dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Statistical analysis for qRT-PCR gene expression was performed by two-way repeated measures of ANOVA with Bonferroni adjustment for multiple comparisons. Factors were group (CTRL, healthy controls; and PCOS, women with PCOS) and treatment (non-treated control, TGFβ1 treatment, AMH treatment, EPS treatment, and EPS+TGFβ1 treatment groups). This analysis was carried out using GraphPad Prism software version 8. 2. 1 (GraphPad Software Inc. , La Jolla, CA, USA).
05) and 4719 differentially expressed (2419 upregulated and 2300 downregulated; FDR < 0. 05) in myotubes from women with PCOS (Fig. 2B  and Supporting information Dataset S1). GSEA using Reactome pathways database showed upregulation of genes involved in skeletal muscle contraction in both groups following EPS in first 20 enriched terms ranked by normalised enrichment score (NES) (Fig. 2C  and Supporting information Dataset S2). Only in myotubes from women with PCOS, genes related to interleukin (IL)-10 signalling, extracellular ligands and chemokine binding, and cholesterol biosynthesis pathways were upregulated following EPS, while genes related to androgen receptor signalling, oxidative stress-induced senescence and epigenetic regulation of both DNA methylation and histone acetylation were downregulated (Fig.
2D ). Relevant genes included in the IL-10 signalling and chemokines pathways are CXCL10, TNF, IL1A, CXCL8, CCL20, PTGS2, CX3CL1, LIF, IL6 and CSF3, which were all highly induced following EPS (log 2-fold change greater than 1. 6) (Fig. 2D  and Supporting information Dataset S2). In contrast, myotubes from healthy women displayed a downregulation of genes related to several inflammatory pathways and immune responses after EPS treatment amongst the top 20 downregulated enriched terms (Fig. 2C  and Supporting information Dataset S2). These results show that myotubes from women with PCOS display a greater responsiveness to EPS compared to myotubes from healthy women and support a pro-inflammatory response after muscle contraction in PCOS.
TGFβ1 upregulates genes related to extracellular matrix remodelling and reduces the expression of contractile genes in myotubes To understand if elevated levels of TGFβ ligands could negatively affect skeletal muscle as observed in the reproductive tissues, we assessed changes in the transcriptomic profile in human primary myotubes following 16 h treatment with TGFβ1 or AMH. MDS plot of RNA-seq data showed a marked separation by TGFβ1 but no effect of AMH treatment on myotubes from both women with PCOS and healthy controls (Fig. 3A ). We detected 1682 upregulated and 1500 downregulated genes (FDR < 0. 05) in myotubes from healthy women following treatment with TGFβ1 (Fig. 3B  and Supporting information Dataset S3). Similarly, in myotubes from women with PCOS there were 1456 upregulated and 1187 downregulated genes (FDR < 0.
05) following TGFβ1 treatment (Fig. 3B ). GSEA using the Reactome pathways showed upregulation of genes related to extracellular matrix remodelling, collagen synthesis and TGFβ signalling in both groups following TGFβ1 treatment (Fig. 3C  and Supporting information, Dataset S4). In addition, TGFβ1 treatment resulted in the suppression of genes related to muscle contraction and NOTCH signalling in both groups (Fig. 3C ). TGFβ1 treatment induced downregulation of genes associated to IL-6-type receptor ligand interactions and activation of matrix metalloproteinases and extra-nuclear oestrogen signalling only in myotubes derived from women with PCOS (Fig. 3C ). Differential expression of a selection of genes (ACTA1, MYL2, COL10A1 and COL7A1) from relevant enriched terms was validated by qRT-PCR (Fig.
TGFβ1 inhibits the EPS-induced increase in genes related to myotube contractile functions To test the hypothesis that TGFβ1 interferes with contraction-induced gene expression adaptations in muscle cells in PCOS, we examined the effects of TGFβ1 on the EPS treatment of primary myotubes from both women with PCOS and healthy controls. Differential gene expression detected with TGFβ1 and EPS compared with EPS alone in both groups was similar (healthy controls: 2003 upregulated and 1908 downregulated genes, FDR < 0. 05; and PCOS: 1465 upregulated and 1023 downregulated, FDR < 0. 05) (Fig. 4A  and Supporting information Dataset S5). A selection of differentially expressed genes, ACTA1, MYL2, TNNC2, IGF1, COL10A1 and COL7A1, was validated by qRT-PCR (Fig. 4B ). To get functional insight into the effect of TGFβ1 on the gene expression changes induced by in vitro contraction in human primary myotubes, we performed both GSEA and cluster analysis.
Values are represented as means ± SD (CTRL, n = 5; in white) and PCOS women (PCOS, n = 6; in grey). * P adj < 0. 05 TGFβ1 vs. no-treatment; #P adj < 0. 05 TGFβ1 vs. AMH treatment within each group; †P adj < 0. 05 TGFβ1 between groups. information Dataset S6). In addition, myotubes derived from women with PCOS displayed an upregulation of genes related to DNA methylation and histone acetylation, and a downregulation of genes related to GPCR signalling and fatty acid metabolism, which was not observed in the healthy control group (Fig. 4C ). Cluster analysis revealed 12 groups of genes with similar expression pattern (Fig. 5A  and Supporting information Dataset S7). We identified three clusters of genes, labelled 2, 4 and 11, whose expression was activated by EPS but were downregulated when TGFβ1 treatment was added in both primary myotubes from women with PCOS and healthy controls (Fig.
5A ). Conversely, expression of genes in clusters 3, 6 and 9 was higher when TGFβ1 was added to the EPS (Fig. 5A ). Consistent with our GSEA findings of the RNA-seq data, over-representation analysis of these clusters using gene ontology, biological processes (GO:BP) or molecular function (GO:MF), respectively, revealed that genes in cluster 2 were involved in SMAD/activin signalling and phosphatidylinositol 3-kinase (PI3K) binding (Fig. 5B  and Supporting information Dataset S8), while genes in cluster 4 were related to muscle contraction, muscle development and myotube differentiation, actin-filament based movement, and cellular calcium ion transport (Fig. 5C  and Supporting information Dataset S8). No enriched pathways were found for genes in clusters 3, 6, 9 and 11. Collectively, these results show that the presence of TGFβ1 during muscle contraction affects the normal gene expression response such as pathways controlling intracellular signalling and constituents of the contraction apparatus.
Acknowledgements The authors acknowledge The Single-Cell Omics platform at the  Centre for Basic Metabolic Research (CBMR)  for the technical expertise and support,  Dr Soulmaz Shorakae  for confirming the diagnosis of women with PCOS. The authors also wish to acknowledge the passing of their esteemed colleague and friend  Professor Nigel Stepto , who had a vital contribution in the conception and design of this study. Open access publishing facilitated by  Victoria University , as part of the  Wiley -Victoria University agreement via the Council of Australian University Librarians.
The aim of this work is to assess the impact of fluorescence by aerosols on the long-term water vapor records from these three Raman lidars, to provide a method to correct the errors, and to determine whether the accuracy of the correction meets the requirements of long-term water vapor trend studies. Section 2 presents an overview of the instruments and auxiliary datasets used in this work. Section 3 summarizes the suspected impact of aerosol fluorescence on the three NDACC water vapor lidar datasets mentioned above. In Sect. 4, we propose a possible correction method and evaluate its performance. The paper concludes with a discussion of the results and the implications for long-term water vapor trend analysis based on Raman lidar measurements.
Instruments and datasets In this study we include long-term datasets from three NDACC water vapor Raman lidars that use a third harmonic Nd:YAG laser transmitter (355 nm), which is a common choice for water vapor lidar systems aiming to reach the UTLS due to a combination of high Raman scattering efficiency and availability of affordable high-power laser sources. Two of these lidar systems are in the northern hemispheric mid-latitudes, while the other is located in the southern hemispheric sub-tropics. By considering several independent instruments with different designs, we can rule out any specific instrumental issue as the cause of the observed biases. Additionally, since stratospheric aerosols play an important role in this study, and not all the lidar systems involved have aerosol characterization capabilities in the UTLS, we decided to use the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) stratospheric aerosol products extracted at each lidar station as the source of aerosol distribution information.
The measurement schedule of TMWAL typically implies four or five measurements per week, at nighttime only, with each measurement period corresponding to an effective acquisition time of 2-3 h. The results are archived at the NDACC data handling center on a quasi-monthly basis. The primary calibration source is a co-located radiosonde. As part of the hybrid calibration approach described in  Leblanc and McDermid (2008)  and  Leblanc et al. (2012) , three to five radiosonde launches per month are typically conducted. These launches are normally conducted around new moon and in clear skies in order to maximize the lidar signal-to-noise ratio and thus the accuracy of the calibration. As part of the hybrid calibration, a NIST-calibrated (NIST: National Institute of Standards and Technology) lamp is used just before and after each measurement period to monitor the changes in the lidar receiver's transmission from night to night between two periods of absolute calibration from the radiosonde.
Raman The returned signal is an envelope of the 355 nm elastic-and Raman-backscattered signals, i. e. , pure rotational Raman, water vapor, oxygen, nitrogen, and Rayleigh. The receiver's telescope consists of four 30 cm mirrors, fiber-coupled to a polychromator based on a holographic diffraction grating (3600 mm -1 , 85 × 85 mm 2 ). The grating-based polychromator is used instead of an interference-filter-based polychromator to achieve long-term data consistency and to minimize the temperature dependency. The data acquisition software ensures autonomous operation of the system and real-time data availability. The data used for this study are the water vapor mixing ratio (WVMR) time series from 2008 to 2021 available publicly at the NDACC database. The WVMR profiles are integrated in time over the entire night, from an hour after astronomical sunset to an hour before astronomical sunrise.
Only profiles with a signal-to-noise ratio (SNR) larger than one in the region 0-12 km are retained and submitted to the NDACC database. The condition imposed on the SNR ensures that the data have good quality at least up to the tropopause, assuming good atmospheric conditions and correct calibration of the humidity channel. RALMO WVMR profiles shall then reach at least 12 km in clear-sky conditions and in wintertime, when the integration time is longer. The data used for the analysis and shown in Fig. 2  are monthly averaged and have been filtered using a relative error threshold of 700 % between 10 and 20 km. The relative error is the combination of the systematic and random errors. We apply Gaussian error propagation through the WVMR equation and account for the main sources of uncertainty, which are measurement noise and the calibration coefficient.
, 2019) , released in August 2018, to serve as correlative aerosol measurements at the three NDACC lidar water vapor sites. This spaceborne lidar product reports monthly mean profiles of aerosol extinction, particulate backscatter, attenuated scattering ratio (SR), and stratospheric aerosol optical depth on a spatial grid of 5 • in latitude, 20 • in longitude, and 900 m in altitude. As part of this dataset, two different aerosol products are reported. One is labeled as "background" and the other is labeled "all aerosols". While the first corresponds to profiles retrieved after removing clouds, aerosols, and polar stratospheric clouds (PSCs), the second only screens out clouds and PSCs. For this study, we use the all-aerosols data product from dataset version 1. 0 before July 2020 and from version 1.
what pushes the random uncertainty of the retrieval below the cutoff threshold. As in TMWAL, the RALMO water vapor dataset does not show any wet bias after the Raikoke/Ulawun eruption despite the large scattering ratio observed by CALIOP, confirming that insufficient blocking of Mie scattering by RALMO can be ruled out. As for the Lidar1200 lidar at Reunion Island (southern hemispheric tropics), Fig. 3b  exhibits the same behavior as for the other two lidars. There was no apparent impact of the Calbuco volcanic aerosol plume observed by CALIOP over Reunion Island during the 2015-2016 period. On the other hand, a strong apparent water vapor increase was observed by Lidar1200 during the second half of 2020 between 18 and 22 km a. s. l. , an increase coinciding with the presence of the smoke plume produced by the January 2020 Australian fires.
To reduce both cost and complexity, it was therefore decided to use a filter wheel instead. The new filter wheel is mounted in place of the original 407. 4 nm interference filter, and now includes both the original filter at 407. 4 nm and an additional 410. 3 nm filter for the fluorescence data. The stability of the system is maximized, since the same 407. 4 nm filter, same PMT, and same electronics are used for the water vapor channel. The new fluorescence channel actually uses the same hardware as the water vapor channel, except for the filter on the filter wheel. The data are acquired alternately for each filter as the wheel rotates through a pre-defined duty cycle. To minimize the effect of atmospheric variability between the acquisition of the water vapor and the fluorescence data, an interleaved acquisition scheme was implemented.
, 2022). When smoke layers in the UTLS are sounded by water vapor Raman lidars transmitting in the UV range (355 nm), they fluoresce and can cause significant contamination of the weak water vapor lidar signals collected at these altitudes. Such contamination was identified in the present study using the long-term water vapor datasets of three ground-based UV water vapor Raman lidars contributing to NDACC, together with the aerosol scattering ratio profiles from the spaceborne CALIOP lidar. Between 2009 and 2017, no large wildfire event was identified, and only volcanic eruptions caused observable injection of aerosols in the stratosphere. Over this period, the TMWAL lidar was able to provide excellent-quality water vapor profiles, typically up to 20 km. However, since 2017, the impact of the increased wildfire activity on TMWAL, RALMO, and Lidar1200 water vapor data was clearly identified in the UTLS, with a high bias of up to 75 % at 17 km for TMWAL.
Acknowledgements. The research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  (grant no. 80NM0018D0004 ). The authors also acknowledge the  European Communities , the  Région Réunion, CNRS , and  Université de La Réunion  for their support and contribution in the construction phase of the research infrastructure  OPAR  ( Observatoire de Physique de l'Atmosphère de La Réunion ). OPAR is presently funded by  CNRS (INSU) ,  Météo France , and  Université de La Réunion , and managed by  OSU-R (Observatoire des Sciences de l'Univers de La Réunion ,  UAR 3365 ). OPAR is supported by the  French research infrastructure ACTRIS-FR (Aerosols, Clouds , and  Trace gases Research In-fraStructure -France ).
Data availability. TMWAL data are publicly available at https://www-air. larc. nasa. gov/missions/ndacc/data. html?station= table. mountain. ca/hdf/lidar/ (NDACC, 2022a). RALMO data are publicly available at https://www-air. larc. nasa. gov/missions/ ndacc/data. html?station=payerne/hdf/lidar/ (NDACC, 2022b). The 2013-2018 OPAR Lidar1200 dataset is publicly available at https://www-air. larc. nasa. gov/missions/ndacc/data. html? station=la. reunion. maido/hdf/lidar/ (NDACC, 2022c). The 2019-2021 dataset is available under open-access by request to valentin. duflot@univ-reunion. fr. CALIOP L3 data is available at https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_LID_L3_ Stratospheric_APro-Standard-V1-01 (NASA/LARC/SD/ASDC, 2020).
s. l. ), and then increases again in 2020 and 2021 as a result of further smoke injection associated with large PyroCbs registered in the western United States and Canada (NASA Earth Observatory, 2021). The present results are consistent with a case study previously reported by Immler et al. (2005) , during which a Raman lidar located in Lindenberg (Germany) showed a water vapor wet bias inside a passing smoke plume that originated from wildfires in Portugal. In order to further support our findings on the origin of the observed biases, we conducted a similar analysis on the RALMO (Fig. 2) and Lidar1200 (Fig. 3) lidar datasets. Because of the zonal symmetry and widespread nature of the volcanic and smoke plumes considered here in the northern hemispheric mid-latitudes, we expect the RALMO dataset to be influenced in a similar way to TMWAL's dataset.
This indicates that the COSMOS-Europe data could be beneficial for model applications at the continental scale despite the limited coverage in some areas of Europe, i. e. , even though the measurements are not area-wide such as remote sensing data. Furthermore, at present only a low number of CRNS stations are automatically transferring neutron count data to the TERENO database that hosts the COSMOS-Europe data. However, a near-real-time availability of the data would be necessary in particular for the use of the data for the improvement of flood models, e. g. , in context of the European Flood Awareness System (EFAS,  Smith et al. , 2016). Efforts should be made in the future to equip more stations with auhttps://doi. org/10. 5194/essd-14-1125-2022 Earth Syst. Sci. Data, 14, 1125 Data, 14, -1151 Data, 14, , 2022  tomatic data transfer capabilities to enable rapid transfer of neutron counts to the TERENO database.
Sample Preparation for Flow Cytometry Immunophenotyping was performed on blood or single-cell suspensions that were previously obtained from bone marrow centrifugation (as described in Fan Y. et al. [18] ), visceral adipose tissue collagenase digestion (as previously described  [19] ), or liver dissociation and stratification. Whole blood was incubated with an antibody mix for 30 min at 4 • C and then fixed and lysed (eBioscience, San Diego, CA, USA) following the manufacturer's instructions. Cell suspensions from each tissue were stained with the antibody mix at 4 • C for 30 min. Washes and the final resuspension for acquisition were performed using PBS with FBS 2% and EDTA 2 mM. Samples were acquired with LSRFortessa X-20 (BD) and analyzed with Novoexpress software (version 1. 6. 0, Agilent, Santa Clara, CA, USA).
Femurs, Liver, and Adipose Tissues Histology Mouse femurs were fixed in 4% paraformaldehyde for 4 h at 4 • C, decalcified in 0. 5 M EDTA pH 8 for 5 days at 4 • C, and frozen in liquid nitrogen-cooled isopentane. Longitudinal femur sections (10 µm thick) were incubated with anti-perilipin 1 antibody (1:200; Cat. No. 9349, Cell Signalling Technology®, Danvers, MA, USA). An anti-rabbit secondary antibody (Jackson ImmunoResearch, West Grove, PA, USA) diluted to 1:200 in PBS plus 1% BSA was incubated for 40 min at 37 • C. Nuclei were counterstained with Hoechst 33342 (Merck KGaA, Darmstadt, Germany). Images (10×) were acquired with a Leica DM6B automated upright microscope (Leica Biosystems S. r. l. , Milan, Italy) and then processed with Fiji/ImageJ software (version 1. 50, NIH, Bethesda, MD, USA).
Adipocytes were manually quantified with Fiji/ImageJ based on perilipin-1 immunostaining. Part of the liver, VAT, SCAT, and BAT was fixed overnight in 4% buffer formalin (Sigma-Aldrich, St. Louis, MO, USA) embedded in paraffin and then 5 µm tissue sections were stained with haematoxylin and eosin (H&E) (Sigma-Aldrich). Images of H&Estained sections were acquired using the Axiovision Zeiss software (version 4. 8, Carl Zeiss, Oberkochen, Germany) at 10× magnification. We evaluated liver steatosis using ImageJ software and the adipocytes area of VAT and SCAT using the Adiposoft Plugin, considering two fields per section, for a total of 10 sections per animal. To assess hepatic neutrophil activation, immunohistochemistry staining of liver sections was performed using the Anti-Neutrophil Elastase antibody (1:200; Cat.
No. ab68672, Abcam, Cambridge, UK) followed by the HRP-conjugated anti-rabbit antibody (1:750; Sigma-Aldrich), a reaction with DAB Liquid Substrate System (Sigma-Aldrich), and nuclei counterstaining with haematoxylin (Sigma-Aldrich). Images were acquired with Axiovision Zeiss software at 10x magnification and elastase-positive areas were quantified with ImageJ software.
Statistical Analysis GraphPad Prism (version 9, GraphPad Software, San Diego, CA, USA) was used for graphical representation and statistical analyses of the data. Results are given as the mean per group ± standard error of the mean (SEM). For comparison between the two groups, an unpaired parametric two-sided T-test with a 95% confidence interval was used when the data sets followed a normal distribution. For non-normal data, the Mann-Whitney nonparametric unpaired T-test was applied. A p-value of <0. 05 was considered significant. The DAVID (The Database for Annotation, Visualization, and Integrated Discovery, NIAID, North Bethesda, MD, USA) platform was used for gene ontology (GO) and KEGG enrichment analyses. GO and KEGG were performed using our protein LFQ dataset, and results for significant terms associated with the gene set were selected based on the FDR < 0.
This medullary rewiring is reflected in changes to the circulating immune subsets and systemic inflammatory status. To explore this hypothesis, untargeted proteomic analysis of the plasma from the different experimental groups was performed. Out of 4306 proteins identified, 4. 4% were downregulated and 3. 4% were upregulated in the plasma of obese Mrc1 -/-mice compared to that of controls (Figure  2A, B ). The different proteomic signature associated with Mrc1 deficiency was confirmed by Principal Component Analysis (PCA, Figure  2C ), which showed a cluster clearly separated from that of plasma proteins from WT mice. To better characterize this phenotype, differently abundant proteins were analyzed using the KEGG database to assess enriched pathways in the plasma of Mrc1 -/-mice compared to that of WT mice (Figure  2D ).
This limitation should be addressed in future studies. Notably, the adipocyte area was not different in the VAT or SCAT from Mrc1 -/-and WT mice (Figure  4D-F ), but smaller lipid droplets were observed in brown adipose tissue (BAT) (Figure  4D ). These smaller lipid droplets were coupled to significant changes in the gene expression profile of genes associated with BAT function such as Ppara, Pgc1a, Atgl and Ucp1 (Figure  4G ). Reduced lipid accumulation was also observed in the liver (Figure  4H , I) prompting us to profile liver proteome. Among 2114 proteins, 344 were downregulated and 363 were upregulated in Mrc1 -/-mice (Supplementary Figure  S6A, B ). Differently abundant proteins were analyzed using the KEGG database to assess enriched pathways (Figure  4J ), which were identified as being mainly metabolism related.