dataesr/scientific-paragraphs-categorization huggingface dataset:
11   Stephan's Quintet (SQ, co-moving radial distance = 85 ± 6 Mpc, taken from the NASA/ IPAC Extragalactic Database (NED)  1  ) is unique among compact groups of galaxies  [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. Observations have previously shown that interactions between multiple members, including a high-speed intruder galaxy currently colliding into the intragroup medium, have probably generated tidal debris in the form of multiple gaseous and stellar filaments  6, 8, 13  , the formation of tidal dwarfs  7, 14, 15  and intragroup-medium starbursts  16  , as well as widespread intergalactic shocked gas  5, 10, 11, 17. The details and timing of the interactions and collisions remain poorly understood because of their multiple nature  18, 19. Here we report atomic hydrogen (H i) observations in the vicinity of SQ with a smoothed sensitivity of 1σ = 4.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-05206-x. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Data reduction and calibration For each sky pixel observed by a given beam, we reduced the spectral data following a similar procedure to that presented in ref. 34. The spectra of the two polarizations were reduced separately (and eventually combined after the consistency check). The data were grouped in ON-OFF cycles. Each ON (or OFF) has 300 samplings which were averaged and calibrated (that is, converted from digital counts to kelvin), resulting in a single raw spectrum. During the observations, a calibration signal of 10 K was injected for a duration of 20 s at the beginning of every ON-OFF cycle, and these data were used to calibrate the antenna temperature T a. The calibration error is on the order of 10%. Repeating this, we obtained a raw spectrum for every ON or OFF. As examples, the upper panel of Extended Data Fig.
The r. m. s. noise of each spectrum was measured in the two velocity intervals of 4,700-5,000 km s -1 and 7,000-7,500 km s -1 , where no H i signal was detected. Beam M16 stands out as the noisiest beam in the array, with a mean r. m. s. of 0. 26 mJy per beam and a standard deviation of 0. 14 mJy beam. The false colour map of the r. m. s. noise in the left panel of Extended Data Fig. 2  shows that indeed pixels in the top-right corner that were covered by beam M16 have higher noise than others. The histogram of the distribution of the r. m. s. noise at all sky pixels is shown in the right panel of Extended Data Fig. 2. The mean of the r. m. s. is 0. 16 mJy per beam with a standard deviation of 0. 05 mJy per beam. It is worth noting that strong radio frequency interference (RFI) in the frequency range of our observations was a serious issue in the early stages of this project.
Acknowledgements This work is supported by the  National Key R&D Programme of China  No. 2017YFA0402704  and  National Natural Science Foundation of China (NSFC)  No. 11873055  and sponsored (in part) by the  Chinese Academy of Sciences (CAS)  through a grant to the  CAS South America Center for Astronomy. C. K. X. acknowledges  NSFC  grant No. 11733006. C. C. acknowledges  NSFC  grant No. 11803044  and  12173045. N. -Y. T. is supported by the  National key R&D program of China  under grant no. 2018YFE0202900  and the  Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS. J. -S. H. acknowledges  NSFC  grant No. 11933003. U. L. acknowledges support from project  PID2020-114414GB-100 , financed by  MCIN/AEI/10. 13039/501100011033 ,from project  P20_00334  financed by the  Junta de Andalucia  and from  FEDER/Junta de Andalucía-Consejería de Transformaciòn Econòmica, Industria ,  Conocimiento y Universidades/Proyecto  A-FQM-510-UGR20.
F. R. acknowledges support from the  Knut and Alice Wallenberg Foundation. This work made use of data from  FAST , a Chinese national mega-science facility built and operated by the  National Astronomical Observatories, CAS. We thank  P. Jiang ,  L. Hou ,  C. Sun  and other  FAST  operation team members for supports in the observations and data reductions, and  H. -C. Feng  and  Y. Huang  for helping with the optical spectroscopic observation of NGC 7320a. Support of the staff from the Lijiang 2. 4 m telescope is acknowledged. Funding for the Lijiang 2. 4 m telescope has been provided by the  CAS  and the  People's Government of Yunnan Province. This research has made use of the NASA/IPAC Extragalactic Database, which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
Code availability The Python and IDL code for the data reduction pipeline is available at https://www. scidb. cn/s/jiIfee. 32. Jiang, P. et al. The fundamental performance of FAST with 19-beam receiver at L band. Res. Astron. Astrophys. 20, 64-92 (2020). 33. Zhu, M. et al. FAST discovery of a long HI accretion stream toward M106. Astrophys. J. Lett. 922, L21-L26 (2021). 34. Cheng, C. et al. The atomic gas of star-forming galaxies at z ∼ 0. 05 as revealed by the Fivehundred-meter Aperture Spherical Radio Telescope. Astron. Astrophys. 638, 14-21 (2020).
The platform records at a sampling rate of approximately 18 kHz electrode -1 when using the full 64 × 64 array. Recordings are filtered at 50 Hz high-pass filter using BrainWaveX software (3Brain) and stored in hdf5-based data format. Spikes were detected and sorted using Herdingspikes2 (https://github. com/mhhennig/HS2) as in  [16]. Briefly, spikes were first detected as threshold crossings individually on each channel and then merged into unique events based on spatial and temporal proximity. For each detected spike, a location was estimated based on the signal centre of mass. Spike sorting was performed by clustering all events using a feature vector consisting of the locations and the first two principal components of the largest waveform. Light stimuli were projected onto the retina as described elsewhere  [17].
Registering RGC activity wit immuno staining-Grik4/Scnn1a DREADD RGC identification A full protocol for the next steps with code and example data will be made publically available on our GitHub repository (https://github. com/GerritHilgen/DREADD_ RGCclass). Briefly, units that did show a significant change in spontaneous firing rate (sampled during at least 5 min) before and after adding CNO to the chamber were selected for Grik4/Scnn1a RGC identification and response clustering. Units with greater than 50% change in firing rate in CNO were considered as potential Grik4/Scnn1a candidates. We also used the burst index  [34]  to look at potential changes in activity levels induced by CNO, with potential Grik4/ Scnn1a cells having changes in their burst index exceeding 50%. This was followed by correlating the physical position of these identified RGCs with micrographs of DREADD-GFP-expressing cells in the GCL.
The chirp responses of the remaining Grik4 and Scnn1a stationary/non-stationary RGC units were used to establish response groups. We used a similar approach to calculate the SPIKE distance as described in our earlier work  [10]. Briefly, the spike distances were computed using the open-source package PySpike  [40]. The pairwise distances between two units were determined by computing the pairwise distances of all trials of the chirp stimulus. The resulting distance matrix was then clustered with a hierarchical clustering algorithm (Python, Scipy library dendrogram). Matlab and Python scripts and other extended material will be made available via GitHub. Ethics. This study used explanted tissues (retina) from transgenic mice. All experimental procedures were approved by the ethics committee at Newcastle University and carried out in accordance with the guidelines of the UK Home Office, under control of the Animals (Scientific Procedures) Act 1986.
RESULTS This section contains the main results of our work. It is divided into four sub-sections. We first present a minimalist model where we explicitly discuss the difference between V and V and the impact of its definitions on the ME solution. Then, we introduce a toy model of the metabolism and connect it with the dynamics of a chemostat considering a heterogeneous culture. The numerical data about the macroscopic quantities obtained with this model will allow us to finally expose how the understanding of the chemostat dynamics, and its influence over the cellular metabolism, can explain the data-driven findings of. 45 Additionally, the artificial data generated will be used to feed FBA and ME to evaluate the effects of heterogeneity and the metabolic spaces definitions over the inference.
The area outside the heatmap (shadow area) represents the unfeasible fluxes. As can be noticed, all experiments (triangular markers) are very close to the limit of the feasible space at the darkest region of the map. In this case, we say that the set of steady state parameters approaches the restrictive limit imposed by the chemostat's constraints (minimum V volume), similarly to the scenario described in panel A of Figure  2. The heatmap shows the results for one dataset, but the others displayed a similar behavior. Figure  6. Continued character triple where: ''m'' means minimization, ''M'' maximization, ''f'' that the flux was fixed to a given value, and ''0'' that no further action was required. The position of the character expresses the action over z, u g or u o respectively (Ex: ''M00'' means that the maximization of z lead to a single solution).
In this case, although the growth rate is constrained to match D, the formulations overestimate u g. Given glucose was the limiting nutrient affecting the growth rate of the network, a mirror effect between these two formulations is expected. On the other hand, the remaining two FBA formulations show major deviations from the experimental values. As shown in Figure  10 , the minimization of u g has the worst correlations for the reactions of the Krebs cycle and is not that good for the pentose phosphate pathway. In the case of the maximization of ATP, it has at the same time, the best performance for the Krebs cycle but the worst for the pentose phosphate pathway from all formulations. Furthermore, the FBA model that maximizes u g and ME inferred non-zero acetate production rates (see Figure  9 ) for all datasets, although the ME ill-prediction is less pronounced.
ACKNOWLEDGMENTS We are indebted to  A. de Martino  for useful discussions and with  A. Muntoni  for providing help with the implementation of the EP algorithm. The work was supported by the  Horizon 2020 Marie Skłodowska-Curie Action-Research and Innovation Staff Exchange (MSCA-RISE )  2016  grant agreement  734439  (INFERNET: New algorithms for inference and optimization from large-scale biological data). It was also partially funded by the  CITMA Project of the Republic of Cuba ,  PNCB-Statistical Mechanics of Metabolic Interactions - PN223LH010-015. The authors declare no conflict of interest.
Observation-based datasets: Due to potential uncertainties in the global observation-based estimates, we used an ensemble of six GPP datasets, four soil carbon (Csoil) and four vegetation carbon (Cveg) stock datasets. The data ensembles, spanning a wide range of sources and methods, represent the current state-of-the-art of global observation-based estimates of the carbon cycle components and, likely, cover the full range of observational uncertainties. As we use only a single ensemble estimate from each product, we assume that the differences and uncertainties are larger across the datasets than within a single dataset. The GPP ensemble includes Model Tree Ensembles (MTE) (Jung et al. , 2011) and its recent successors from FLUXCOM based on remote sensing and that with additional meteorological forcing  38  , solar-induced fluorescence (SIF) based GOSIF GPP  39  , light use efficiency based GPP from vegetation photosynthesis model (VPM)  40  , and lastly, an independent machine learning prediction from FluxSat 41 that uses corrected satellite-based reflectance.
All GPP products are either upscaled from or extensively evaluated against eddy covariance observations of carbon fluxes from FLUXNET (www. fluxdata. org) sites (Baldocchi et al. ,     2001). For example, the MTE, FLUXCOM, and FluxSat products use different combinations and corrections of training data and drivers from satellite remote sensing and meteorological dataset to upscale site-level observation to the global scale using different machine learning algorithms. VPM, a light use efficiency model, is similar to MODIS GPP but with an additional validation for FLUXNET sites, and GOSIF GPP uses a set of GPP-SIF relationships to produce an ensemble of high resolution global GPP fields that are independent of reflectance-based remote sensing products. While the spatial variability of the mean GPP is one of the most robust features when comparing different observation-based GPP products, it is desirable to use an ensemble as no single data is superior to all others universally  43.
For Csoil, we use the full depth soil organic carbon estimates including a data that combines inventories from Harmonized World Soil Database 44 with the Northern Circumpolar Soil Carbon Database 45 extrapolated to full depth  18  ; from SoilGrids that uses machine learning method to upscale soil profile measurements with land and climate characteristics as predictors  46  ; and from Sanderman et al. that corrects for land use and forest cover  47. Due to differences in number and locations of observed soil profiles used, predictors, and prediction methods, the soil carbon estimates are uncertain, especially in high latitude regions, with a large Csoil but a relatively fewer measurement profile. Nonetheless, the global distributions of Csoil in these products are robust  48  , and their differences reflect potential uncertainties across all observation-based global estimates.
Climatic regions based on aridity index: The aridity index, calculated as the ratio of mean precipitation and potential evapotranspiration (PET), is used to delineate different climatic regions based on relative availability of moisture and energy. For consistency, the CRU-NCEP precipitation, used to force the CRESCENDO model simulations, is also used for calculating the aridity index. The PET data is based on an estimate from the latest climate datasets at a high resolution  51. Different climatic regions were then delineated based on the aridity index ranges suggested by UNEP 52 as: arid (<0. 2), semi-arid, sub-humid, and humid (see Figure  A1 ). The average annual precipitation (temperature) varies from 245 mm/year (7. 6°C) in arid regions to 880. 3 mm/year (0. 9°C) in humid regions. These regions constitute 8.
Acknowledgments The CRESCENDO simulations and analysis for this study were carried out under the support of " Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO) " project of the  European Union 's  Horizon 2020 Framework Programme  (grant agreement no. 641816 ). SK acknowledges the additional support of the "Erdsystemforschung:  Afrikanische Grundwasserressourcen im Zuge des globalen Wandels" (Earth System Research: Groundwater Resources in Africa under Global Change)  project of the  Max Planck Society.
schemes were used in the ESM simulations for the sixth phase of coupled model intercomparison project (CMIP6). Following the TRENDY protocol 54  , the models were forced by CRUNCEP v7 55  forcing dataset (https://vesg. ipsl. upmc. fr/thredds/catalog/work/p529viov/cruncep/V8_1901_2016/catalog. ht ml), observed atmospheric CO2 concentration, nitrogen deposition, and annual land-use changes. The offline simulations forced with an observation-based dataset are suitable for evaluating the response of the land carbon cycle processes to the climate forcing, as they are less prone to biases and uncertainties arising from differences in carbon-climate feedbacks in the coupled simulations of ESM.
The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the radiative transfer model PAMTRA as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; doing so, it leverages the spatial consistency of the measurements to mitigate the problem's ill-posedness. The method was implemented on X-and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura in January 2021. An in-depth assessment of the retrieval's accuracy was performed through comparisons with colocated aircraft in-situ measurements collected during 3 precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets.
This, combined with the complexity of our forward model (i. e. PAMTRA), has led us to explore a different direction. Machine learning techniques offer the possibility to tackle inverse problems in a different way, i. e. with a statistical rather than an analytical approach. Note that, as pointed out by  Geer (2021) , the overarching framework in both cases ultimately remains that of Bayesian probabilities, viewed through different prisms. The typical machine learning route to solve an inverse problem (e. g. Chase et al. , 2021)  has the following structure: the available forward model is first used to create a large synthetic dataset {(x k s , y k s = f (x k s )), k = 1. N }; the s subscript denoting synthetic values and N the size of the dataset. Then, a machine learning model is trained on this dataset to learn a statistical relation between y and x, i.
e. an approximation of the inverse mapping g. Ultimately, this produces a gate-to-gate inversion of the problem which can be implemented on real data. One major limitation of this "direct" method is when the problem itself is ill-posed, i. e. when several values of x may yield similar outputs y: in such cases, the retrieval may yield arbitrary outputs. The proposed approach, illustrated in Fig. 1 , can mitigate this issue. -In a first step, a neural network is trained on a synthetic dataset of (x s , y s = f (x s )). should be close to the true profile of microphysical descriptors X r. The architecture of the decoder and encoder will be detailed further on (Sect. 4), but one key property should already be underlined. The encoder network contains convolution kernels: thanks to this feature, it can capture the spatial structure of the Doppler spectrograms, e.
Forward model assumptions To generate this synthetic dataset, PAMTRA is run by prescribing snowfall microphysics through several parameters. These parameters are the snowfall properties that the algorithm will then learn to retrieve. The definitions of the microphysical parameters are summarized in Table  1. The PSD is assumed to be a negative exponential (N (D) = N 0 exp(-D/D 0 ), e. g. Straka, 2009) , whose size parameter D 0 is prescribed; here and further, the size of a particle is defined as its maximum dimension: D = D max. For an exponential PSD, D 0 is equal to the mean diameter; the effective diameter, often relevant for radiative transfer models, is D ef f = 3D 0. Mass-size and area-size relations are considered to be power laws, whose prefactors and exponents are prescribed (m = a m D b m , A = α a D βa ).
Forward model inputs When generating this training set, a trade-off has to be defined: if the dataset is too narrow, i. e. if it doesn't cover a large enough range of values and combinations for the microphysical descriptors, this will cause a bias in the retrieval; conversely, if the range of values is much too large, this will hinder the training process, for it will include non-realistic values. It was therefore chosen to parameterize PAMTRA by sampling the microphysical properties using a large observational dataset collected using the Multi-Angle Snowflake Camera during 10 field deployments. This data was organized into a database in  Grazioli et al. (2022). We follow the method presented in this study  (Grazioli et al. , 2022, Sect. "Technical Validation")  to derive from the database the microphysical parameters required in the forward model (Table  1 ).
Four categories of particles are used: aggregates, planar crystals, columnar crystals and graupel. For each type of particle and for each parameter, a distribution is fitted to the empirical histogram calculated from the database. We refer to Appendix B1 for more detail. When generating the training set, parameters are then randomly sampled from those distributions. It is worth highlighting that all parameters are sampled independently, with the exception of a m and α a. Indeed, as pointed out in  Grazioli et al. (2022) , a strong correlation exists between a and b, and between α a and β a ; thus, empirical fits are used from which a m (resp. α a ) is sampled for a given b m (resp. β a ), with the addition of randomness using the mean squared error of the fit. The definition of aspect ratio A r is slightly different between the MASC dataset and the SSRGA parameterization: in the former, it is equal to the ratio of minor axis length to major axis length  (Garrett et al.
Generation of the training set Each item of the dataset is generated through the following procedure. 1. A particle type is randomly sampled among the four aforementioned types. Given the large variety that exists within the aggregate category, it is given more weight in the sampling procedure (aggregates: 40% -planar crystals: 20% -graupel: 20% -columnar crystals: 20 %). 2. Microphysical descriptors are randomly sampled using the MASC-based distributions. 4. Then, turbulent broadening and spectrum shift due to radial wind are added, with randomly sampled values. While these could be computed directly in PAMTRA, it was more computationally efficient to implement them in post-processing in a vectorized way. The radial wind parameter includes the wind shift that could be caused e. g. by vertical wind, beam misalignment, etc.
The broadening parameter is the size of the Gaussian broadening kernel and includes the effect turbulent eddies but in reality also accounts for all other possible broadening causes (e. g. horizontal wind, beam width). It is computed by sampling a value of atmospheric turbulence, represented by the eddy dissipation rate (sampled as a negative exponential with 10 -3 m 2 s -3 mean, consistent with some literature standards, e. g. Sharman et al. (2014) ). The resulting broadening is derived following equations of  Shupe et al. (2012)  using for the radar's beam width those of the W-and X-band radars used in the study, described in Section 3. 2. 5. Finally, spectra are reduced to 256 points through bin averaging, for computational reasons. Ultimately, each item of the synthetic dataset contains an input vector with 13 dimensions (see table  1 ) and the corresponding simulated Doppler spectra (X-and W-band) with each 256 bins.