dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to STdeconcolve and SpatialDWLS for the coronal mouse brain section We applied STdeconvolve  [16]  to the coronal mouse brain section data (for run setting see Additional file 1: Section S6). The obtained topics visibly cluster in space; however, again as in the case of sagittal section data, the results lack topics that would correspond to omnipresent cell types that are expected due to their function in the mouse brain, such as microglia and endothelial cells (Additional file 1: Fig. S3 ). This dataset was also previously analyzed with SpatialDWLS  [13]. In contrast to Celloscope, Spa-tialDWLS benefits from using a reference scRNA-seq dataset. Despite this handicap, this method reported similar subtypes as found by Celloscope, with small differences. For example, SpatialDWLS reported slightly different spatial localization of granule neurons (Additional file 1: Fig.
Celloscope elucidated the source of inflammation in a human prostate tissue Next, we applied Celloscope to analyze human prostate data  [20]. The analyzed dataset contained twelve sections from different regions of a resected prostate, which were profiled using ST. Several of these sections contained cancerous tissue. We selected two sections (3. 1: Fig. 5B ; 4. 2: Fig. 6B ), where infiltrations of immune cells were visible in their respective H&E images. We applied Celloscope to investigate whether those infiltrations could be associated with an ongoing tumorigenesis in these areas, as was observed by  [34, 35] , or whether it was due to some other inflammatory process. Notably, this information could not be derived from the H&E image alone, as the fine subtypes of the detectable cells were not distinguishable visually.
Comparison to methods designed for immune cell type decomposition in bulk RNA-seq Finally, we compared the results obtained by Celloscope for the human prostate data, section 3. 1, to the results obtained by methods designed for immune cell type decomposition in bulk RNA-seq data (using the function deconvolute from the R package immunedeconv  [38] ). The four compared methods included ABIS  [39] , EPIC  [40] , quanTIseq  [41] , and xCell  [42]. We did not include MCP counter  [43]  in this analysis, as this method provides scores in arbitrary units that are only comparable between samples, but not between cell types. Thus, the output of this method does not allow to perform decomposition into proportions occupied by the different cell types. Firstly, we evaluated the ability of the four methods to decompose each individual spot of ST data into cell types.
Comparison of model performance and data quality between the mouse brain and the prostate cancer datasets The convergence diagnostics using the Gelman-Rubin method showed high evidence for the convergence of Celloscope's sampling procedure for both mouse brain and prostate cancer data (Additional file 1: Fig. S9 ). Still, in-depth analysis of the data quality and the inferred model parameters indicated a much higher quality of the mouse brain dataset in comparison to the prostate dataset. Specifically, the Kendall and Pearson correlation coefficients between the expression of the lead genes (Methods) and the rest of the marker genes were much higher for the mouse brain data (Additional file 1: Fig. S10 A ), as compared to the prostate data (Additional file 1: Fig. S10 B ). Therefore, for the prostate data, the agreement of gene expression among the marker genes for the cell types was lower and the model had a harder task identifying where the cell types were located.
Additionally, accept-reject steps require drawing random numbers from predefined probability distributions (inbuilt functions in SciPy or inverse transform samplers). Calculating parameters of those distributions requires matrix multiplication (in NumPy  [46] ), which is the major operation with the highest computational cost, taking into consideration all steps of the algorithm. Dimensions of the multiplied matrices are (K, N) and (N, M), where K denotes number of marker genes, M denotes number of spots and N denotes number of cell types. Thus, the pessimistic computational complexity of the algorithm can be estimated as O(I • K • N • M) , where I is the number of the iterations of the sam- pler. The number of independent chains does not affect computational complexity, as independent runs are paralleled with the use of Python functions Parallel and delayed from the Python package joblib.
Discussion The results of our extensive simulation study demonstrated the Celloscope's accurate performance. Moreover, the outcomes of the conducted analyses of mouse brain data showed the Celloscope's capability to unravel spots' percentage composition with respect to cell types. Finally, the obtained insight into the source of inflammation in the human prostate dataset showed the Celloscope's usefulness to investigate heterogeneous tissue's functionality. The correctness and effectiveness of our approach are additionally demonstrated by the high spatial correlation for inferred cell type proportions measured with the use of the Moran's I coefficient. Celloscope not only has numerous, significant advantages, and shear strengths; it also substantially stands out from previously proposed methods that rely on integrating scRNA-seq with ST data.
As the number of analyzed high-throughput sequencing datasets will grow, our current understanding of cell types and marker genes will expand even more. This will trigger the growth and strengthen the importance of marker-gene-driven methods such as Celloscope. In the current study, Celloscope was applied to localize different cell types, as defined by their marker genes. It is worth noting that the same framework is readily applicable to localizing cell types in different states. Indeed, we can also use marker genes to define cell states, which can be thought of as laying one step lower in the hierarchical structure of cell types. For example, Celloscope could be applied not only to identify spots harboring B cells, but also to separate them into activated cells B cells and resting B cells.
Conclusions In this contribution, we proposed a probabilistic Bayesian framework for comprehensive and accurate decomposition of cell type mixtures in ST spots based on marker genes. In this way, we enable spatial mapping of known cell types and indication of the presence of novel ones in tissues examined using the ST technology. Importantly, we are able to complete this task without a reference scRNA-seq dataset. However, we do incorporate prior domain knowledge about cell types and their corresponding marker genes, with the aim to limit the hypothesis space and guide toward feasible solutions  [17, 18]. Thus, our method benefits from knowledge that has been accumulated throughout many years, via different techniques and by independent researchers  [19]. In summary, Celloscope is a step forward in developing new probabilistic methods used to analyze spatial transcriptomics data and answer diverse biological questions.
Mouse brain datasets ST profiling of the anterior part of the mouse brain tissue sagittal section and mouse brain coronal sections was generated with the Visium technology from 10x Genomics. Raw data can be downloaded from  [22, 31]. The analyzed count matrices  [22]  are outputs of the spaceranger pipeline  [49]. Sagittal data was accessed via SeuratData package  [50, 51]  (Additional file 1: Section S7). The sagittal dataset contains expression of 31,053 genes in 2,696 spots, given by read counts. One hundred seventy-nine genes were selected and modeled as markers (see below). Across the spots, a minimum of 14, on average 119, and a maximum of 153 genes had non-zero expression. The minimal total expression per spot was 18, maximal was 13,788, while average total expression was 1199. 061.
Human prostate dataset The analyzed human prostate data was generated by Berglund et al. [20]. The raw files generated with ST were preprocessed by the authors as described in  [52]. The data comprised twelve sections from different regions of a resected prostate affected by cancer. We restricted our analyses to spots that were contained in an area annotated as inflamed by an expert pathologist based on the respective H&E images. We present results for 140 spots from section 3. 1 and 87 from section 4. 2. A semi-automatic procedure selected 173 genes as marker genes (see below). Across the analyzed spots, a minimum of 7, on average 88, and a maximum of 163 genes had non-zero expression. The minimal total expression per spot amounted to 21, maximal was 5150, while the average total expression was 921.
Defining the marker genes for cell types Celloscope expects as input a binary matrix specifying marker genes for each cell type. This matrix reflects prior knowledge and should be curated manually by the user. To make the task of determining the specific marker genes for each cell type easier for the user, Celloscope implements a semi-automatic procedure of marker selection. Importantly, the procedure does not use scRNA-seq data, therefore Celloscope remains independent of a reference in the form of a scRNA-seq dataset. More specifically, the input to the marker selection procedure is given by the following: (i) sets of (possibly large) candidate marker genes for each cell type-these sets could be collected from published datasets and literature, (ii) threshold r N for the num- ber of spots a marker gene should be expressed in (five by default), (iii) threshold r K for marker gene expression level, (iv) for each cell type t, a single lead gene g * t -a core marker gene with high expression in the analyzed ST data which the user is sure that it definitely is representative of t, i) threshold for normalized Kendall correlation τ * t and for normalized Pearson correlation ρ * t.
Annotation of areas of interest based on the H&E image for the human prostate sample In order to assess the type of tissue present in each spot for the human prostate dataset, we annotated contiguous tissue regions using QuPath  [60]. The annotated tissue types were as follows: suspected cancer, cancer, immune cells: chronic inflammation, immune cells: acute inflammation and suspected acute inflammation. To obtain lists of spots per each annotated area, we overlapped spot coordinates with tissue annotations using a custom script in QuPath.
Cell counting We estimated the number of cells in each spot using a dedicated, custom script extending upon the functionality of QuPath  [60]. First, areas occupied by the circular spots in the analyzed H&E images were identified by their coordinates and diameter. Then, cell nuclei placed within these circular spot areas were detected using QuPath's inbuilt cell counting algorithm for H&E image analysis (function WatershedCellDetection). To enssure the counted cells were correctly distinguished from the background noise in the images by the algorithm, we manually, carefully adjusted the algorithm's parameters, so that the returned cell counts in randomly selected spots were in agreement with counting cells by eye. The cell counting procedure was performed for all spots on the mouse brain slide and for the pathologist-selected inflammation area for the human prostate dataset.
Synthetic data simulation We set s M = 800 , t N = 7 , a = 10, b = 1, a 0 = 0. 1, b 0 = 1, 0 = 0. 2 and the num- ber of marker genes g K = 149 (marker genes distributed across cell types as:  15, 31, 35, 23, 17, 33, 0). The value of the parameter α depends on the data simulating sce- nario, i. e. for the dense scenario we fix α = 2t N and for the sparse scenario α = 0. 45t N. To sample values for gene expression level , we first calculate the average gene expression for cell types found in a scRNA-seq dataset on the mouse brain cortex  [62]. The obtained values were resampled to finally get values of for all marker genes. The values for p g were sampled from Unif(0, 1), π according to the distribution (2), Z according to distribution (3) and according to  (4). We computed H based on , from Eq.
Quality measure of the inference accuracy in synthetic data For each of 60 synthetic datasets, we calculate: where ǫ k denotes the average error across spots and cell types for the kth datasets, k = 1,. , 60 , h (k) ij denotes the true value of a proportion of a type j in a spot i for the kth dataset and ĥ(k) ij is the proportion of a type j in a spot i estimated by Celloscope for the kth dataset, Stereoscope or RCTD. (16) ǫ k = s M i=1 t N j=1 |h (k) ij - ĥ(k) ij | t N s M , Table  3  Values of hyperparameters used for mouse brain data sagittal section, if different for mouse brain coronal section {in curly bracket} and human prostate data (in parentheses) Variable.
Run settings for mouse brain and human prostate data In the following, we describe Celloscope's run settings for the mouse brain dataset sagittal section, mouse brain data coronal section in curly brackets and provide the corresponding settings used for the human prostate data in parentheses. We ran three (ten) independent chains with the same hyperparameters (Table  3 ), but random starting values with 120,000 ( 10 6 ) {40000} total iterations, including the burn- in phase of the first 90,000 (900,000) {30000} iterations. The estimated total number of cells in each spot ( l s ) was incorporated as priors for the N s variable. For , , 0 , p g , we introduced adaptive step sizes (with the optimal acceptance ratio of 23%). Table  4  presents the starting values for the step sizes. Additionally, we set the thinning parameter to 10 (100), which resulted in keeping every 10th (100th) value and discarding the rest of them.
Cell counting Default parameters values for the cell counting procedure are provided in the script available at GitHub (https:// github. com/ szczu rek-lab/ qupath-spot-utils). Due to the fact that the results of the estimation can be validated on the respective H&E images in QuPath for a representative, a limited number of spots, the parameters given to the algorithm can be tuned manually based on the user's visual inspection. Importantly, QuPath inbuilt cell counting algorithm is guided by numerous parameters, e. g. , setting too high sigma (which is responsible for smoothing) may result in blurring some nuclei together. Moreover, while on the one hand a too high threshold parameter may result in too few pixels passing it and lowered sensitivity of the cell counting algorithm, on the other hand, a too low threshold value may result in many false positives.
These modifications to the equations solved by the MESA code are extended to the case of non-solid body rotation. Results. We find that the impact of having different initial inclinations is mostly present in the evolution towards an equilibrium state that is independent of the inclination. If the binary separation is small enough such that the interaction happens when the star is burning hydrogen in its core, this state is reached before the beginning of a mass-transfer phase, while for a wider binary not all conditions characterizing the equilibrium are met. We also explore the effect of having different initial rotation rates in the stars and how the Spruit-Tayler dynamo mechanism affects the angular momentum transport for a non-coplanar binary. Conclusions. These findings show that including the inclination in the equations of tidal evolution to a binary after a kick is imparted onto a newly born compact object changes the evolution of some parameters, such as the eccentricity and the spin period of the star, depending on how large this inclination is.
In order to do so, we consider the set of differential equations of tidal evolution derived by  Repetto & Nelemans (2014) , which we incorporate in the publicly available stellar evolution code MESA. Before using it to analyse the evolution of HMXBs, we compare our code with the outcome of other codes in the literature. The paper is organized as follows: in Sect. 2 we describe the model used together with the additions included in the stellar evolution code. In Sect. 3 we present the results of some exemplary cases of study. We present a summary of our findings and concluding remarks in Sect. 4.
Naturally, given the physical origins of the convective turbulence and the radiative damping, their efficiencies are different with convective envelope stars presenting more tidal friction than those with radiative envelopes. Then we integrate the set of equations for tidal evolution described above and couple them with the evolution of the orbital AM. Once the value of i is known at a given timestep from solving Eq. (  4 ), we use the hooks provided by the MESA code for the following: the other_edot_tidal subroutine is used to solve the evolution of e, while the other_sync_spin_to_orbit subroutine computes the tidal torque acting on the non-degenerate companion 1.
Stellar modelling The detailed evolution of binary systems is computed using the stellar evolution code MESA (version 15140,  Paxton et al. 2011 Paxton et al. , 2013 Paxton et al. , 2015 Paxton et al. , 2018 Paxton et al. , 2019)). All computed stars are assumed to have a solar metallicity content, Z = Z = 0. 017  (Grevesse & Sauval 1998). We use the default nuclear reactions networks present in MESA, basic. net, co_burn. net and approx21. net, which are switched dynamically as later stages in the evolution are reached. Convective regions, which are defined according to the Ledoux criterion  (Ledoux 1947) , are modelled using the standard mixing-length theory (Böhm-Vitense 1958) with a mixinglength parameter α MLT = 1. 5. Semi-convection is modelled following  Langer et al. (1983)  with an efficient parameter α SC = 1.
We also neglect the evolution prior to this point of the interior of the companion star to the compact object, considering that it is represented by a zero age main sequence (ZAMS) star. Binary interaction is computed with the MESAbinary module of MESA. When the star in the binary overflows its Roche lobe, the mass transfer (MT) rate is implicitly computed following the prescription of  Kolb & Ritter (1990). A key factor in the Roche model is the assumption of synchronous orbit, which in our case is not imposed. As we show, in some of our simulations there are cases where the binary is close to a MT stage, but have not yet achieved a synchronous state. Moreover, the model of  Hut (1981)  considers only quadrupolar terms as a perturbation to a central force field, which for a star nearly filling its Roche lobe might not be the best representation.
2005) , while kicks in BHs are more uncertain  (Brandt et al. 1995; Willems et al. 2005; Wong et al. 2012 Wong et al. , 2014;; Vanbeveren et al. 2020; Callister et al. 2021; Stevenson 2022 ). In addition, if these stars are members of binary systems, the momentum kick imparted to the remnant will, through linear momentum conservation, modify the orbital parameters of the binary: P orb , e, and i. After that, the orbital and internal AM evolution will be dominated by the effect of tides operating on the binary system. In this work we address the topic of how these tides operate on the progenitors of HMXBs in which, after such an asymmetric kick is imparted, the spin of the star is not aligned with the orbital AM. For this purpose, we include the formulation of  Repetto & Nelemans (2014)  for arbitrary inclinations in the stellar evolution code MESA.
Acknowledgements. A. S. B. is a fellow of  CONICET. F. G. and J. A. C. are CONICET researchers. F. G. and J. A. C. acknowledge support by  PIP 0113  ( CONICET). J. A. C. is a  María Zambrano  researcher fellow funded by the  European Union -NextGenerationEU- ( UJAR02MZ ). This work received financial support from  PICT-2017-2865  (ANPCyT). J. A. C. was also supported by grant  PID2019-105510GB-C32/AEI/10. 13039/501100011033  from the  Agencia Estatal de Investigación of the Spanish Ministerio de Ciencia, Innovación y Universidades , and by  Consejería de Economía, Innovación, Ciencia y Empleo of Junta de Andalucía  as research group FQM-322, as well as  FEDER  funds. S. C. acknowledges the  CNES (Centre National d'Etudes Spatiales)  for the funding of  MINE  (Multi -wavelength INTEGRAL Network ).
Comparison of the speed expressions from 4 Implementation in AerSett v2. 0. 2 Equation 33 along with Eqs. 8-9 to express A λ,φ , Eqs. 20-21-22 to express C φ c and Eq. 31 giving the expression of function S gives the expression of the settling speed of a falling particle as a function of: d eq , the mass-equivalent diameter of the particle ρ p , density of the particle ρ a , density of air µ, dynamic viscosity of are -, the mean-free path of air molecules λ, the aspect ratio of the particle φ ∈ {0 • ; 90 • }, the attack angle of the particle Eq. 33 extends the method exposed for spherical perticles in  Mailler et al. (2023b)  to the calculation of the settling speed of prolate spheroidal aerosols. This permits to generalize the AerSett module to the calculation of the settling speed of prolate spheroidal aerosol.
Code availability. The source code of Aersett v2. 0. 2 is available online with doi: 10. 5281/zenodo. 10261378. It is distributed under the GNU General Public Licence v3. 0. All the scripts and programs used to produce the figures in this manuscript are available with doi: 10. 5281/zenodo. 10254770. They are distributed under the GNU General Public Licence v3. 0.
, 2020; Durazzi et al. , 2021). Analysis using metagenomic species reconstructed from non-redundant reference gene catalogs allows specific identification and quantification of the microbial species  (Plaza Oñate et al. , 2019). On the other hand, shotgun metagenomics sequencing is a much more expensive technique that generates large and complex datasets, which can be difficult to process, analyze, or interpret  (Liu et al. , 2021). Shotgun sequencing is also less suitable for samples with relatively low bacterial biomass (e. g. , intestinal biopsies), where 16S rRNA sequencing is able to amplify these genes. The specificity of the generated microbiome data has several implications which depend on the sequencing techniques used: (1) The total reads per sample (or depths of coverage) can vary by orders of magnitude within a single sequencing run.
Similarly, there are several transformation methods for microbiome data. Since microbiome datasets are essentially compositional, these methods follow Aitchison's methodology for compositional data to convert the feature counts to log-ratios within each sample by applying an additive, centered or isometric log-ratio transformation  (Aitchison, 1982; Egozcue et al. , 2003). Again, a reference feature (gene, taxon, or function) is sought to be used for eliminating the effect of the sampling fraction. However, one should be cautious on how to replace the zeros during calculations for which there is no clear consensus. For example, zeroes can be eliminated by either incorporating a pseudo count or imputing values using probability or zero-inflated models. However, both approaches pose certain challenges.
Understanding the cause can help determine the appropriate approach to handling outliers. If outlier samples are due to measurement errors or sample contamination, it may be appropriate to remove them from the dataset. In terms of non-informative features or taxa that are biologically irrelevant or known contaminants, filtering can be based on thresholds on their abundance/prevalence, variance or correlation. Low-abundance or prevalence filtering involves eliminating features present in less than, e. g. , 10% of the samples  (Cao et al. , 2021). Variance filtering involves removing features of zero or low variance across the samples as they are less likely to contribute to the overall variation in the data and may be less informative. The threshold for variance filtering can be determined based on the distribution of the variance in the data.