dataesr/scientific-paragraphs-categorization huggingface dataset:
The step of training the neural networks and using them in the fitting routine makes the exploration of the large, multi-dimensional parameter space of solutions computationally possible. This approach is particularly potent for the case of Bennu as both day-side and night-side data are available with a wide spectral wavelength range  15, 17. The 70% of model radiances is used for training via stochastic gradient descent and a neural network architecture with 1 hidden layer of 10 neurons, which is the optimal scheme  17. Another 15% of the dataset is used to protect the networks against overfitting the training data. We use the last 15% of the dataset to assess the networks' performance on unseen data in terms of mean squared error between the predicted and target radiances. The networks generalise well the prediction of the model radiances at testing: the average errors are equal to 0.
43 on PolyCam images at a spatial resolution of 5 cm pixel -1 (thus, >l s ). The area of each rock is computed as that of a circle with diameter equal to the rock's longest dimension. One minus the sum of rocks' areas divided by the area of the OTES spot is provided as % of unresolved material in Extended Data Figure  3 , along with the value of α. We also check that the size distributions of the mapped rocks are consistent with that globally mapped on Bennu  34  , meaning that the two sites are representative of average Bennu. We use the two-sided Spearman test to reject the null hypothesis that a random distribution of Γ R -and α-values could produce the observed correlation of Figure  1  (Extended Data Figure  4 ). To take into account uncertainties in the values of Γ R and α, we perform the Spearman test 10,000 times, where at each trial we vary Γ R and α within their uncertainties.
We repeat the thermophysical modelling of all OTES spots for a low-end value of φ=15%, which is an estimate for the whole asteroid based on a boulder size-frequency distribution analysis  44  , and a high-end value of φ=60%, which is a compromise reduction from much higher values used in previous studies (e. g. φ=80%, ref. 45, which we consider unlikely for a polydisperse size-frequency-distribution). We perform a 3σ test on the solutions to identify those areas where (Γ P , Γ R , α) for φ=15% and φ=60% are statistically distinct from those for φ=40%. This test is done considering only those spots where a converged solution is found for both macroporosities: 93 spots (φ=40% versus φ=15%) and 90 spots (φ=40% versus φ=60%). We repeat the Spearman test to assess the robustness of the correlation against removing the areas with statistically distinct solutions from the dataset.
049-0. 074). These boulder properties could mimic the Γ R -α correlation of Figure  1  if α was also negatively correlated with the area of the largest boulder in the OTES spot. Using the boulder database of ref. 46 we plot the α-value as a function of the size of the largest boulder and perform the Spearman test to investigate whether these quantities are correlated (results are in Extended Data Figure  7 ).
and 92% of the cases, respectively. The correlations are robust against removing the areas whose solutions are statistically distinct from the dataset with macroporosity φ=40% (Spearman correlation index: 0. 55±0. 07 and p<0. 05 in 100% of 10,000 trials). The error bars correspond to 1 standard deviation (Supplementary Table  1 ; Methods) computed on ~450 and ~880 samples on average. The results for a regolith macroporosity of φ=40% are described in the main text (Figure  1 ).
Acknowledgments This material is based upon work supported by  NASA  under contract  NNM10AA11C  issued through the  New Frontiers Program. We are grateful to the entire OSIRIS-REx team for making the encounter with Bennu possible, to  C. Wolner  and  F. Murphy  for editorial help, and to the  OPAL infrastructure of the Observatoire de la Côte d'Azur (CRIMSON)  for providing computational resources and support. S. C. thanks the  University of Arizona  for supporting this study. M. D. , C. A. , J. D. P. D and A. B. acknowledge the  French space agency CNES. C. A. and M. D. acknowledge support from  ANR  " ORIGINS " ( ANR-18-CE31-0014 ). C. A. was supported by the  French National Research Agency  under the project " Investissements d'Avenir "  UCAJEDI ANR-15-IDEX-01. G. P. and J. R. B. were supported by  Italian Space Agency  grant agreement  INAF/ASI  n.
Data Availability Raw through calibrated OTES  52  and OCAMS  53  data are available via the Planetary Data System at https://sbn. psi. edu/pds/resource/orex/. The SPC/OLA v34 shape model is available via the Small Body Mapping Tool at http://sbmt. jhuapl. edu/. The IDs of the OTES observations used in this study and the best-fit solutions for the thermophysical model are in Supplementary Table 1. The boulder size, location and reflectance used to test the robustness of the results are in refs. 34 and 46. Code Availability The thermophysical analysis reported here uses a custom code based on the Thermophysical Model of ref. 16, available at www. oca. eu/images/LAGRANGE/pages_perso/delbo/thermops. tar. gz. The code to compute the geometry of the OTES acquisitions and boresight is available in Zenodo with the identifier 10.
5281/zenodo. 4781752 (ref. 54). The code to compute the values of Γ c for the thermophysical analysis is available in Zenodo with the identifier 10. 5281/zenodo. 4763783 (ref. 55). The rock mapping of Extended Data Figure 3 was performed using the SAOImageDS9 software available at cfa. harvard. edu/saoimageds9. Other codes that support the findings of this study are available in Zenodo with the identifier 10. 5281/zenodo. 4771035 (ref. 56).
Analysis of mRNA expression levels HepaRG cells were differentiated in 12-well plates and treated with different concentrations of the test compounds or solvent control (0. 5% DMSO) for 24 h. Cells were washed twice with ice-cold PBS and lysed with 350 µl RLT buffer (RNeasy Mini Kit, Qiagen, Hilden Germany). Total RNA was extracted according to the manufacturer protocol. For first-strand cDNA synthesis, 1 μg of total RNA was reverse-transcribed into cDNA in a total volume of 20 μl, using the PrimeScript RT reagent Kit Perfect Real Time for RT-PCR (Takara Bio, Europe) with oligo-dT primers and random hexamers for the reaction according to the manufacturer's instructions. Specific primers were designed using the Primer-BLAST tool (NIH) for 63 genes linked to liver steatosis, nuclear receptor activation and hepatotoxicity.
All genes were amplified by real-time PCR in the Step One Plus detection system with StepOnePlus Software v2. 3 (Thermo Fisher Scientific, Waltham, USA) using SYBR green as the detection dye. Each amplification reaction was carried out in a total volume of 20 μl containing 10 μl SYBR Select Master Mix (Thermo Fisher Scientific), 0. 3 μM of each primer and 0. 002 μg cDNA. The reactions were cycled 40 times using the following parameters: 95 °C for 3 s and 60 °C for 30 s during which the fluorescence data were collected. Melting curves were generated to verify the identity of the products. A non-template control was run with every set of primers and no indication of PCR contamination was observed. Lack of PCR products from the non-reverse transcribed RNA control indicated that contamination by genomic DNA did not serve as amplification template.
54 µm). Identification of neutral lipid spot was done by tracking Nile Red green emission with an XF100-485-20 filter set. Identification of the nuclei was done by tracking DAPI with an XF100-386-23 filter set and used to focus the instrument. The Spot Detector V3 Bioapplication analysis algorithm (software V. 6. 5) was used to identify nuclei upon fluorescent size and intensity. The nuclear mask was dilated in order to define the cytoplasmic region. Two parameters were measured at the cell level as follows: nuclei (defined as area as well as total and average intensity for each cell) and neutral lipid spots (defined as spot number, spot intensity and spot area as well as total fluorescence of spot intensity within each cell). To quantitatively assess the data obtained after the image analysis, a workflow was built in Statistica v13.
Analysis of mixture effects and compound potencies Concentration-response modeling and RPF analysis were performed as previously described  (Lichtenstein et al. 2020). Data were loaded into PROAST (https:// www. rivm. nl/ en/ proast, RIVM, Bilthoven, The Netherlands). Concentration-response data were statistically analyzed by fitting with an exponential four-parameter model (1): where y denotes the response, x denotes the concentration, a reflects the response at concentration zero, b relates to the potency of the tested chemicals, c reflects the maximum response and d reflects the steepness of the curve. Based on the obtained fits, RPFs were calculated for a benchmark response of 50% (BMR 50 ). Mixture compositions were thereafter determined based on the estimated RPFs to design equipotent mixtures of the test compounds.
For mixture effect analysis, the concentration-response data for the mixture and the single compounds were compared using the same approach. First, the single compounds were analyzed for calculation of the RPF. The curve fit results were expressed visually as described above. If the data points of the mixture fit with the curve derived from the single compounds, dose addition can be assumed. In cases of synergism or antagonism, the concentration-response data of the mixture will not fit with the response of the single substances and shift either to the left or to the right. Additionally, the ratio of overlap was calculated to provide a quantitative evaluation. The ratio of overlap is used to characterize the degree of dose addition or the degree of deviation from dose addition and describes numerically what can be seen graphically in the PROAST plots.
Statistical analysis All statistical analysis were performed with GraphPad Prism v. 8 (GraphPad Software, La Jolla, USA). Statistical analysis for cell viability, nuclear receptor and triglyceride accumulation endpoints was performed by doing one-way ANOVA followed by Dunnett's test (*p < 0. 05; **p < 0. 01; ***p < 0. 001). Statistical analysis for gene regulation endpoint was performed by doing the non-parametric Kruskal-Wallis test followed by Dunn's test (*p < 0. 05; **p < 0. 01; ***p < 0. 001).
Nuclear receptor activation All mixtures were tested in the reporter gene assays for NRs, which showed the strongest activation in the screening assay, i. e. PPARα and PPARγ. Additionally, GR and RXRα were tested. Please note that only data from nuclear receptors showing unidirectional activation by at least two compounds could be used for subsequent modeling, due to limitations of the chosen BMD-based RPF approach. Therefore, only the mixtures of PHX and PHP were analyzed in that way. Raw data for the combinations including CTD can be found in Figs. S18-S21. Overall, mixtures with CTD (i. e. PHP + CTD, PHX + CTD and PHP + PHX + CTD) showed a concentration-dependent activation of GR, RXRα, PPARα, and PPARγ. Regarding PHX + PHP, data also showed a concentration-dependent activation for all tested receptors.
AP-1 was shown to mediate the release of inflammatory mediators such as IL-8  (Qiao et al. 2016; Wang et al. 2013). Additionally, some inter-connection with NFκB has been documented  (Fujioka et al. 2004 ). Therefore, inflammation-related processes might be triggered in parallel to the triglyceride accumulation and counteract possible XME induction by the activated NRs. The assessment of mixture effects in the case of dissimilarly acting component mixtures (i. e. CTD with PHX or PHP PROAST software and data are presented as mean ± SEM. The curves represent the four-parameter exponential model; see Eq. 1. The concentration-response data of the mixture (green diamonds in binary mixtures; blue inverse pyramids in ternary mixtures) indicate no deviation from the overall concentration-response fit.
Moreover, it is not always easy to define Note that concentration levels were modified due to observed cytotoxicity with the concentrations indicated in Table  1  (inter-laboratory variability). Concentration-response modeling was performed using PROAST software and data are presented as means ± SD. The curves represent the four-parameter exponential model; see Eq. 1. The concentration-response data of the mixture (green diamonds in binary mixtures; blue inverse pyramids in ternary mixtures) indicate no deviation from the overall concentration-response fit. Thus, dose addition can be assumed (colour figure online) similarity or dissimilarity of the MoA in case of complex biological processes such as the steatosis AOP and partially overlapping molecular targets of many test compounds. It should also be noted that dose addition may apply at the common endpoint level (i.
Acknowledgements The research leading to these results received funding from the European Union´s Horizon 2020 research and innovation program under Grant Agreement 633172 (EuroMix; European Test and Risk Assessment Strategies for Mixtures). This publication reflects only the author's views, and the Community is not liable for any use made of the information contained therein. We would like to thank Beatrice Rosskopp and Regina Al-Hamwi for their excellent technical support.
Moreover, comprehensive DMS datasets comprising protein single and double mutants were used for detailed analysis of intramolecular interactions, based on which the three-dimensional protein structure could be inferred  (6). Very recently, DMS was also used to analyze the impact of mutations in the SARS-CoV-2 receptor binding domain on the binding of ACE2, a central host factor required for virus entrance  (7). Overall, these studies showcase the power of mutational scanning for protein functional dissection and structure-mechanistic analysis. Anti-CRISPR (Acr) proteins are natural inhibitors of CRISPR-Cas systems  (8, 9)  and play an important role in the co-evolution of phages and their bacterial hosts  (10). Hundreds of experimentally validated anti-CRISPR proteins have been described and many thousands have been additionally predicted  (11).
By applying our pipeline to AcrIIA4  (19)  and AcrIIA5  (14) , both targeting Streptococcus pyogenes (Spy)Cas9, we created comprehensive datasets on the impact of individual amino acid exchanges on CRISPR-Cas9 inhibition as well as the overall Acr mutational tolerance. We then aligned our DMS data to known sequence and structural features, thereby further characterizing the observed mutational tolerance. Finally, we selected several mutations that improve, preserve or modulate Acr function and cross-validated their activity in mammalian cell gene editing assays as well as in vitro affinity measurements. Our work establishes mutational scanning as a powerful strategy for Acr characterization and engineering.
Acr mutant activity regression As a proxy for Acr activity, linear regression of the binned distribution and mean of log fluorescence intensities was performed for a set of single Acr mutants (benchmarks). To this end, log fluorescence intensity data from flow cytometry for the benchmark mutant set were binned (bin width 0. 5, range from 0 to 12) and bins normalized to sum to one. An affine regression model relating the read distribution across sorting fractions to the distribution of log fluorescence intensity was zero initialized and fitted using gradient descent to minimize squared error under L² regularization. Model fits were evaluated using mean square error calculated via leave-one-out cross-validation on the set of benchmark mutants. Log fluorescence intensity distributions were predicted for all single Acr mutants.
Affinity measurements Biolayer interferometry binding data were collected with a Gator Bioanalysis System and processed using the instrument's integrated software. For the binding assay, His tagged designs were loaded onto anti-NTA biosensors (Gator probes) at 5 µg mL -1 in binding buffer (PBS pH 7. 4) for 120 s. Homemade CRISPR-Cas9 RNP was diluted from 200 nM to 6. 25 nM in binding buffer. After baseline measurement in the binding buffer alone, the binding kinetics were monitored by dipping the biosensors in wells containing the Cas9 RNP at the indicated concentration (association step) and then dipping the sensors back into baseline/buffer (dissociation). First, to implement a robust gene circuit for Acr selection, we created a three-plasmid system encoding (i) a (mutant) Acr protein, (ii) a catalytically impaired SpyCas9 (dSpyCas9) and sgRNA, and (iii) a red fluorescent protein (RFP) reporter (Figure  1A , right).
We observed distinct fluorescence distributions in particular for FACS fractions 1-5, which corresponded to the low to mediumhigh fluorescence fractions (Figure  1D  and Supplementary Figure  S1C ). This suggested that each fraction was enriched for Acrs with distinct inhibition potency. We then sequenced the Acr mutant pools in all individual fractions on the Illumina MiSeq platform. To analyze general mutant coverage, we first summed up all NGS reads in all fractions and calculated the proportion of reads corresponding to any given mutant (Figure  1E  and Supplementary Figure  S2 ). Overall, 93% of all possible AcrIIA4 and -5 mutants could be robustly detected within the library (>10 reads), and the majority of mutants were covered by more than 100 reads. Next, to determine the inhibition potency for each Acr mutant in the library, we calculated the frequency of NGS reads corresponding to each Acr mutant in each fraction (Supplementary data 2 and 3).
We then fitted an affine regression model to the obtained NGS read distributions (Figure  1F ). The model comprised a fully parameterized affine transformation and was trained on NGS read frequencies and corresponding FACS fluorescence profiles of 16 individually measured benchmarks mutants for AcrIIA4 (Figure  1C , right) and 12 benchmark mutants for AcrIIA5 (Supplementary Figure  S3 , see Material & Methods for details). We then used the trained model to predict RFP fluorescence profiles corresponding to each Acr mutant within our E. coli library from the individual NGS read profiles (Figure  1G ; see Supplementary data 4 and 5 for the predicted fluorescence profile for every single AcrIIA4 and -5 mutant, respectively). Leave-one-out cross-validation on the benchmark mutant dataset (Figure  1C , right) revealed that our model could accurately infer the reporter fluorescence distribution, and hence the activity of the Acr variant (Figure  1H ).
From the predicted fluorescence distribution, we finally calculated the log fluorescence intensity. This value corresponds to the model-predicted ability of the Acr variant to prevent CRISPRi as a measure for the Acr inhibition potency. To cross-validate our analysis, we compared the obtained Acr inhibition potencies (log mean fluorescence values) with two previously reported, smaller AcrIIA4 mutant datasets  (50, 51)  (Supplementary Figure  S4 ). Despite the fact that the readouts underlying these studies were profoundly different from our DMS readout, namely measurement of SpyCas9 gene drive activity in Saccharomyces cerevisiae in case of Basgall et al. (51)  and in vitro DNA cleavage in case of the Dong et al. study  (50) , our DMS data correlated with both literature datasets reasonably well (R = -0.
82 and 0. 79 for the Basgall et al and Dong et al datasets, respectively; Supplementary Figure  S4 ). On top of predicting the Acr activity from the NGS profiles, we also estimated the confidence in each data point. To this end, we calculated the relative entropy, i. e. the distance of the NGS read count distribution over the different fractions from a uniform distribution, for each Acr mutant. Hence, for each mutant in our library we obtained two values, namely its predicted activity as well as the confidence in this prediction.
This underlines the importance of the disordered protein region for AcrIIA5 inhibitory function. Moreover, the AcrIIA5 dataset contained various mutants that showed an activity higher than the wild-type Acr (Supplementary Figure  S5A ). This suggests that unlike AcrIIA4, which naturally prevents Cas9 DNA binding very effectively, the ability of AcrIIA5 to inhibit Cas9 DNA binding is naturally rather weak but might be enhanced by mutagenesis. Taken together, our DMS analysis of AcrIIA4 and -5 revealed that both CRISPR inhibitors show a considerable mutational robustness.
Our data shows that regions in AcrIIA4 and-5 considered important for CRISPR-Cas inhibition tolerate at least a subset of possible substitutions, provided the remainder of the Acr remains intact. In the future, it will be possible to extend our DMS pipeline to other CRISPR-Cas orthologues and Acr types and families. While we employed a gene circuit based on CRISPRi as readout, since it could be easily implemented in case of SpyCas9 and provided a decent dynamic range of measurement under our experimental conditions, it should also be possible to set up selection circuits based on DNA or RNA target cleavage or even Cas effector binding. Taken together, our study establishes Acr mutational fitness landscapes created via deep mutational scanning as powerful resource to study the Acr evolutionary design space and inform the engineering of Acrs that are optimized for selected applications in CRISPR genome engineering.
ACKNOWLEDGEMENTS We thank members of the  Niopek and Correia labs at TU Darmstadt and EPFL Lausanne , as well as  Max Waldhauer  and  Stefan Holderbach ,  Heidelberg University , for helpful discussions. We would like to thank the  Protein Production and Structure Core facility at EPFL  for their support on the protein biophysical characterization experiments, the ZMBH flow cytometry core facility ( Heidelberg University ) for help with bacterial cell sorting and the  EMBL Genomics Core Facility  ( EMBL Heidelberg ) for performing amplicon deep sequencing.
DATA AVAILABILITY Plasmid sequences (Genbank files) are provided as Supplementary data 1. NGS read distributions for AcrIIA4 and -5 are available as Supplementary data 2 and 3, respectively. Predicted flow cytometry plots for each single AcrIIA4 and -5 mutant are available as Supplementary data 4 and 5, respectively. Code is available on GitHub (https://github. com/mjendrusch/acr-dms) and the entire datasets are accessible on Zenodo (https://dx. doi. org/10. 5281/zenodo. 5221560). Data will also be made available from the corresponding author upon reasonable request.
0 database  (Laske et al. , 2013). For a given source model, the surface displacements at each receiver are computed by summing the contribution of each sub-fault. We search for the spatial distribution of slip amplitude and rake angle independently for each time step. For the slip amplitude, we limit the search to values between 0 and 1 m, thus ensuring the positivity of slip. For the rake angle, we only explore values that are plus or minus 15 • from the rake angle of the mainshock given by the GCMT catalog (i. e. , 109 • ). Both parameters are drawn from a uniform prior. The spatial distribution of the source parameters is obtained using an optimization procedure similar to that of  Pianatesi et al. (2007). We use a heat-bath simulated annealing algorithm, an evolution of the Metropolis-Hastings that lowers the rejection rate by computing the relative probabilities from a The mean and a linear trend are calculated using the 6 d prior to the mainshock origin time (red dashed line) and are removed from the time series.