dataesr/scientific-paragraphs-categorization huggingface dataset:
Section 4 describes the physical elements that matter for the development of CLIMP-LT. Section 4. 1 presents the cloud detection scheme used in CLIMP-LT to detect the same cloud as CALIPSO-GOCCP despite the instrumental differences between ATLID and CALIOP. Then we analyze a long-term (multi-decadal, monthly averaged), global-scale space lidar virtual dataset built from climate models + COSP/lidar simulation (Sect. 4. 2) to illustrate how a merged dataset "CLIMP-LT + CALIPSO-GOCCP" could help evaluate climate models' predictions of multi-decadal cloud changes (Sect. 4. 3). We conclude in Section 5. https://doi. org/10. 5194/egusphere-2022-1187 Preprint. Discussion started: 18 November 2022 c Author(s) 2022. CC BY 4. 0 License.
The CLIMP short-term dataset In this section, we search for useful cloud information for model evaluation that can be got thanks to ATLID beyond the CALIPSO data. For this purpose, we use high resolution cloud scenes (Sect. 3. 1), simulate how they are observed by ATLID and CALIPSO (Sect. 3. 2), and compare the SR(z) profiles seen by the 2 lidars (Sect. 3. 3) and the clouds detected by the 2 instruments (Sect. 3. 4). To address the comparability of clouds observed by two space-borne lidars, we used the existing methodology  (Reverdy et al. , 2015; Feofilov et al. , 2022) , but with used a much finer-scaled cloud model, updated instrumental parameters of ATLID, and a new simulation chain, which estimates noise at the detector level and propagates it to cloud product level. The main question we sought to answer in this section was whether ATLID can observe optically thinner clouds than CALIPSO in daytime, a useful capability to evaluate thin cirrus clouds in climate models.
These gridded sets were converted to pseudo orbits by slicing them along the diagonal lines and arranging the slices into "lidar curtains", each comprising 20000 individual profiles and split to daytime and nighttime parts, 10000 profiles each. This way we got almost seamless cloud distributions, which followed the variability prescribed by 3DCLOUD_V3 model and at the same time resembled parts of real lidar orbits. We show the most representative parts of these pseudo orbits in Fig. 2  and 3  for cirrus and stratocumulus clouds, respectively, and we discuss them below. With these two datasets covering both the daytime and the nighttime scenes, we performed a full series of simulations explained in Fig. 1. Namely, we fed the high-resolution atmospheric inputs described above to the CALIOP and ATLID simulators  (Chepfer et al.
To address the information content of the backscattered radiance, it makes sense to define a useful signal and to estimate the SNR for this signal. For CALIOP the useful signal is represented by 𝐴𝑇𝐵(𝜆, 𝑧) (see Eq. 1) whereas the ATLID can measure the molecular and particulate backscattered radiances separately, so it would be logical to call the 𝐴𝑃𝐵(𝜆, 𝑧) (see Eq. 2) a signal, which carries the information about the cloud, and look at its SNR. For the sake of simplicity, we do not discuss here To quantify the lidar cloud detection agreement and disagreement regarding the reference cloud dataset, we distinguish four cases: (1) when the lidar detects the actually cloudy layer as cloudy (YES_YES case), (2) when there is no cloud and the lidar does not detect a cloud (NO_NO), (3) when the lidar does not detect an existing cloudy layer (YES_NO), and (4) when the lidar detects a cloud whereas the layer does not contain a cloud (NO_YES).
However, not all the noise from Fig. 6b  propagates to Fig. 6d. This is because of a second condition of (Eq. 9): the variations are partially filtered out by imposing a condition on the 𝐴𝑇𝐵(532𝑛𝑚, 𝑧) signals regarding 𝐴𝑇𝐵 𝑚𝑜𝑙 (532𝑛𝑚, 𝑧). Still, the daytime scene contains a lot of false detections marked by red in Fig. 6d. The overall characteristics of CALIOP cloud detection for this scene estimated over the whole simulated cloud dataset can be found in the 2 nd and 4 th columns of cloud fraction similar to (Eq. 6) and then compared the resulting series of cloud fractions with the reference one defined from the source dataset. The "total score" line refers to the cloud detection statistics and is defined in the caption. As one can see, for CALIOP the daytime noise increases the disagreement with the reference cloud dataset.
To reduce the number of plots, we do not show the averages, but in Table  2  we provide the estimates for averaging effects of the clouds defined under (Eq. 9) (seek columns marked with Averaged=Y). For CALIOP, the 1 km averages reduce the number of false detections and improve the total score for daytime simulations for cirrus. For ATLID with its lower daytime noise, the averaging procedure does not change the cloud detection quality. For the stratocumulus clouds, the averaging procedure is not required and sometimes it can lead to overestimate the cloud fraction (e. g. Chepfer et al. 2008 , Feofilov et al. 2022 ). Still, it improves the score for CALIOP because of suppression of sporadic-noise-induced "clouds" above the real cloud layer (Fig. B1d ). Overall, the ATLID-related columns in Table  2  demonstrate more consistency between daytime and nighttime cloud amounts and reference data than the CALIOP-related ones, and ATLID daytime cloud quality is better than that of CALIOP whereas the nighttime results are comparable.
6 , 7, 8, and 9 and the corresponding columns in Table  3 ). For stratocumulus clouds (figures B3 and B4 of the Appendix B), with their strong signals, the agreement between CALIOP and ATLID is also better than for the clouds defined by Eq. 9 (compare the "c" and "d" panels of Fig. B1 , B2, B3, and B4). The 1 km averaging further improves the agreement between the data sets (Table  3 ). Summarizing, using the thresholds (Eq. 5) to define the clouds makes the cloud data sets from CALIOP and ATLID comparable. Further adjustment will be needed for real ATLID data to compensate the effects of diurnal cycle  (Noel et al. , 2018; Chepfer et al. , 2019; Feofilov and Stubenrauch, 2019)  and for others, actual differences that will show up when ATLID actual data will be available, which are not included in the present study.
Numerical chain to simulate long-term lidar record and method to quantify time of emergence The previous section showed that ATLID and CALIOP data may be merged to build a long-term dataset. Here we build a longterm space lidar synthetic dataset spanning more than 30 years, and examine when would a change in cloud properties attributable to human-induced warming would be detectable in this dataset if the climate model simulations are trusted. This approach is directly inspired by the one pioneered in  (Chepfer et al. 2018) , and later expanded in  Perpina et al. (2021). We use climate predictions from IPSL-CM6  (Boucher et al. 2020 ) and CESM2 (Community Earth System Model,  Hurrell et al. 2013) , two ocean-atmosphere-coupled GCMs which took part in the Climate Model Intercomparison Project (CMIP) phase 6  (Eyring et al.
The missing years in the record would delay the detection of a reliable trend by at least the same time period  (Chepfer et al. , 2018). Perfect intercalibration supposes the effects of instrumental differences in technical specifications (wavelengths, pulse energy, field of view, etc. ) and orbital characteristics (local time of overpass, altitude) on lidar measurements are reconciled somehow. For instance, ATLID operates at 355nm and CALIOP at 532nm, and the impact this has on measurements can be reconciled by converting ATLID signal at 532nm as done in the current study, but the costs of this conversion are not completely understood and will require re-examination when actual ATLID data will be available. Imperfect intercalibration could lead to offsets in one spaceborne   et al. , 2018; Chepfer et al.
Conclusions This study presents the physical basis for the ATLID Cloud CLIMate Product named CLIMP. This product builds on previous work on CALIPSO, a space lidar dedicated to cloud and aerosol observations like ATLID. CALIPSO data have been being used for 16 years to evaluate the description of clouds in climate models using a dedicated product named GOCCP and a dedicated lidar simulator named COSP/lidar. The present work also builds on recent work on AEOLUS, a space lidar with HSRL capability operating in the UV like ATLID. Based on this legacy, we have defined the CLIMP short-term (ST) and CLIMP long-term (LT) products, both dedicated to cloud climate studies. Both contain the same variables as GOCCP (see Table  D1  in Appendix D) on the same horizontal and vertical resolutions, but CLIMP-ST and CLIMP-LT have different cloud detection thresholds because they aim to tackle slightly different science objectives.
Acknowledgements This work is supported by the  Centre National de la Recherche Scientifique (CNRS)  and by the  Centre National d'Etudes Spatiales (CNES)  through the  Expecting EarthCARE ,  Learning from A-Train  ( EECLAT ) project. The authors are grateful to  Dr. Valery Shcherbakov  ( Laboratoire de Météorologie Physique, Université Clermont Auvergne, Aubière, France ) for the discussion of multiple scattering coefficient in application to CALIOP and ATLID. 695 https://doi. org/10. 5194/egusphere-2022-1187 Preprint. Discussion started: 18 November 2022 c Author(s) 2022. CC BY 4. 0 License.
Optimization Software Tool In order to simplify a code-coupling process between each research problem and the proposed unified algorithm, we extend the optimization tool presented in  [6] , based on the open-source EASEA (EAsy Specification of Evolutionary Algorithms) platform, by implementing QIU-NSA as a template of the EASEA platform. The EASEA provides a compiler for automatically merging a problem description file *. (the users' guide is provided in the online documentation 1  ), which specifies a scientific research task, with an optimization algorithm template into a CPU parallel C++ code  [2]. 3 EXPERIMENTS AND RESULTS.
Input Parameters and the objective function. The input parameters and their boundary values are presented in Table  1. More details about the method for studying physical properties of MCMs can be found in  [6]. Value 0. 1 -2. 5 0. 1 -2. 5 0. 1 -2. 5 = 1 -0. 0-2. 5 2 https://github. com/numbbo/coco The objective function is defined as follows: = + (1) =| (Δ ( 0 ) -Δ ( 0 )) | + | (Δ ( 1 ) -Δ ( 1 )) | (2) =| (Δ ( 0 ) -Δ ( 0 )) | + | (Δ ( 1 ) -Δ ( 1 )) | (3) where Δ ( 0 ), Δ ( 0 ), Δ ( 1 ) and Δ ( 1 ) is the the temperature interval width of heat capacity curve peak under different magnetic fields upon cooling and warming process.
Image analysis and quantification All images were analyzed using the FIJI software (Fiji is just ImageJ) (National Institutes of Health, Bethesda, MD, USA). Z series images were corrected from bleaching then projected to obtain a 'focused' single image corresponding to the different focal planes, using the Stack Focuser plugin (https://imagiej. nih. gov/ij/plugins/stack-focuser. html). Mitotracker images were used to delineate cell shape and assign lipid droplets to corresponding cells. Lipid droplets were segmented and quantified in single cell using Cellp-Profiler v4. 2. 1  (22). Nucleic Acids  Research, 2022 , Vol. 50, No. 3 1353  QuantSeq and SLAM-Sequencing sample preparation 3T3-L1 cells were grown as described in 'Adipocyte culture, differentiation and treatment' and treated with DMSO or ML-792.
SLAMseq and QuantSeq data analysis mm10 mouse genome assembly and Refseq 3 UTR coordinates were downloaded from UCSC (4 August 2020) using Table  Browser. Sequencing reads were mapped and filtered with SlamDunk pipeline v0. 4. 3 (http://t-neumann. github. io/slamdunk/docs. html #document-Dunks). SlamDunk all (http://t-neumann. github. io/slamdunk/docs. html#all) was applied to full analysis for all samples. Reads with ≥ 1 T > C conversions were considered as labeled reads. Default settings for other parameters was followed. Two parallel differential gene expression analyses were performed using DESeq2 R package (1. 26. 0). The total RNA reads were used for Quantseq analysis. The normalization size factor in Quantseq analysis was applied to global normalization for labeled reads. Time course experiments design (http://master.
ChIPseq sequencing data analysis Reads were mapped to the Mus musculus genome (assembly mm10) using Bowtie (24) v1. 0. 0 using the following parameters -m 1 -strata -best. Reads mapped in genomic regions flagged as ENCODE blacklist were removed  (25). SUMO peaks were called with the ENCODE ChIPseq pipeline v1. 3. 6. Briefly, the pipeline ran quality controls and called peaks with spp v1. 15. 5  (26). Reproductible peaks were kept after the IDR analysis was run (optimal IDR sets of peaks were kept). Peaks were annotated relative to genomic features using Homer annotatePeaks. pl v4. 11. 1  (27). Known or de novo TF motifs were identified using HOMER findMotifsGenome. pl with default parameters. Heatmaps and mean profiles presenting read enrichments at various genomic locations were generated using Easeq software v1.
111  (28). To compare SUMO peaks enrichments over time, the union of all peak positions was computed with BEDtools v2. 26. 0  (29). Then, read counts per peak (union peak set) were normalized across libraries with the method proposed by Anders and Huber  (30)  and implemented in the Bioconductor package v1. 24. 0  (31). Regions varying due to time effect were identified using a likelihood ratio test (LRT) with DESeq2. Resulting P-values were adjusted for multiple testing using the Benjamin and Hochberg method  (32). Significant regions were those which adjusted P-value ≤ 0. 05, absolute fold change > 1. 5.
Analysis of previous RXR and PPAR␥ ChIPseq datasets RXR and PPAR␥ data were mapped to the Mus musculus genome (assembly mm10) using Bowtie (24) v1. 0. 0 using the following parameters -m 1 -strata -best. Peaks were called using MACS2 callpeak with default parameters except for -g mm -nomodel -extsize 200. Peaks were annotated relative to genomic features using Homer an-notatePeaks. pl v4. 11. 1  (27). For integrating SUMO-2/3, RXR and PPAR␥ ChIPseq, heatmaps were generated using Deeptools v3. 5 using the tool bamCoverage to generate bigwigs files with a step of 10 nt. Bigwig files were normalized using the CPM (counts per million mapped reads) method  (33). Then, the tool Deeptools computeMatrix v3. 5 was used to generate a count matrix at the positions of interest and finally the tool Deeptools plotProfile v3.
Analysis of MS data All MS RAW data were analyzed using the freely available MaxQuant software, version 1. 5. 3. 30 (35). All data were processed in a single computational run, and default MaxQuant settings were used, with exceptions specified below. For generation of the theoretical spectral library, the mouse FASTA database was downloaded from Uniprot on the 14 February 2020. The mature sequence of SUMO2 was inserted in the database to allow for detection of free SUMO. In silico digestion of theoretical peptides was performed with Lys-C, Asp-N and Glu-N, allowing up to 8 missed cleavages. Variable modifications used were protein N-terminal acetylation, methionine oxidation, peptide N-terminal pyroglutamate, Ser/Thr/Tyr phosphorylation (STY) and Lys SUMOylation, with a maximum of 3 modifications per peptide.
To further minimize false-positive discovery, additional manual filtering was performed at the peptide level. All modified peptides were required to have a localization probability of >75%, be supported by diagnostic mass remnants, be absent in the decoy database, and have a delta score of >40 in case SUMO modification was detected on a peptide C-terminal lysine not preceding an aspartic acid or glutamic acid. All multiply-SUMOylated peptides were discarded, unless the corresponding SUMO sites were also identified by singly-SUMOylated peptides. SUMO target proteins were derived from the 'proteinGroups. txt' file, and all post-filtering SUMO sites were manually mapped. Only proteins containing at least one SUMO site were considered as SUMO target proteins, and other putative SUMO target proteins were discarded.
Data analysis The hierarchical clustering and heatmaps were generated using Perseus software v1. 6. 10. 50  (36). For SUMO-2/3 proteomics, the label-free quantified (LFQ) value for four bioreplicates was averaged. The averaged value was log2 transformed, imputed with default setting, and normalized by row Z score transformation. For SLAMseq data, the normalized count for three bio-replicates was averaged. The averaged normalized count was log2 transformed and normalized by row Z score transformation. The row dendrogram was generated based on Euclidean distance and processed with k-means. Z score was used to generate cluster profiles in parallel. GO analysis was performed using clusterProfiler R package  (37). The statistical significance was specified as adjusted P value lt; 0. 05. Redundant GO terms were simplified according to similarity measured with 'Wang' method.
The scatter plot was generated using Perseus software. Log2 transformed normalized counts were plotted. Meanwhile, Pearson correlation coefficient and P value were calculated. Venn diagram was generated using VennDiagram R package (https://CRAN. R-project. org/package = VennDiagram). For each single gene, Pearson correlation coefficient was calculated by comparing time-course profiles in different datasets in Excel. The density plot for the distribution of Pearson correlation coefficient was generated using EaSeq software v1. 111  (28). The ggplot2 R package v3. 3. 2 (https://ggplot2. tidyverse. org) was used to generate box, histogram, bar, dot and line plots in the study. Integrative Genomics Viewer  (38)  was used for SLAMseq and ChIPseq data visualization with normalized bigWig files.
Other genes (248) showed a more gradual increase in transcription levels and their expression remained high during later stages of AD (modules 9, 10 and 11), such as Pparg and Adipoq, both known to be highly expressed in MAs (Figure  1C ,D; Supplementary Figure  S3E ; Supplementary Table  S2 ). Contrary to QuantSeq, pathway enrichment analysis of SLAMseq data revealed a partial overlap between terms in PA and CE (Figure  1E ). These common terms notably included mitotic regulation of the cell cycle, indicating that transcriptional upregulation of cell cycle genes required for CE is one of the earliest events following adipogenic induction. In contrast, MA-specific transcription modules 9-11 showed a strong enrichment in genes involved in fat cell differentiation and mature adipocyte functions, including triglyceride metabolism, glucose homeostasis and lipid storage (Figure  1E ).
These data indicate that sumoylation inhibition alters transcription dynamics more significantly than it alters total mRNA dynamics. We therefore decided to focus our investigation on SUMO-regulated transcription using SLAMseq DEGs. Among them, 636 DEGs (927 DE transcripts) were significantly regulated over the normal time course, while 407 genes were uniquely affected by ML-792 treatment (Supplementary Figure  S4D ). Regarding the 636 DEGs, pathway enrichment analysis revealed various processes including regulation of mitosis and fat cell differentiation, the latter being enriched beyond normal expectations (Supplementary Figure  S4E, F ). Hierarchical clustering revealed that sumoylation regulates the transcription of many genes located in PA, CE and MA modules (Figure  2B ,C; Supplementary Table  S4 ).
The same was observed for CE modules 5-7 that contained 170 peaks with highest intensity at day 1 or 3 (Figure  3E ). Finally, MA modules 8-12 contained 1136 peaks with highest intensity in MAs and a very strong enrichment for genes involved in adipogenic functions as well as adaptive thermogenesis (Figure  3E ). These data indicate that chromatin-bound SUMO may support adipocyte function and metabolism via transcriptional regulation. By comparing the SLAMseq transcription modules identified in Figure  1D  with SUMO ChIPseq data, followed by hierarchical clustering, we found that distinct peaks of SUMO were present both at repressed genes and at activated genes upon induction of adipogenic differentiation (Figure  3F ,G and Supplementary Table  S5 ). Pearson scoring revealed a general positive correlation between transcription and the presence of SUMO (Figure  3H ,I), which was especially true for exons, introns and distant promoters (Figure  3H ).
However, the presence of SUMO at promoter regions both positively and negatively correlated with transcription (Figure  3H ). Most PA genes, including the AD inhibitor Klf10, appeared to be repressed in presence of SUMO upon induction of adipogenic differentiation (Figure  3I  and Supplementary Figure  S5D ). In clear contrast, the presence of SUMO at CE genes and in particular at MA genes, such as the adipogenic gene Fabp4, showed a positive correlation with transcription (Figure  3I  and Supplementary Figure  S5D ). Based on these findings, we hypothesized that adipogenic stimulation on the one hand leads to increased sumoylation of transcription factors to inhibit transcription of PA genes, while on the other hand it increases transcription of MA genes. We integrated ML-792 SLAMseq data with CTRL SLAMseq and ChIPseq datasets by hierarchical clustering (Supplementary Figure  S5E  and Supplementary Table  S5 ).
Site-specific characterization of the SUMOylome during adipocyte differentiation Our ChIPseq data show that SUMO binds to specific TF-BSs during AD. To identify adipogenesis-specific SUMO-2/3 substrates, we carried out site-specific characterization of the endogenous SUMOylome by mass spectrometry during AD (Figure  4A )  (4). PCA of SUMO-modified lysine residues of four independent experiments demonstrated high reproducibility between replicates and considerable differences between the time points (Supplementary Figure  S6A, B ). Across all time points, fractions and biological replicates, we identified 5230 SUMO-modified peptides that mapped to 3706 unique SUMO sites. Out of all SUMO sites, 3137 (∼85%) could be quantified in quadruplicate (Supplementary Table  S8 ). In accordance with SUMO western blots (Supplementary Figure  S1C ), MS experiments showed that the overall density of SUMO increases upon adipogenic induction (Figure  4B ).
Sumoylation supports transcription of PPAR␥/RXR target genes in mature adipocytes Our data indicate that the SUMO peaks detected at PPAR␥ /RXR binding sites (Figure  3  and Supplementary Figure  S3 ) are due to the presence of sumoylated TFs and co-activators, including PPAR␥ /RXR itself (Figure  4 ). We integrated the SUMO MS, SUMO SLAMseq, SUMO ChIPseq, and PPAR␥ /RXR ChIPseq datasets and focused on PPAR␥ /RXR target genes of which the regulation is critical during AD  (41). We identified 143 DEGs, both normal time-course DEGs and ML-792 DEGs, at which SUMO perfectly overlapped with PPAR␥ /RXR (Figure  5A  and Supplementary Table  S9 ). Note that these genes are adipogenic genes (Supplementary Figure  S7A ) that are either repressed (PA genes) or activated (MA genes) by adipogenic stimulation (Figure  5B  and Supplementary Figure  S7B ).
As previously published in other cellular systems, the abundance of mature mRNAs compared to nascent RNAs and compensatory mechanisms that increase mRNA stability upon transcriptional inhibition may occlude transcriptional changes that could lead to the discovery of factors that regulate transcription  (48, 49). To investigate adipogeneic transcription, we characterized nascent transcriptome dynamics during adipogenesis using SLAMseq. Interestingly, comparison of SLAMseq data with total mRNA dynamics (QuantSeq) revealed important deviations. As previously observed, total mRNA dynamics featured gene expression modules involving large numbers of genes and intricate dynamics  (41, 46). In contrast, SLAMseq revealed a severe transcriptional switch from PA to MA stages: PA genes (92%) are repressed and MA (also some CE) genes (8%) are induced.