dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine the orientation of the sICA metagenes, two alternative approaches are considered: "long tail-pointing" and "disease-pointing". As shown in Figure  2A , for each metagene, the "long tail-pointing" approach checks the distribution of its weights; if the long tail of such distribution corresponds to the positive side of the metagene, no further operation is performed. If instead the long tail of the metagene distribution corresponds to negative weights, then we flip the metagene Each transcriptomic dataset is separately decomposed using MF. The framework proposed here can be combined with any MF algorithm of interest. In this work, we chose stabilized independent component analysis (sICA)  [12, 27, 28] , a stabilized version of ICA  [28] [29] [30]. sICA was indeed previously shown to outperform alternative MFs in the extraction of relevant biological knowledge from collections of transcriptomic datasets derived from the same biological condition (e.
Metasamples and metagenes are learned based upon the assumption that the number k of components occurring in the input matrix X is smaller than either its rows or columns. Here, we selected the number k of components to be 100 for those datasets with more than 100 samples and equal to half of the samples for smaller datasets. These chosen values of k are higher than the estimation of the optimal transcriptomic dimension (optimal k value defined in  [28] ), due to the fact that overdecomposition in sICA was proven not to be detrimental for the interpretability of the resulting components  [28]. To determine the orientation of the sICA metagenes, two alternative approaches are considered: "long tail-pointing" and "disease-pointing". As shown in Figure  2A , for each metagene, the "long tail-pointing" approach checks the distribution of its weights; if the long tail of such distribution corresponds to the positive side of the metagene, no further operation is performed.
Step 2: Construction of the Reciprocal Best Hits (RBHs) At Step 2, the reciprocal best hit (RBH) network is constructed. A positive/negative RBH is defined as follows: given two sets of metagenes {M 1. M k } and {N 1. N k } obtained from the transcriptomic datasets T M and T N , respectively, we define M i and N j as a positive reciprocal best hit (+RBH) if: max cor M i , {N t } k t=1 = max cor {M t } k t=1 , N j > 0 (1) and M i and N p as a negative reciprocal best hit (-RBH) if: min cor M i , {N t } k t=1 = min cor {M t } k t=1 , N p < 0 ( 2 ) where cor corresponds to the Spearman correlation. Briefly, as defined in Equations (  1 ) and (  2 ), the two metagenes M i and N j are linked by a ±RBH if the absolute value of their correlation is maximal with respect to both the correlations of with all other metagenes obtained from dataset T N and the correlations of N j with all other metagenes obtained from dataset T M.
For defining a +RBH (Equation (  1 )), positive correlations are considered, while for -RBH (Equation (  2 )), negative correlations are taken into account. As a result, each metagene will thus find a maximum of two associated metagenes in another independent transcriptomic dataset, corresponding to +RBH Equation (  1 ) and -RBH Equation  (2). Repeating the same procedure for the metagenes of all the available transcriptomes, we obtain a weighted network whose nodes are the metagenes computed in all the transcriptomic datasets and whose links correspond to their +RBHs and -RBHs computed as in Equations (  1 ) and (  2 ). These links are weighted based on the correlation computed in Equations (  1 ) and (  2 ). The procedure used to construct the ±RBHs preferentially builds a sparse network with high weights; however, a cutoff threshold can be always introduced on the weights if required.
Step 3: Subnetwork Isolation and Community Detection In Step 3, given our interest in the processes that are differentially altered between two diseases such as AD and LC, we delineate the relevant subnetwork of RBHs. For example, if we want to study the inverse comorbidity between AD and LC, we restrict the analysis to the negative RBHs (-RBHs) connecting metagenes of AD with metagenes of LC. Once the subnetwork of interest is selected, we detect communities in this subnetwork with the MCL algorithm  [31, 32]. When running MCL, the weights associated to the links of the RBH network are taken into account for community detection. The obtained communities correspond to biological components involved in the DD relationship and consistently present in multiple independent datasets. Moreover, having previously isolated the subnetwork of interest (such as negative RBHs between AD and LC), we are sure to only identify communities that are altered in the direction consistent with the type of comorbidity under analysis (oppositely regulated in the case of inverse comorbidity and concordantly regulated in the case of direct comorbidities).
Investigation of the Orientation Methodology for the sICA Components Among the various modifications made to the framework, of particular importance is the choice of the procedure for the orientation of the metagenes. As described previously, two alternative approaches were considered: "long tail-pointing" and "disease-pointing". We tested how such a choice impacts the following steps of the framework and, in particular, the structure of the obtained RBH network. To do this, we selected a specific DD relationship, i. e. , the inverse comorbidity between Alzheimer's disease (AD) and lung cancer (LC) as a case study  [22] [23] [24]. We considered 17 transcriptomic datasets, spanning AD and LC patients and containing case and control samples, (see the Methods section for further details). Following our framework (Figure  1 ), each dataset was decomposed separately through sICA (see Supplementary Table  S1  for the number of components) and the orientation of the components was established both with the long-tail-pointing and the disease-pointing approaches.
The resulting metagenes were then compared according to multiple criteria (see Figure  3 ). transformed expression matrix, it is computed as    -. In fact, to associate a metagene to a specific biological function or pathway, we need to perform enrichment tests using databases of functional annotations (e. g. , Reactome, Gene Ontology). Generally, this interpretation step is just aimed at associating a function to each metagene without considering the sign of activity of the identified pathways/processes. However, when dealing with comorbidities, it is important to not only associate a function to each metagene, but also to infer the sign of activity of such pathways/functions. This task can be easily achieved once the metagenes are positively correlated with the gene fold-change. As shown in Figure  3A , the disease-pointing orientation produces metagenes that are significantly more correlated with the gene fold-change than the long tail-pointing one (significance tested with Wilcoxon test, resulting p-values available in Supplementary Table  S1 ).
First, the correlation between the obtained metagenes and the case vs. control fold-change of expression was considered to decide between the two orientation methods. Fold change measures expression changes between two states (case vs. control in our case) and for each gene in the log2-transformed expression matrix, it is computed as Avg(Case samples) -Avg(Control samples). In fact, to associate a metagene to a specific biological function or pathway, we need to perform enrichment tests using databases of functional annotations (e. g. , Reactome, Gene Ontology). Generally, this interpretation step is just aimed at associating a function to each metagene without considering the sign of activity of the identified pathways/processes. However, when dealing with comorbidities, it is important to not only associate a function to each metagene, but also to infer the sign of activity of such pathways/functions.
This task can be easily achieved once the metagenes are positively correlated with the gene fold-change. As shown in Figure  3A , the disease-pointing orientation produces metagenes that are significantly more correlated with the gene fold-change than the long tail-pointing one (significance tested with Wilcoxon test, resulting p-values available in Supplementary Table  S1 ). We have then applied Step 2 of the framework and independently constructed an RBH network for "long-tail-pointing" and "disease-pointing" oriented metagenes. In both cases, the nodes of the network correspond to the metagenes independently identified in the 17 datasets (369 total nodes) and their links are +/-RBHs, defined as in Equations (  1 ) and  (2). Changes in the orientation of the metagenes alter the sign of the correlations, giving rise to different RBH networks.
Moreover, the fenretinide community is tightly linked with the monocytes one, in agreement with its mechanisms of action involving the regulation of the secretion of pro-inflammatory cytokines in human monocytes  [40]. S3. Colours are linked to the diseases: red for AD and blue for LC. In AD, datasets obtained from the same region of the brain are denoted with different shades of red (normal and light red). The nodes are organized into communities. Each community is denoted with a number corresponding to its ID and the main biological annotation associated to them (see Supplementary Table  S2  for an extensive report). HDAC = histone deacetylase. The majority of the communities present in the network are associated to the immune system and mitochondrial functioning, confirming the results of previous transcriptomics meta-analyses on the inverse comorbidity between AD and LC  [25, 26].
Additionally to mitochondria and the immune system, community 36 has been associated to   S3. Colours are linked to the diseases: red for AD and blue for LC. In AD, datasets obtained from the same region of the brain are denoted with different shades of red (normal and light red). The nodes are organized into communities. Each community is denoted with a number corresponding to its ID and the main biological annotation associated to them (see Supplementary Table  S2  for an extensive report). HDAC = histone deacetylase. The communities associated to mitochondria span different processes related to their activity: oxidation-reduction processes (communities  16, 23, 40) , hypoxia (community 10) and phosphate metabolic processes (community 38). Enrichment in hypoxia could correspond to a confounding factor linked to the state of the profiled tissues (post-mortem for AD and fresh tissue biopsy for LC).
Data Collection Three microarray datasets from NCBI Gene Expression Omnibus (GEO) (https://www. ncbi. nlm. nih. gov/geo/) were collected for Alzheimer's disease: GSE4757 and GSE48350 obtained from four brain regions: the hippocampus, entorhinal cortex, superior frontal cortex, post-central gyrus and GSE5281 obtained from six brain regions: the entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, superior frontal gyrus and primary visual cortex. The last two datasets were split based on the region of the brain in which the samples were collected, obtaining a total of 11 AD datasets composed of both case and control samples. Concerning lung cancer, three microarray datasets from NCBI GEO were collected: GSE19188, GSE19804 and GSE33532. The last one, involving four biopsies from the same sample, was split into four datasets.
We thus obtained a total of six LC datasets composed of case and control samples. All these microarray datasets were obtained with the same microarray platform, the Affymetrix HG U133Plus2, to reduce bias due to inter-platform differences. Moreover, each study was normalized separately using the frozen multiarray analysis (fRMA)  [60]. Additionally, the RNA-seq lung dataset downloaded from The Cancer Genome Atlas (TCGA; https://tcga-data. nci. nih. gov/tcga/) was added to the analysis. In this case, RSEM normalized Level 3 data were downloaded from TCGA. Genes having zero values in more than 30% of samples were filtered out and the data were then log2 transformed.
We then defined the top-contributing genes of a community as those genes having a weight in the consensus metagene higher than three standard deviations in absolute value. The top-contributing genes were then divided into "up" and "down" based on their sign in the consensus metagene and tested for their intersection with the various collections of signatures. For cell type specific signatures, we used a Fisher's exact test with Bonferroni correction; for MsigDB, we employed its default enrichment test  [33]. After testing the association of each community with all the considered annotations (MSigDB signatures, MCP counter cell type signatures, the lung cancer subtypes available for TCGA data and the CIT metagenes), we associated the annotation that is more consistently found across the different tests to each community.
In Supplementary Table  S2 , the annotations associated to each community together with their associated p-values are more extensively described. Finally, to test the reproducibility of the identified consensus metagenes in other cancers, we used the metagenes computed with sICA on TCGA transcriptomics data from 32 different cancer types  [28]. Then, for each community in the -RBH AD/LC subnetwork, we computed the number of cancers having at least one correlated metagene. The resulting values are reported in Supplementary Table  S2.
2010) , no such universally shared pattern exists for viruses. A consequence is that no universally accepted strategy or tool currently exists to define OTUs and evaluate viral alpha or beta diversity from virome data  (Nooij et al. 2018; Simmonds 2015). We have developed an automated routine addressing this OTU definition problem and integrated it in VirAnnot, a bioinformatic virome sequence analysis pipeline that already performs the assembly of reads, the identification of viral contigs, and their Blast-based taxonomic assignation, steps considered standard for the analysis of HTS data  (Nooij et al. 2018 ). These steps are then followed by a new clustering strategy that allows for grouping together in the same OTU contigs that share more than a set identity threshold. Sequence datasets.
In order to analyze the reproducibility of the VirAnnot OTU clustering output and the closeness of the identified OTUs to taxonomic species recognized in the current International Committee for the Taxonomy of Viruses (ICTV), several datasets generated from complex pools of plants  (ca. 40 species and 200 individual plants, Ma et al. , unpublished data)  were used. These datasets have been sequenced on an Illumina HiSeq 3000 system at the GeT-PlaGe platform (INRA, Toulouse, France) and deposited in the INRA National Data Portal under the identifier https://doi. org/10. 15454/TVWBCQ. VirAnnot pipeline workflow. The VirAnnot pipeline integrates standard bioinformatic tools in three main steps: (i) reads cleaning, (ii) contigs assembly, and (iii) taxonomic classification. Briefly, the first step consists of raw reads quality trimming with a minimum score of 20 and a minimum read length of 70.
The reads are then demultiplexed followed by adapters, polyA, and if necessary, multiplex identifier (MID) tag removal using cutadapt  (Martin 2011). In order to limit intersample cross talk associated with index-hopping  (Illumina 2017; van der Valk et al. 2019) , a subroutine can be implemented to retain only reads having identical MID tags on both pairs. The second step performs the assembly of the reads from all selected libraries into contigs using either IDBA-UD  (Peng et al. 2012)  or SPAdes  (Bankevich et al. 2012). There is however no specific step implemented to identify terminal redundancies to identify and assemble circular viral genomes. Through the third step, the contigs are annotated using BlastN or BlastX  (Altschul et al. 1990 ) against the NCBI GenBank nr or nt sequence databases with a user defined significance threshold (default e-value of 10 -4 ).
In addition to similarity searches against protein databases, a search against the PFAM database  (Punta et al. 2012 ) is carried out in all six reading frames using RPS-Blast (reverse-position-specific BLAST)  (Marchler-Bauer et al. 2002)  with again a user defined threshold (default e-value of 10 -4 ). Clustering approach of OTU identification. After the RPS-Blast and BlastX searches, the VirAnnot pipeline identifies OTUs based on a clustering approach performed on sequences encoding conserved viral protein domains  (Itzhaki 2011; Koo et al. 2009). All contigs encoding a given virus-specific conserved protein motif, identified by the RPS-Blast annotation, are aligned with reference sequences using the ETE3 Toolkit, taking into account the motif-coding information so that all sequences are aligned in the correct orientation  (Huerta-Cepas et al.
Validation of the OTU definition threshold in relation to ICTV taxonomic species. Various datasets (see above) were analyzed using a 10% clustering cut-off value for OTUs defined on the basis of the RNA-dependent RNA polymerase (RdRp) conserved motifs. For several singlestranded RNA (ssRNA) or double-stranded RNA (dsRNA) viral families, the average pairwise distance between OTUs (amino acid divergence in a 100 amino acid small region extending on both sides of the GDD conserved triplet) was then compared with that between viral species recognized by the ICTV using the MEGA software  (Kumar et al. 2016). Pipeline availability. VirAnnot is freely available at https://github. com/marieBvr/virAnnot. All documentation about the implementation, installation, and use of the pipeline is available at https://virannot-docs.
readthedocs. io/en/latest/. Repeatability. To validate the clustering routine implemented in VirAnnot, its repeatability was evaluated by running the whole pipeline analysis five times on two datasets. A comparison of the OTUs defined on the basis of the well-known RdRp signature sequences of RNA viruses (RdRp1, RdRp2, RdRp3, and RdRp4) in these five independent analyses was then performed. For RdRp1, 26 ≤ OTUs ≤ 27 were obtained; for RdRp2, 45 ≤ OTUs ≤ 46; for RdRp3, 40 OTUs; and for RdRp4, 111 ≤ OTUs ≤ 117. Comparison of the OTUs identified between the different clustering repetitions showed that the same OTUs were repeatedly defined (not shown). These comparisons therefore show a good stability of the VirAnnot output and the limited variations observed are likely due to the known variability of the assembly and clustering processes.
Both top and bottom plastics are sandwiched between two 5 mm thick Aluminum plates. Whole assembly is then bolted to ensure leak tightness. Test section is checked for leakage by applying a pressure of almost 10 bars between the inlet and outlet connectors and closing the outlet manifold. Pressure inside the assembly is monitored for at least 5 h to spot any major leakage. Such a test is repeated before initiating experimental test campaign. Small holes of 200-250 µm are drilled in top plastic at 3 axial locations x = 0. 58L, 0. 72L and 0. 87L. Local static pressure is read through these holes using a solenoid valve-switching assembly. At each pressure tap, differential pressure between the port and atmosphere is measured using the differential pressure gauge. The atmospheric pressure is also measured at every data point and is finally added to differential reading to obtain absolute pressure of the specific cross section.
Therefore, a limited set of simulations with only 3 MCs with different hydraulic diameters and aspect ratios are chosen to emphasize minor losses dependence on the MC dimensions. Geometry and meshing is done using Design Modeler and ANSYS Meshing software, respectively. A mesh of 45 × 30 × 200 is used in the MCs. A structured mesh locally refined at the walls of the channel and manifolds is employed as shown in Figure  4. The mesh expansion factor is kept as 1. 1 and first node point is placed such that y+, which is non-dimensional distance between first mesh node and MC wall, is in between 1 and 4 for the highest Re simulated. Orthogonality of mesh elements inside MC is between 0. 95-1 in all the simulated cases. A commercial solver CFX based on finite volume methods is used for the flow simulations.
Kinematic viscosity dependence on gas temperature is defined using Sutherland's law. Further details of boundary conditions can be seen in Table  4. To estimate friction factor and minor loss coefficients, five different cross-sectional planes are defined at x/L of 0. 005, 0. 25, 0. 5, 0. 75 and 0. 98, respectively. In addition, two planes defined at x/L of 0. 0005 and 0. 9995 are treated as the inlet and outlet of MC, respectively. Results from these planes are further post processed in MATLAB to deduce required flow quantities. Numerical friction factors are then evaluated simply by using Equation (4).
Experimental Average Friction Factor Pressure-drop experiments are performed for MC1 using nitrogen gas as working fluid. Results in terms of f f in laminar flow regime are compared with Shah & London correlation (Equation (  12 )) for rectangular channels while Blasius law in turbulent flow regime is used for comparison. Laminar f f is also compared with the correlation of Hong et al. [12]  that caters for an increase in laminar f f due to compressibility. Numerically obtained values of K in are used in data reduction M1. When outlet is open to atmosphere, a convenient practice is to estimate the experimental f f by assuming a fully expanded flow at the MC outlet. This assumption essentially makes K out to be zero and is quite realistic in the laminar and early turbulent flow regimes as shown numerically in Figure  6b.
Conclusions From the above-mentioned discussion followings points can be inferred: 1. For the correct experimental estimation of f f from total pressure drop and inlet temperature measurements employing minor losses methodology proposed for incompressible flows (M1), it is essential to have inlet and outlet loss coefficients calculated numerically as a priori else results will be misleading in the presence of strong compressibility effects. 2. Gas flow properties at MC inlet can be obtained by considering an isentropic expansion of gas between the entrance manifold and MC (M2). This, when coupled with the assumption of perfect expansion at MC outlet, results in a self-sustained experimental data reduction method with no numerical input as a priori. Furthermore, this avoids possibility of inducing errors in inlet minor loss estimation due to poor numerical modeling.
8 M single nucleotide polymorphisms and confirmed that the major histocompatibility complex (MHC) loci and immunoglobulin gene family exhibited high nucleotide diversity in the tree shrew genome. We updated the tree shrew genome database (TreeshrewDB v2. 0: http://www. treeshrewdb. org) to include the genome annotation information and genetic variations. The new high-quality reference genome of the Chinese tree shrew and the updated TreeshrewDB will facilitate the use of this animal in many different fields of research.
, 2013) , visual cortex function  (Bosking et al. , 2002; Lee et al. , 2016; MacEvoy et al. , 2009; Mooser et al. , 2004; Veit et al. , 2014) , brain development and aging  (Fan et al. , 2018; Wei et al. , 2017) , and neuropsychiatric disorders induced by social stress  (Fuchs, 2005; Meyer et al. , 2001). Previously, we successfully sequenced the genome of the Chinese tree shrew (Tupaia belangeri chinensis) using Illumina short-read sequencing (KIZ version 1: TS_1. 0) and showed their close relationship to non-human primates, thereby settling a longrunning debate regarding the phylogenetic position of tree shrews within eutherian mammals  (Fan et al. , 2013). Furthermore, to advance the use of the tree shrew genome, we developed a user-friendly tree shrew database (TreeshrewDB: www. treeshrewdb.