dataesr/scientific-paragraphs-categorization huggingface dataset:
GroEL metainference simulations The crystal structures of apo GroEL (PDB: 1XCK) (63) and GroEL-ADP complex in the relaxed allosteric state (PDB: 4KI8) (64) were used to generate a synthetic cryo-EM map, using the following procedure. Chains A were extracted from the two PDBs (Fig. 1 A ) and aligned using UCSF Chimera  (23). MODELLER v9. 17  (26)  was used to generate a comparative model (GroEL-ADP*) of the sequence of apo GroEL based on the GroEL-ADP complex template  (64). The gmconvert utility  (65)  was then used to separately convert the apo-GroEL and GroEL-ADP* atomistic models into two density maps (Fig. 1 B ). Radius and weight for the conversion were determined by the residue-type method implemented in gmconvert. The final synthetic map was computed as the average of the two individual maps, with equal weight (Fig.
1 C ). A divide-and-conquer approach (49) was used to fit a GMM with 4000 components, which resulted in a cross correlation with the original map of >0. 99 (Fig. S1 ). Initial models for the metainference production run were randomly extracted from the 1-ns-long equilibration run initiated from the apo-GroEL model. The metainference ensemble was simulated using four replicas for a total aggregated time of 50 ns. s SEM r;i was kept constant for all replicas and set to 0:01 ov DD;i. This parameter determines the maximum intensity of the cryo-EM restraint in the case of the absence of data noise (s B r;i ¼ 0) and was set to the minimum value that allowed a proper integration of the cryo-EM restraint. To enhance sampling, we used well-tempered metadynamics with W 0 ¼ 1000 kJoule/mol and g ¼ 150,000.
STRA6 metainference simulations The cryo-EM map of the complex of zebrafish STRA6 with copurified calmodulin at 3. 9 A ˚resolution (EMD: 8315 (66)) was fit with a GMM using a divide-and-conquer approach  (49). The final GMM was composed of 11,585 Gaussians and resulted in a cross correlation with the original map equal to 0. 97 (Fig. S2 ). The cytosolic loop (residues 575-597) missing from the deposited model (PDB: 5SY1) was modeled using the software MODELLER v9. 17. The residue numbering scheme was kept as in the deposited model. The resulting comparative model was then equilibrated at 300 K, as previously described. Initial models for two independent production runs (labeled as ''RUN I'' and ''RUN II'') were then randomly extracted from the equilibration run. The metainference ensemble was simulated using 16 replicas for a total aggregated time of 355 ns per production run.
Stereochemistry assessment of the STRA6 ensemble To measure the stereochemical quality of the ensemble of STRA6 models generated by the metainference method, we calculated the distribution of the backbone dihedral angles 4 and j on the conformations sampled in the two independent simulations. To achieve this, we used the program PROCHECK  (67) , specifically the procheck_nmr collection of codes designed to evaluate the quality of NMR ensembles. This program classifies all residues in all models in four regions of the Ramachandran plot (Fig. S3  A): residues in most favored regions (red), in additional allowed regions (yellow), in generously allowed regions (light yellow), and in disallowed regions (white). The percentages of residues in each of these regions for the two independent metainference simulations were, in both cases: 87.
Structure and dynamics of the STRA6 membrane receptor We then applied our metainference approach to the integral membrane receptor STRA6  (66) , which mediates the cellular uptake of retinol by extracting it from its carrier FIGURE 2 Structure, dynamics, and noise characterization of the STRA6 membrane complex. Compared to the single-structure model (PDB: 5SY1, A), the metainference ensemble displays a higher degree of flexibility (C). We calculated the predicted cryo-EM maps from the single-structure model (B) and metainference ensemble (D) and evaluated the global and local CC with the experimental map. The metainference map provides a better CC with the experimental map (global CC ¼ 0. 91) compared to the single-structure map (global CC ¼ 0. 86), especially in the more dynamical regions of STRA6.
Cross-correlation maps are visualized at a threshold of 3. 5. The pie charts report the distributions of local CC in the regions of the single-structure and metainference maps with density between 3. 4 and 3. 6. The level of relative error in the experimental map inferred by metainference is rather uniform, with the exception of the regions occupied by cholesterol and amphipols (E). To see this figure in color, go online. (retinol binding protein, or RBP) and moving it across the membrane  (69). Recently, zebrafish STRA6 was determined at 3. 9 A ˚resolution by single-particle cryo-EM (EMD: 8315) (66). The structural model obtained from the cryo-EM map (PDB: 5SY1) revealed a dimer of STRA6 in complex with the protein calmodulin, and enabled the characterization of the regions involved in RBP binding and retinol translocation across the membrane  (66).
The STRA6 metainference ensemble Starting from the 5SY1 structure (Fig. 2 A ), we modeled a thermodynamic ensemble of conformations by integrating cryo-EM data (Fig. S2 ) with an a priori, physico-chemical knowledge of the system (Materials and Methods). In the generated ensemble (Figs. 2 C and S4 C), STRA6 presents a significant degree of flexibility around the single-structure model, in particular in the N-terminal and C-terminal domains, the lid peptide (LP) and the RBP-binding motif, the cytosolic loop, and the JM helix (Fig. S7 A ). These regions correspond to areas at lower resolution in the experimental map  (66) , as well as in the back-calculated maps (Figs. 2, B and D and S4, B and D). The cytosolic loop, which is not included in the PDB: 5SY1 structure, displays the largest fluctuations (Fig.
S7  A, magenta), whereas calmodulin (Fig. S7 B ) and the STRA6 trans-membrane (TM) domain (Fig. S7  A, gray) are instead more rigid and deviate less from the PDB: 5SY1 model (Fig. S7, C  and D ). The latter result is particularly relevant, given the low accuracy of the prior in the TM region (Materials and Methods). We then measured the agreement of the metainference ensemble with the experimental data by calculating the maps predicted from the ensemble and the single-structure model (Supporting Material). We found that the metainference ensemble provided a better cross correlation with the experimental map (global CC ¼ 0. 91) than the single-structure model (global CC ¼ 0. 86), especially in the more dynamical regions of STRA6 (Figs. 2, B  and D  and S4, B  and D ). We also verified that the improved agreement with the experimental data was not achieved at the expense of the stereochemical quality of the models (Fig.
CONCLUSIONS We have reported a method to determine structure and dynamics of macromolecular systems by modeling thermodynamic ensembles from cryo-EM density maps. The application to the integral membrane receptor STRA6 has illustrated how functional dynamics might remain hidden in areas of the map at lower resolution. The method is capable of revealing such dynamics by disentangling the effect of noise in the maps from conformational heterogeneity. This method is implemented in the PLUMED-ISDB module  (51)  of the open-source PLUMED library (http://www. plumed. org) (52) , allowing the integration of cryo-EM with other ensemble-averaged experimental data, thus readily enabling integrative structural and dynamical biology studies  (18, 19). The approach can be extended to model multiple ensembles using three-dimensional reconstructions obtained from different two-dimensional class-averages and to thoroughly characterize the conformational landscape, dynamics, and function of complex biological systems.
Hence, it is reasonable to assume the existence of a possible cross-talk between TFs and miRNAs that could modulate ABCA1 gene and RCT circuit. However, such mechanisms of regulation are still not well defined and could represent an important future area of research. Circular RNAs (circRNAs), a class of RNAs with the linking of 3′ and 5′ ends, are predicted to function as robust transcriptional and posttranscriptional regulators of gene expression. Evidence is arising that some circRNAs might regulate miRNA function as microRNA sponges. Relevant information in this respect can be provided by the web tool CircInteractome, freely available at http://circinteractome. nia. nih. gov. This new database has been developed for exploring circRNAs and their interacting proteins and microRNAs  [14]. Further studies have suggested that circRNA-miRNA-mRNA axes play a prominent role in different pathologies including CVD  [15].
nia. nih. gov. This new database has been developed for exploring circRNAs and their interacting proteins and microRNAs  [14]. Further studies have suggested that circRNA-miRNA-mRNA axes play a prominent role in different pathologies including CVD  [15]. Some circRNAs have been predominantly localized in the nucleus where they can directly promote host-gene transcription through interaction with RNA polymerase II (pol II) in the promoter region of genes. Furthermore, these RNAs may interact with TFs to influence various diseases  [16]. Because no data are yet available regarding the impact of circRNAs on lipid homeostasis and HDL biogenesis, this has prompted us to propose a model integrating TFs, miRNAs, RBPs and CirRNAs into the ABCA1 regulatory gene network (Figure  1 ). However, we are aware that certain circuits of this model are much more difficult to directly translate in vivo mostly due to the complexity of several highly connected pathways with feedback and feedforward loops.
Reconstructions of sediment provenance and potential source areas in these basins are important for understanding source-to-sink relationships and the evolution of the Western Alps foreland basin in general. Provenance studies based on heavy mineral analysis and sediment petrology in the Barrême, Annot and Saint Antonin basins (e. g. ,  Stanley, 1964; Bodelle, 1971; Evans and Mange-Rajetzky, 1991; Evans et al. , 2004)  provided important information on sediment source areas and routing systems. These studies showed that during the Eoceneearly Oligocene sediments were delivered to these three basins from the Maures-Esterel massif and the Corsica-Sardinia block located to the south. A change of sediment provenance between about 30-29 Ma has been reported for the Barrême basin in terms of sediment petrology, paleocurrent transport directions and zircon fission-track and U-Pb data (e.
g. ,  Evans and Mange-Rajetzky, 1991; Evans et al. , 2004; Jourdan et al. , 2013) , but not for the Saint Antonin or the Annot basin, because the Saint Antonin basin never received sediments from the Western Alps, and deposition in the Annot basin had ended by that time. Detrital apatite fission-track (AFT) and zircon fission-track (ZFT) and U-Pb single grain double dating can provide additional information on sediment provenance, as well as the exhumation history of sediment source areas (e. g. ,  Zeitler et al. , 1986; Cerveny et al. , 1988; Carter, 1999; Carter and Moss, 1999; Garver et al. , 1999; Bernet and Garver, 2005; Bernet et al. , 2006; Jourdan et al. , 2013). The purpose of this study is combine sediment provenance information with rates of source area exhumation. Therefore, we present geothermochronological data from six samples of detrital apatite and zircon collected from the three main sedimentary units of the latest Eocene to early Oligocene sedimentary sequence preserved in the Saint Antonin basin syncline (Tab.
1). Similar data have already been published for the Barrême and Annot basins  (Bernet et al. , 2009; Jourdan et al. , 2013) , which allows for a good comparison of the thermochronological signal in the different foreland basin remnants. The times of deposition of the sedimentary sequences in the Saint Antonin basin have previously been constrained by biostratigraphy  (Callec, 2001). We use the minimum age approach for comparing the youngest age population of each sample with the biostratigraphically constrained depositional ages of the three formations for estimating maximum source area exhumation rates. Central ages of the AFT and ZFT grain-age distributions of our samples are used for first-order estimates of average long-term exhumation rates of the paleo-drainage areas. Zircon U-Pb ages of grains already dated with the fission-track method were determined in order to identify a possible Paleogene volcanic contribution, which may perturb the exhumation signal, as it was done in a previous Barrême basin study  (Jourdan et al.
The zircon samples, also covered with white mica detectors, were irradiated together with Buluk and Fish Canyon Tuff age standards and CN1 dosimeter glasses. After irradiation, all mica detectors were etched at 21 °C for 18 min in 48% hydrofluoric acid. All fission-track analyses were done in the ISTerre fissiontrack laboratory in Grenoble, France. Fission-tracks were counted dry at 1250Â magnification in grains mounted parallel to their C-axis, using an Olympus BX51 microscope and the FTStage 4. 04 system of T. Dumitru. The grain age distributions of all samples are presented in radial plots, and minimum and central ages  (Galbraith and Laslett, 1993)  were calculated with the RadialPlotter program of  Vermeesch (2009). Given the relatively close age of deposition of about 31 ± 2 Ma of the sampled units, we combined all AFT and ZFT single grain age data for binomial peak fitting using BINOMFIT of Brandon (see  Stewart and Brandon, 2004; Ehlers et al.
Zircon U-Pb and fission-track/U-Pb double dating results A total of 109 zircons were dated with LA-ICP-MS analysis (Fig. 8a ) and 62 of these zircons were previously dated with the fission-track method (Fig. 8b ). With the exception of one single zircon from sample 10SJ10 in Formation 3, with a crystallization age of 30 ± 1 Ma, all other zircons have U-Pb ages ranging between 277-2812 Ma (Fig. 8a ). Almost 40% of   the grains show Hercynian ages (∼360-280 Ma) and another 20-25% Pan African ages (∼460-660 Ma). The zircon grain with a 30 Ma U-Pb age also has a 34 Ma ± 25 ZFT cooling age, which means that this grain has to be considered as the only Oligocene volcanic zircon grain observed in our samples. The ZFT data show that a few grains with Hercynian and Pan African crystallization ages have Eocene-Oligocene cooling ages, but otherwise show a large spread of apparent cooling ages between ca.
9 ;  Lucazeau and Mailhé, 1986; Morillon, 1992 Morillon, , 1997;; Jakni, 2000). Paleozoic basement and Triassic-Jurassic cover rocks in eastern and western Sardinia have AFT ages over 50 Ma and increase to 100 to 306 Ma in southwestern Sardinia  (Rossi et al. , 2005; Zattin et al. , 2008 , Malusà et al. , 2016). AFT cooling ages of 30 Ma and younger can be found along the central graben in Sardinia, but the majority of AFT ages in Sardinia ranges from about 40 to 170 Ma  (Rossi et al. , 2005; Malusà et al. , 2016). ZFT ages throughout Sardinia are between 140-240 Ma (Fig. 9 ;  Malusà et al. , 2016). Single grain apatite (U-Th)/He data for Sardinia are as variable as the AFT ages, ranging from 26-204 Ma  (Malusà et al. , 2016). A relatively dense AFT and ZFT dataset exists for Corsica (e. g.
Provenance information from geo-thermochronological data Based on structural, paleomagnetic and petrographic data,  Westphal et al. (1976)  had already shown that the Corsica-Sardinia block was directly connected with the Porvence area of southern France, with Corsica in a location close to where Nice and Cannes are today  (Lacombe and Jolivet, 2005). In addition, from the sediment petrological and sedimentological evidence of the Saint Antonin basins mentioned above, the sediment source areas were located to the south and south-west of the basin, including the Maures-Esterel massif, Corsica and Sardina  (Stanley, 1980; Campredon and Giannerini, 1982). The question is which of the three potential source areas is the most important? We first look at the zircon U-Pb data to elaborate this question.
Zircons with Pan-African and older U-Pb ages are most likely derived from Permo-Triassic sedimentary cover rocks in the Maures-Esterel massif, Sardinia and Corsica, or from meta-sedimentary rocks of the Alpine Corsica zone. However, large fractions of zircons dated in this study have Hercynian U-Pb ages between 280 and 350 Ma. Hercynian granitic intrusions and migmatites are widespread throughout the Maures-Esterel massif, Sardinia and Corsica (e. g. ,  Morillon et al. , 2000; Duchesne et al. , 2013). Therefore, the zircon U-Pb data alone do not provide solid criteria for distinguishing between the three source areas. Only one zircon grain with an Oligocene crystallization age was identified, which is, given its corresponding ZFT cooling age, interpreted to be derived from a volcanic source.
Because this contribution is very small, and because the andesite breccia we sampled in the Saint Antonin basin did not yield any apatite or zircon, we think that Oligocene volcanic rocks are not a major source and the detrital AFT and ZFT cooling age signals discussed below represent source area exhumation. Here the questions is how can the AFT and ZFT ages be linked to source areas? A basic assumption is that for apatite and zircons with Eocene and early Oligocene cooling ages only bedrock exposed at the surface today which have early Oligocene or younger cooling ages can be considered as potential source areas (Fig. 9 ). In the Saint Antonin basin, we interpret apatites with early Oligocene cooling ages being mainly derived from source areas in Corsica and possibly from the Maures-Esterel massif, but there outcrops with early Oligocene AFT cooling ages are rather limited, at least from what is known from the published data (Fig.
The lack of a dense fission-track dataset in the Maures-Esterel massif prohibits more precise constraints. However, the published ZFT data from Corsica and Sardinia support potential source areas in the eastern, central and western parts of Hercynian Corsica for zircons with Early Cretaceous and older apparent fission-track cooling ages and Alpine Corsica for zircons with Late Cretaceous and Paleogene fission-track cooling ages  (Fellin et al. , 2006; Danisik et al. , 2007; Malusà et al. , 2016). We think that all of these Corsican sources were exposed and eroded during the early Oligocene and some of these sediments were deposited in the Saint Antonin basin, before Corsica and Sardinia drifted away during the late Oligoceneearly Miocene  (Lacombe and Jolivet (2005) , and were removed as potential source areas, and the Saint Antonin basin was fully inverted and uplifted  (Campredon and Giannerini, 1982).
Source area exhumation rates The minimum and central ages of the individual AFT and ZFT samples are clearly older than the given depositional ages between about 33 and 29 Ma. This, together with the relatively shallow burial of the sampled sedimentary rocks, suggests that fission-tracks in apatite and zircon were not affected by strong post-depositional annealing. As outlined above with the zircon double-dating results, a volcanic contribution seems to be negligible. Therefore, the AFT and ZFT data presented in this study can be used for estimating drainage basin average and maximum exhumation rates. The lag times shown in Table  4  can be used with Figure  11  to obtain first-order estimates of exhumation rates. Drainage basin average long-term exhumation rates determined from the mean central-age lag times are on the order of 0.
Conclusions The geo-thermochronologic data of the Saint Antonin basin presented in this study are consistent with sediment provenance from the Maures-Esterel massif, Sardinia and Corsica. The Saint Antonin basin clastic formations were deposited between 33 and 28 Ma, at the end of the Pyrenean-Provencal collision phase and just before the initiation of the opening of the Ligurian Sea and rifting of Corsica and Sardinia away from the Maures-Esterel massif, and the single grain zircon fission-track and U-Pb dating shows that a volcanic contribution is negligible, and apatites and zircons with early Oligocene fission-track cooling ages are in the Saint Antonin basin, even if rare, are regarded as the result of erosional exhumation at maximum rates of up to 0. 4-0. 7 km/Myr. The majority of the AFT and ZFT cooling ages however indicate overall relatively slow long-term average erosional exhumation rates on the order of 0.
1-0. 2 km/Myr in the Maures-Esterel, Sardinia and Corsica, consistent with the rather limited amount of sediments deposited in the Saint Antonin basin. In comparison, the published geo-thermochronologic data of the Barrême basin, show similar age ranges but with the difference of a large proportion of zircons with early Oligocene fissiontrack cooling ages, partly of volcanic origin and partly derived from rapid erosional exhumation in the Western Alps. The Saint Antonin basin deposits did not record a significant change in sediment provenance at about 30 Ma, as it has been documented in the Barrême basin. This means that the Saint Antonin basin was not affected by the same changes in foreland basin dynamics and sediment routing systems as the Barrême basin, even if the two foreland basin remnants are less than 50 km apart.
). • Knowing current ecological dynamics F (denoted as knowledge K 2 ). Interactions within the exploited ecosystem are assessed (social and ecological interactions). Interactions between the exploited ecosystem and the decision makers as well as the exogenous drivers (such as climate change or inherent variability) are also known. It requires experts in several interacting areas (climate scientists and biologists). Earth's Future 10. 1029/2018EF000852  • Knowledge of future events based on the properties of uncertainties U (denoted as knowledge K 3 ) such as the probability distribution of drivers is used during the decision process. Standard and extreme events are characterized from data or from expertise (from climate scientists to mathematicians). • Knowing exploitation levels based on the users' declaration (denoted as knowledge K 4 ): managers aim at assessing how the ecosystem is exploited.
Microarray Profiling Microarray profiling was performed as previously described  (Osorio et al. , 2011) , using a GeneChip Mouse Gene 1. 0 ST Array (Affymetrix). Raw data were processed with the RMAExpress program (http://RMAExpress. bmbolstad. com), using default settings. Differentially expressed genes in each condition were identified using the R/Bioconductor package limma  (Ritchie et al. , 2015). A proportional Venn diagram was generated using nVenn (http://degradome. uniovi. es/cgi-bin/nVenn/nvenn. cgi)  (Perez-Silva et al. , 2018). GSEA v2. 2. 0 and MSigDB release 5. 1 (http://software. broadinstitute. org/gsea/index. jsp) were used for pathway enrichment analysis. Weighted enrichment scores were calculated with gene expression lists ranked by signal-to-noise ratio (maximum gene set size: 500; minimum gene set size: 20; number of permutations: 1,000; gene set database: Hallmark; false discovery rate [FDR] # 0.
Untargeted and Targeted Metabolomics Analysis Metabolites were extracted from 30 mg of each tissue using a mix of cold methanol/water/chloroform (9:1:1). Samples were homogenized, and phase separation was performed by centrifugation. For untargeted metabolomics, supernatants were collected and evaporated, and dried extracts were solubilized in methanol. Two aliquots were produced for gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) analysis. For targeted metabolomics, collected supernatants were evaporated, dried, and solubilized with MilliQ water. One aliquot was transferred to LC vials and injected into LC-MS. Targeted metabolomics were performed using ultra-high-pressure liquid chromatography coupled to mass spectrometry (UHPLC-MS). Analytical methods and data processing were performed as previously described  (Enot et al.