dataesr/scientific-paragraphs-categorization huggingface dataset:
The mass limit for the IFS data is cut to the lowest mass computed by the model grid for both pipelines. The K1 mass curve reaches smaller values than the K2 mass curve because the K2 filter matches a methane absorption band that strongly dims the flux of cold giant planets. Our detection limits are corrected for the coronagraphic transmission  (Boccaletti et al. 2018 ) and for small sample statistics  (Mawet et al. 2014). Our results from ANDROMEDA tell us that the IFS data are sensitive to planets more massive than ∼1 M Jup beyond ∼6 au, and more massive than 2 M Jup beyond 4. 5 au. While with TRAP we see an additional improvement in the sensitivity of planets down to 2 M Jup at 3 au. We therefore achieve an improvement of about 2 M Jup in sensitivity over previous studies (e. g. SAM17).
Retrieval analysis We used the 1D radiative transfer code petitRADTRANS 2  (pRT;  Mollière et al. 2019 ) in its scattering implementation  (Mollière et al. 2020)    as an input the spectra of the planet along with prior distributions for the metallicity, C/O ratio, log g, radius, a list of molecules to be included, quench pressure, and cloud parameters such as f sed , K zz , and log τ cloud.
Input data Data set. Our Y H-spectrum, as all SPHERE/IFS spectra, comprises 39 channels, all of which were fed into the retrieval process. In addition, we use the IRDIS H2/H3 photometric filters and broad-band H from SAM17, and K1/K2 from this work. Also included are the L P and M S Keck/NIRC2 data points from  Rajan et al. (2017). We do not include GPI spectra in these retrievals due to the difficulty in quantifying systematic differences in the calibration of different instruments. A study combining data from SPHERE and K-band GRAVITY to expand the wavelength coverage using the retrieval approach is reserved for future work  (Samland et al. , in prep. ). As described in SAM17 and originally in  Greco & Brandt (2016) , the spectral covariance of the residual speckle noise should be taken into account when computing the likelihood of a model that matches the data from an IFS-type instrument.
Atmospheric retrieval on previous data In order to determine the extent to which the use of a retrieval method alone impacts the outcome in terms of the derived atmospheric parameters with respect to the SAM17 grid of self-consistent models, we ran an additional set of pRT retrievals (nominal and enforcing clouds) on the original data used by SAM17. These attempts can also tell us the extent to which the outcomes are a result of the better quality of the SPHERE 2017 data. We included all the data cited in the work of  Samland et al. , that is, the Y J and the Y H spectra, and the H2H3, K1K2, L P , and broad-band H photometric points, all resulting from 2015 and 2016 observations, as well as the GPI spectrum published in the discovery paper  (Macintosh et al. 2015). We note that SAM17 used an SDI+ANDROMEDA data reduction, which resulted in the reference channels of their spectra being unusable in the model comparison.
Run New nominal New enforced clouds SAM17 nominal SAM17 enforced clouds SAM17 Note. The last column shows the results of SAM17 for comparison. The 'New' in Cols. 2 and 3 refers to the retrieval results using our new data + the photometry in SAM17 + Rajan et al. (  2017 )'s M S and L P. When comparing the parameter values for the 'SAM17 nominal' retrieval in Table  5  to the ones in Table  5  of SAM17 (top row, 'PTC-C', shown in the last row of Table  5 ), we notice that we find a significantly lower metallicity, and very little indication of the presence of clouds. Other parameters differ as well, but are mostly within the 16th and 84th percentile limits. We discuss this issue, and in particular our attempt to determine whether the object is cloudy or not, in depth in Sect. 5. 6.
4 ± 0. 08 for the new data and C/O ∼ 0. 8 ± 0. 17 for the data in SAM17), respectively. Compared to the solar C/O ratio assumed by SAM17 (i. e. C/O = 0. 55), all our retrieved values within error bars differ by ∼0. 1. However, no definitive conclusions can be drawn because the value for the C/O ratio of the star remains undetermined. The lower flux from the M S photometric point hints to the presence of carbon monoxide (CO), but further observations would be needed to use it to constrain the C/O ratio of 51 Eri b.
[Fe/H] In our retrievals using the data in SAM17, we obtain a metallicity [Fe/H] in the range of -0. 09 to 0. 30, including uncertainties, which differs from the results of SAM17, who find [Fe/H] = 1. 0±0. 1. However, when comparing with retrieval results for two benchmark brown dwarfs, these latter authors find that they tend to fall in the lower end of the estimated metallicity range for the host star. In our case, we observe a similar behaviour, even for the new value we obtain of [Fe/H] ∼ 0. 26, which is slightly super-solar. As discussed at length in SAM17, the derived metallicity strongly depends on the K-band flux, and indeed our retrieval tends to slightly underfit the K1flux, whereas it was overfitted in SAM17. When artificially enhancing the importance of the K1-flux point by lowering its uncertainty by a factor of ten, the resulting fit for the SAM17 data shows an increased metallicity ([Fe/H] = 0.
Radius and surface gravity The values we obtain for the radius and surface gravity are in agreement with previous results for the data in SAM17 within uncertainties. As seen in Figs. 6  and D. 2, the retrieval finds the best-fitting models towards the lower R p prior boundary for the new data, while this is not the case for the retrievals using the data in SAM17  (Figs. D. 4 and D. 6). We attribute these variations to the different input data. Nevertheless, all values are consistent with the radius of Jupiter within uncertainties, which according to planetary evolution models are slightly smaller than expected for the possible age of the system (i. e. 10-20 Myr, ∼1-1. 2 R Jup ;  Mordasini et al. 2012).
Summary and conclusions In this work, we present VLT/SPHERE spectro-photometric observations of 51 Eridani b. The new Y H spectrum and K1K2 observations show improved S/N compared to previously reported data, allowing us to revise the published flux measurements. We used the radiative transfer code petitRADTRANS, which uses a retrieval approach to fit the atmospheric parameters. In addition, we attempted to reproduce previous results (obtained with self-consistent models) using this approach and compared A98, page 10 of 19  S. B. , et al. : A&A proofs,  the outcomes of retrievals to self-consistent models. Our results can be summarised as follows: -We extracted the spectrum of 51 Eri b using the ANDROMEDA algorithm (Fig. 2 ). We obtained new photometric measurements for the K1K2 filters (M K1 = 15.
Acknowledgements. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France) ,  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France) ,  LESIA (Paris, France) ,  Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zürich (Switzerland) ,  NOVA (Netherlands) ,  ON ERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE  also received funding from the  European Commission Sixth and Seventh Framework Programmes  as part of the  Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566 for FP6 (2004 )(2005)( 2006 )(2007)(2008), grant number  226604  for  FP7  ( 2009-2012 ) and grant number  312430 for FP7 (2013 )(2014)(2015)(2016).
Increasing numbers of datasets and experimental assays that capture the organization of chromatin inside the nucleus warrant an effort to develop tools to visualize and analyze these structures. Alongside polymer physics or constraint-based modeling, network theory approaches to describe 3D epigenome organization have gained in popularity. Representing genomic regions as nodes in a network enables visualization of 1D epigenomics datasets in the context of chromatin structure maps, while network theory metrics can be used to describe 3D epigenome organization and dynamics. In this review, we summarize the most salient applications of network theory to the study of chromatin contact maps, demonstrating its potential in revealing epigenomic patterns and relating them to cellular phenotypes.
Introduction In the last twenty years, we have started producing maps of the structure of the genome inside the nucleus, through chromatin capture experiments and other complementary techniques. Our ability to chart out chromatin structure is revolutionizing our mental models of the genome. For years, we have been representing the genome as a line, prompted by the availability of extremely useful genome browsers, which we used as visualization tools for different types of datasets (gene definitions, chromatin mark peaks, replication origin activity, etc. ). Diverse experiments have undeniably shown that DNA is intertwined at various levels in complex 3D structures. Moreover, a variety of RNA and proteins are associated with chromatin, constituting what has been recently termed the nucleome, an important entity that connects the genome (intended as purely genome sequence) to gene regulation and cellular phenotypes  [1].
The transition between single-cell microscopy-based assays to chromosome conformation capture technologies has produced an explosion in the quantity and diversity of chromatin structure datasets available. The popularization of next-generation sequencing has enabled the efficient mapping of genome-wide chromatin contacts by conformation capture, for example, through Hi-C  [2] , or of interactions mediated by specific chromatin-bound factors  [3] , painting the first pictures of 3D genome structure. Further developments have led to new experimental approaches that lead to even more detailed descriptions of the 3D genome  [4] [5] [6] [7] [8] [9] [10] [11] [12]. Each experimental technique gives us a partial but useful view on chromatin organization, so comparison and benchmark of these methods are necessary  [13, 14].
Independent of the different assays that can be used to detect 3D chromatin contacts, we can distinguish between at least 3 types of chromatin structure maps that can be built from these datasets: 1) real spatial descriptions of chromatin inside single cells, through advanced labeling and microscopy approaches, 2) spatial models of the chromatin produced by polymer physics-based or constraint-based simulations, starting from experimental chromatin contact data, and 3) abstract topological representations of chromatin contacts through network models, derived directly from the experimental data. In this review, we will focus specifically on network theory approaches to visualize, interpret, and compare chromatin structure datasets.
The igraph package  [67]  and NetworkX  [68]  offer numerous algorithms for calculation of the most common network statistics (see Table  1  for some examples) as well as network visualization. Networks can be represented in 2D using specific algorithms that assign node positions based on userdefined requirements. The most common representations aim to put nodes that are connected close to each other and optimize the layout for visibility (avoiding node overlap). Widely used network visualization tools that allow interactive exploration of the nodes as well as basic statistics calculations are Cytoscape  [69]  and Gephi  [70] , while MuxViz  [71]  can be used to visualize and investigate multilayer networks and multiplexes that are representations in which the same elements in a specific system can be connected according to different criteria, which are represented by different networks shown as layers.
We can represent chromatin as a network by considering nodes to represent chromatin regions/fragments, while edges joining two nodes indicate 3D proximity or contact between the corresponding genomic regions. One of the most popular techniques to map these contacts is Hi-C, which uses the principle of chromosome conformation capture, involving cross-linking of chromatin with formaldehyde, digesting it, and religating it to preserve information about which sequence fragments were close in 3D space, but on a global genome-wide scale. Since 2010, very soon after the first HiC datasets had been produced, models of chromatin structure as a network of chromatin fragments have led to many interesting results. Since genome-wide chromatin structure datasets produced by Hi-C started being represented as matrices, drawn as heatmaps, showing essentially the number of contacts between all possible pairs of chromatin regions, they were quickly interpreted as adjacency matrices (see Box 1) of a chromatin contact network  [15] [16] [17] [18] [19].
In this case, the number of contacts between specific regions could be used as weight (strength) of the interactions between nodes that represent these regions in the network. The main limitation of the early Hi-C datasets was a lack of resolution and the fact that most of the datasets would describe noncoding regions of the genome (the most abundant overall), making it hard to associate structure patterns to genes and biological function. Since then, an increasing interest in network analyses was justified by improvements in the quality of the datasets available  [20]. At present, there are several web servers that allow visualizing chromatin as networks  [21] [22] [23] [24]. Before the appearance of two 3D genome browsers  [25, 26]  in 2022, all other genome browsers still represented the genome as a linear object, or as a triangular/square heatmap of contacts  [27].
Using chromatin networks to integrate 1D epigenomic datasets in a 3D context One of the most powerful approaches in biology to extract biological hypotheses from different experimental assays involves data integration. For example, if we have discovered that specific genomics regions are enriched in peaks of a particular transcription factor, we can study which other factors also bind in similar regions and deduce that potentially the two factors are needed for a specific process. An analysis of integration of chromatin marks has led to the definition of different chromatin states  [34]  and successively chromatin colors  [35] , which help to define a set of rules that epigenomes tend to obey. Similarly, we might be interested in integrating datasets describing genomic regions (the same definitions of chromatin states for example) with the 3D structure of chromatin, to identify patterns in how regions with specific chromatin states might be organized inside the nucleus.
This type of analysis is greatly facilitated by the network analogy. As is the case in other applications of network theory, nodes can be characterized by feature vectors, enabling the projection of a variety of datasets on the chromatin network (Figure  2a ). Chromatin fragment features can allow us to establish 'classes' of nodes and analysis of the topology of the different classes has led to several insights. Integrating histone modification data from Chromatin ImmunoPrecipitation sequenecing (ChIPseq) in three different human cell lines, Thibodeau et al. identified the importance of broad domains and superenhancers as hubs (highly connected nodes) in the chromatin interaction network and proved that the topology around regulatory elements can predict the functional importance of regulated genes associated with them  [36].
ChAs were applied to the set of features projected on an mESC promoter-capture Hi-C dataset, identifying polycomb group proteins as central players in the maintenance of the interaction core, while we identified biologically relevant differences in ChAs of specific forms of RNA Pol II (paused, initiating, and elongating) in promoter-enhancer subnetworks  [37]. ChAs was also applied to analyze monocytes, neutrophils, and T cells' promoter-centered networks to study the relationship between 3D structure and gene expression (Figure  2c ), DNA methylation, as well as the most important histone marks. The ChAseR package and the GARDEN-NET web server provide the community with tools to calculate assortativity of any desired feature on any chromatin network  [22]. We also reported positive and very high assortativity of replication timing (Figure  2d ), generalizing previous observations that TADs represent domains of consistent replication timing  [22].
Finally, assortativity of replication origin efficiency across distances even longer than TAD sizes suggests a strong link between replication and 3D chromatin structure at multiple scales  [39]. ChAs was also used to detect the localized nature of contacts affected by STAG2 knockdown in bladder cancer cell lines and to identify the typical range of distances spanned by such altered contacts  [40]. Finally, ChAs of several transcription factors was used to assess the performance of bioinformatic tools to identify significant contacts from promoter-capture Hi-C datasets  [41].
Using network frameworks to relate chromatin structure and gene expression Some controversies still exist over the strength and direction of the connection between 3D genome structure and gene expression. As soon as Hi-C datasets became available, it was reported that co-expressed genes were often found close or in 3D proximity of each other  [15, 42]. TADs were soon identified as domains of coordinated gene regulation, although the existence of coregulated domains distinct from TADs was also reported  [43]. Researchers have often considered gene regulation as one of the main purposes behind chromatin structure organization  [44]. Recent experiments that were designed to thoroughly test this hypothesis have provided a yet unclear picture. On one side, it is still unclear whether interactions detected by chromosome capture methods really capture physical contacts or proximity, on the other side, simultaneous measurements of chromatin structure features, such as TADs, and live-transcription detection do not find a particularly strong mechanistic link between the two  [10, 45, 46].
Finally, we can exploit very powerful tools to compare and classify networks using graph neural network approaches. Representing the genome as a network calls for specific deep learning frameworks. GraphReg  [51]  is a recent tool based on Graph Attention Networks that can predict enhancer activities and gene expression, integrating a variety of features including TF motifs, 1D epigenomic datasets such as chromatin accessibility or histone modifications, with chromatin long-range loops detected by Hi-C-derived techniques. Machine learning has also been used to predict enhancer-promoter 3D contacts from RNA-seq and Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAseq) in samples for which obtaining chromatin structure data would be challenging, such as cancer patient samples  [52].
Chromatin networks as a step toward multilevel phenotypic regulation Networks are ideally suited to represent chromatin structure datasets produced by different experimental assays. In addition, nested networks of networks, in which nodes can represent entire networks, can be used to visualize the genome in an inherently hierarchical way, thus allowing the integration of disparate information about 3D contacts, without many constraints on the definition of what a network node should correspond to (a gene, a chromatin fragment, an entire TAD  [39] , and potentially an entire chromosome). This makes networks ideal for descriptions of multiscale and multilevel systems, such as the genome. Using simulations of polymer models, Brackley et al. were able to predict that spatial patterns in 3D interactions between promoters and regulatory regions lead naturally to the emergence of small-world gene-regulatory networks  [59] , which are networks in which the average distance between any two nodes is shorter than would be expected at random.
Conclusions The availability of detailed maps of the organization of chromatin inside the nucleus has undoubtedly revolutionized our understanding of the subtle connection between epigenomes and phenotypes. We are progressively discovering that projecting all genomic datasets on a 1D line, an imaginary straightened-out chromosome, was very useful but quite misleading, as it downplayed the role of the physical localization of genome regions in the space inside the nucleus. Just like specific projections of the world map distort the relative sizes between countries, forcing our understanding of chromatin structure into one dimension through traditional Hi-C heatmaps and linear browsers has affected our ability to comprehend some of the genome's organizational principles. Fortunately, old and new techniques that force us to preserve spatial relationships between genomic regions explicitly, albeit with their own set of limitations, have helped us to develop new, richer visualization and analysis frameworks.
Constraint-based and polymer physics-based models of chromatin structure are extremely useful to rescue the three-dimensional character of the epigenome, but the increasing number of applications of this framework suggests that network representations are a useful and complementary approach. To summarize some of the advantages of this formalism, we highlights the following points: chromatin networks are extremely efficient to build, starting from any kind of chromatin structure datasets, and integrate with additional 1D datasets or information; the flexibility in deciding what a node represents provides the potential to consider hierarchical structures, while mathematical multiplex frameworks simplify the task of integrating epigenome-level regulation with additional regulatory cellular processes.
We conduct the MUSICAv0 simulation for the year 2017 to compare with a previous WRF-Chem simulation  (Kumar et al. , 2022). MUSICAv0 and the WRF-Chem simulation and the observational data used in this study are described in Section 2. The MUSICAv0 model simulation results are evaluated against in situ observations and compared with satellite retrievals in Section 3. In Section 4, we provide an example application of MUSICAv0 over Africa -identifying key potential regions in Africa for future in situ observations and field campaign(s).
, 2020)  and the four-mode version of the Modal Aerosol Module (MAM4;  Liu et al. , 2016)  for the aerosol scheme. The MUSICAv0 users have the option to create their own model grid. MUSICAv0 is currently being developed and tested for applications over various regions globally (https://wiki. ucar. edu/display/MUSICA/Available+Grids), including North America, India, East Asia, South America, Australia, and Korea, among others. (e. g. ,  Schwantes et al. , 2022; Tang et al. , 2022; Jo et al. , 2023). In this study, we develop a model grid for applications in Africa (ne0np4. africa_v5. ne30x4). As shown in Figure  1a , the horizontal resolution is ~111 km ´ 111 km (i. e. , 1° latitude 1° equatorial longitude) globally, and ~28 km ´ 28 km (i. e. , 0. 25° latitude 0. 25° equatorial longitude) within the region over Africa.
Our simulation uses the default option for vertical layers (i. e. , 32 layers from the surface to ~3. 64 hPa). Here we run MUSICAv0 with the model grid for Africa for the year 2017, saving 3-hourly output. We use the Copernicus Atmosphere Monitoring Service Global Anthropogenic emissions, (CAMS-GLOB-ANTH) version 5. 1  (Soulie et al. , 2023)  for anthropogenic emissions and the Quick Fire Emissions Dataset (QFED) for fire emissions  (Darmenov and da Silva, 2013). Plume rise climatology is applied to fire emissions following  Tang et al. (2022). In addition, we also include open waste burning (https://www. acom. ucar. edu/Data/fire/;  Wiedinmyer et al. , 2014)  emissions in the simulation. The model has the option of a free-running atmosphere or nudging to external meteorological reanalysis.