dataesr/scientific-paragraphs-categorization huggingface dataset:
6 and ∼1. 7 R ⊕ , respectively, making them two super-Earths with mean densities of ∼6 g cm -3. We searched the TESS light curves for possible transits of the two outer companions with the DST code (Section 2. 1) masking out the transits of the UPS planet, but we found no other significant transit signals. Under the assumption that the orbits of the three planets are coplanar, the impact parameters of planets c and d would be b c ≈ 6 and b d ≈ 13, respectively. This would account for the nondetection of the transit signals of GJ 367 c and GJ 367 d in the TESS light curves. In this scenario, GJ 367 c and d would transit their host star only if their radii were unphysically large, i. e. , R c > 1. 9 R e and R d > 5 R e. The other configuration for the outer planets to transit is if they are mutually inclined with planet b (i c ≠ i b ; i d ≠ i b ).
Acknowledgments We thank the anonymous referee for a thoughtful review of our paper and very positive feedback. This work was supported by the  KESPRINT collaboration , an international consortium devoted to the characterization and research of exoplanets discovered with space-based missions (www. kesprint. science). Based on observations made with the ESO-3. 6 m telescope at  La Silla Observatory  under programs 1102. C-0923  and  106. 21TJ. 001. We are extremely grateful to the  ESO  staff members for their unique and superb support during the observations. We acknowledge the use of public TESS data from pipelines at the  TESS Science Office  and at the  TESS Science Processing Operations Center. TESS data presented in this paper were obtained from the  Milkulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute.
The specific observations analyzed can be accessed via doi:10. 17909/rkwv-t847. Resources supporting this work were provided by the  NASA High-End Computing (HEC)   Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center  for the production of the SPOC data products. E. G. acknowledges the generous support from  Deutsche Forschungsgemeinschaft (DFG)  of the grant  HA3279/14-1. D. G. gratefully acknowledges financial support from the  Cassa di Risparmio di Torino (CRT)  foundation under Grant No. 2018. 2323 " Gaseous or rocky? Unveiling the nature of small worlds ". Y. A. and J. A. E. acknowledge the support of the  Swiss National Fund  under grant  200020_192038. C. M. P. gratefully acknowledges the support of the  Swedish National Space Agency  ( DNR 65/19 ).
Westermayr et al. [3]  showed that it would just cost 24 s to simulate 100 fs surface hopping dynamics for the same CH2NH2 + through SchNarc (an approach combining SchNet and SHARC) using one CPU (2x Intel Xeon E5-2650 v3 CPU). Lopez et al. [62]  have performed a 10 ns simulation for trans-hexafluoro-2-butene in 2 days, using the machine learning ab initio molecular dynamic package PyRAI2MD. Akimov  [63]  has recently applied ML to propagate nanostructures dynamics for 20 ps. Table  1  surveys several of such works. It is, of course, a dated and incomplete picture of the field; however, it may help debutant researchers find out the type of method that is the most suited for their application. While most applications of ML-assisted NAMD have been made with TSH, significant progress has also been made in ML-assisted system-bath quantum dynamics.
Case studies This section demonstrates how ML accelerates two different types of excited-state dynamics: full quantum dynamics and trajectory surface hopping. Before that, we also show how to perform full quantum dynamics with the traditional HEOM approach for a better comparison. The full quantum dynamics is demonstrated on a standard example of the two-state spin-boson Hamiltonian, which is a general representation for many chemical systems. TSH is demonstrated on a specific chemical system -fulvene. This molecule is an excellent test system for surface hopping thanks to its nontrivial PES topography, with recurrences between S1 and S0 at an extended crossing seam with peaked and sloped conical intersections  [108] [109]. Instructions and materials needed to perform these case studies are provided at https://github.
Case study 3: Trajectory-surface hopping with machine learning In this case study, we demonstrate in a tutorial way step-by-step how to perform a TSH NAMD simulation using trained machine learning models. The fulvene molecule will be used as a prototype of a photoactive system. This molecule undergoes significant geometry deformation (C-CH2 bond stretching and CH2 rotation) during the internal conversion process from the S1 to S0 state  [93, 114]. Two independent program packages will be necessary to follow the tutorial and run the simulations: (i) the MLatom program  [111] [112] [113]  will be used to train the machine learning model, and (ii) the Newton-X software  [115] [116]  interfaced with MLatom will be responsible for propagating the dynamics. Instructions and materials needed to perform this case study are provided at https://github.
Create the training setThis is a crucial step to guarantee good accuracy for the machine learning model. It is, however, rather involved, as we discussed in the Methods section. We provide the training set by selecting points from a set of NAMD trajectories already computed at the quantum mechanical level. It can be downloaded from https://figshare. com/articles/dataset/Fulvene_DC-FSSH/14446998/1, but it is not necessary for this study. This data set contains 200 TSH CASSCF trajectories, run at the SA-2-CAS(6,6)/6-31G level with a time step of 0. 1 fs up to a maximum time of 60 fs. More than 120000 data points are available in this fulvene dataset with information of molecular geometries, gradients, the potential energy of the S0 and S1 states, and the nonadiabatic coupling vectors between the two energy surfaces.
) with  7 as the Boltzmann constant and T as temperature. The total number of generated trajectories is 1000, propagated up to the maximum run-time of a trajectory  T = 20 (a. u. ) or ≈ 0. 48 fs using timestep dt = 0. 05 (a. u. ). The data and script for running dynamics with HEOM are available at https://github. com/maxjr82/MLinQCbook16-NAMD. Here we have considered both symmetric ( = 0) and asymmetric ( ≠ 0) cases of our two-state system.
Acknowledgements POD acknowledges funding by the  National Natural Science Foundation of China  (No. 22003051 ), the  Fundamental Research Funds for the Central Universities  (No. 20720210092 ), and via the  Lab  project of the  State Key Laboratory of Physical Chemistry of Solid Surfaces. MB and MPJ were financially supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  832237  (project  SubNano ).
1 : 1 Create the training setThis is a crucial step to guarantee good accuracy for the machine learning model. It is, however, rather involved, as we discussed in the Methods section. We provide the training set by selecting points from a set of NAMD trajectories already computed at the quantum mechanical level. It can be downloaded from https://figshare. com/articles/dataset/Fulvene_DC-FSSH/14446998/1, but it is not necessary for this study. This data set contains 200 TSH CASSCF trajectories, run at the SA-2-CAS(6,6)/6-31G level with a time step of 0. 1 fs up to a maximum time of 60 fs. More than 120000 data points are available in this fulvene dataset with information of molecular geometries, gradients, the potential energy of the S0 and S1 states, and the nonadiabatic coupling vectors between the two energy surfaces.
) with  7 as the Boltzmann constant and T as temperature. The total number of generated trajectories is 1000, propagated up to the maximum run-time of a trajectory  T = 20 (a. u. ) or ≈ 0. 48 fs using timestep dt = 0. 05 (a. u. ). The data and script for running dynamics with HEOM are available at https://github. com/maxjr82/MLinQCbook16-NAMD. Here we have considered both symmetric ( = 0) and asymmetric ( ≠ 0) cases of our two-state system. 1, 0. 2, 0. 3, 0. 4, 0. 5, 0. 6, 0. 7, 0. 8, 0. 9, 1. 0}, 𝜔 1 ∈ {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, and 𝛽 ∈ {0. 1, 0. 25, 0. 5, 0. 75, 1}, here 𝛽 = 1/(𝑘 7.
INTRODUCTION The dialog of cells in a tissue through the secretion of ligands and sensing by receptors plays an essential role in development, homeostasis, and diseases  (1). The advent of single-cell omics has led to remarkable progresses in the analysis of the cell composition and ligand-receptor networks within a tissue  (2) (3) (4) (5). Nevertheless, these technologies remain expensive and single-cell data on cohorts are limited compared with bulk datasets, particularly for patient cohorts associated with clinical data. Moreover, bulk techniques are more sensitive to low-abundant molecules. Therefore, tools to untangle cellular networks from bulk data are needed as a complementary solution to single-cell studies.
Nucleic Acids Research, 2023 Here, we describe BulkSignalR, a R package that builds on our previous work on bulk  (6)  and single-cell data  (7). BulkSignalR exploits reference databases of known ligandreceptor interactions (LRIs), gene or protein interactions, and biological pathways to assess the significance of correlation patterns between a ligand, its putative receptor, and targets in a downstream pathway. This integrated modeling provides the increased specificity that is required by the convoluted bulk format. It also allows generating gene signatures to report the LRI activity and their downstream consequences, which may facilitate sample comparisons. As BulkSignalR uses correlation patterns to determine the statistical significance of LRIs, datasets can be analyzed without any prior knowledge of sample groups or clusters.
Despite the popularity of LRI inference in the single-cell bioinformatics community with many existing tools  (8) , its equivalent in bulk data has not attracted much attention. A seminal paper explored LRIs in non-small-cell lung cancer (NSCLC) (9) using an empirical algorithm, called CCCExplorer, that required separate bulk transcriptome datasets for different cell populations. Exploiting dual mouse and human microarrays, i. e. dual transcriptomes, Komurov  (10)  proposed an algorithm to infer interactions between cancer and stromal cells using bulk data from a lung adenocarcinoma mouse xenografts model. Among the studies on LRI inference from single-cell data, two tools indicate in their documentation that they may be applied to bulk data. CellPhoneDB  (11)  mentions the possibility to process bulk data provided they were obtained from pure cell populations, and ICELLNET  (12)  is able to exploit two separated bulk datasets to predict interactions.
Due to the need for separated bulk datasets, CCCExplorer, CellPhoneDB and ICELLNET cannot be compared with BulkSignalR completely. They offer a less general approach. After single-cell omics, ST  (13)  is another revolution in functional genomics. Spatial data are often obtained at multicellular resolution, e. g. with the popular Visium™ system, and therefore, they are localized bulk data. Consequently, bulk-specific approaches could be used to assess ST data analysis. However, most software tools developed for ST target single-cell or subcellular resolution datasets, where the individual transcriptomes of adjacent or nearby cells can be directly accessed. By simply adjusting a few parameters, we found that BulkSignalR could be used for multicellular resolution ST successfully. We compared the performance of BulkSignalR and of three tools for multicellular resolution analyses, CellPhoneDB  (11) , stLearn  (14)  and SpaTalk  (15).
Expression data and randomized expression data We denote with A the n × m matrix that represents the expression of n genes (or proteins) in m samples. To compute the null distributions of the Spearman rank correlation coefficients, we need to generate randomized expression datasets. To do this, we assign each gene to b equally sized bins of comparable average (over the samples) expression levels and we shuffle genes within the same bin. By default, we use b = 20. This procedure preserves the global distribution of gene pairwise correlations, which is essential for realistic null distribution estimates. It also preserves a potential relation between pairwise correlations and average gene expression levels.
Null distributions of the spearman rank correlation coefficients BulkSignalR statistical model requires null distributions of Spearman correlation coefficients between a ligand and a receptor (L-R null distributions) and also between a receptor and its target genes (R-T null distribution). By default, an empirical algorithm selects the appropriate statistical model for these null distributions because their shapes depend on the dataset. Empirical random Spearman L-R correlations are obtained by generating r 1 randomized expression datasets. For each dataset, Spearman correlation (across samples) is computed for each L-R pair documented in the LRdb database. We typically use k 1 = 5 because each random dataset yields a large number of random correlations, one for each L-R interaction in LRdb with ligands and receptors in the matrix A.
We pool all correlations to estimate the null distribution. For some datasets, a censored--or truncated--normal distribution (correlations lie in [-1; 1]) provides an Nucleic Acids Research, 2023 3 accurate fit (Figure  S1A ). With f (t) = 1 σ √ 2π e -1 2 ( t-μ σ ) 2 the density function of a N(μ, σ ) distribution, the density of the censored normal distribution is f c (t) = 1 q f (t), with q = 1 ∫ -1 f (t)dt. The parameters μ and σ are estimated with a maximum-likelihood (ML) approach. However, in some cases, it is necessary to use a mixture of two censored normal due to a slight asymmetry (Figure  S1B ). A mixture of two normal distribution density functions is given by g(t) = α 1 σ 1 √ 2π e -1 2 ( t-μ 1 σ 1 ) 2 + (1 -α) 1 σ 2 √ 2π e -1 2 ( t-μ 2 σ 2 ) 2 , and the censored -or truncated -mixture distribution is obtained as above: g c (t) = 1 r g(t), with r = 1 ∫ -1 g(t)dt.
The censored--or truncated--stable density is obtained with h c (x) = 1 s h(x) and s = 1 ∫ -1 h(x)dx. The four parameters α, β, c, μ are estimated by ML. The stable density and cumulative distribution functions are provided by the R package stabledist  (20). The R-T null distribution is obtained in a similar manner. For each randomized dataset, we consider all combinations of receptors (from the LRdb database) and containing downstream pathways. For each combination, we identify the target genes and we compute their Spearman correlation with the receptor. We then pool all correlations for all receptors and pathways. We repeat this for k 2 randomized datasets, again pooling all correlations. We typically use k 2 = 2 because each iteration yields >100 000 random correlations. In the special case of the censored stable distribution, subsampling is used to avoid endless computations by randomly selecting the same number of random correlations as obtained for the L-R null distributions.
As the L-R null distribution and the R-T distribution are usually very similar, BulkSignalR offers the possibility to use the L-R null distributions for R-T to save training time. We do not recommend this option for accurate computations, but it may be convenient for preliminary analyses, especially when using stable distributions where parameter estimation can take up to 15 min on a powerful processor. We tried the expectation-maximization algorithm implemented in the R package alphastable  (21) , but it did not fit our distributions and required comparable computing time (data not shown). The user can impose any model manually. A control plot displaying the empirical histograms of (L-R) and (R-T) correlations and the chosen fitted model can be generated by BulkSignalR training function (Figure  S1 ) for control purposes.
Moreover, we wanted BulkSignalR to learn from the expression dataset directly, without any manual intervention, and to be applicable to datasets of virtually any size. Therefore, we opted for a simple but robust approach. Once the target genes g 1 , • • • , g N of a pathway pw downstream a receptor R are identified, we can compute the Spearman correlations between R and each of g i. We use order statistics (making the assumption of independence between R-T correlations under the null hypothesis) to integrate the information provided by several target genes, and also to take into account branches of a large pathway that are not all active, or regulatory mechanisms that cannot be detected with the used technology, e. g. posttranslational modifications in transcriptomic data. Namely, all the R-T correlations are sorted by increasing order and the kth percentile is used.
LRI association with cell types We assume that two matrices of gene signature scores are available, a t × m matrix C for cell types and a u × m matrix I for LRI activity. That is, t different cell type signatures and u distinct LRI signatures were scored over the m sample of the dataset. To associate the LRI on row l of the matrix I with one or several cell types, a preliminary filter requires a minimum Spearman correlation between a cell type signature and the LRI signature I l,. (default = 0. 25). If any cell type passes this filter, the selected cell types are used to construct a regularized linear model with the LASSO and by imposing non-negative coefficients (otherwise default parameters of the glmnet R library  (22)  were used to optimize the weight λ of the penalty term). If we denote P t the set of cell types with non-zero weights and P t = ∅, the linear model j ∈P t α j c j,.
Bulk datasets and their preparation All the examples provided in this article were based on public data made available by their respective authors, but for the CRC-LM spatial data that were generated by us (see liver metastasis below). We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse. org (March 2019). The frontal cortex transcriptome data were from the Genotype-Tissue Expression (GTEx) Project v8 (2017-06-05) RNASeQCv1. 1. 9 gene reads gct. Lung cancer cell line transcriptome (RNA-seq) data were from DepMap (23) 22Q2 Portal. SDC data are available from GEO (GSE138581) and from Ref. (24)  authors. BulkSignalR includes a data preparation function that eliminates non-or barely expressed genes/proteins and performs common normalization procedures.
We chose default parameters for RNA-seq data: genes were retained if a minimum read count of 10 was found in at least 10% of samples (percentage and minimum value can be changed to adapt to different data). The default normalization is upper quartile, but total count is available as well. Pre-normalized data can be used to allow filtering and normalization according to more advanced strategies. We processed datasets with default parameters unless otherwise specified. For the datasets presented in Supplementary Information, refer to Supplementary Methods. For the DepMap lung cancer cell line data, we imposed a minimum read count of 1 and logtransformation.
Pseudo-receiver operating characteristics (ROC) curves To estimate true and false positives (TPs and FPs), we applied BulkSignalR to the original data matrix A and also randomized matrices (see above for the randomization procedure). By varying a threshold on the Q-values we obtained estimates of the number of FPs from the randomized data for that specific Q-value. The corresponding number of TPs was estimated by the number of selected LRIs at the same Q-value from the original matrix A minus the number of FPs. This procedure is strongly inspired by decoy databases in proteomics  (25). We named the resulting curves pseudo-ROC curves because the estimates were obtained in the absence of an exact reference. To estimate their variability, we generated 100 randomized expression matrices Aper dataset.
We also generated pseudo-ROC curves for single-cell scores (similar to what done by ICELLNET and CellPhoneDB). We computed ICELLNET-like scores using normalized data according to the ICELLNET original publication  (12). We obtained CellPhoneDB-like P-values from 1000 shuffled datasets. We did not implement any specific treatment for multimeric receptors, thus departing from the original CellPhoneDB and ICELLNET implementations. We estimated the pseudo-ROC curve variability based on 50 randomized expression matrices A in this case.
Comparison with other ST software tools We used BulkSignalR with default parameters except min. count = 1, method = 'TC', min. prop = 0. 01 when calling the method prepareDataset(), min. positive = 2 when Nucleic Acids Research, 2023 5 calling the method learnParameters(), and min. cor = -1 when calling the method initialInference(). For stLearn, we followed the steps provided by the cellcell interaction tutorial in the stLearn documentation website (version 0. 4. 8, October 2022). We used the function st. tl. cci. run() with distance = 0 to compute LRIs in the within-spot mode, and we set n pairs (the number of random pairs generated to compute the background distribution) to 1000. We did not apply any P-value correction for multiple hypotheses. We replaced the stLearn L-R database by LRdb  (7).
Then, we exported all L-R and the associated P-values from data. uns['lr summary'] and data. obsm['p vals]. CellPhoneDB v3 was used to compute inferences of LRIs according to the documentation provided by the Cell-PhoneDB website. CellPhoneDB database v4 was used. We set the method parameters to 'statistical analysis'. Spatial information of cell types was incorporated via the microenvironments file. We used SpaTalk to directly infer cell-cell communications, thus skipping the deconvolution mode as described in its Wiki documentation. We called the function createSpaTalk() with the following parameters: if st is sc = FALSE, spot max cell = 1. We defined the major cell type at each spot according to the data released by the authors of the different datasets. We used the LRdb database as the core database for LRI, thus replacing the native database.
Liver metastasis spatial gene expression analysis Prior to Visium analysis the quality of RNA extracted from the selected formalin-fixed paraffin-embedded (FFPE) CRC-LM samples was analyzed. To this end the percentage of fragments with length >200 nucleotides (DV200) was evaluated. Briefly, two 10 m-thin FFPE tissue sections were devaxed, lysed and the RNA extracted using the High Pure FFPE RNA Isolation Kit (Roche Diagnostics GmbH, Mannheim, Germany; cat. no. : 06650775001). The DV200 analysis was conducted using microfluidics-based automated electrophoresis Bioanalyzer (Agilent, Santa Clara, CA, USA). All tested samples had a DV200 >50% (data not shown). Following this, a serial section from the same tissue blocks was stained with Hematoxylin Eosin Saffron (HES) to identify an area of ∼6 × 6 mm containing the liver metastasis.
The size of the area was selected such so that it provides optimal coverage of the 6. 5 × 6. 5 mm capture area on the Visium slide. Following macro-dissection, 10 m-thin sections of the selected areas was fixed on the Visium slide, followed by dewaxing, HES staining, imaging, and decrosslinking according to the manufacturer's instructions (10X Genomics, Pleasanton, CA, USA; Visium Spatial Gene Expression for FFPE kit, Human Transcriptome, protocol no. : GC000408 and GC000409). Visium libraries were prepared according to manufacturer's instructions (10X Genomics; protocol no. : GC000407). Sequencing was performed using the NovaSeq 6000 system (Illumina, San Diego, CA, USA; S1 flowcell) to obtain ∼50 000 reads per spot. Raw data were demultiplexed, aligned to the human probes, barcode assigned and counted by unique molecular identifier counts using the Space Ranger Software Suite (10X Genomics; version 1.