dataesr/scientific-paragraphs-categorization huggingface dataset:
Mutational Analysis of Alpha Variant Sublineages in the USA We subset the USA data explained earlier (n = 1,739,797 SARS-CoV-2 sequences) to only include genomes assigned to the Alpha lineage (n = 140,100). Additionally, genomes assigned to the Alpha lineage collected from the entire world (n = 906,114 excluding the USA) were retrieved from the GISAID as of 31 May 2022. Using the ViralVar "Mutational Analysis" module, the mutation profile for the Alpha VOC in the USA was compared to tigate these mutations, all genomes containing Spike K1191N (n = 51,713) and NSP12 P227L (n = 190,869) mutations were retrieved from the GISAID and uploaded into Vi-ralVar. A majority of genomes with the Spike K1191N mutation were in the Alpha variant genomes (80. 4%, 41,558 of 51,713 genomes), of which the vast majority came from the USA (93.
The majority of studies using phylogenetic trees to study SARS-CoV-2 variants of concern (VOCs), therefore, must rely on subsampling approaches  [52, 53]. The k-means-based clustering of SARS-CoV-2 genomes based on Euclidean distance is one way to overcome this challenge as the method calculates the distance of each datapoint to the centroid using pairwise distances instead, decreasing the computational cost of analyzing the additional sequences (Figure  S3 ). Furthermore, the k-means clustering of genomes based on protein mutations can be leveraged to the group genomes in a way directly related to the phenotype  [54, 55]. The congruence between the approach taken by ViralVar (Figure  5C ) and the phylogenetic analysis results (Figure  5D ) support the potential use of k-means clustering for the rapid analysis of large genomic datasets to facilitate tracking emerging protein mutations using a generic clustering method.
Acknowledgments: We gratefully acknowledge the authors from the originating laboratories and the submitting laboratories, who generated and shared via the GISAID genetic sequence data on which this research is based, as well as the structural biology groups for contributing their structures to the PDB. This research is supported by grants ( R21 AI163912  to Hultquist,  U19 AI171110  to Hultquist,  U19 AI135964  to Ozer) from the  National Institutes of Health ; a grant (to Lorenzo-Redondo) from the  Northwestern University Havey Institute for Global Health ; and a grant (to Ozer and Hultquist) from the  Walder Foundation's Chicago Coronavirus Assessment Network. This research was supported, in part, through the computational resources and staff contributions provided by the Quest high performance computing facility at  Northwestern University , which is jointly supported by the  Office of the Provost , the Office for Research, and  Northwestern University Information Technology.
A techno-economic analysis of production of bio-oil from catalytic pyrolysis of olive mill wastewater sludge has been performed with two different cooling schemes. The two configurations differ in the manner how the bio-oil vapors are quenched. In scheme-1, a vapor compression refrigeration machine is utilized for condensation of biooil vapors while in scheme-2, the vapor compression refrigeration machine is replaced by absorption refrigeration machine. The two schemes are modelled in Aspen Plus which provides mass and energy balances. For techno-economic analysis, Aspen process economic analyzer is employed. The model is first validated against experimental data from lab scale and then upscaled to an industrial scale of 100 tonnes/day wet biomass (93 tonnes/day dry biomass). Results show that the model with absorption refrigeration machine (scheme-2) has a slightly better process efficiency and a lower MFSP compared to the model with compression refrigeration machine (scheme-1).
In fast pyrolysis, biomass is heated to elevated temperature in a very limited time. The process is favorable if the bio-oil yield is maximum among other products. [9]. The use of the Aspen Plus modeling software in process modeling has grown in recent years. It is a modeling software that calculates a process's physicochemical and biological properties. The tool is popular because it may be used for all phases of the process (solid, liquid, and vapor)  [10]. A few recent studies have shown that the simulation tool may be used to analyze pyrolysis experiments and forecast the process yields  [11]. The tool is also useful for manipulating pyrolysis process parameters such as heating rate, temperature, solid residence time, feedstock size, and so on. Many simulation studies on different types of biomass such as beachwood  [12] , empty fruit bench, sawdust and giant miscanthus  [13] , lignocellulosic feedstock  [14] , Napier grass bagasse  [15] , pinewood  [16] , pine and forest residues  [17] , hybrid polar  [18] , spruce, wheat straw and corn stover  [19]  have been performed.
The Aspen Plus model was effectively used to forecast pyrolysis product production and to study the effect of temperature on pyrolysis product output. So far, pyrolysis models which have been studied and developed in Aspen Plus are based on the work of Ranzi et. al. [20]  which target the cellulosic biomass. In these studies, the simulation and experimental data were found to be in good agreement, demonstrating that Aspen Plus simulator may be used to forecast the yield of pyrolysis products. The simulation findings also showed that utilizing Aspen Plus, the pyrolysis operating parameters can be successfully adjusted, and the findings may be utilized for experimental testing. The bio-oil derived from the pyrolysis is acidic in nature, has low energy content, contains oxygenated compounds and high viscosity  [21].
These studies have estimated the cost of bio-oil to range between €1. 75 and €8. 67/GGE (€0. 014/MJ -€0. 07/MJ). Capital cost for the study conducted by Wright et. al. [32]  is €166 million for a pyrolysis plant of 2000 tons/day. The economic analysis of a plant of 49 kg/hr Napier grass bagasse indicates that the capital cost is estimated to be €1. 77 million and operating cost €0. 25 million  [15]. In this study, we propose to develop an Aspen Plus model which takes into account the global kinetic parameters for the production of pyrolytic bio-oil from fast pyrolysis of OMWS (catalytic and noncatalytic) and compare the simulation results with the experimental data of Agblevor et. al. [34]. After validation of results, the model is upscaled to commercial scale of 100 tonnes/day and two schemes are proposed.
Experimental data Experiments were performed by Agblevor et. al. [34]  in Utah State University. Olive mill wastewater sludge (OMWS) was collected from a Tunisian evaporation pond. It is dried at ambient temperature before being ground to a size of around 2 mm. The 160 g of ground biomass are mixed with 40 g biochar to make it less sticky. Nitrogen gas at 25 • C, 1. 01 bar is used as carrier to blow it into the fluidizing bed reactor. The fluidized bed reactor has a diameter of 50 mm and a length of 500 mm. The mixture of ground biomass, biochar and nitrogen is introduced into the pyrolysis reactor at ambient temperature. Nitrogen as fluidizing gas enters from bottom of pyrolysis reactor at 300 • C, 1. 02 bar. Catalyst; red mud, is heated at 550 • C for five hours before using it in the reactor.
Pilot scale Aspen Plus is a comprehensive powerful tool used by engineers and researchers for a variety of applications including chemical processes, power generation, polymerization, distillation, pharmaceuticals and fertilizers. It contains several tools to calculate mass and energy balance, chemical equilibrium, reaction kinetics and perform optimization studies. Users can benefit from extensive databases thermodynamic models and physical properties to develop complicated and comprehensive processes. Its strength lies in the handling of conventional and non-conventional solids, liquid and gas fuels  [35]. Aspen Plus has been used by many researchers for pyrolysis of biomass  [36] [37] [38] [39]. Components in Aspen Plus are divided into two categories: conventional and non-conventional.
The molecular structure of conventional components is known, and they may be found in numerous Aspen Plus databanks. Because non-conventional components lack a molecular formula, proximate and ultimate analyses are used to represent them  [37]. In this work, catalytic and non-catalytic pyrolysis of OMWS is modelled using ASPEN Plus ® software as shown in Fig. 3. OMWS and biochar are defined as non-conventional components and described on the basis of proximate and ultimate analysis (Table  1 ). OMWS, biochar carrier gas and fluidization gas are fed into the fluidized bed reactor where dehydration takes place. After dehydration, feedstock is fed into RCSTR (PYR) reactor where biomass is disintegrated into bio-oil, biochar, water and gases according to the global reaction equation  (2). OMWS = Biooil + Biochar + Biogas + Water (2) The products exiting the reactor are cooled to 400 • C using a simple cooler exchanger (Heater) to keep the products at same temperature as cyclone.
As both conventional and non-conventional solids are involved in the simulation model, stream class MCINCPSD is a suitable choice. Molecular weights and other thermos-physical properties are retrieved from NIST database. Aspen plus is flexible enough to integrate other programs like Fortran user-subroutines or Matlab. This feature is intended to model highly complex reactor models which otherwise cannot be used in predefined blocks in Aspen Plus. The kinetic reactions of pyrolysis are written in a separate Fortran file and then compiled using an intel compiler. The external Fortran user-subroutine is then coupled with Aspen Plus using a dll file. This method of coupling is different from the Fortran calculator blocks already embedded in Aspen Plus. Reactor inputs e. g. mass flow rate, temperature, volume etc.
4 ). A general reaction is given by equation  (2). The reaction rate of the pyrolysis of OMWS is determined from the analysis of experimental data of Agblevor et. al. [34]  and formulated according to the modified Arrhenus law proposed by Diaz and Broun  [41]. R = k. C ko. ( F k F ko ) n (8) where R, k, C ko ,F k ,F ko and n are reaction rate (kg/m 3. s), rate constant (1/s), concentration of OMWS (kg/m 3 ), remaining mass flow (kg/s), initial mass flow of OMWS (kg/s) and Order of reaction (n = 1) respectively. The rate constant can be written by the following equation: k = Aexp ( E a RT ) ( 9 ) where A is pre-exponential factor (A = 2. 55 × 10 4 s -1 ), E a is Activation Energy (E a = 69. 56kJ/mol/K), T is temperature (K) and R is gas constant (J/mol. K). The subsequent production of bio-oil, biochar, NCG and water is calculated by the following relation  [41, 42].
Catalytic fast pyrolysis The non-catalytic fast pyrolysis results of OMWS investigated by Agblevor et. al. [34]  showed that the pyrolytic OMWS bio-oil had high oxygen content and viscosity. To upgrade the quality of this bio-oil, Agblevor et. al. [34]  investigated in situ catalytic fast pyrolysis process using red med as a catalyst. In this case, the Aspen Plus model was also developed to simulate the catalytic fast pyrolysis of OMWS at the same temperature used in non-catalytic pyrolysis. The model was validated against the experiments of Agblevor et. al. [34]  conducted in a fluidized bed pyrolyzer. The simulation results with experimental data of the products yield distribution are shown in Figs. 17, 18, and 19 and the absolute average deviation values are depicted in Table  11. It can be seen that the simulation model of the biochar and water yield distribution correlates better with experimental data of Agblevor et.
It can be seen that the deviations between the model predictions and the experimental data are noticeable for all bio-oil compositions. These deviations are due to the fact that apart from a large number of compounds determined, there is still about 30% of biooil composition that could not be determined experimentally. In addition, there are some compounds that could not be found in the Aspen Plus directory so they were termed as unknown compounds. So, this  validation is attributed to the limitation of the model to simulate the compounds yields which are present in the catalytic bio-oil.
As compared to the parameters discussed above, the MFSPs demonstrated less exposure to capital cost, with a 30% rise in capital cost triggering a 11% increase in scheme-1 and an 8% increase in scheme-2 (scheme-1: €3. 61; scheme-2: €2. 96), and vice versa (scheme-1: €2. 88; scheme-2: €2. 38). The small impact of higher capital investment on MFSPs, compared to the strong impact of higher bio-oil yield, indicates that the profitability could be scaled up by increasing plant capacity. The above sensitivity analysis considered only single point variation of a parameters on MFSP while other parameters were kept constant. To understand the variation of more than one parameter at a time, a Monte Carlo sensitivity analysis was performed using ModelRisk software  [53]. In the Figs. 26 and 27, a spider chart has been created to analyze the sensitivity of the mean of the MFSP.
Acknowledgments The authors thank the financial supporters of the  Waste2Fuel-ERANETMED2-72-298  collaborative  Euro-Mediterranean  project. The  Tunisian Ministry of Higher Education and Scientific Research , as well as the  ANR in France , provided financial and administrative support to the writers. This work is also financed and carried out as a result of international collaboration between  University of Loraine, France  and  University of Sfax ,  Tunisia  under the mechanism of  DrEAM  within the framework of  Lorraine University of Excellence Initiative (LUE). Fig. 27. Spider chart for scheme-2.
Using a novel computational pipeline, we compare the transcriptional landscape of rabbit and mouse at the scale of the entire organism, revealing that extra-embryonic tissues, as well as gut and PGC cell types, are highly divergent between species. Focusing on these extra-embryonic tissues, which are highly accessible in the rabbit, we characterize the gene regulatory programs underlying trophoblast differentiation and identify novel signaling interactions involving the yolk sac mesothelium during hematopoiesis. Finally, we demonstrate how the combination of both rabbit and mouse atlases can be leveraged to extract new biological insights from sparse macaque and human data. The datasets and analysis pipelines reported here set a framework for a broader cross-species approach to decipher early mammalian development, and are readily adaptable to deploy single cell comparative genomics more broadly across biomedical research.
We also investigate the molecular programmes underlying trophoblast differentiation and yolk sac hematopoiesis in the rabbit and use the fine-grained cell type annotations of both rabbit and mouse atlases to obtain a more detailed view into the cell type diversity of sparse human and macaque data. Our transcriptional and chromatin accessibility atlases can be explored via the interactive website available through https://marionilab. github. io/RabbitGastrulation2022/.
In a recent atlas of mouse gastrulation and early organogenesis, 430,339 cells spanning embryonic day (E) 6. 5 to E9. 5, were classified into 87 curated cell type labels  26. Given the overlapping developmental stages (Figure  1B ), we trained a label-transfer model on the full mouse dataset and used this to predict cell type annotations for the rabbit (Methods, Figure  S4A ). The automatic transfer of existing annotations ensured that our rabbit cell types were defined as consistently as possible with the mouse, facilitating cross-species comparisons downstream. The preliminary annotations obtained from this automated stage were subsequently verified by manual annotation via assessment of known cell type markers and by cross-referencing with additional information such as the developmental stage, histology (Figure  1A , S1D) spatial information via embryo microdissection (Figure  S1C ), RNA-scope images and independent data integration (Figure  S4C ).
Moreover, in organisms where the embryo is deeply embedded within the uterine lining (such as human and mouse), capturing trophoblast cell types without also capturing a lot of maternal material is experimentally challenging. By contrast, in the rabbit, implantation occurs alongside gastrulation at GD 7 and 8  4, 24  , when the embryo is larger, enabling the straightforward capture of large numbers of extraembryonic-ectoderm cells (Figure  1D , S1A-B). Consistent with this, in comparison to the mouse gastrulation dataset  10  which only captures a subset of cells labeled as extraembryonic-ectoderm, we are able to profile a diverse set of extraembryonic ectoderm cell types (Figure  2A ), encompassing the maturation of early trophoblast cells (characterized by expression of GATA3, HAND1, and DPPA3)  29, 30  into cytotrophoblast (characterized by expression of CD9 and CYP19A1;  31, 32  and finally into syncytiotrophoblast (SCT) progenitors (characterized by expression of GCM1 and of TFAP2A/C)  33, 34  that will later fuse with the maternal decidua and become multinucleated.
In cells assigned a cytotrophoblast identity, corresponding to the middle of the pseudotemporal ordering, we observed accessibility in regions associated with DLX5/6, ZNF740, and TP63 motifs. Later, during syncytiotrophoblast differentiation, we observed open chromatin in regions associated with the GCM1 motif  38  , a known regulator of CEBPA and syncytin genes, the latter of which are directly involved in cell-cell fusion with the maternal decidua  39, 40. Finally, in the most mature syncytiotrophoblast cells, motifs associated with the binding of TFEB and MITF are highly accessible. These TFs are known to form homodimers or heterodimers, and are involved in vascularization and placental labyrinthine development  41. Overall, these patterns seen in rabbit in vivo are consistent with the limited information available for human, not only suggesting conservation of key temporal ordering of changes in chromatin accessibility during trophoblast development, but also establishing the first comprehensive in vivo dataset capturing this key process at single cell resolution for both transcription and open chromatin.
A neighborhood-based comparison across species identifies rabbit-mouse differences in embryonic and extra-embryonic cell states and developmental trajectories In the previous section, we focused on characterizing extraembryonic cell types that are present in the rabbit but which are underrepresented in mouse datasets at this stage of development. Next, we focused on cell types and developmental trajectories that are present across both species and examined which developmental processes were conserved and which showed divergence. To do this, we developed a computational approach to evaluate transcriptional similarity between k-nearest neighbor (kNN) graphs, constructed from scRNA-seq data of different species (Figure  3A ). Independently for each species, we defined neighborhoods of cells across the entire kNN graph  43  allowing us to locally approximate the transcriptional profile in precise regions of the gene expression manifold.
To investigate these observations in more detail, we examined how similarities between neighbourhoods vary along trajectories of differentiation. Specifically, we visualized mappings between maximally correlated neighborhoods along the reduced dimensional spaces of rabbit and mouse datasets for a subset of cell types relating to the development of specific lineages (Figure  3E -F, S7B-D). While neighbourhoods map very strongly across the whole spectrum of mesodermal cell types (Figure  3E ), the trajectory of endoderm development is much less correlated between species (Figure  3F ). As in Figure  3D , the extraembryonic component of the gut trajectory exhibits the lowest similarity between the rabbit and mouse, whereas neighbourhoods representing the epiblast and definitive endoderm, are more strongly correlated.
Close to the point of intercalation, across neighbourhoods of the developing gut, the correlation is intermediary between values at the embryonic and extraembryonic origins, potentially as a result of cell mixing and convergence of transcriptional signatures. Moreover, many neighborhoods of the rabbit gut tube at GD9, have highest correlation with mouse neighborhoods at E8, suggesting a change in the timing of development (Figure  S7A ). Taken together, our neighborhood-based analysis provides a general approach to compare single-cell RNA-seq datasets at a high level of granularity and independently of cell type annotations. Using correlations in expression, we are able to quantitatively assess differences between transcriptional profiles, which are often obfuscated, or difficult to interpret using integration-based methods.
Applying this to the rabbit and mouse atlases we observe differences in extraembryonic tissues and in cell types such as the gut and PGCs, whose development is known to be influenced by extraembryonic structures and are linked to the maternal environment and cup vs disc embryo morphology  47, 48. We are also able to map neighbourhoods along continuous and discontinuous paths of differentiation, where we find varying dynamics in the conservation of cell states along different trajectories (Figure  3E -F, S7B-D). These results have implications for how we interpret observations from specific cell types and lineages in any one species and sets the scene for more extensive cross-species analysis when suitable datasets are reported for additional species.
Leveraging the rabbit atlas to expand our understanding of early primate development The quantity of cells, tissues and developmental stages captured in the rabbit and mouse atlases provide a comprehensive view into the development of these organisms. Given the scarcity of transcriptomic data from primate embryos, we next investigated whether the comprehensive mouse and rabbit resources could be leveraged, using automated cell type annotation tools, to gain deeper insight into the cellular makeup of sparse human and macaque datasets. Manually annotating cell types de-novo can be a challenging and time-consuming process, particularly when cell types are represented by small numbers of cells. In these cases, there is often little statistical power to confidently detect signals in marker gene expression above transcriptional noise.
Being able to overcome these difficulties using model organism reference atlases will become increasingly relevant as more studies of early human development take place. To take advantage of our newly generated rabbit data, as well as the existing mouse datasets, we used SingleR to train cell type annotation models on the rabbit and mouse atlases. We then used these models to predict cell type annotations from the transcriptomic profiles of a representative human and macaque single-cell query dataset. Tyser et al. 2021 reported a SMART-seq v2 dataset of 1,195 cells, obtained from a single CS7 human embryo between 16 and 19 days post-fertilisation  25. At this stage of development, the human embryo appears as a flat-disc, similarly to the rabbit embryo at day 7. Based on the transcriptomic profiles of these cells, we observe that our rabbit annotation model assigns several new cell type annotations, in addition to those consistent with the original publication (Figure  4A , S8A).
Other differences are found in the prediction of epiblast cells, which the mouse-trained model labeled as ectoderm. We applied the same approach to a single-cell RNA-sequencing dataset of cultured in-vitro macaque embryos spanning embryonic days 10, 12 and 14  50. These stages of development correspond with Carnegie stages 5-6  51  reflecting an earlier developmental stage. CC-BY 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted October 6, 2022. ; https://doi. org/10. 1101/2022. 10. 06. 510971 doi: bioRxiv preprint than captured in our rabbit dataset, although partially overlapping with the E6.