dataesr/scientific-paragraphs-categorization huggingface dataset:
(  1 ) using satellite observations of AOD; and 4) finally, AOD errors in individual models were attributed to emission, lifetime and MEC contributions. This method allowed for an integrated assessment of model AOD errors based on three interpretable components that are constrained using observational datasets. Such a quantitative attribution of AOD errors due to multiple sources has, to our knowledge, not been conducted previously. The resulting information can be immediately used to improve model performance and has several key implications detailed below. Notably, our constraining analysis was conducted on a seasonal and regional scale. The constrained results cannot be directly applied to a smaller scale within the regions. Similarly, evaluations/interpretations of individual fires are beyond the scope of this study.
Attributing AOD errors to emissions, lifetime and MEC Model errors in AOD can now be attributed by comparing a model's emissions, lifetime, and MEC with the constrained values (see Methods). Figure  4  shows that a significant balancing of errors existed in almost all models. We found a substantial bias induced by emissions, mostly underestimation except for the Amazon and Eastern Siberia   4 ). Total BB emissions consist of OA, BC, and SO 2. The OA emissions in the four inventories are converted from OC emissions with a ratio ranging from 1. 4 to 2. 6 as used in the AeroCom models (shown as the range of bars). The 1. 67 OA/OC ratio (thick black lines) from a recent field measurement is used for direct comparison  37. The four inventories are Global Fire Emission Database version 4. 1 s (GFED)  35  , Fire Energetics and Emission Research version 1.
2 (FEER)  34  , Global Fire Assimilation System version 1. 2 (GFAS)  18  , and Quick Fire Emissions Dataset version 2. 5 (QFED)  36. regions, as mentioned previously. The differences in the emissionrelated bias were associated with the choices of emission datasets and assumptions on OA/OC ratios in the models (see Supplementary Table  1 ). However, AOD errors cannot be fully explained without considering errors related to lifetime and MEC, which either compensated for or exacerbated errors caused by emissions. In fact, AOD errors were less contributed by the emission errors (38 ± 19%) than the total errors induced by MEC (27 ± 19%) and lifetime (22 ± 15%) errors for the whole ensemble of models. The cross effects between the three factors were nonnegligible but less important (13 ± 9%) for the whole ensemble, although individual models contained large cross-term errors.
Biomass burning regions The study focused on aerosols over five key BB regions: the Amazon, Southern Hemisphere Africa, Southeast Asia, Boreal North America, and Eastern Siberia. The fire season in 2010 was defined for each region based on recorded BBA emissions and satellite-observed AOD peaks. The spatial distribution and monthly evolution of BBA emissions and AOD over the five BB regions are mapped in Fig. 1. According to the GFED database, the major burned biome types that contributed significantly to the global BB aerosol budget were included in these five BB regions (i. e. , tropical forest, savanna, grassland, and boreal forest). In total, emissions from these five regions accounted for more than half of the global total BBA emissions for BC and OC  35. Based on BBA emissions and AOD observations, we defined the BB seasons in 2010 as follows: July to October for Amazon, June to September for Southern Hemisphere Africa, March for Southeast Asia, June to August for Boreal North America, and July for Eastern Siberia.
AeroCom models and observation data We used modelled AOD data at 550 nm from two control experiments (CTRL2016, CTRL2019) in phase III of the Aerosol Comparisons between Observations and Models project (AeroCom). AeroCom is an open international initiative aiming at a better understanding of global aerosols and their impact. The two phase III control experiments represent the state-of-the-art aerosol modelling for 2010. A total of 17 models were involved, as detailed in Supplementary Table  1. In addition to AOD data, data on emissions, aerosol burdens, precipitation, and Ångström Exponent (AE, calculated from AOD at 550 nm and 440 nm) were also used. Observations of AOD and AE in 2010 were taken from the POLarization and Directionality of the Earth's Reflectances (POLDER) sensor on the PARASOL platform using the Generalized Retrieval of Atmosphere and Surface Properties algorithm  43.
The POLDER data were validated previously on the global scale and have shown better performance than other satellite datasets  44, 45. We used precipitation from the Global Precipitation Climatology Project (GPCP) version 2. 3 as observations. GPCP combines satellite estimates and ground measurements to estimate precipitation globally and has been used to evaluate global climate models before  46, 47. BBA particle size and MEC from field measurements and flight campaigns were also collected to further verify the modelled aerosol properties (see Supplementary Tables  2, 3 ). The modelled AOD was first regridded into 1°× 1°grid cells and compared against POLDER observations using 3-hour average data for CTRL2016 models and daily data for most CTRL2019 models. The normalized mean bias for the five fire regions is listed in Supplementary Table  1 , showing a diverse, generally negative bias.
ECHAM-HAM simulations The ECHAM-HAM model was run at T63 horizontal resolution, with 47 vertical layers with the M7 submodule for aerosol microphysics  54. Emissions for major aerosol sources were taken from GFED for BB, the Community Emissions Data System for anthropogenic sources, the AeroCom phase II dataset for biogenic volatile organic compounds, and online calculations for dust and sea salt. Meteorology was nudged with ECMWF Reanalysis v5 (ERA5). The simulations covered the entire fire season for Southern Hemisphere Africa and started three months earlier for model spin-up. We compared three different cases within ECHAM-HAM (see Supplementary Table  6 ): 1) the default model configuration; 2) the EC case using default model configuration but with emissions over the Africa source regions scaled to match POLDER AOD observations; and 3) the MFC case with a modified model configuration based on the error attribution done for ECHAM-HAM.
Acknowledgements This work was financially supported by  Netherlands Organization for Scientific Research (NWO ;  ALWGO. 2018. 052and Vici scheme 016. 160. 324 ). The ECHAM-HAM simulations were carried out on the Dutch national e-infrastructure with the support of the SURF Cooperative. We acknowledge AeroCom modellers for preparing and submitting model data that were used in this work. We would like to acknowledge the use of POLDER data with GRASP algorithm available from  CNES/GRASP/LOA/Cloudflight/ICARE. The POLDER/ PARASOL Level-1 data was originally provided by  CNES. Products and images and browse images were produced by  LOA/ICARE , distributed by  ICARE Data and Services Center  (https://web-backend. icare. univ-lille. fr). H. M. was supported by grants from  MEXT/JSPS  ( JP19H05699 ,  JP19KK0265 ,  JP20H00196 ,  JP20H00638 ,  JP22H03722 ),  MEXT-ArCS-II  ( PMXD1420318865 ) and ERTDF of ERCA (JPMEERF20202003).
Data availability The AeroCom model data can be accessed from https://aerocom. met. no. POLDER observations can be downloaded from https://www. graspopen. com/products/polder-data-release. GPCP data can be downloaded from https://climatedataguide. ucar. edu/climate-data/gpcpdaily-global-precipitation-climatology-project. BBA emissions from the four emission inventories can be downloaded from https://www. geo. vu. nl/~gwerf/GFED/GFED4 for GFED, https://apps. ecmwf. int/ datasets/data/cams-gfas for GFAS, https://feer. gsfc. nasa. gov/data/ emissions for FEER, https://portal. nccs. nasa. gov/datashare/iesa/ aerosol/emissions/QFED/v2. 5r1/0. 1/QFED for QFED. Sources of independent observations can be found in the Supplementary Information. The ECHAM-HAM simulation data are deposited in Zenodo (https:// doi.
, 2016). Whole genome sequencing (WGS) data and clinical data were obtained from dbGaP (phs001963. v1. p1) for the DLB cohort and AMP-PD https://amp-pd. org/whole-genome-data) for the PD cohort. The NPC1 gene region was extracted from these datasets using PLINK v1. 9  (Purcell et al. , 2007).
Quality Control Quality control for the RBD cohort (MIPs data) was performed using PLINK v1. 9  (Purcell et al. , 2007)  and Genome Analysis Toolkit (GATK) as previously described  (Ouled Amar Bencheikh et al. , 2020). In brief, variants were excluded if coverage depth was lower than 50x and if the variant appeared in less than 25% of the reads. We further excluded variants based on the following criteria: genotyping rate less than 90%, missingness difference between cases and controls set at p = 0. 05 after Bonferroni correction, and deviation from the Hardy Weinberg equilibrium set at p = 0. 001. Samples were removed if their average genotyping rate was less than 90%. The pipeline is available at https://github. com/gan-orlab/MIPVar. Sequence data for the DLB and PD cohorts was run through standard genome-wide association study (GWAS) quality control for samples and variants as previously described (https://github.
Statistical Analyses To test for associations between common variants (MAF > 0. 01) and rare variants (MAF < 0. 01) in NPC1 and disease status, we performed logistic regression for all three cohorts using PLINK v1. 9  (Purcell et al. , 2007). Optimized sequence Kernel association tests (SKAT-O, R program) were also performed on rare variants for each cohort, including tests for all rare variants, nonsynonymous variants, and pathogenic variants, as well as a combination of nonsynonymous, frameshift, splice and stop-gain variants  (Lee et al. , 2012). Pathogenic variants were determined as those with predominantly pathogenic or likely pathogenic reports in the ClinVar database, extracted using VEP  (McLaren et al. , 2016). All regression and SKAT-O analyses across the three cohorts included adjustment for sex, age and the top 10 principal components (based on scree plots).
Thus, the lack of association of NPC1 with synucleinopathies supports that specific dysfunction of the sphingolipid metabolism pathway may be the key lysosomal disease mechanism in synucleinopathies. This study has a few key limitations. First, we only included individuals of European ancestry, as there was no sufficient available genetic data from non-European populations to perform these analyses. The synucleinopathies included in the present study are complex disorders, thus it is possible that NPC1 variants may contribute to RBD, DLB or PD in other populations. Another limitation is that there are differences in age and sex between patients and controls across all three cohorts. Both common and rare variant analyses were adjusted for sex and age to account for these differences. Lastly, our study cannot rule out the role of very rare NPC1 variants in alpha-synucleinopathies, or variants not present in our cohorts.
Acknowledgements We thank the participants for contributing to this study. We thank  Meron Teferra ,  Helene Catoire  and  Sandra B. Laurent  for their contributions. The work performed in this study was financially supported by grants from the  Michael J. Fox Foundation , the  Canadian Consortium on Neurodegeneration in Aging (CCNA) , the  Canadian Institutes of Health Research (CIHR) , the  Canada First Research Excellence Fund (CFREF) , awarded to  McGill University  for the  Healthy Brains for Healthy Lives initiative (HBHL) , and  Parkinson Canada. J-FG holds a  Canada Research Chair on Cognitive Decline in Pathological Aging. DLB data used in preparation for this article were obtained from dbGaP. More information on this study can be found at phs001963. v1. p1, and we acknowledge principal investigators.
Study design, sources and population In this nationwide population-based cohort study, we used data from the Programme de Médicalisation des Systèmes d'Information (PMSI database), the French national hospital database in which administrative and medical data are systematically collected for acute (PMSI-MCO) and psychiatric (PMSI-PSY) care. The PMSI database is based on diagnosisrelated groups with all diagnoses coded according to the 10th version of the International Classification of Diseases (ICD-10) and procedural codes from the Classification Commune des Actes Médicaux (CCAM). The PMSI database is used to determine financial resources and is frequently and thoroughly verified by both its producer and the paying party with possible financial and legal consequences  [20]. Data from the PMSI database are anonymized and can be reused for research purposes.
Procedure We defined two groups. The first group included patients who had a diagnosis of schizophrenia according to specific ICD-10 codes (i. e. , F20*, F22*, or F25*). To control for downcoding, patients with schizophrenia were researched in either the acute or psychiatric PMSI databases from 2015 to 2020. The second group included patients who did not have a diagnosis of severe mental disorder according to the same procedure. Patients without a diagnosis of severe mental disorder were matched to patients with schizophrenia in a 10:1 ratio according to age (±5 years) and sex.
Limitations and perspectives The impact of COVID-19 on non-COVID-19 mortality and hospital admissions may depend on health care organization and public management strategies. Our results may not be extrapolated to other countries and should thus be replicated. The impact of context (e. g. , social distancing, isolation, lockdown) could also be explored and it would be interesting to determine whether it has affected patients with schizophrenia more than control patients. A weakness of medico-administrative databases is the miscoding of diagnoses during hospital stays that can underestimate patients' comorbidities and severity at admission. Smoking addiction (Yes/No) and alcohol addiction (Yes/No) are crude binary classifications that cannot adequately measure these important confounding influences.
Administrative data are known to exhibit high specificity but low sensitivity for capturing adverse events; thus, these events are often underreported  [44]  and were not analyzed in our study. These issues may be accentuated in schizophrenia given the phenomenon of diagnostic overshadowing  [33] [34] [35]. In addition, several data, such as social isolation, race/ethnicity and treatments, were not available in our database and are known to influence health outcomes. Our work describes only patients who were admitted into acute care hospital, and we have no information on care and death occurring outside the hospital setting. These deaths might be differentially distributed by study group and time period with likely higher rates of out-of-hospital deaths among people with schizophrenia. Future work should study the impact of COVID-19 on non-COVID-19 out-of-hospital mortality.
In our analysis, we did not distinguish index hospital stays from hospital readmissions. Future studies should determine whether increased mortality was related to early mortality during the index stay or later during hospital readmission. Some patients with schizophrenia may not have been identified in our study because we only selected patients diagnosed in acute and psychiatric hospitals. However, this problem should be minimized, as this study covered 5 years of hospitalization, and previous studies have shown that 83% of patients with schizophrenia are followed by public mental health hospitals in France  [45]. In addition, since the PMSI database collects nationwide data, admissions of subjects diagnosed with schizophrenia in France and admitted to hospitals outside of France would have been missed.
ACKNOWLEDGEMENTS We acknowledge  Matthew Carty ,  Cyrille Colin ,  Quentin Cordier ,  Violaine Fernandez ,  Jean-Christophe Lifante ,  Stéphanie Polazzi ,  Thomas Rimmelé ,  Lea Pascal ,  Cécile Payet ,  Vincent Pradel  and  Marie Viprey  and the health care workers who dealt with the COVID-19 pandemic.
DSST calculation To calculate past DSST, the data from different cores have been resampled following a linear interpolation with a resampling step of 2,500 years, which best resembles the initial resolution in both cores. DSST was then determined by subtraction: D ("#$% '("#$% ))+, =  "#$% ',+, - "#$% ),+, (. 1) with tx describing the same age point in the cores, core 1 being MD11-3357 and core 2 being MD11-3353.
Acknowledgments We gratefully thank  H. Eri Amsler ,  Julia Gottschalk ,  Thomas Frölicher ,  Gianna Battaglia ,  Fortunat Joos  and  Xavier Crosta  for fruitful discussions. We thank  Bichen Zhang  for help with setting up the quantitative framework. Financial support for this study was provided by the  Swiss National Science Foundation  (grants  P1BEP2_168625  to LMT and  PP00P2_144811  and  PP00P2_172915  to SLJ), the  Max Planck Society (AMG) , and the  ERC starting GRANT OceaNice (PKB).
7. The figures of the French specimen were composed with Adobe Illustrator CC2019 and Photoshop CC2019 software. The Spanish wing specimen and plant remains (on the same slab's surface) were photographed using a compound microscope Olympus BX53 with an integrated digital camera, and with an Olympus Tough TG-5, respectively. The photograph of the wing fragment is a stacked image, unlike the plant remains images. The plate with these images was composed using Photoshop CS2 version 9. 0. The small ancient iron quarry of Tante Victoire (Playes Massif, Six-Fours-les-Plages town, Var department) is of Gzhelian age (late Stephanian), and the palaeoflora was composed of Pecopteris, Calamites, Walchia, and Sigillaria ('Notice de la Carte Géologique à 1/5000, BRGM, France' of Toulon). The holotype MNHN.
Cell viability Assessment MTT assay U87 cells were seeded at 10 4 or 5 × 10 3 cells per well overnight and then treated by different concentrations of the synthetic molecules for 24 or 72 h. Then, cells were incubated for 3 h with MTT (0. 5 mg/mL). Afterward, DMSO was added to solubilize formazan crystals and the OD was measured at 600 nm to quantify the percentage of living cells. IC 50 were calculated using Prism Graphpad software. LDH release assay Cellular membrane integrity was monitored by the permeability assay based on the release of lactate dehydrogenase (LDH) into the media. LDH release from U87 cells was determined by using the LDH Cytotoxicity Detection Kit-PLUS test (Roche Applied Science, Mannheim, Germany) according to the manufacturer protocol. Briefly, cells were seeded at 3 × 10 5 in 6-wells plate and treated by the synthesized molecules, at their respective IC 50, or Triton X-100, as positive control, for 24 h.
Videomicroscopy Migration Assay A 24-well plate was coated with fibronectin matrix at 10 µg/mL in PBS. Cells were seeded (10 4 cells/well) for 2 h at 37 • C until they adhered and then treated with different concentrations of the synthetic molecules and incubated at 37 • C with 5% CO 2. Five fields in each well were photographed every 5 min for 4 h using a Coolsnap HQ camera (Photometrics, Tucson, AZ, USA) operated by NIS-elements AR 2. 30 software (Nikon, New York, NY, USA). Results were analyzed by Metamorph ® image analysis software (Roper Scientific, Evry, France). Several parameters were calculated for each cell, including total migration distance from origin, velocity, and directional persistence of cell migration. Total migration distance represents the sum of distances between each measurement.
Acknowledgments: We would like to thank  Lamia Guizani-Tabbane  ( LR 16 IPT 06 ),  Khadija Essafi-Benkhadir  ( LR 16 IPT 04 ) and  Mohamed-Chiheb Saada  ( LR 20 IPT 01 ) from  Pasteur Institute of Tunis  for their technical support.
Data Availability Statement: AaTs-1 sequence was obtained from publication under name "AaTs-1: A Tetrapeptide from Androctonus australis Scorpion Venom, Inhibiting U87 Glioblastoma Cells Proliferation by p53 and FPRL-1 Up-Regulations" (Molecules 2021, 26, 7610. https://doi. org/10. 3390/ molecules26247610 SM, accessed on 20 November 2021), and the sequence data will appear in the UniProt Knowledgebase under the accession number C0HLZ5.
Abstract: Background & Aims: Obesity-associated inflammation is a key player in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of macrophage scavenger receptor 1 (MSR1, CD204) remains incompletely understood. Methods: 170 NAFLD liver biopsies were processed for transcriptomic analysis and correlated with clinicopathological features. Msr1 -/-and WT mice were submitted to a 16 week high-fat and highcholesterol diet. Therapeutic intervention with monoclonal antibody against MSR1 was performed in mice and ex vivo human liver slices. Genetic susceptibility was assessed using GWAS data from 1,483 NAFLD patients and 430,101 participants of the UKBiobank. Results: MSR1 expression was associated with the occurrence of hepatic lipid-laden foamy macrophages and correlated with the degree of steatosis and steatohepatitis in NAFLD patients.
Patient selection Cases were derived from the European NAFLD Registry (NCT04442334), approved by the relevant Ethical Committees in the participating centres, and all patients having provided informed consent. 12 For the histopathological and nanoString® study, 194 formalin-fixed paraffin-embedded (FFPE) or frozen liver biopsies samples were obtained from patients diagnosed with histological proven NAFLD at the Freeman Hospital, Newcastle Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK and at the Pitié-Salpêtrière Hospital, Paris, France (Table  S1 ). For the Genome Wide Association Study, 1,483 patients with histological proven NAFLD were included as previously described. 13 All liver tissue samples for the histopathological and nanoString® study were centrally scored according to the semi-quantitative NASH-CRN Scoring System by an expert liver pathologist (DT).
6b , Table  S5 ). To verify whether these transcription factors bind any regulatory regions near the MSR1 gene, and in particular the rs41505344 SNP locus, we investigated ChIP-sequencing data for these proteins. MITF, MAF, THRA and NR1H3 proved to bind in the vicinity of the rs41505344 locus, suggesting an indirect role for the SNP in the transcriptional regulation of MSR1 (Fig. 6c ). When assessing the rs41505344 genotype in our nanoString cohort, a significant increase in MSR1 transcript was observed in patients carrying the SNP (Fig. S7 ). Taken together, these results suggest there is an increased frequency in NAFLD of variants potentially affecting MSR1 expression during monocyte-macrophage differentiation, which thereby could influence features of obesity-related diseases.