dataesr/scientific-paragraphs-categorization huggingface dataset:
For each dimension of each embedding, we compute the variance of the subset of observations from each batch and scale by the overall variance in that dimension as a measure of under-or over-dispersion of the subset's embeddings in that dimension. For example, in the SCMixology benchmarking dataset, biologically identical samples (mixture of three cell lines) were assayed using three library preparation methods (Dropseq, Celseq2, and 10X), with each batch expected to have the same distribution of cells. SV values closer to one indicate better integration (more similarity in dispersion) in a given dimension by batch. Consistent with the plots in Figure  4A , the SV plots (Figure  4B ) showed that SCTransform had the best integration, with all SV points very close to one. Similarly, corralm and Harmony with corralm also showed good batch integration, and both outperform Harmony with PCA, which had SV values far from one.
Computational performance of corral's CA implementation The corral implementation of CA leverages the fast, approximate, partial SVD method from the irlba package in R (60); even when allocated just a single core on a 2019 MacBook Pro laptop (with 2. 3 GHz 8-core Intel Core i9; 64 GB memory), corral runs in under a minute for a dataset of 1,500 features and over 20,000 cells (top 50 components). Figure  5A  shows that for the analogous task, glmPCA takes a little over an hour, and that across a range of dataset sizes (1,500 features), the glmPCA run-time increases rapidly with the number of cells, while CA (corral) scales much more favorably (Figure  5 ). As faster and more efficient implementations of SVD are developed, run-time and/or memory use may be further reduced by modularly incorporating these approaches into the corral pipeline.
The performance of dimension reduction approaches depends the variance structure, noise, and other characteristics of a dataset; we find that as has been reported elsewhere  (18) , the performance of these methods varies depending on the characteristics of individual datasets. This underscores the importance of including process quality controls and tests to ensure that methods perform consistently and are selected appropriately for a given dataset and application. Benchmarking studies are limited by lack of robust reference datasets that reflect the depth of complexity and nuance in actual biological research contexts. To date, most high-quality, "ground-truth" benchmarking datasets are derived from simple, in vitro, synthetic or "pseudo"-cell mixtures, derived from pools of distinct cell types which neither reflect either the true diversity of cell types found in human tissues or properties of real-world research data.
In biological tissue, parameters like number of "true" clusters are unknown a priori and depend heavily on the specific research question and context. A complementary approach is to consider benchmarking datasets obtained by sequencing complex tissue samples, although these datasets also have their own disadvantages; cells derived from tissue in such studies are largely assigned identities based on one analytical method (and for one particular set of study objectives) without a way of independently validating the assignments-and therefore set an unreliable standard for future benchmarking studies of other methods which can never outperform the method used for initial assignment. As the field advances with respect to systematic benchmarking frameworks for complex datasets in different contexts, we will be better equipped to test the merits of each approach and identify optimal approaches based on data characteristics.
The analysis we present here is limited by the annotations of our benchmarking datasets. With the exception of the SCMixology and Zhengmix datasets (which both comprise well-defined cell clusters and are artificially simpler than data from complex tissues), the datasets we analyzed did not have independently validated cell type annotations and so our assessment of performance is limited by the original cell type assignments. Even if a given method better distinguishes important sub-populations or a rare cell type from clustering, these advantages may not be reflected in the ARI, and the method would actually receive a small penalty for differences from "reference. " Furthermore, given the complexity of and subjectivity inherent in cell cluster annotation, researchers may call different cell populations or clusters even on the same dataset, depending upon the research objectives.
The diversity of research questions and data challenges that arise in single cell biology necessitate the breadth of statistical and computational approaches; it is unlikely that a "one size fits all" pipeline or tool that will be superior in all analytical tasks. That said, the robust conceptual framework for CA and its empirical performance advantages over PCA argue for its incorporation in scRNAseq analyses. We implemented CA, CA-FT, and other variations that adjust for overdispersion for scRNAseq in the R/Bioconductor package corral, enabling its integration into commonly used analytical pipelines. Moreover, detailed documentation, tutorials, and vignettes are available  (3, 37). We conclude with ideas for future development-CA, especially when situated within the broader duality diagram framework, can serve as both a platform for and rich source of further methods adaptation and development.
By simultaneously visualizing both cell and gene embeddings, the CA biplot emphasizes the row-column duality inherent in these data and facilitates joint analysis of genes and cells. Furthermore, the unified approach to analysis of gene and cell embeddings and a natural framework to extend and/or integrate with other approaches, including gene set enrichment analysis, supervised decomposition, and projection of supplementary data into a shared latent space-for example, with a similar approach as used previously in mogsa and omicade4  (10, 34, 36). Cell and gene embeddings can be used as matrix operators to project supplementary data into shared latent space, which facilitates approaches for multi-modal and multi-batch integration, as well as fast approximation methods. Matrix projection via multiplication is fast and scalable, even for very large datasets, and in future extensions, can be used as the basis for fast, approximate dimension reduction approaches based on decomposing a small, representative subset of the data and then projecting into the space the full matrix.
corralm: multi-table adaptation of correspondence analysis The adaptation of correspondence analysis for to the integration of multiple tables is similar to the method for single tables with additional matrix concatenation operations. When integrating datasets, we employ indexed residuals, by dividing the standardized residuals by the square root of expected proportion to reduce the influence of column with larger masses (library depth), which is a known source of batch effect in scRNAseq studies. Indexed residuals have a straightforward interpretation for example a value of 0. 5 indicated that the observed value is 50% higher than the expected value. A value of -0. 5 indicated that the observed value is 50% less likely than expected to have a gene-cell association than expected. 1. Match tables and select features.
Identify the intersection of features across the  matrices to be integrated, and subset the tables for only those  * features. While in these analyses we focus on batch integration and therefore match on features, the tables can either be matched by features, for integration across batches, or by cells, for multi-modal integration across 'omic types. 2. Transformation from counts to indexed residuals. Given each table with  cells and  * features, the row weight  !. , column weight. " , and abundance  !" for each observation are computed as described above for standard CA. The indexed residuals  !" can be computed: 𝑟 !" = observed -expected expected = 𝑝 !" -𝑝 !. 𝑝. " 𝑝 !. 𝑝. " Each table is scaled separately, so as to preserve the internal structure of each dataset. 3. Concatenate matrices.
Scaled variance plot When integrating embedding representations across batches, measures for cluster evaluation are effective for assessing group compactness and recovery of cell populations via clustering. However, they do not directly assess how well dataset embeddings are integrated across batches. To focus specifically on batch integration, we developed and applied a heuristic scaled variance metric, which captures the relative dispersion of each batch with respect to the entire dataset. The scaled variance of component dimension  * for the subset of observations in batch  * ,  4 * ,6 , is computed with: 𝑆𝑉 4 * ,6 = Var(𝐄 𝐛%𝐛 * ,𝐝%𝐝 * ) Var(𝐄 𝐝%𝐝 * ) where  is the matrix of embeddings, and  indexes the rows (observations by batch) while  indexes the columns to indicate which component dimension to evaluate.
When the datasets are well integrated, SV values for each batch are close to 1, indicating that each batch has similar dispersion as compared to the entire embedding. This metric is appropriate when the types of cells represented in different datasets are expected to be similar but cannot account for situations where the expected distribution of cell types (and therefore, embeddings) is fundamentally different between batches. that involve no transformations apart from computing chi-squared residuals. The second set (rows 3-5) feature variance stabilizing transformations performed on counts prior to standard CA. The third approach (row 6) smooths the chi-squared residual matrix with a minor "power deflation" prior to decomposition with SVD. B. Table  of  NNGraph cluster recovery performance achieved by each method (rows), in nine datasets (columns), reporting the maximum ARI selected across a range of PCs (full results of ARI by PC shown in Figure  2C ), with ARI from ten runs of glmPCA were averaged prior to selecting the maximum.
Highest ARI (to two decimal places) in each dataset is circled, and the cell clusters in the original datasets are used as the reference groupings. Freeman-Tukey residuals exhibit the best overall performance, with the highest ARI in 6 of the 9 datasets. C. Plot of ARI by number of components in each of nine datasets (same as B), colored by method. Results for glmPCA (gray) include ten seeds. The results from multi-table SVD and PCA from Part I, which show that data must be transformed and centered for effective integration with SVD. C. ) Scaled variance plots for each method. The colors correspond to PC dimension (PC1, PC2, PC3) and shape corresponds to type of count (raw, log). SCTransform, corralm, and Harmony with corralm all have values close to 1 on this metric, indicating better integration across batches.
B. Scaled variance by batch Values closer to 1 indicate similar dispersion by dataset (i. e. , better integration) A. Batch integration methods Top 2 components, with approx. run-time for each method 39 The results from multi-table SVD and PCA from Part I, which show that data must be transformed and centered for effective integration with SVD. C. ) Scaled variance plots for each method. The colors correspond to PC dimension (PC1, PC2, PC3) and shape corresponds to type of count (raw, log). SCTransform, corralm, and Harmony with corralm all have values close to 1 on this metric, indicating better integration across batches. The dotted gray rectangles indicate results from Part I that are included here for ease of comparison. library preparation platforms, computed on the first three components of counts and logcounts presented in Figure  4A , colored by batch.
clusters, whereas standard PCA is driven by a gradient in the second component and fails to resolve the groups. C. Plots show the first two components generated by CA (corral; top row) and PCA (bottom row) on both counts (left column) and logcounts (right column) of the Zhengmix4eq dataset, which comprises approximately 4,000 purified PBMCs in approximately equal mixtures. Cells are colored by type. CA is robust for use with counts or logcounts, whereas PCA on counts results in a Guttman, horseshoe, or arch effect. D. CA (green) and PCA (purple) were applied to counts (left column) and logcounts (right column) from six benchmarking datasets (SCMixology, distributed in the CellBench R package, and Zhengmix datasets, distributed in the DuoClustering R package). Embeddings from all approaches were used as input for NNGraph clustering, with performance in recovering published clusters assessed using Adjusted Rand Index (ARI).
Availability of data and materials Code and documentation are available in the corral R/Bioconductor package: https://www. bioconductor. org/packages/corral R code to reproduce the figures and analysis in this manuscript is available on Github at: https://github. com/laurenhsu1/corral_manuscript A tutorial describing different implementations of PCA and CA, including corral, is available at: https://aedin. github. io/PCAworkshop The datasets used in these analyses are detailed in the Benchmarking section of Methods, including citations and where the data can be accessed directly through R data packages.
Test site and data presentation The test site is located in the Remningstorp forest, southern Sweden, (58°28′ N, 13°38′ E) and includes 1200 ha of managed hemiboreal forest  (Hajnsek et al. , 2008 (Hajnsek et al. , , 2009;; Ulander et al. , 2011). The forested area is dominated by Norway spruce (Picea abies), Scots pine (Pinus sylvestris) and birch (Betula spp). The topography is fairly flat, with an elevation range of approximately 20 m, between 120 and 140 m above sea level. The forest height is between 5 and 35 m, and the maximum biomass reaches 370 tons/ha at the stand level  (Ulander et al. , 2011). DLR (German Aerospace Center)'s E-SAR system acquired data over the Remningstorp forest at L-and P-bands, during the BIOSAR campaign, at three different dates: March 9, March 31, and May 2, 2007.
The flight altitude is 3900 m above the ground and the look angle varies from 25°to 55°from near to far range. The slant range resolution is 2 m and azimuth resolution is 1. 6 m. The temporal baseline does not exceed some hours and the nominal spatial baseline spacing is 8 m at L-band and 19 m at P-band. LiDAR data was acquired over the Remningstorp estate in August and September 2010 by the helicopter-borne laser scanning system TopEye. A raster image file that covers the most parts of the Remningstorp estate contains one of the LiDAR metrics, i. e. the 95th percentile of LiDAR return values above the height threshold. The height threshold was 1 m and 10% of the maximum height (perc95). In the ground truth data, there were stands that were dominated by pine, spruce and birch with their coordinates.
Fig. 7  shows the offset of ground height estimation from Pol-InSAR L-and P-band data compared to LiDAR derived ground level. Ground interferometric phase is estimated after linear extrapolation of the interferometric coherences associated with Pauli and lexicographic bases, in the complex plane. We found that the bias was higher at L-band and the estimated values show important dispersion at P-band for lower forests. It is noticeable that insertion of the optimal interferometric coherences in the linear regression problem did not provide better results. Therefore, to reduce the bias in the ground phase estimation, we investigated the reduction of the problem to a dual-pol formulation. The short temporal baselines satisfy the condition of polarimetric stability and the interferometric coherence expression can be contracted as, = T ( ) † (1/2) 12 (1/2) (10) In fact, at dual-pol, as T -(1/2) Ω 12 T -(1/2) is a two dimensional complex quadratic form.
Conclusions This paper proposes a dual frequency approach to estimate both forest height and vertical structure, which constitutes the main objective of Pol-InSAR over forests  (Caicoya et al. , 2010; Renaudin et al. , 2012). In fact, height is linked to above ground biomass through speciespecific allometric relation. The forest vertical heterogeneity differs from a specie to another then, this parameter is essential to accurately estimate the above ground biomass. The sensitivity of the Pol-InSAR signal to the complexity of the forest vertical structure at long wavelengths is evaluated using GBm over SAR fully polarimetric interferometric data acquired at L-and Pbands. Vertical heterogeneity of the forest has been inverted through the estimate of two Gaussian parameters, the normalized standard deviation and the normalized elevation.
Introduction In the framework of the MICADO (Measurement and Instrumentation for Cleaning And Decommissioning Operations) European Union (EU) project  [1] , whose goal is to establish new, fully digital assessment and management of low-and intermediate-level radioactive waste, we have produced and characterized a full set of radiation counters suitable for monitoring gamma rays and neutrons. In particular the goal of the Work Package 7 is to set up a granular radwaste monitoring system to be used during the final MICADO demonstration  [2]. Indeed, the practice so far has been to store the radwaste drums following their initial characterization, the only monitoring consisting in an overall set of few ambient detectors and periodic manual checks done by operators. The proposed system for online real-time monitoring consists of an array of many radiation sensors to be deployed all around a number of radioactive waste drums, in order to collect counting-rate data in real time and to make them available to a software platform under development named DigiWaste.
Funding: This work was funded within the framework of European Union's  Horizon 2020 research and innovation program  under grant agreement No  847641 , project  MICADO (Measurement and Instrumentation for Cleaning and Decommissioning Operations ). This work was also supported by the  Centro Siciliano di Fisica Nucleare e di Struttura della Materia  [Grant  02/2019 ]. Institutional Review Board Statement: Not applicable.
, 2016; Tranchant-Dubreuil et al. , 2019). While recent progress has been made to compute and store pangenomes  (Garrison et al. , 2018; Li et al. , 2020) , the tool landscape is particularly barren when it comes to visualization. Tools such as Pan-Tetris  (Hennig et al. , 2015) , PanViz  (Pedersen et al. , 2017)  or PanX  (Ding et al. , 2018)  were designed for genebased pangenomes and do not scale well to large-scale eukaryotic studies. Indeed, this gene-centric definition does not take into account positions within genomes, thereby blending paralogs together, and ignoring non-coding sequences despite their crucial influence on phenotypes  (Maston et al. , 2006). The current trend for sequence-based pangenomes is to use graph visualization software like Bandage  (Wick et al. , 2015) , a general tool for navigating assembly graphs, but alternatives dedicated to pangenomes and their inner properties are yet to be refined and adopted.
Implementation Panache is a JavaScript application built with Vue. js 2 and additional librairies, namely D3. js v5 (enabling linkages between data and SVGs) and Vuex. For easy deployment, we have created a docker instance that can run Panache through nginx but the production files are available for deployment through other means. Panache can take two types of files as input. The main file is a presence/absence matrix file in a BED-like format. Each line stores information about one pangenomic block (either a gene or a sequence) detailed through multiple columns, starting with linear position data and ending with the presence/absence information within every genome. An optional GFF file of annotations on the linear pangenomic coordinates may be loaded in addition to the matrix file. Panache can be used to display graph based pangenomes if they are previously linearized with tools such as BioGraph.
Acknowledgements The authors wish to thank  Romain Basset  and  Mel Florance  for their help on Vue. js and improvements brought to the code, as well as  Dr. Eric Ganko  and  Steve Graham  for their useful feedback. The authors acknowledge the ISO 9001 certified IRD itrop HPC (member of the  South Green Platform) at IRD  montpellier for providing HPC resources that have contributed to the research results reported within this paper. URL: https://bioinfo. ird. fr/http://www. southgreen. fr.
However, in this step, we ignore all the complex axioms. Once the inferences are deduced, a mapping is established to add all individuals (asserted and inferred) to the ontology designed with axioms (the set of rules are ignored). This mapping leads the axiomatic reasoning to infer the classification of the amino acids. Another mapping is then established to populate the same model with this classification. At the end, the rules related to polyproteins definition (SWRL rules 7 and 8) are considered, and axioms are ignored. The polyproteins sequence can be finally fetched with SPARQL queries (Protocol and RDF Query Language). The pipeline was developed with Java. Technical parameters motivated us to opt for Openllet 5  which is an OWL 2 DL reasoner. We combined it with both Jena 6  and OWL-API 7  libraries.
The polypeptide N-acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates O-linked mucin-type glycosylation. The family constitutes 20 isozymes in humans-an unusually large number-unique to O-glycosylation. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates. In this work, we deciphered the sequence and structural motifs that determine the peptide substrate preferences for the GalNAc-T2 isoform. Our approach involved sampling and characterization of peptide-enzyme conformations obtained from Rosetta Monte Carlo-minimization-based flexible docking. We computationally scanned 19 amino acid residues at positions -1 and +1 of an eight-residue peptide substrate, which comprised a dataset of 361 (19x19) peptides with previously characterized experimental GalNAc-T2 glycosylation efficiencies.
To recapitulate experimentally observed specificities for a large dataset, we need an efficient, high-throughput computational method that can capture the key mechanisms of enzymatic catalysis. Enzymatic catalysis relies primarily on selective transition state stabilization, ground state (reactants) destabilization, dynamics, and active-site gating. 19, 20  In practice, these effects occur at different length-and time-scales and therefore cannot be accurately captured by a single method. Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have been able to recapitulate catalytic proficiency or mechanistic details for many enzymes such as Kemp eliminases  21  or glycoside hydrolases  22  as they are well-suited to characterize the transition state. QM/MM simulations, however, are not suitable to capture binding or dynamics over longer timescales and are prohibitively expensive for a larger dataset.
Clustering of low interaction energy decoys reveals that peptides exhibit multiple competing low-energy conformations We studied all 361 (19x19) sequons obtained by scanning 19 amino acids (all amino acids except cysteine) at positions -1 and +1 with respect to the modified threonine (Thr0). The experimentally determined glycosylation efficiencies for all of these sequons was determined by Kightlinger et al. 12  and replotted in Figure  1A. For each sequon, we started with the co-crystal structure of the peptide and UDP-sugar bound to the enzyme (pdb ids: 4d0z and 2ffu, respectively). 16  We mutated the residues at the -1 and +1 position of the peptide to the target sequon and repacked and minimized the nearby side chains to obtain a starting enzyme-peptide configuration for the target sequon.