dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, two case studies indicate that systematically recorded (C) Heatmap shows protein signatures that report on COVID-19 severity. Visualization was performed using the ComplexHeatmap R package  (Gu et al. , 2016). Black ''squares'' indicate missing values. Patients labeled with an asterisk (*) had a fatal outcome of the disease. (D) Proteins upregulated (top panel) and downregulated (lower panel) depending on COVID-19 severity (WHO grade; SS, standard serum; GS, Generation Scotland), as well as the population spread of the protein abundance in 199 randomly selected individuals of an independent cohort (Generation Scotland; GS). As the absolute quantities from the COVID-19 and GS studies cannot be compared directly (samples were obtained in a different manner), we simplified the visual assessment of the population spread, by normalizing by the median of GS quantities to the median of WHO grade 3 (no oxygen support) COVID-19 cases (the normalized values were used for illustration purposes only and not used for testing for statistical significance).
The boxes show first and third quartile as well as the median (middle) and the whiskers extend to the most extreme data point, which is no more than 1. 5 times the interquartile range from the box. Proteins upregulated with increasing severity of COVID-19: A1BG, ACTB;ACTG1, C1R (complement C1r), C1S (complement C1s), C8A (complement C8 alpha chain), CD14 (monocyte differentiation antigen CD14), CFB (complement factor B), CFH (complement factor H), CFI, CRP, FGA, FGB and FGG, HP, ITIH3, ITIH4, LBP, LGALS3BP, LRG1, SAA1, SAA1;SAA2, and SERPINA10; proteins downregulated with increasing severity of COVID-19: ALB, APOA1, APOC1, GSN, and TF. proteomes provide information that is beyond that of currently applied clinical assays. When we first obtained the dataset, two individuals (patients 4 and 6) initially clinically assessed as severe  (Kurth et al.
, 2020)  clustered with patients that suffered from the mild form of COVID-19 (Figure  S3 ). These results triggered a retrospective assessment, which revealed that patient 4 turned out to be suffering from type B influenza rather than a SARS-CoV-2 infection, whereas patient 6 was classified as severe due to recent chemo-immunotherapy due to a hematological malignancy, applied just ten days before his COVID-19related hospitalization. We have, as a consequence, excluded patient 4 from the COVID-19 dataset (all results shown in Figure  4 ). Both clinical case findings indicate however a high prognostic precision of the proteomic biomarker signatures and demonstrate that proteomes may very well outperform conventional clinical assays: no current clinical assays would have identified this type of outliers in the clinical assessment of the two individuals.
1ml, Square well, V-bottom). Collected material was completely dried on a vacuum concentrator (Eppendorf Concentrator Plus (Biomek NXp protocol) or Fisher Scientific, SPD300P1 (Biomek i7 protocol) and redissolved in 50ml 1% ACN, 0. 1% formic acid (Biomek NXp protocol) or 50ml 0. 1% formic acid (Biomek i7 protocol), then stored at -80 C until data acquisition. The samples for the SARS-CoV-2 studies were analysed without freezing. QC samples for repeat injections were prepared by pooling commercial serum samples and were spiked with iRT peptides (Biognosys).
5 minutes and was kept for 0. 2 minutes at this composition before going back to 1% B in 0. 3 min. The equilibration times were 2. 8 minutes (Water H Class protocol) or 4. 2 minutes (Agilent Infinity II protocol). Data was acquired in high sensitivity mode and the amount of total proteins injected was 5mg (GS study) and 10mg (SARS-CoV-2). The sample load was optimised (Figure  S2A ) and is a balance between how frequently one needs to clean the instrument and the identification numbers. The DIA/SWATH method consisted of an MS1 scan from m/z 100 to m/z 1500 (20ms accumulation time) and 25 MS2 scans (25ms accumulation time) with variable precursor isolation width covering the mass range from m/z 450 to m/z 850 (Table  S6 ). Ion source gas 1 (nebulizer gas), ion source gas 2 (heater gas) and curtain gas were set to 50, 40 and 25 respectively.
QUANTIFICATION AND STATISTICAL ANALYSIS Mass Spectrometry Data Processing, Batch Correction and Quality Control The raw data were processed using DIA-NN 1. 7. 10 in the ''robust LC (high precision)'' mode with RT-dependent median-based cross-run normalisation enabled. MS2 and MS1 mass accuracies were set to 20 and 12 ppm, respectively, and scan window size set to 6. Although DIA-NN can optimise such parameters automatically, we fixed them to these values to ensure comparability. For all the experiments, we used a project-independent public spectral library  (Bruderer et al. , 2019). Human UniProt (UniProt Consortium, 2019) isoform sequence database (3AUP000005640) was used to annotate the library. The library was first automatically refined based on the dataset in question at 0. 01 global q-value (using the ''Generate spectral library'' option in DIA-NN).
Finally 41-point running median smoothing was applied. Protein quantification was performed using the MaxLFQ algorithm  (Cox et al. , 2014)  as implemented in the diann R package (https://github. com/ vdemichev/diann-rpackage, version 1. 0, commit ''eb4607a''). Data completeness was defined as the proportion of non-missing values in the proteins x samples quantities matrix. The coefficient of variation (CV) was calculated for each protein as its empirical standard deviation divided by its empirical mean. PCA analysis was always performed only on ubiquitously identified proteins: imputation was not used.
Differential Expression Analysis In the exploratory cohort, differential expression was tested only for proteins quantified using at least five peptide precursors in one of the acquisitions. Further, protein groups were quantified using only precursors detected in at least 10% of patient samples (both cohorts). As we were specifically interested in proteins which could serve as biomarkers of COVID-19 severity, the test was performed using the Kendall's Tau test for the Theil-Sen trend estimator (as implemented in the EnvStats R package  (Millard, 2014) ) against the disease severity as classified according to the WHO ordinal scale (Table  S1 ). The input for the test was obtained by calculating, for each patient, median protein levels across the timepoints or replicates measured (Table  S4  for timepoint information).
Multiple testing correction was performed using the Holm-Bonferroni method for FWER control, as implemented in the p. adjust R function, and the significance threshold was set to 0. 05. The choice of a nonparametric test (Theil-Sen) was dictated by the fact that such widespread methods as ANOVA or linear regression are only valid under the assumption of Gaussian errors with the same variance across all conditions. In the case of this dataset, however, we observed very significant differences in the variance, e. g. many proteins seem a lot more variable between patients with severe and critical COVID-19 than mild COVID-19. For such a situation a nonparametric test is ideal: although it would typically have less power (less proteins detected as differentially expressed), the p-values produced are reliable.
(Perez-Riverol et al. , 2019)ortium via PRIDE(Perez-Riverol et al. , 2019)partner repository with the dataset identifier Pride:PXD018874. According to the terms of consent for GenerationScotland participants, access to individual-level data (omics and phenotypes) must be reviewed by the GS Access Committee. Applications should be made to access@generationscotland. org. The DIA-NN software suite and DiaNN R package are open source and are freely available for download at https://github. com/vdemichev/DiaNN and https:// github. com/vdemichev/diann-rpackage respectively.
ACKNOWLEDGMENTS We thank  Trefor Mitchell  for his help with the quality management system;  Stephan Kamrad  and  Simran Aulakh  for proofreading the manuscript;  Jan-David Manntz  ( Beckman, Germany ) for help with the Biomek i7; and  Robert Lane ,  Jean-Baptiste Vincedent , and  Nick Morrice  ( SCIEX ) for help with the TripleTOF 6600. This work was supported by the  Ministry of Education and Research (BMBF) , as part of the  National Research Node "Mass spectrometry in Systems Medicine" (MSCoresys) , under grant agreement  031L0220A. The study was further supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001134 ), the  UK Medical Research Council  ( FC001134 ), and the  Wellcome Trust  ( FC001134 ) and received specific funding from the  BBSRC  ( BB/ N015215/1  and  BB/N015282/1 ) and the  Wellcome Trust  ( 200829/Z/16/Z ) as well as a  Crick Idea to Innovation (i2i) initiative  (grant number:  10658 ) as well as the Crick lifeArc Project  1290305  (to M.
R. ). The Generation Scotland study received core support from the  Chief Scientist Office of the Scottish Government Health Directorates  ( CZD/16/6 ) and the  Scottish Funding Council  ( HR03006 ) and is now supported by the  Wellcome Trust  ( 216767/Z/19/Z ). A. C. is funded by  HDR UK  and the  Wellcome Trust  ( 216767/Z/19/Z ). C. H. is supported by an  MRC   University Unit Programme grant  ( MC_UU_00007/10 ) (QTL in Health and Disease). R. E. M. is supported by an  Alzheimer's Research UK  project grant ( ARUK-PG2017B-10 ). L. E. S. is supported by the  German Research Foundation  (DFG,  SFB-TR84 114933180 ) and by the  Berlin Institute of Health (BIH) , which receives funding from the  Ministry of Education and Research (BMBF). M. Witzenrath is supported by grants from the  German Research Foundation ,  SFB-TR84 C6  and  C9  and by the  German Ministry of Education and Research (BMBF)  in the framework of the CAPSyS (01ZX1304B) SYMPATH ( FKZ 01ZX1906A ) and  PROVID  project ( FKZ 01KI20160A ).
(Perez-Riverol et al. , 2019)ortium via PRIDE(Perez-Riverol et al. , 2019)partner repository with the dataset identifier Pride:PXD018874. According to the terms of consent for GenerationScotland participants, access to individual-level data (omics and phenotypes) must be reviewed by the GS Access Committee. Applications should be made to access@generationscotland. org. The DIA-NN software suite and DiaNN R package are open source and are freely available for download at https://github. com/vdemichev/DiaNN and https:// github. com/vdemichev/diann-rpackage respectively. EXPERIMENTAL MODEL AND SUBJECT DETAILSClinical Samples of COVID-19 Patients Sampling was performed as part of the Pa-COVID-19 study, a prospective observational cohort study assessing pathophysiology and clinical characteristics of patients with COVID-19 at ChariteUniversit€ atsmedizin Berlin(Kurth et al.
The stakeholders primarily concerned with PBA in the physiotherapy workplace are physiotherapy students and work-based educators. In the Republic of Ireland, two types of An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice. J o u r n a l P r e -p r o o f 5 work-based educators exist; clinical educators, primarily service providers who voluntarily undertake student supervision, and practice tutors who are qualified clinicians employed to provide dedicated educational support to students and clinical educators but who do not engage in service provision  (3, 4). These are often employed in large teaching hospitals or regions where large numbers of students are accommodated at one time. Notwithstanding, in physiotherapy education globally, clinical educators provide up to one third of the academic content of university programmes through practice-based modules  (8, 9)  thus highlighting their significant contribution to the preparation of physiotherapy students for practice.
INSERT FIGURE 1 HERE In line with recommendations for DCE design  (17, 18) , focus groups were conducted with three stakeholder groups; physiotherapy students (n=33), clinical educators (n=27) and practice tutors (n=19) in order to inform the DCE. These focus groups identified An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice. 7 stakeholders' perceived challenges and facilitators of the current PBA process and their priorities for its development  (3, 4). This data informed the selection of attributes for the DCE, i. e. following thematic analysis  (19)  of focus group data, the first and last author reread all transcripts independently and collaboratively identified attributes reflecting the most relevant challenges of PBA in the physiotherapy workplace.
Within the gamma-proteobacteria, all known vascular pathogens in our dataset have a copy of cbsA localized in the context of type 1, 2, or 4 neighborhoods. Within Xanthomonas, sequences from the clade of CbsA homologs found in both vascular and non-vascular pathogens are located in type 3 neighborhoods, while sequences from the clade of CbsA homologs found exclusively in vascular pathogen genomes are located in type 4 neighborhoods, further supporting the hypothesis that sequences belonging to either of these two clades have separate functions (Figure  2 ).
We therefore speculate that cbsA's absence may be selected for to dampen recognition by the host and/or the elicitation of host immunity; however, these hypotheses remain to be tested. Gene loss is a fundamental mechanism of adaptation  (19). Especially for loci with large effects such as cbsA, only a minimal number of loss events are required to incur drastic changes to phenotype. Adaptive phenotypes arising through loss of function may emerge over shorter timescales compared with adaptive phenotypes arising through gains in function, as genes typically have more mutational opportunities for losing functions than for gaining functions  (20). Even within our own limited dataset, we observed multiple mutational routes in the form of sequence deletions and transposable element insertions that led to the convergent loss of cbsA in different non-vascular pathogen lineages, which suggests that non-vascular phenotypes readily emerge in the Xanthomonadaceae.
Comparative genomics for identification of vascular pathogen-specific genes Using Orthofinder v2. 2. 3  (25) , we first created ortholog groups (OGs) from all predicted amino acid sequences derived from 171 complete and 8 partially complete publicly available assemblies from the Xanthomonadaceae and representative lineages across the beta-and gammaproteobacteria in order to obtain a comprehensive comparative genomic dataset (Table  S1 ). Consensus functional annotations for each OG were obtained by determining the most frequent protein family domain present among the members of the OG using InterProScan version 5. 25-64. 0 (26). Predicted proteins across all genomes were classified into 36,905 OGs using Orthofinder (Supplemental Table  2 )  (25)  Genomes were classified as vascular, non-vascular or unknown based on available information in the literature (Supplemental Table  1 ).
Phylogenetic analyses To decrease redundancy among strain-or species-specific genomes in our dataset while maintaining sample power, we built a preliminary 50% majority-rule consensus tree based on the maximum likelihood (ML) phylogenies of 139 amino acid alignments of single copy orthologs. We used this tree to guide our selection of at most 3 representative genomes from each Xanthomonas pathovar, ultimately arriving at a final dataset of 86 genomes (Table  S1 ). Using this de-replicated genomic dataset, we then built a final 50% majority-rule consensus tree based on 81 amino acid-based ML phylogenies of single copy orthologs that had greater than 60% average bootstrap support, our rationale being that consolidating multiple gene trees with high support increases the robustness of species-level phylogenetic inference  (31).
Analysis of cbsA homologs and neighboring genomic regions In order to determine the precise mechanisms and relative order through which cbsA was gained and lost from the genomes in our dataset, we analyzed the evolutionary history and structural features of all gene neighborhoods that flank cbsA. Using custom scripts, we first explored the gene neighborhoods surrounding cbsA homologs (+/-15kb) in the 86-genome dataset for conserved synteny, as defined by orthogroup content conservation. For each of the four conserved neighborhood types that we identified, we then re-searched all genomes for regions composed of these genes, thus identifying all instances of each neighborhood in each genome, regardless of whether cbsA was present or not (Table  S3 ). In doing so, we could then leverage phylogenetic evidence from flanking genes to support or reject competing hypotheses of gene duplications, horizontal gene transfers and losses that may have resulted in cbsA's extant distribution.
We built nucleotide-based ML phylogenies of cbsA and the genes from each neighborhood type, and manually reconciled their evolutionary histories with the consensus species tree using a combination of parsimony-based gene tree-species tree reconciliation and likelihood-based phylogenetic testing (Supplemental Figures  6 7 8 ; Supplemental Tables  4 5 6 ). In order to robustly root the cbsA tree for reconciliation analysis, we first retrieved the top 1000 hits in the NCBI nr protein database (last accessed: 03/09/18) to the cbsA sequence in X. campestris (accession: WP_076057318) and used them to build a midpoint rooted ML tree (available on the Figshare repository). This tree was then used as a reference to root subsequent ML trees that focused only on this study's clade of cbsA sequences of interest.
We additionally built a ML tree with cbsA sequences from the full 179 genome dataset in order to verify the final topology of the cbsA tree built with the 86 genome de-replicated dataset (available on the Figshare repository). All other gene trees were midpoint rooted. All genomic regions were further annotated for transposable elements with BLAST using the ISFinder database in order to ensure a comprehensive structural annotation of mobile elements  (36). Nucleotide sequences of the genomic regions that were missing cbsA were searched using BLASTn with a cbsA query to ensure any missing or incomplete cbsA coding regions were identified. The Mixture Model and Hidden Markov Model from the PhyML package were used to detect homologous recombination breakpoints in the untrimmed nucleotide alignments that were then manually inspected and refined if necessary  (37).
Funding: An  NSF   Postdoctoral Fellowship in Biology  ( 1306196 ) to JMJ, a  US Fulbright Scholar Award  to Belgium to JMJ, a  USDA-NIFA Postdoctoral Fellowship  ( 2017-67012-26116 ) to JMJ, a COST SUSTAIN travel grant to JMJ and a  NSF-NIFA joint PBI grant  ( 2018-05040 ) to JMJ, JML and JEL. NSF ( DEB-1638999 ) to JCS. A  Fonds de Recherche du Quebec-Nature et Technologies Doctoral Research Scholarship  to EGT. AJ and LDN are supported by the  NEPHRON  project ( ANR-18-CE20-0020-01 ). This work was supported by a Ph. D. grant from the  French Ministry of National Education and Research  to AC. LIPM is part of the  TULIP LabEx  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
PoS(CORFU2019)175 (σ 1 , σ 2 , σ 3 ) ∼ ( γ 1 σ 1 γ, γ 2 σ 2 γ, γ 3 σ 3 γ ) , γ i ∈ S n [S 2 ] , γ ∈ S n , (4. 1) where S n [S 2 ] is the so-called wreath product of S n by S 2 (that is the semi direct product S n (S 2 ) n ). Its action describes as follows: (S 2 ) n permutes independently the half-lines of the n v j i 's; then, S n permutes the n vertices v j i of a given color j, hence the three copies of the wreath products in rank 3. Up to equivalence, any contraction belongs to the double quotient S n [S 2 ] × S n [S 2 ] × S n [S 2 ] \(S 2n × S 2n × S 2n )/Diag(S 2n ). (4. 2) The computation of the cardinality of double coset requires a technique different from the complex case but it still involves the Burnside lemma. Its last stage makes use of software programs. The number of invariants in rank d = 3, that we denote Z o 3 (2n), follows the sequence: Other counting sequences at rank d = 4 are provided in  [7] , and its appendices list Mathematica programs computing both general and connected sequences at arbitrary d.
Conclusion We have enumerated rank d real and complex tensor invariants that are orthogonal and unitary invariants relying on techniques build on symmetric groups and their representation theory. From this enumeration, we find several bridges with other formalisms. In particular, we were interested in TopFT allowing us to interpret in a different way these countings as the number of covers of topological objects (punctured 2-sphere for the complex case and torus with defects for the real case) and even to foresee geometrical pictures attached to them. If this program is successfully achieved then one could establish, for instance, connections between string theory and tensor models. Such correspondences deserve attention. From another side, the representation theory of the symmetric group brings a different perspective on these countings and might connect them with the problem of the combinatorial interpretation of the Kronecker coefficient.
Acknowledgments I gratefully thanks the organisers of  Corfu Summer Institute 2019  "School and  Workshops on Elementary Particle Physics and Gravity"  ( CORFU2019 ) and the  Humboldt Kolleg "Frontiers in Physics, From the Electroweak  to the  Planck Scales", especially George Zoupanos ,  Patrizia Vitale  and  Ifigeneia Moraiti. I am also particularly indebted to  Sanjaye Ramgoolam  for introducing me in the subject and for years of collaboration without which this work could not stand. I wish also to warmly thank  Avohou Remi Cocou  and  Nicolas Dub , for collaboration on the joint work presented here.
Results An extensive, highly-connected trial network. Field trial data were generated by Arvalis (https://www. arvalisinstitutduvegetal. fr) and included 25,841 grain yield records collected from 1998 to 2014 at 242 locations (Fig. 2 ). There were 752 year-location combinations with one (un-replicated) trial per year-location. In total, 481 French-registered cultivars were tested. All trials were connected through a common tester and by many other cultivars that generated partial connections between trials. On average, each trial was connected with at least 340 other experiments through at least five genotypes (see "Methods" and Supplementary Fig. 1  for further details). All testing locations had a meteorological station within 10 km from where environmental Fig. 1  Computer-simulated performance of candidate genotypes in target locations.
The proposed computer-simulation platform uses phenotype, genotype (e. g. , SNPs), and environmental covariate data collected in an existing network of trials and historical weather records at target locations ("virtual network of trials") to simulate the performances of selected genotypes in target locations, under possible weather conditions. data were retrieved. All trials received fungicide and seed treatments. The average grain yield (standardized at 15% moisture) was 9. 49 (±1. 50) tons per hectare. The distribution of grain yield was reasonably symmetric (Supplementary Fig. 2 ). Each variety was genotyped with an Axiom high-density genotypic platform (Affymetrix Inc. , Santa Clara, CA) containing 420K SNPs 7 ; these genotypes were generated within the Breedwheat project (ANR-10-BTR-03).