dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present study, we selected cases with drastic NEPCa criteria, either pure or with a majority NE component. Furthermore, since our objective was to identify both immunoprofile and prognostic markers, we selected NEPCa cases with sufficient amount of cancer tissue available, and extensive clinical data including at least 6 months follow-up after tissue collection. Considering the markers of NE differentiation, we observed that 94% of post-treatment cases expressed chromogranin A and only 44% synaptophysin, suggesting that chromogranin is a better marker of NED in treatment induced NEPCa. In contrast, both markers were found to be equally expressed in de novo cases. Other studies, composed mainly of de novo NEPCa cases, have reported a more frequent positivity for synaptophysin than for chromogranin A  [3, 4, 9].
Data collection We collected and analysed medical history, physical examination, and haematological, biochemical and immunological data obtained in patients with SARS-CoV-2 infection from electronic medical records. Data collection forms were anonymised and reviewed independently by two investigators. Data collected at ICU admission included age, gender, cardiovascular risk factors, comorbidities (obesity defined by a body mass index > 30 kg. m -2 , hypertension, diabetes, cardiovascular disease, chronic respiratory disease, chronic kidney disease defined by a creatinine clearance < 60 ml. min -1. 1. 73 m -2 ). Disease severity was evaluated using Simplifier Acute Physiology Score (SAPS II)  [9] , Sequential Organ Failure Assessment without neurological points (called SOFA # )  [10]  and organ support therapy were measured at Baseline (Day 0).
Acknowledgements We are indebted to  Pierre Aucouturier ,  Guillaume Lefevre ,  Jerome Guéchot ,  Michel Vaubourdolle ,  Jérémie Joffre ,  Judith Leblanc  for their help and l' Entrepôt de Données de Santé de l' Assistance Publique -Hôpitaux de Paris (AP-HP)  for their help in data extraction.
, 2016; Torres-Oliva et al. , 2018). Comparison of the RNA-seq data among these three developmental timepoints showed that transcriptomes of 72 h AEL eye imaginal discs were the most different in comparison to transcriptomes from both 96 h AEL and 120 h AEL for both species (Suppl. Fig. 1 ). This reflects the distinctive processes that are occurring at these developmental stages  (Torres-Oliva et al. , 2018). We next focused on the expression of genes located within the mapped 0. 66 Mb X-linked region and, in particular, on expression differences at 120 h AEL, when at least the most posterior ommatidia are acquiring their final size. Of the 62 genes located in this region, 49 were expressed in at least one of our RNA-seq datasets and only eight of these genes were differentially expressed between the eye discs of these two species at this timepoint (Suppl.
simulans and D. mauritiana further, we performed ATAC-seq  (Buchberger et al. , 2021; Buenrostro et al. , 2013; Kittelmann et al. , 2018)  on D. simulans and D. mauritiana eye imaginal discs at 96 and 120 h AEL. We mapped our datasets against both genomes in order to detect common, differentially accessible and species-specific regulatory regions (Fig. 4a ). The ATAC-seq peak calling of the four datasets (two developmental stages and two species) revealed a total of 22 peaks in the otd locus, all of which were located within alignable orthologous regions in the two species (Fig. 4a ). 19  (D. sim chrX: 8,129,876 -8,131,170 , padj = 0. 0317) located upstream of otd (Fig. 4 ).
mauritiana-specific regions in peak 16. We predicted 164 putative TFBM in D. simulans and D. mauritiana in Peak 6 using JASPAR with a threshold of 85%. This included predicted TFBMs for Cut (Ct), Odd-paired (Opa), Optix, Sine oculis (So) and the Iroquois complex TFs (Araucan (Ara), Caupolican (Caup) and Mirror), which all have detectable expression in our RNA-seq datasets (Suppl. Table  3 ) and are known to be involved in eye-   5 ) and 60 were D. simulans-specific (Trl, Dll, Optix, Iroquois Complex, Dref, BEAF-32, Ems, Cut, Deaf1, E5, Ovo, Sd, Bgb::run). Overall, we identified several regions in the otd locus that may function as new eye enhancers for this gene in D. mauritiana and D. simulans, and that their differential activity could be responsible for the temporal differences in the onset of otd activation between these two species.
Differences in otd targets during eye development between D. simulans and D. mauritiana Next, we investigated whether differences in the onset of expression of otd between D. simulans and D. mauritiana promoted further changes in its Gene Regulatory Networks (GRNs) that ultimately may be responsible for the differences in the final size of ommatidia. To this end, we called open chromatin peaks in each sample and searched for the Otd-binding motif in these accessible regions of genes expressed during eye development. Based on this analysis, we found 1,148 putative Otd target genes. We next examined which of these accessible chromatin peaks were associated with genes that were differentially expressed in our transcriptome datasets. We found that peaks associated with 161 of the 1330 genes that are upregulated in D.
mauritiana contained Otd binding motifs, and 111 out of 1249 genes upregulated in D. simulans had associated peaks containing Otd binding motifs (Fig. 5a, c , Suppl. Table  6 ). We then performed Gene Ontology (GO) term enrichment analysis for these differentially expressed genes with accessible chromatin containing Otd binding motifs. The D. mauritiana dataset exhibited enrichment in terms related to developmental processes, cell morphogenesis, axonogenesis, regulation of differentiation or growth, among others (Fig. 5b ). By contrast, genes that were upregulated in D. simulans with associated Otd-peaks were enriched in terms such as the MAP kinase network, signalling by interleukins and cellular response to insulin stimulus (Fig. 5d ).
Volume renderings of the two channels were used to visualize the data and identify ommatidia that were Rh3-positive (Rh3 + ) and those that did not express Rh3 (Rh3 - ) (Figure  4a ). Landmarks were used to label the ommatidia and the 3D-measuring tool was used to measure the diameter of the lens of each ommatidium. For landmarks and the measuring tool to work, an isosurface of the autofluorescence channel was created to provide a reference as to where in the 3D space these were placed (Figure  4b ). Using this technique, ommatidia diameters of Rh3 + and Rh3 -ommatidia were measured in a central region of the compound eye. Also, in a central region, a quadrant of 10x10 ommatidia was defined and the number Rh3 + and Rh3 -ommatidia was counted (Figure  4b ). All raw diameter measurements and ommatidia counts are available in the supplementary material (Suppl.
Accurate quantitation of cDNA libraries was performed using the QuantiFluor™dsDNA System (Promega, Madison, Wisconsin, USA). The size range of final cDNA libraries was determined by applying the DNA 1000 chip on the Bioanalyzer 2100 from Agilent (280 bp). cDNA libraries were amplified and sequenced using cBot and HiSeq 2000 (Illumina): only 120h eye-antennal imaginal disc samples were sequenced as paired-end (PE) reads (2 x 100 bp), all the rest of samples were sequenced in single-end (SE) reads (1 x 50 bp). Sequence images were transformed to bcl files using the software BaseCaller (Illumina). The bcl files were demultiplexed to fastq files with CASAVA (version 1. 8. 2). The reciprocally re-annotated references described in  (Torres-Oliva et al. , 2016)  were used to map the species-specific reads.
, 2013). These reads were then converted to the D. melanogaster coordinate system using liftOver (1. 14. 0) with custom prepared chain files, one for the conversion of D. mauritiana coordinates to D. melanogaster coordinates and one for the conversion of D. simulans coordinates to D. melanogaster coordinates. Peaks were then called using MACS2 (version 2. 1. 2, (Zhang et al. , 2008) ) with the following parameters: -shift -100, extsize 200, -q 0. 01. We used the Diffbind package (version 2. 12. 0,  (Ross-Innes et al. , 2012) ) in R (version 3. 6. 1. ) to search for differentially accessible ATAC-seq regions. A consensus peak set of 19,872 peaks (96h AEL) and 15,868 peaks (120h AEL) was used for all samples and the reads were counted for each identified peak with the dba. count command. For each time point separately we used the dba.
analyze command with default parameters to get differentially accessible peaks between the two species. This command uses by default the DESeq2 analysis. All plots were as well generated with the DiffBind package. To search for TFBM of potential otd regulators, we used the JASPAR core database and its online tool for screening DNA sequences of ATAC-seq peaks with all possible TFB motifs from insects (153 profiles) with a relative profile score threshold of 85%.
Gene regulatory network reconstruction To search for TFBM of potential otd regulators, we used the JASPAR core database and its online tool for screening DNA sequences of ATAC-seq peaks in the otd locus (between the two flanking genes) with all possible TFB motifs from insects (153 profiles) with a relative profile score threshold of 90%. To define a list of potential Otd target genes, we used an Otd-motif (Dmelanogaster-FlyFactorSurvey-Oc_Cell_FBgn0004102) from the MotifDB package (version 1. 16. 1), which provides a collection of available transcription factors in R (version 3. 3. 3). We searched for Otd binding sites in accesible chromatin regions with the findMotifsGenome. pl command implemented in the HOMER (version V4. 10. 4, (Heinz et al. , 2010) ) in all samples. All peaks with a predicted Otd motif were annotated to an associated gene using the annotatePeaks.
pl command by HOMER and combined all time points and both species into one file. We then looked for the number of genes with an annotated Otd motif and found 1,148 unique genes, which we overlapped with our RNA-seq dataset to find out which of these target genes were differentially expressed between the two species. GO term enrichment analysis of putative Otd target genes was performed using the online tool Metascape  (Zhou et al. , 2019). We used the online STRING database that integrates all known and predicted associations between proteins based on evidence from a variety of sources  (Szklarczyk et al. , 2020) , to construct networks of DEG encoded proteins. To visualize the network and map genes/prot with Otd motifs we applied the Cytoscape software  (Shannon et al. , 2003).
Acknowledgements This work was funded by grants from the  ERC  ( 242553 ) to A. P. M. and  BBSRC  ( BB/M020967/1 ,  BB/T000317/1 ) to A. P. M. and I. A. and A. P. M. and M. K respectively. M. T. -O. was supported by a  "la Caixa-DAAD " fellowship. N. P. and E. B. were funded by the  Deutsche Forschungsgesellschaft (DFG , Grant No. PO 1648/3-1  to N. P. ). We acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at the TOMCAT beamline X02DA of the  SLS , and the invaluable assistance of  Dr Christian Schlepütz. We want to thank the  Transcriptome and Genome Core Unit, University Medical Center Göttingen (UMG)  for support with next generation sequencing and  Franck Pichaud  for giving us access to otd microarray data.
FastQC software (version 0. 10. 1, Babraham Bioinformatics) was performed. All RNAseq reads are accessible in the Short Read (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is Before the mapping step, PE 100 bp reads were converted into SE 50 bp by splitting the reads in half and merging right and left reads into a single file. bioRxiv preprint doi: ; https://doi. org/10. 1101/2021. 03. 17. 435774 this version posted March 17, 2021. The copyright holder for this preprint. CC-BY 4. 0 International license made available under a PRJNA666524). Archive through umbrella BioProject PRJNA666691 (containing PRJNA374838 and.
In particular, grinding provides a relatively smooth surface, whereas sand-blasting produces a rougher surface characterized by a chaotic pattern. The roughness of the treated surfaces was measured using a Focus-Variation 3d optical microscope (Bruker Alicona, Graz, Austria) and the Sa roughness data are respectively 0. 15 µm for ground and 3. 2 µm for sand-blasted surfaces. behind this is to compare the mechanical behavior of hybrid specimens with their current manufacturing counterparts. Mechanical testing of the materials of this study includes microhardness, tensile, and fatigue tests. Tensile tests were carried out according to the standard ISO 6892-1:2009 using a Autograph AG-X plus 100kN universal testing machine (Shimadzu, Kyoto, Japan), fitted with Trapezium X software and TRViewX image acquisition).
The motive behind this is to compare the mechanical behavior of hybrid specimens with their current manufacturing counterparts. Mechanical testing of the materials of this study includes microhardness, tensile, and fatigue tests. Tensile tests were carried out according to the standard ISO 6892-1:2009 using a Autograph AG-X plus 100kN universal testing machine (Shimadzu, Kyoto, Japan), fitted with Trapezium X software and TRViewX image acquisition). A constant displacement rate of 0. 2 mm/min at ambient temperature was set for all tests. An axial fatigue test campaign was run using an Instron 8801 servo-hydraulic machine with 100 kN load cell (Instron, Norwood, MA, USA) under the following conditions: (i) stress ratio R = -1, (ii) frequency of 5 ~ 10 Hz. Testing was realized under stress control at three levels of stress amplitude: 140 MPa, 170 MPa, and 200 MPa.
, 2019; Mucko et al. , 2020, in press) , but these studies and some of the single gene studies have limited taxon sampling relative to the known diversity of the Bacillariaceae. Until now, the largest single gene study had 195 (28S rDNA) and 175 (rbcL) non-identical Bacillariaceae sequences  (Carballeira et al. , 2017) , and the largest three gene study had 56 sequences (though two may be identical)  (Kim et al. , 2019). Here we have assembled and reanalysed existing molecular data (including 987 sequences from GenBank: Supplementary Table  1 ) and have also taken advantage of many new Bacillariaceae sequences (Supplementary Table  2 ) obtained during recent projects (including a marine benthic diatom project at Szczecin University and French and UK projects to provide barcodes for freshwater biomonitoring).
Molecular phylogeny Five markers were used: nuclear-encoded 18S rDNA (nSSU) and partial (D1/D2 or D1-D3) 28S rDNA (nLSU), choroplast-encoded rbcL and psbC, and mitochondrial cox1 sequences (Supplementary Tables  1, 2 ). Data were assembled from the following sources: (1) existing GenBank accessions (those available up to the beginning of 2018), (2) the Marine Benthic Diatom Project of Szczecin University (http://geocentrum. usz. edu. pl/en/szczecin-diatom-culturecollection-szcz/); (3) sequences obtained from the Thonon Culture Collection (TCC:  Rimet et al. 2019  and see https://www6. inrae. fr/carrtel-collection_eng/) that have hitherto been available only from the online TCC database; (4) sequences obtained to support DNA-based biomonitoring of UK rivers; and (5) unpublished sequences accumulated over many years in Edinburgh by R.
All previously unpublished sequences have been deposited in GenBank and their accession numbers are given in Supplementary Table  2. Sequences were first aligned preliminarily using MUSCLE or Clustal and quick ML analyses were then performed using MEGA v. 6 or v. 7  (Kumar et al. , 2016)  to identify and assess similar sequences. To produce the most compact matrix and hence achieve faster analysis in the single-gene analyses (for which hundreds of accessions were available in the case of nSSU, rbcL and nLSU), sequences were removed if they were identical to others that were more complete or differed from them only in ambiguities, or apparently artefactual at the 5′ or 3′ ends. However, such 'suboptimal' sequences remained eligible for inclusion in concatenated 2-or 4gene alignments. For the concatenated gene analyses, sequences of different genes  (rbcL+nSSU, rbcL+nLSU, rbcL+nSSU+nLSU+psbC)  were almost always admitted only if they were obtained from the same clone, to avoid the possibility of creating chimaeric data by combining sequences from different taxa as a result of inaccurate or inconsistent identification (it became evident during our analyses that morphology-based identifications are often problematic in the Bacillariaceae).
A very few exceptions to the rule were allowed, particularly in Pseudo-nitzschia and Fragilariopsis, where the species-level taxonomy is rather well understood and the sampling of diversity has been particularly intense, but where sequences for different genes are not always available for the same clone. In the 4-gene set (rbcL+nSSU+nLSU+psbC), clone data were included if data for three out of the four genes were available; those more often missing were the shorter regions, i. e. nLSU and psbC. Combining existing and new sequence data, the datasets finally comprised 298 (nSSU), 279 (nLSU), 309 (rbcL), 119 (psbC), 111 (cox1), 188 (rbcL+nSSU), 163 (rbcL+nLSU) and 120 (rbcL+nSSU+nLSU+psbC) Bacillariaceae sequences. Outgroups added were (1) Craspedostauros, Achnanthes and Staurotropis (the CAS group), Undatella, and three Amphora species  (A.
Overall tree topologies The Bacillariaceae are not monophyletic in any of the trees except cox1, which has the least satisfactory sampling of all the datasets (compare Supplementary Fig. 7  with Figs  3  and 4 , and with Supplementary Figs  2 3 4 5 6 ). In the nSSU tree (Supplementary Fig. 4 ), only four sequences interfere with monophyly, namely a single sequence of Craspedostauros alatus (GenBank KX981860: this appears within clade 8C-I, which otherwise comprises Fragilariopsis sequences) and the three Staurotropis sequences (which branch off the Bacillariaceae close to clade 1). In the four-gene tree (Fig. 3 ) and rbcL-nSSU (Supplementary Fig. 2 ), the whole CAS (Craspedostauros-Achnanthes-Staurotropis--) group lies within the Bacillariaceae, with other non-Bacillariaceae (namely 'Psammamphora', the three naviculoid taxa and Eunotiales) appearing as outgroups.
These are clades W, X and Y in rbcL and rbcL-nSSU (Fig. 4  and Supplementary Fig. 2 ); B and F in nSSU (Supplementary Fig. 4 ), and apochlorotics-B (a clade of obligate heterotrophs) in nLSU (Supplementary Fig. 5 ). There are also some unsupported clades ('groups': Z in rbcL and rbcL-nSSU; E in nSSU) and grades (e. g. A, C and D in nSSU; V in rbcL). Some taxa that are included within the major clades in the fourgene tree are separated from them in individual gene trees; for example, Tryblionella cf. compressa and Nitzschia cf. aequorea are included in clade 8B in the four-gene tree (Fig. 3 ) but form a separate grade ('V') in rbcL (Fig. 4 ). Cox1 (Supplementary Fig. 7 ) has been sampled less than the other genes and its dataset lacks representatives of three major clades (clades 1, 3 and 5).
Apparently misplaced taxa In all trees there are sequences that seem misplaced, given the identifications made by their authors. We give three examples, which probably have different explanations, discussed later and in Supplementary File 10. The first is the cox1 sequence KJ671739 of clone A2ni, which was identified as from "Pseudo-nitzschia sp. ". Whereas Pseudo-nitzschia sequences generally form a well-supported homogeneous monophyletic group (clade 8C), either alone or with Fragilariopsis (in the nLSU tree: Supplementary Fig. 5 ), in the cox1 tree (Supplementary Fig. 7 ) KJ671739 is placed anomalously, outside the Pseudo-nitzschia-Fragilariopsis clade, although GenBank BLAST of KJ671739 does indeed return Pseudo-nitzschia species (P. lundholmiae, and P. hasleana) as the nearest matches, though with low percent identity (c.
85%). The second example is several sequences from a single study  (Huang et al. , 2017: strains  Xmm11S4, Xmm20S5, Xmm16S2 and Xmm28S1), supposedly representing N. dubiiformis, N. palea, N. capitellata and N. epithemioides. Each of these species is also represented in our SSU dataset by other sequences (from other clones), none of which group with the Xmm strains. We consider that all for these Xmm clones were misidentified, since none of the photographs provided by  Huang et al. show the diagnostic characteristics of the four species named. The third example of apparently misplaced sequences is given by two of our own strains, BC0799 and TCC886. TCC886 is referred to in our trees  (nSSU, rbcL, Supplementary Figs 2, 4)  as "Nitzschia cf. palea" but it was originally identified by us with LM, and also by Luc Ector (personal communication), as N.
  The sigmoid Nitzschia species (e. g. Fig. 1G ) do not constitute a monophyletic group (see, for example, the positions of taxa belonging to Grunow's Sigmoideae, Sigmata and Obtusae in Fig. 4 ) and there is therefore a conflict with the amalgamation of all into an expanded Sigmoideae that was made by  Hustedt (1956). The sigmoid species fall into clades 2 (which contains Grunow's Sigmoideae species), 6A (containing Grunow's Sigmata) and 6B (containing Grunow's Obtusae but also representatives of other Grunowian sections).  Grunow's sections Lanceolatae, Lineares and Nitzschiella are polyphyletic (Fig. 4 ). The artificiality of the Lanceolatae was already evident from the single-gene nSSU study by  Rimet et al. (2011)  but is even clearer with expanded datasets.  The single Denticula species included (D.
Can the major molecular clades and subclades be characterized morphologically? We have mapped some morphological features onto the rbcL tree (Fig. 4 ) in order to see whether any of them characterize the major clades and could perhaps serve for identifying taxa where molecular data cannot be obtained (e. g. in fossils or rare species). The features comprise some that are not used in traditional classifications (because they cannot be assessed in LM), such as the structure of the striae and raphe canal. In addition, of course, the Grunowian sections to which particular sequences are assigned in Fig. 4  summarize the distribution of the LM characters listed in Table  1. Wherever possible, we used information derived from the clone that was sequenced. However, it is important to have in mind that there is often little or no extra information about a clone in GenBank beyond a name and a source location and author(s); in such cases we usually used morphological information published for the species to which the sequences were assigned.
6D, E , G; see also  Trobajo et al. , 2006 Trobajo et al. , , 2013)) , which in our dataset is almost restricted to clade 8A-I (Fig. 4 ); the exception is one species of clade 8B (N. amphibia: Fig. 6D ). Another, though more homoplasious character is the number of longitudinal rows of areolae in the canal, which can be zero (e. g. in Pseudo-nitzschia and Fragilariopsis: Fig. 6B ); consistently only one in most species of clades 8B and 8A-II (Fig. 6C ) but occurring also elsewhere; one to two (e. g. in N. palea, Fig. 6F ); strictly two, as in many of clade 8A-II and some clade 6B (Fig. 5K ); through to three or more rows, e. g. in the type species of Nitzschia, N. sigmoidea (in clade 2: Fig. 6A ), and N. sigma (in clade 6A; Fig. 6H ). A difference in the number of longitudinal rows of poroids within the raphe canal appears to distinguish clade 8A-I, with two or more rows (Fig.