure to SAS by the proteomic experiments have been validated through targeted experiments. Overall, proteomics showed that precipitated SAS induced a more important macrophage response than fumed SAS at equal dose. Nevertheless, validation experiments showed that most of the responses detected by proteomics are indeed adaptive, as the cellular homeostasis appeared to be maintained at the end of the exposure. For example, the intracellular glutathione levels or the mitochondrial transmembrane potential at the end of the 10 days exposure were similar for SAS-exposed cells and for unexposed cells. Similarly, no gross lysosomal damage was observed after repeated exposure to SAS. Nevertheless, important functions of macrophages such as phagocytosis, TNFα, and interleukin-6 secretion were up-modulated after exposure, as was the expression of important membrane proteins such as the scavenger receptors, MHC-II, or the MAC-1 receptor.
These studies have shown that SAS is selectively toxic for macrophages  [17, 18]  but not for other cell types  [19] , and also induces a pro-inflammatory response  [20] [21] [22] [23]. This suggests a strong parallel between the toxicology of SAS and the one of crystalline silica. However, the in vitro studies that have investigated the pro-inflammatory effects of SAS have used a classical acute exposure mode, and thus cannot investigate the persistence of the effects, which is a key dimension in the etiology of silicosis. In vivo studies have shown that the effects of SAS are transient  [11] [12] [13] [14] , which has been recently transposed (and confirmed) using dedicated in vitro systems  [24]. When assessing the toxicology of SAS, another point to be taken into account is that the exposures to this nanomaterial are generally chronic, i.
e. , repeated and at low concentrations over a long period, e. g. , through the use of SAS as a food additive or in cosmetics. This is a critical dimension to consider when studying the effects of SAS on macrophages in vitro. Indeed, the few studies that have investigated the effects of repeated exposures, mostly on silver nanoparticles, have reported different effects than those observed after an exposure to a single dose  [25] [26] [27] [28] [29] [30]. Such studies are much less common for silica  [31]  and have centered more on the structural determinants of the effects of silica on cells rather than on the cellular responses. This is an active area of research  [32] [33] [34] [35] , as the chemical determinants of the effects of silica on cells are much less obvious than for metallic nanoparticles, which often produce their effects through partial dissolution and liberation of toxic metal ions  [36].
In complement to the studies devoted to the understanding of the chemical features that drive silica toxicity, we decided to carry out a study centered on the cellular responses of macrophages to SAS. We thus chose to investigate the effects of two different types of SAS, namely precipitated silica and fumed silica. Precipitated silica is prepared via a wet route starting from alkaline silicates in water, while fumed silica is prepared via a pyrolytic route starting from silicon halides that are burnt in a flame in the presence of oxygen. Both types of silica are produced and sold as solids and can be used in a variety of applications ranging from tires and resins reinforcement to additives in food and cosmetics. As exposures to these forms of SAS are typically occupational, we chose to investigate the effects of a repeated exposure on macrophages, using a combination of proteomics and targeted studies.
Nanomaterials and Reagents The precipitated SAS used in this study was provided based on a collaboration with Solvay (Solvay GBU Silica, Lyon, France), reflecting a commercial grade of SAS. The primary particle size was in the 10-20 nm range, the agglomerate size in the micron range, and the specific surface area close to 220 m 2 /g. The fumed silica used in this study was purchased from Sigma (catalog #S5055, St. Louis, MI, USA). Its primary particle size was in the 20-30 nm range, the agglomerate size in the 250-nm range, and the specific surface area close to 200 m 2 /g. More characterization details have been provided in previous publications  [24, 39]  and are also available in Supplementary Materials Figure  S1. For use in cell cultures, both forms of silica were suspended in ultrapure water at the concentration of 10 mg/mL and pasteurized overnight after 10 min of sonication in an ultrasonic bath.
ICP-AES Dosage The multi-elemental standards for ICP were purchased from Sigma-Aldrich (ref 92091) and were first prepared in 10% HNO 3. At the end of the standard measurement, samples were prepared two at a time (base-mineralized silica is not stable in HNO 3 ). From 250 to 500 µL of mineralisate was added to 10% HNO 3 to reach a final volume of 6 mL. The samples were measured in an ICP-AES Shimadzu 9000 with ICPE solution Launcher software (v1. 31, Shimadzu France, Noisiel, France).
Sample Preparation The cell pellets obtained after exposure to SAS (or from mock-exposed cells cultivated under the same conditions) were lysed in were extracted with 10 pellet volumes of lysis solution (7 M urea, 2 M thiourea, 15 mM spermine base, 15 mM spermine tetrahydrochloride, 10 mM Tris (carboxyethyl) phosphine hydrochloride and 4% (w/v) CHAPS). After extraction at room temperature for one hour, the extracts were clarified by centrifugation (15,000× g 15 min), the supernatants collected, and their protein concentration determined by a modified Bradford assay  [42]. The protein extracts were stored frozen at -20 • C until use.