dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations and future works We used the reanalysis ERA5-Land monthly climatic data to model the signal differences. As a result, whether signal differences can be accurately modelled partly depends on the accuracy of the ERA5-Land climatic data. Future work will test the effectiveness of other climatic data sets for modelling the signal differences. The accurate mapping of some climatic variables, such as snow depth, is challenging  (Orsolini et al. , 2019; Clifford, 2010; Pulliainen et al. , 2020). This is critical in the high-latitude regions such as northern Alaska, where snow depth is the most important variable predicting the signal differences. The estimation of rainfall is also challenging in regions with sparse climate stations such as the tropics. An increasing number of climate data sets has been made publicly available, including the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2;  Gelaro et al.
NDVI therefore contains kinds of multiple environmental information. In addition, aerosol could be a contributing factor to the signal differences, especially in deserts such as the Sahara, where the rRMSE values in the final LHScat data set remained relatively high (Fig. S3 ). The Sentinel-5P mission provides near-real-time, high-resolution aerosol products starting from the year 2018  (Ingmann et al. , 2012). Analysis will soon be conducted to assess whether NDVI and aerosol layers can further improve the data-merging quality. Another potentially useful data set to be included into our data-merging framework is the Oceansat-2 scatterometer (OSCAT). OSCAT also provides Ku-band backscatters akin to QSCAT but operating in a different period  (between 2009 and 2014; Bhowmick et al. , 2013). QSCAT operated in full mode between 1999 and 2009 and overlapped with ASCAT during 3 years  (2007) (2008) (2009).
Higher spatial resolution can be achieved by merging only QSCAT and ASCAT images. As stated in the introduction to this paper, the BYU data centre provides QSCAT and ASCAT images at the resolution of 4. 45 km. It is therefore possible to generate a global Cband radar data set at 4. 45 km resolution but with a shorter time span (since the QSCAT mission started in 1999). It is also possible to have higher temporal resolutions, such as time-averaged (such as weekly) resolutions  (Lin et al. , 2016). New versions of LHScat will be made publicly available at https://doi. org/10. 6084/m9. figshare. 20407857. C-band radar data have been widely used in Earth science studies for monitoring vegetation dynamics, mapping deforestation and soil moisture, and estimating snow water equivalent  (Chang et al.
, 1982; Clifford, 2010; Kelly et al. , 2003; Liu et al. , 2009; Saatchi et al. , 2013; Steele-Dunne et al. , 2017; Smith and Bookhagen, 2018). Thus, the merged radar signals are expected to be useful in a range of research disciplines. A possible outcome is to separate the signal into soil moisture and vegetation optical depth (VOD). In this way, the signals can be more directly related to the soil or vegetation dynamics. Technically, extracting VOD and soil moisture from LHScat signal is feasible with the help of the Water Cloud Model  (Liu et al. , 2021) , and efforts are being devoted to developing a LHScat VOD data set at the global scale. Considering its long time span (since 1992) and high resolution, LHScat VOD would be suitable for the assessment of long-term global vegetation changes.
Using the optical Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index data, a recent study found that most of the world's vegetated areas are becoming greener, particularly in China and India  (Chen et al. , 2019). Using the optical vegetation index NDVI, another recent research explored the long-term (2000-2020) resilience change in global forests  (Forzieri et al. , 2022). It would be interesting to re-evaluate the vegetation trends using LHScat VOD data. While radar signal penetrates the upper forest canopy and interacts directly with the water molecules contained in forest biomass, optical greenness data reflect the canopy features of the topmost leaf layer which could be maintained due to leaf demography or light availability  (Guan et al. , 2015; Wu et al. , 2016). We therefore expect the LHScat VOD to provide new insights into the long-term changes in global forests.
Acknowledgements. We thank the  BYU data centre  and  David Long  for providing the enhanced-resolution radar images. Financial support. This research has been supported by the  National Natural Science Foundation of China  (grant no. 31988102 ), the  Chinese Academy of Sciences  (grant no. XDA26010303 ),  ESA  ( CCI-BIOMASS ), and the  Agence Nationale de la Recherche  (CEBA, grant no. ANR-10-LABX-25-01 ; TULIP, grant no. ANR-10-LABX-0041 ;  ANAEE-France , grant no. ANR-11-INBS-0001 ). Review statement. This paper was edited by  Jing Wei  and reviewed by three anonymous referees.
3B  & C ) and associated with p65 by co-immunoprecipitation (Sup. Fig. 2C ). In contrast, the TIGR4 challenged p65 mass spectrometry dataset enriched for different NF-κB associated targets. Gene Ontology and KEGG pathway analysis enriched for protein degradation pathways, including proteins such as HDAC6, a p62, and ubiquitin (Sup. Table  1 )  33. Indeed, p62 is a classical receptor of autophagy, HDAC6 an ubiquitin-binding histone deacetylase known to be important in modulating autophagy, and together have been shown to degrade protein aggregates through a process termed aggrephagy  [33] [34] [35]. Therefore, our proteomic data suggested that p65 could be targeted for degradation through an aggrephagy pathway under TIGR4 infection conditions. To begin testing this hypothesis we probed whole cell lysates obtained from A549 cells 2 hrs post challenge with either 6B ST90 (+/-IL-1β) or TIGR4 (+/-IL-1β) for HDAC6 levels, as HDAC6 is degraded along with its cargo during aggrephagy (Fig.
Fig. 2C ). This observation further supports that TIGR4 challenge is targeting NF-κB p65 complex as a whole. Overall, these data demonstrate TIGR4, in contrast to 6B ST90, induces diverging NF-κB p65 signaling cascade that results in: 1) differential protein binding and 2) degradation of p65 through aggrephagy. COMMD2 associates with both p65 and p62 and translocates to the nucleus Since aggrephagy primarily occurs in the cellular cytosol we analyzed the p65 interactome data for an additional element that could link degradation with nuclear export. Upon deeper investigation we noted our interactome identified COMMD2 and COMMD4 as among the most highly enriched proteins associating to p65 under TIGR4 infection conditions compared to 6B ST90. All of the COMMD protein family associate with NF-κB subunits, with the archetype member COMMD1 being shown to terminate NF-κB signaling through proteasomal degradation via export by exportin 1 (CRM1) and displacement from chromatin at kappa-binding sites  16, 17, 19, 21, 41.
Of the two COMMD proteins found in our TIGR4 dataset, COMMD2 was previously shown, by ectopic expression, to reside in the cell cytosol and had a higher association with RelA and NF-κB1  14, 22. Given these previous studies and our data we focused on COMMD2. We hypothesized that COMMD2 through association with p62, could be involved in p65 turnover and thus a link to the aggrephagy pathway. Herein, we generated an A549 GFP-COMMD2 ectopic expression stable cell line, from which GFP-COMMD2 was immunoprecipitated from lysates 2 hrs post-challenge with either 6B ST90 (+/-IL-1β), TIGR4 (+/-IL-1β) or from our uninfected and IL-1β controls. Samples were probed for p65 or p62 to detect interaction with COMMD2. Our results show that only under TIGR4 challenge conditions do p65 and p62 interact with COMMD2 (Fig.
7A-D ; sup. Table  3 ). By crosscomparison of these targets 254 unique proteins were associated with COMMD2 under TIGR4 challenge with 92 being also found in our p65 interactome dataset (Fig. 3A ; sup. Table  1 ). From the analysis we also identified a putative COMMD2 posttranslational modification associated with ubiquitination or neddylation at Lysine residue 50. Finally, using Panther ontology protein class analysis of the significant targets obtained from the interactome underscored a unique TIGR4 enrichment for host metabolite and protein modifying enzymes as well as proteins associated with translation (Fig. 7E ). Altogether, these results showed during TIGR4 challenge COMMD2 function was specific with a potential role in host inflammatory response. We previously showed during TIGR4 challenge the host gene transcriptional signature was substantially repressed  31, 32.
Whole cell lysates were ran on 4 -20% pre-cast polyacrylamide SDS PAGE gels (BioRad), transferred to PVDF membrane (BioRad TransBlot) and blocked 1 hr in 5% BSA TBST at room temperature. Membranes were probed overnight at 4°C in 5% BSA TBST with primary antibody to p65 ) as per manufacturer's recommendations. COMMD2 immunoblots were blocked for 1 hr at room temperature in 3% milk TBST and probed overnight at 4°C with the primary antibody (Sigma ref# HPA044190) suspended in 3% milk TBST. Appropriate secondary-HRP conjugated antibodies in 5% Milk TBST were incubated for 1 hr at room temperature prior to development with clarity ECL (BioRad) developing reagents with a ChemiDoc Touch (BioRad). Detroit562 immunoblots were developed using Licor Odessey using secondary antibodies at 1:7,500 -Goat anti-rabbit IgG H&L (IRDye 800CW) and goat anti-mouse IgG H&L (IRDye 680RD) from abcam.
Immunofluorescence microscopy and Cellprofiler analysis. For microscopy the desired cell line were seeded on acid washed and UV treated coverslips in 24well or 96well plates as described above. Two hours post-challenge media was aspirated, cells washed in DPBS, and fixed with 2. 5% PFA for 10 mins at RT. Fixed cells were blocked and permeabilized overnight in 5% BSA 0. 5% Tween20 at 4°C. Primary antibody to p65 (CST ref #6956 or CST ref# 8242), COMMD2 (Sigma ref# HPA044190-25UL; only works for immunofluorescence), or p62 (SQSTM1; abcam ref# ab109012) were diluted at 1:1,000 in 5% BSA 0. 5% Tween20 and incubated overnight at 4°C. Cells were washed 3x 10 mins at RT in PBS + 0. 1% Tween20 prior to 1 hr incubation at 1:1,000 dilution of either Alexa Fluor 594 or Alexa Fluor 647 secondary antibody.
Nuclei were stained with 10 ng/mL final concentration of Hoechst 33342 for 15 mins. Coverslips were rinsed in PBS and molecular grade water prior to mounting with Fluoromount-G Mounting Medium (INTERCHIM). Confocal microscopy images were acquired on a Nikon TiE inverted microscope with an integrated Perfect Focus System (TI-ND6-PFS Perfect Focus Unit) and a Yokogawa Confocal Spinning disk Unit (CSU-W1). Nine images per well were acquired using a 20X air objective (NA 0. 75) at a step-size of 0. 9µm in z-plane. Deconvoluted epifluorescent images were acquired on a Cytation 5 (BioTek) using a 20X air objective (NA 0. 75) with a grid of 3 x 3 (9 images en total). Images were processed for background using Fiji  75  , and segmented using Cell Profiler  [76] [77] [78]. Briefly, the pipeline for image analysis consisted of sequential modules to 'IdentifyPrimaryObjects' based on channel signal for nuclei (DAPI stain), p65 (Alexa594), or p62 (Alexa594).
Mass spectra were acquired in data-dependent acquisition mode with the XCalibur 2. 2 software (Thermo Fisher Scientific, Bremen) with automatic switching between MS and MS/MS scans using a top 12 method. MS spectra were acquired at a resolution of 70000 (at m/z 400) with a target value of 3 × 10 6 ions. The scan range was limited from 300 to 1700 m/z. Peptide fragmentation was performed using higher-energy collision dissociation (HCD) with the energy set at 27 NCE. Intensity threshold for ions selection was set at 1 × 10 6 ions with charge exclusion of z = 1 and z > 7. The MS/MS spectra were acquired at a resolution of 17500 (at m/z 400). Isolation window was set at 1. 6 Th. Dynamic exclusion was employed within 30 s. Recorded spectra were searched with MaxQuant (version 1. 5. 3. 8) using the Andromeda search engine  80  against a human database (74368 entries, downloaded from Uniprot the 27 th of September 2019), a Streptococcus pneumoniae R6 database (2031 entries, downloaded from Uniprot the 1 st of January 2020) and a Streptococcus pneumoniae serotype 4 database (2115 entries, downloaded from Uniprot 1 st of January 2020).
120028 Da was used for internal calibration (lock mass) and QCloud 81 has been used to control instrument longitudinal performance during the project. Recorded spectra were searched with MaxQuant (version 2. 0. 3. 0) using the Andromeda search engine with default search settings including a false discovery rate set at 1% on PSM, peptide and protein level. Spectra were searched against the Streptococcus pneumonia setotype 4 (TIGR4) (Taxid: 170187) protein sequences in the Uniprot database (database release version of 04_2022, UP000000585), containing 2,109 sequences (www. uniprot. org), the Streptococcus pneumoniae (Taxid: 1313) protein sequences in the Uniprot database (database release version of 04_2022, UP000046310), containing 2,115 sequences (www. uniprot. org) and the human protein sequences in the Swiss-Prot database (database release version of 2022_01, reference proteome: UP000005640), containing 20,588 sequences (www.
Next, missing values were imputed using the impute. MLE function of the R package imp4p (https://rdrr. io/cran/imp4p/man/imp4p-package. html). Statistical testing was conducted using a limma t-test thanks to the R package limma  83. An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values thanks to the function adjust. p of R package cp4p 84 using the robust method described in ( 85 ) to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins associated to an adjusted p-value inferior to a FDR level of 1% have been considered as significantly differentially abundant proteins. Statistical analysis. All experiments, unless otherwise noted, were biologically repeated 3-5 times and the statistical test is reported in the figure legend.
Data normality was tested by Shapiro-Wilk test, and appropriate parametric or non-parametric tests performed depending on result. P values calculated using GraphPad Prism software and the exact values are in source data. Microscopy data obtained from analysis of 3 -5 image fields per biological replicate after being automatically acquired by the microscope software to ensure unbiased sampling with the total number of analyzed cells or nuclei noted in the figure legend. Data Availability: All data in the present study is available upon request from the corresponding authors. The mass spectrometry proteomics data of GFP-p65 and GFP-COMMD2 have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD032970 (p65) and PXD043886 (COMMD2).
Acknowledgements: We would like to thank Emmanuelle Varon and Thomas Kohler for their generous gifts of S. pneumonie strains. We are appreciative of Pierre-Henri Commere and the Institut Pasteur, Flow Cytometry Platform (Paris, France) for sorting of the COMMD2 stable cell line. Displayed as a dot plot with each data point and a bar representing the mean. 10µg chromatin input from A549 cells either untreated (light gray), IL-1β treated (10 ng/ml; dark gray) or 2 hrs post-challenge with TIGR4 (+/-IL-1β; 10 ng/ml; MOI 20; light blue or dark blue respectively). B) Schematic representation of PTGS2 or CSF2 promoter with ChIP-qPCR primer locations and NF-κB sites denoted as predicted by Alibaba2-curated eukaryotic transcription factor DNA-binding motifs from the TRANSFAC database  67. C & D) ChIP-qPCR represented as % recovery against input of p65 at indicated NF-κB sites.
Lines represent -log10(pV) and fold-change cutoffs with targets of interested denoted. D) Venn diagram describing the numbers of unique and shared significant proteins and their total % from the mass-spectrometry dataset. Table break down of identified partners that were significant, of the significant targets that are unique, and of the significant targets shared with the p65 interactome. E) Panther classification analysis of protein class of identified COMMD2 interactors unique to TIGR4, shared between TIGR4 and 6B ST90, or unique to 6B ST90. FIGURE 4 FIGURE 7 FIGURE 8.
Finally, we like to thank Daniel Hamaoui for his help processing blots during COVID-19 related work personnel restrictions. Michael G. Connor is supported by a Springboard to Independence grant (AirwayStasis) from the French Government's Investissement d'Avenir program, the Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases" (ANR-10-LABX-62-IBEID). Work in the laboratory Chromatin and infection unit (headed by Melanie A. Hamon) is supported by the Institut Pasteur, the Fondation pour la Recherche Médicale (FRM-EQU202003010152), the Fondation iXCore-iXLife and the Pasteur-Weizmann research fund. Caroline M. Weight was supported by the Medical Research Council (MR/T016329/1). Sebastian Baumgarten is supported by the Institut Pasteur and the European Commission (ERC-STG "PlasmoEpiRNA").
The views expressed in this article are those of the authors and not necessarily those of the NIHR, or the Department of Health and Social Care. Jost Enninga and Lisa Sanchez, members of Dynamics of host-pathogen interactions unit (Institut Pasteur), are supported by the European Commission (ERC-CoG-Endosubvert), the ANR-HBPsensing, and are members of the IBEID and Milieu Interieur LabExes. Filipe Carvalho was supported by a postdoctoral grant through the "Investissement dʼAvenir" as part of a French "Laboratoire dʼExcellence" (LabEx) research program: Integrative Biology of Emerging Infectious Diseases (ANR-10-LBX-62 IBEID). This work was also supported by EPIC-XS, project number 823839, funded by the Horizon 2020 program of the European Union. Author contributions: Conceived and designed all experiments: MGC and MAH.
Graphed as the relative expression of each indicated transcript to matched uninfected/unstimulated control per time point (n = 3 biological replicates; 2 technicals per replicate). Displayed as a dot plot with each data point and a bar representing the mean. 10µg chromatin input from A549 cells either untreated (light gray), IL-1β treated (10 ng/ml; dark gray) or 2 hrs post-challenge with TIGR4 (+/-IL-1β; 10 ng/ml; MOI 20; light blue or dark blue respectively). B) Schematic representation of PTGS2 or CSF2 promoter with ChIP-qPCR primer locations and NF-κB sites denoted as predicted by Alibaba2-curated eukaryotic transcription factor DNA-binding motifs from the TRANSFAC database 67. C & D) ChIP-qPCR represented as % recovery against input of p65 at indicated NF-κB sites. Tukey box and whisker plot with defined box boundaries being the upper and lower interquartile range (IQR), 'whiskers' (fences) being ± 1.
Statistical Analysis Data were analyzed using the R statistical software (R Foundation for Statistical Computing). Logistic regression analysis was used to investigate which independent variables were associated with practicing healthy lifestyles (dependent variable). These independent variables included the sociodemographic characteristics, health status, and health-related behaviors. To inform future research, the latest 2021 data set was analyzed. Because the percentage of the excluded subjects was as high as 36. 6% and the missing rate of outcome variables were associated with various covariates (missing at random instead of missing at completely random), multiple imputations were conducted to estimate the missing variables among the excluded subjects. Five imputed data sets were created by the multiple imputation via chained equations (MICE) package in R.
MICE imputation starts by a simple imputation such as mean imputations on all variables with missing value. Then, these imputed missing values are updated by running a series of regressions, whereby each variable with missing value is regressed upon other variables repeatedly until convergence. For cases with imputations, logistic regression analyses were performed for each of the imputed data sets. The regression coefficients were combined using the pool() function within the MICE package. In addition, we used the detectseparation package in R to check if there was any multicollinearity in our logistic regression analysis. The results of the 2021 data set logistic regression analyses were presented both for cases without missing data and for those with imputations. A P value <. 05 was considered statistically significant.
Limitations This study has 4 key limitations. First, the variables in the secondary analysis were limited to the data set used. For example, only 1 question in the data set investigated the individuals' physical activity habits. Moreover, the question was rather general and inquired whether the individuals performed any type of physical activity or exercise during the past 30 days. Two essential components-frequency and intensity-were not explored. Thus, our study findings might not adequately represent the level of physical activity as recommended by the WHO  [51]. Second, all variables were reported by individuals via phone call. Therefore, we might have encountered reporting biases arising from the participant's recall from memory, and the interviewers may have recorded the responses incorrectly.
The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1. 5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyse fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. A retrospective prediction for the years 2021-2022 is made to test the model skill, followed by the generation of fCO2 and fluxes from January to August in 2023. Results indicate a modest degradation of the model skill in prediction mode and open the possibility to provide robust information about marine carbonate system variables with low latency.
, 2023). Measurements of surface ocean CO 2 fugacity (f CO 2 ) from ships, drifters, moorings, and autonomous surface platforms are the main reference to document the actual variation of air-sea fluxes (fgCO 2 ) in space and time  (Friedlingstein et al. , 2022)  because the two are linearly related. Long-term efforts in maintaining and expanding international observing networks together with a coordinated data aggregation of the Surface Ocean CO 2 Atlas database -SOCAT  (Bakker et al. , 2016 (Bakker et al. , , 2023) )  have provided millions of individual f CO 2 observations since the 1950s and associated gridded products. However, f CO 2 data are poorly sampled leaving out most areas for some or all of the year. Statistical databased reconstructions of f CO 2  (Rödenbeck et al. , 2013; Landschützer et al.
, 2016; Gregor & Gruber, 2021; Chau et al. , 2022b)  have emerged to gap-fill the SOCAT database using auxiliary data, resulting in reconstructions of f CO 2 global monthly maps. They are still the topic of active research to improve the reconstruction quality, but these maps lag behind real time by 0. 5 to 1. 5 years: the update of the SOCAT archive follows an annual pace with a public release usually in June after measurement collection, quality control, and processing. This lag is problematic for the documentation of the carbon cycle as it evolves, while the main variables of the carbon cycle are progressively integrated within operational programmes with much faster data releases. A prominent example of operational programmes in need of a reduced time lag is the operational observation-based anthropogenic CO 2 emissions monitoring and verification support capacity (CO 2 MVS) that the European Commission is building under its Copernicus Earth Observation programme (e.