dataesr/scientific-paragraphs-categorization huggingface dataset:
Test setup and constraints For DTC-2 and DTC-3 (equivalent to the Qualification and Acceptance Tests) a long capsule interface was used, compared to the short capsule used during DTC-1, with a direct build-in experimental setup (Figure  22 ). This was done in favor for better observation possibilities as well as less restricted integration and handling space. The safe encapsulation of the experiment was achieved by placing aluminum plates directly between the 4 structural base trusses of the capsule. For measuring the release velocity highspeed image recoding (Photron Fastcam (MC2-10K) with 250-500 fps) was used. The release rotation was detected using an internal wireless IMU (x-IMU, with a data rate of 256 Hz). Additional action cams (GoPro Hero3) were used at selected locations to capture the global separation behavior (Figure  24 ) as well as localize critical contact points between the LM and the MESS if necessary.
Release rotation The rotational motion was studied with respect to the DTC-3 tests (flights 2 -4 only) as they had the most reliable and consistent IMU data across all the tests. The angular velocities and acceleration components along all the axes (cf. Figure  23 ) had distinct edges and peaks corresponding to the different stages of the drop tower test as expected. Figure  29  (left) shows the angular velocity readings of the LM for DTC-3 flight 2 along with the vertical acceleration of the drop capsule from its release to its deceleration within the drop shaft. data, time was set to zero when the acceleration along the z-axis rose sharply from -1 g to -0. 2 g at (A). Similarly, the end of the test was marked when the acceleration fell sharply to the IMU's limit of -8 g at (G), which was constant for all flights at 4.
Simulation setup The simulation environment used was the software tool SIMPACK  [13]. According to the MBS topologies, the following modeling elements (Table  7  and Figure  30 ) were used to realize a virtual representation of the MASCOT-LM/MESS push-off assembly. The Polygonal Contact Model (PCM) of the multibody dynamics simulation was used to investigate especially the contact conditions and collisions between the LM body and the MESS assembly as well as the LM body and the push-plate. Therefore, the respective body surfaces were meshed and its faces were evaluated for mutually intersections to detect contact and collision events and to derive contact forces therefrom. The LM body in Figure  30  is displayed in a wireframe to illustrate this. The underlying force law and its implementation were part of the MBS tool and is explained in detail in  [14].
It was rotating in the MASCOT reference frame and was needed to be despun for the purpose of the fit. An analysis of the data from the distant descent part close to the Ryugu's surface, where the HY2 field was negligible, produced a stable rotation axis and provided MasMag internal offsets. A following sensitivity analysis showed that the direction of the rotational axis was well determined in the range of ± 15 degrees. Therefore, the estimation of the rotation period (∼ 139 s) is robust and was supported by the flight dynamics analysis. In order to validate the model results, it was necessary to limit the possible position and strength of the HY2 representative dipole moment. For this, the HY2 team provided (personal communication) design information about the ion engine, and in particular the location, strength and orientation of the permanent magnets.
Its functionality was verified by the combined efforts of numerical simulation, extensive subunit components testing as well as in system level tests in its expected microgravity environment. Necessary design improvements, which were the direct results of these investigations, are explained in detail. Finally, we compare the test measurements and final predictions of the mechanisms performance with the analysis of two independent methods using actual flight data taken during the flight separation event. One method applied by the MASCOT flight dynamics group used MASCOT's onboard sensors and images taken by the optical navigation camera from Hayabusa2. The second method applied by the MasMag, one of MASCOT's scientific instruments, used the magnetic field decay from the intrinsic magnetic moment of Hayabusa2 after separation.
Background: The Covid19 infection is caused by the SARS-CoV-2 virus, a novel member of the coronavirus (CoV) family. CoV genomes code for a ORF1a / ORF1ab polyprotein and four structural proteins widely studied as major drug targets. The genomes also contain a variable number of open reading frames (ORFs) coding for accessory proteins that are not essential for virus replication, but appear to have a role in pathogenesis. The accessory proteins have been less well characterized and are difficult to predict by classical bioinformatics methods. Methods: We propose a computational tool GOFIX to characterize potential ORFs in virus genomes. In particular, ORF coding potential is estimated by searching for enrichment in motifs of the X circular code, that is known to be over-represented in the reading frames of viral genes.
Here, we propose a computational tool GOFIX (Gene prediction by Open reading Frame Identification using X motifs) to predict potential ORFs in virus genomes. Using a complete viral genome as input, GOFIX first locates all potential ORFs, defined as a region delineated by start and stop codons. In order to predict functional ORFs, GOFIX calculates the enrichment of the ORFs in X motifs, i. e. motifs of the X circular code  [19] , a set of 20 codons that are over-represented in the reading frames of genes from a wide range of organisms. For example, in a study of 299,401 genes from 5217 viruses  [20]  including double stranded and single stranded DNA and RNA viruses, codons of the X circular code were found to occur preferentially in the reading frame of the genes. This is an important property of viral genes, since it has been suggested that X motifs at different locations in a gene may assist the ribosome to maintain and synchronize the reading frame  [21].
An initial evaluation test of the GOFIX method on a large set of 80 virus genomes  [15]  showed that it achieves high sensitivity and specificity for the prediction of experimentally verified overlapping proteins (manuscript in preparation). A major advantage of our approach is that it requires only the sequence of the studied genome and does not rely on any homology information. This allows us to detect novel ORFs that are specific to a given lineage. We applied GOFIX to study the SARS-CoV-2 genome and related SARS genomes, with a main focus on the accessory proteins. Using the extensive experimental data concerning the SARS-CoV genome and the expressed ORFs, we first show that the reading frames of the SARS-CoV ORFs are enriched in X motifs, including most of the overlapping accessory proteins.
Genome sequences Viral genome sequences were downloaded from the Genbank database, as shown in Table  1. The Genbank reference genomes were used as representative genomes for SARS-CoV and SARS-CoV-2. For the Bat-CoV, Civet-CoV and Pangolin-CoV genomes, we selected well annotated Genbank entries having the highest number of annotated ORFs. All CDS annotations were extracted from the Genbank files, and ORF names were standardized according to the SARS-CoV-2 nomenclature (Table  2 ).
Characterization of ORFs overlapping the N gene The annotation of functional ORFs overlapping the N gene is variable in the different genomes studied here. In SARS-CoV, only ORF9b has been observed to be translated, probably via a ribosomal leaky scanning mechanism and may have a function during virus assembly  [30, 34]. ORF9b limits host cell interferon responses by targeting the mitochondrial-associated adaptor molecule (MAVS) signalosome. However, some SARS-CoV strains have an additional ORF9c, annotated as a hypothetical protein (e. g. Genbank:AY274119). For Bat-CoV and Pangolin-CoV, no overlapping genes are annotated in the corresponding Genbank entries. In contrast, the Civet-CoV genome is predicted to contain both overlapping genes, ORF9b and ORF9c. Similarly, the annotation of overlapping ORFs for SARS-CoV-2 is different depending on the strain: the reference strain has no overlapping ORFs of the N gene, while the U.
Novel ORF overlapping the S gene The GOFIX method predicts a novel ORF, that we called ORFSa, overlapping the RBD (Receptor Binding Domain) of the S (Spike) ORF in SARS-CoV (XME = 7. 6) and Civet-CoV (XME = 7. 6). ORFSa is found in the + 1 frame and codes for a protein with 64 amino acids, as shown in Fig. 8. As the ORFSa sequence was not present in the Bat-CoV reference genome, we also searched for the ORF in the genomes of other Bat-CoV strains, and found one occurrence (XME = 6. 5) in the strain WIV16 (Genbank:KT444582) (Fig. 8 ), another bat coronavirus that is closely related to SARS-CoV  [36]. To investigate whether the novel ORFSa might be a functional protein in SARS-CoV, we used BlastP to search the Genbank database for matches to viral proteins. A significant hit was obtained with a sequence identity of 100% to the protein AAR84376, described as "putative transmembrane protein 2d" from the genome of SARS coronavirus strain ZJ01 (AY28632).
CDS annotations extracted from Genbank, with ORF names standardized according to the SARS-CoV-2 nomenclature (Continued) SARS-CoV annotation for ORF9c was propagated from Genbank entry AY274119: SARS-CoV isolate Tor2, where it is annotated as ORF14 c SARS-CoV-2 annotations for ORF3b, ORF7b, ORF9b and ORF9c were propagated from Genbank entry MN985325: Severe acute respiratory syndrome coronavirus 2 isolate 2019-nCoV/USA-WA1/2020.
Funding This work was supported by the  Institut Français de Bioinformatique (IFB) ,  ANR-11-INBS-0013 , the  ANR projects Elixir-Excelerate :  GA-676559  and  RAin-RARE :  ANR-18-RAR3-0006-02 , and institute funds from the  French Centre National de la Recherche Scientifique  and the  University of Strasbourg.
We employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty.
However, with the exception of Fard et al. 31  and Lv et al. , 2014  29  , the aforementioned studies produced monotonic descent passages during cell differentiation, mimicking closely the Waddington's epigenetic landscape metaphor (see for example  22, 34  ). Also, none of the above studies consider directly the cellular mechanism that generates stochastic gene transcriptional bursts. In the present work, we aimed to shed light on the gene transcriptional mechanism behind the rise-then-fall trajectory of cell-to-cell variability in gene expression observed during the cellular differentiation process. To this end, we analyzed a collection (8) of published single-cell transcriptomic datasets from various cell differentiation systems, comprising both single-cell RT-qPCR (scRT-qPCR)  8, 10, 12, [35] [36] [37]  and single-cell RNA-sequencing (scRNA-seq)  38, 39.
We employed a likelihood-based analysis using a recent method CALISTA (Clustering And Lineage Inference in Single-cell Transcriptomics Analysis)  40. The analysis relied on a mechanistic model of the stochastic gene transcriptional bursts to characterize single-cell gene expression distribution. We defined a new concept, called transcriptional uncertainty landscape, based on the cell likelihood value from CALISTA analysis to characterize the stochastic dynamics of the gene transcription process during cell differentiation. The stochastic gene transcriptional model enabled identifying the specific parameters or mechanisms that explain the observed changes in the the gene transcriptional uncertainty at the single-cell level  41. For two additional single-cell datasets, we also evaluated the single-cell RNA-velocity using the recently published Velocyto method  42.
Transcriptional uncertainty landscape of iPSC cell differentiation to cardiomyocytes In the following, we demonstrated an application of our procedure described above to a single-cell transcriptional dataset from cardiomyocytes differentiation from human induced pluripotent stem cells (iPSCs)  35. The single-cell clustering of CALISTA returned five clusters  40  , in good agreement with the number of cell types reported in the original study. CALISTA identified one bifurcation event in the lineage progression, which led to two cell lineages  35. Fig. 2a-b  gives the single-cell transcriptional uncertainty landscape showing cells exiting the initial epiblast state that is characterized by a valley in the landscape, passing through a hill of high transcriptional uncertainty corresponding to primitive streak (PS)-like progenitor state, before ending up at one of the low transcriptional uncertainty terminal states corresponding to either mesodermal (desired) or endodermal (undesired) fate.
Fig. 2f  clearly illustrates that within cluster 2, some genes show higher NLL values than the others (Supplementary Figure  S4 ). To identify the important genes related to transcriptional uncertainty, we identified genes with NLL values exceeding a threshold  for at least 30% of the cells in each cluster, where  is set to 3 standard deviation above the overall mean NLL for all cells and genes in the dataset (see Methods Eq. (  2 )). None of the genes in clusters 1, 4 and 5 have NLL above the threshold. Meanwhile, 16 and 8 genes in clusters 2 and 3, respectively, pass the above criterion for high uncertainty with 4 common genes between the two gene sets (Supplementary Table  S1 ). Genes with high transcriptional uncertainty in cluster 2 may have functional roles in cell fate determination.
Transcriptional uncertainty landscapes of cell differentiation We further applied the procedure above to seven additional single-cell transcriptomic datasets that were generated using scRT-qPCR  8, 10, 12, 36, 37  and scRNA-sequencing  38, 39  , to assess the universality of the rise-then-fall feature of single-cell transcriptional uncertainty landscape during cell differentiation. The first of these datasets came from 405 cells during mouse embryonic fibroblast (MEF) reprogramming into induced neural (iN) and myogenic (M) cells  38. Like the iPSC differentiation above, the lineage progression has a single bifurcation point. As depicted in Figure  3a , the single-cell transcriptional uncertainty increases from the initial MEF state and reaches a peak around the bifurcation before decreasing toward two end-point cell fates.
The rise-then-fall of transcriptional uncertainty in the MEF reprogramming is in good agreement with what we observed in the iPSCs differentiation above. Higher entropy of gene expression distribution in a cell population has also been reported in the reprogramming of iPSCs  13. Next, we analyzed datasets from cell differentiation processes without a lineage bifurcation and with multiple lineage bifurcations. Three scRT-qPCR datasets came from differentiation systems without bifurcation,   S7-9 ). The single-cell transcriptional uncertainty landscapes of these three differentiation systems, as shown in Figure  3b , exhibit a rise-then-fall profile, creating a hill that the cells traverse through in the differentiation process. A transitory increase in single-cell gene expression uncertainty was reported either directly or indirectly in the original publications.
In Richard et al. 8  and Stumpf et al 10 , the authors adopted the Shannon entropy to quantify cell-to-cell variability (uncertainty), while Moussy et al. 12  reported an unstable transition state with 'hesitant cells' flipping their morphology between polarized and round shapes before committing to the common myeloid progenitors-like fate. Morphological uncertainty therefore corresponded to a higher transcriptional uncertainty. Note that Moussy et al. study looked at only the initial phase of the (hematopoietic) cell differentiation, and thus, it is likely that the differentiation process had not completed for the cells in the dataset. The next set of single-cell gene expression data came from differentiation systems with multi-branching lineage, including Guo et al. study during mouse embryo development from zygote to blastocyst 36 , Nestorowa et al.
39  and Moignard et al. 37  studies on hematopoietic stem cell differentiation. Figure  3c  show the single-cell transcriptional landscape for each of the datasets. For Guo et al. study, we identified 7 cell clusters and identified two bifurcations in the lineage. Here, we observed two hills in the transcriptional uncertainty landscape, each coinciding with a bifurcation event in the lineage progression -one at 32-cell stage (cluster 2 to cluster 3 and 4) and another at 64-cell stage (cluster 4 to cluster 6 and 7) (see Supplementary Figure  S10 ). For Nestorowa et al. 39  (Supplementary Figure  S11 ) and Moignard et al. 37  (see Methods and  40  ) datasets, we again observed peaks in the transcriptional uncertainty landscape that colocalize with the bifurcation points in the lineage progression.
The use of the two-state mechanistic gene transcriptional model within CALISTA enabled us to probe into a mechanistic explanation for the observed shape of the transcriptional uncertainty landscape. Table  1  show the pairwise Pearson correlations between the cell-averaged NLL of each cluster with two biologically interpretable model parameters, namely transcriptional burst size (number of transcripts generated in each burst) and burst frequency (occurrence of burst per unit time)  50  (see Methods). The Pearson correlations indicate that the singlecell gene expression uncertainty increases with higher burst size and burst frequency (p-value ≤ 0. 01). Higher transcriptional burst size and frequency are associated with a lower qoff -a lower rate of promoter turning offand a greater qon -higher rate of promoter turning on.
Coupling cell uncertainty with RNA velocity In a recent paper  42  , La Manno and colleagues introduced the concept of RNA velocity, which involves computing the rate of change of mRNA from the ratio of unspliced to spliced mRNA. A positive RNA velocity indicates an induction of gene expression, while a negative RNA velocity indicates a repression of gene expression. La Manno et al. demonstrated that RNA velocities are able to predict the trajectory of cells undergoing a dynamical transition, such as in circadian rhythms or cell differentiation. In the following, we explored the relationship between RNA velocities and single-cell transcriptional uncertainty. We evaluated the single-cell transcriptional uncertainty and RNA velocity for two single-cell gene expression datasets that were previously analyzed in La Manno et al.
42  ; 42. The first dataset came from human glutamatergic neurogenesis which has a linear (non-bifurcating) lineage progression. Figure  4  (top row) depicts the cell clustering, single-cell transcriptional uncertainty, and RNA velocities (see also Supplementary Figure  S12 ). The single-cell transcriptional uncertainty landscape again has the rise-then-fall shape, as in the other cell differentiation systems discussed above. Interestingly, the same rise-then-fall profile is also seen in the RNA velocity. As illustrated in Figure  4 , the increase and decrease of the RNA velocity preceed the transcriptional uncertainty, and the peak of RNA velocity occurs prior to those of the transcriptional uncertainty (see Supplementary File S1 for an animated illustration). Furthermore, a gene-wise cross-correlation analysis confirms a positive correlation between RNA velocity and single-cell transcriptional uncertainty with a delay for individual genes (see Supplementary Figure  S13 ).
We also compared RNA velocity and single-cell transcriptional uncertainty for another dataset from mouse hippocampal neurogenesis with a multi-branching lineage  53. Figure  4  (bottom row) shows that like in the neurogenesis dataset earlier, the RNA velocity increases and then decreases during cell differentiation, and the change in the RNA precede that of the transcriptional uncertainty (see Supplementary File S2). Also, the RNA velocity peaks take place before the transcriptional uncertainty peaks. The rise-then-fall dynamic of the RNA velocity seen in the two datasets above is consistent with the view that cells engage in an exploratory stochastic dynamics as they leave the progenitor state, and disengage this explorative mode as they reach toward the final cell state.
Therefore, the transcriptional uncertainty landscape in our study is a reflection of the dynamic evolution of gene transcriptional stochasticity within individual cells during the cell differentiation process. The reconstruction of the transcriptional uncertainty landscapes from 10 single-cell transcriptomic datasets of various cell differentiation processes in our study reveals a universal rise-then-fall trajectory in which cells start from a high potency state with a uniform gene expression pattern in the cell population, then progress through transitional cell state(s) marked by increased transcriptional uncertainty (i. e. , higher cell-to-cell variability), and eventually reach one of possibly several final cell states with again a uniform gene expression pattern among the cells. Furthermore, the peaks of the transcriptional uncertainty landscape colocalize with forks in the cell lineage.
Such an idea would be in congruence with the recent demonstration that, in a physiologically relevant cellular system, gene expression variability is functionally linked to differentiation  57, 58. The rise-then-fall trajectory in the transcriptional uncertainty landscape are more pronounced in some datatsets than in others. For example, in Nestorowa 39 and Moignard 37 datasets (see Figure  3c ), peaks in the transcriptional uncertainty landscape are less noticeable than in the other differentiation systems. We noted that cells in the Nestorowa  39  and Moignard 37 studies were pre-sorted by using flow cytometry based on the expression of surface protein markers. We posited that at least some cells in the transition state(s) might have been lost during the cell pre-sorting since such cells might not express the chosen surface markers strongly.
Cell clustering CALISTA clustering follows a two-step procedure. The first step involves a greedy optimization strategy to find cell clustering that maximizes the total cell likelihood, i. e. the sum of the likelihood value for all cells. The singlecell likelihood value is computed as the joint probability of the cell's gene expression data, which is set equal to the product of the probabilities of the mRNA counts for the selected genes based on the mRNA distribution from the two-state stochastic gene transcription model. To avoid issues with numerical overflow, we use the logarithm of the cell likelihood. By performing the greedy optimization multiple times, a consensus matrix containing the number of times two cells in the dataset are put in the same cluster, is generated. In the second and final step, CALISTA generates the cell cluster assignments by using k-medoids clustering based on the consensus matrix.