dataesr/scientific-paragraphs-categorization huggingface dataset:
Within Eurypon sp. 2, KOG class expression patterns in hypoxia vs. anoxia were significantly positively correlated to those of hypoxia (r = 0. 71, p < 0. 01, Figure  1D ). Similarly, in H. stellifera, KOG enrichment in hypoxia vs. anoxia was positively correlated to that of hypoxia (r = 0. 72, p < 0. 001, Figure  1H ) and anoxia (r = 0. 46, p < 0. 05, Figure  1I ). All significant correlations between datasets are shown in (Figure  1B-J ), and all relationships that were not significantly correlated are not shown. At the level of individual KOGs, the KOG class 'replication recombination and repair' (RRR) was significantly enriched in all pairwise comparisons of oxygen conditions for both sponge species (P adj < 0. 05), though in different directions depending on oxygen. It was significantly upregulated in anoxia and anoxia vs.
Expression of the KOG classes 'carbohydrate metabolism' and 'amino acid transport and metabolism' were not significantly changed under any oxygen condition (Padj > 0. 05). There was some significant enrichment of metabolic KOGs, but only in Eurypon sp. 2. 'Coenzyme transport and metabolism' and 'inorganic ion transport and metabolism' were both downregulated in anoxia, and 'nucleic acid transport metabolism' was downregulated in hypoxia vs. anoxia. For both these conditions (anoxia and hypoxia vs. anoxia), 'Lipid transport and metabolism' and 'Secondary metabolites biosynthesis, transport and catabolism' were significantly downregulated. However, the transcription of the majority of genes labeled with metabolic KOGs remained unchanged under anoxia and hypoxia. Comparing the Lough Hyne dataset with external datasets of invertebrate KOG expression in response to stress, only two positive correlations were found.
In Eurypon sp. 2, expression patterns under Anoxia vs. Hypoxia were comparable to those of T. wilhelma under short-term anoxia (r = 0. 44, p < 0. 05, Figure  1E ). The hypoxic response of Eurypon sp. 2 resembled that of diapause in S. mosellana (r = 0. 68, p < 0. 001, Figure  1B ), but this was the only correlation found between Lough Hyne sponge data and diapause or dauer states. Importantly, KOG enrichments under anoxia (versus normoxia) were not positively correlated to any other external datasets for either sponge species. Three significant negative correlations, which corresponded to opposing KOG expression patterns, were observed between Lough Hyne sponges and external datasets. Hypoxia responses in H. stellifera were negatively correlated with those of A. millepora under heat stress (r = -0.
2021). In contrast, different coral taxa generally exhibit more correlations in KOG expression in response to different stressors than sponge taxa do in response to a single stressor, although stress responses in corals can still differ widely between species and stressor  (Kenkel, Mocellin, and Bay 2020). Within a single sponge species, no correlations were seen between KOG enrichments in anoxia or hypoxia (vs. normoxia), highlighting the transcriptomic differences between responses to hypoxia and anoxia. KOG enrichments in hypoxia versus anoxia, on the other hand, were positively correlated with those of anoxia in Eurypon sp. 2 and both anoxia and hypoxia in H. stellifera. When quantitatively compared to transcriptomic datasets from the sponge T. wilhelma using KOGMWU, KOG enrichments in Lough Hyne sponges were only positively correlated between the sponge T.
Sponge gene expression under anoxia did not correlate to either diapause or a dauer state in C. elegans  (Sinha, Sommer, and Dieterich 2012)  or S. mosellana  (Gong et al. 2013) , respectively. Although these dormancy datasets come from distantly related invertebrates,  Strader et al. (2016)  demonstrated a correlation between the KOG expression patterns of a specific phenotype of coral larvae and diapause in S. mosellana, hypothesizing that this potential functional analog of dormancy could serve to increase larval dispersal. Therefore, the sponges of Lough Hyne could only be considered dormant under anoxia if their transcriptional method of dormancy was distinct from that of an insect, nematode and coral, which is possible but not parsimonious given that animals are monophyletic  (Feuda et al.
2017). Paradoxically, Eurypon sp. 2 responses to hypoxia were positively correlated to diapause in S. mosellana (Figure  1B ). Therefore, the hypoxic response in Eurypon sp. 2 species, rather than the anoxic response, was similar to diapause in terms of global KOG expression patterns. The RRR kog was also downregulated in Eurypon sp. 2 under hypoxia as well as in S. mosellana under diapause, which may partially explain the correlation between these datasets. Moreover, diapause can be associated with the reduction of cell division (MacRae 2010), which may occur in sponges under hypoxia but not anoxia based on MCM gene expression. In comparison, KOG enrichments under anoxia were not positively correlated to any external datasets for either sponge species (Figure  1 ), indicating that sponge transcriptomic responses to anoxia are relatively novel compared to those of other taxa in response to various factors in the existing literature  (Dixon et al.
W. Bange , and  R. A. Schmitz. 2012. "Production of  Oceanic Nitrous Oxide by Ammonia-Oxidizing Archaea. " Biogeosciences 9 (July): 2419-29. Love,  Michael I. ,  Wolfgang Huber , and  Simon Anders. 2016. " Moderated Estimation of Fold Change and Dispersion  for RNA-Seq Data with DESeq2. " BMC  Genome Biology  15 (January): 1-22. Lynch,  Michael , and  Georgi K. Marinov. 2015. "The Bioenergetic Costs of a Gene. " Proceedings of the National Academy of Sciences of the United States of America 112 (51): 15690-95. MacRae, Thomas H. 2010. " Gene Expression, Metabolic Regulation and Stress Tolerance during Diapause. " Cellular and Molecular Life Sciences: CMLS  67 (14):  2405-24. Matz, M. V. 2015. "Assembly and Annotation of  Transcriptomes de Novo. " Github. 2015. https://github. com/z0on/annotatingTranscriptomes.
Medini, H. ,  T. Cohen , and D. Mishmar. 2021. " Mitochondrial Gene Expression in Single Cells Shape Pancreatic Beta Cells' Sub-Populations and Explain Variation in Insulin Pathway. "  Scientific Reports  11 (1): 466. Meyer, E. 2016. "A Collection of  Scripts for Analyzing Transcriptome Assemblies. " Github. 2016. https://github. com/Eli- Meyer /transcriptome_utilities. Micaroni,  Valerio ,  Rob McAllen ,  John Turner ,  Francesca Strano ,  Christine Morrow ,  Bernard Picton ,  Luke Harman , and  James J. Bell. 2021. " Vulnerability of Temperate Mesophotic Ecosystems (TMEs) to Environmental Impacts: Rapid Ecosystem Changes at Lough Hyne Marine Nature Reserve , Ireland. "  Science of The Total Environment. https://doi. org/10. 1016/j. scitotenv. 2021. 147708. Micaroni,  Valerio ,  Francesca Strano ,  Rob McAllen ,  Lisa Woods ,  John Turner ,  Luke Harman , and  James J.
Data, scripts, code, and supplementary information availability Raw sequence data as well as genome and transcriptome assemblies are available at NCBI under BioProject number PRJNA893197 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA893197). Scripts, code, and additional data are available here: https://zenodo. org/badge/latestdoi/573040118. Supplemental figures and tables are available on BioRxiv under 'Supplementary material. '.
B-J. Significant (p < 0. 05) correlations between the KOG enrichments of different treatments or datasets (columns in A). All other possible comparisons between the various datasets were not significant. Within Eurypon sp. 2, KOG class expression patterns in hypoxia vs. anoxia were significantly positively correlated to those of hypoxia (r = 0. 71, p < 0. 01, Figure1D). Similarly, in H. stellifera, KOG enrichment in hypoxia vs. anoxia was positively correlated to that of hypoxia (r = 0. 72, p < 0. 001, Figure1H) and anoxia (r = 0. 46, p < 0. 05, Figure1I). All significant correlations between datasets are shown in (Figure1B-J), and all relationships that were not significantly correlated are not shown. At the level of individual KOGs, the KOG class 'replication recombination and repair' (RRR) was significantly enriched in all pairwise comparisons of oxygen conditions for both sponge species (P adj < 0.
This study was based on a retrospective analysis of polysomnographic data from healthy, well-sleeping individuals who participated in strictly controlled laboratory studies at the Centre for Chronobiology, University of Basel, Switzerland, between 1994 and 2020. In our studies, we focused on different age groups and included both females and males (we used this terminology to refer to biological sex defined at birth). We also manipulated sleep pressure levels and different environmental stimuli, such as light and noise. Therefore, we also investigated whether these factors contribute to the observed variance in ultradian sleep cycles. We defined the duration of the NREM-REM sleep cycles according to Feinberg and Floyd 8 using a self-programmed algorithm. We set out to answer the following questions.
Statistics Distribution patterns of the number of sleep cycles, the duration of sleep cycles as well as the duration of NREM and REM sleep episodes were calculated using PROC Univariate in the SAS statistical package (version 9. 1; SAS Institute, Cary, NC), which includes 3 tests for normality distribution (ie, Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling). For a subset of studies aimed at investigating the influence of individual, environmental, and sleep pressure factors (ie, modulators) on sleep ultradian cycles, the effects of these modulators were tested using generalized linear mixed-model analysis of variance for repeated measures (PROC GLIMMIX, statistical package SAS [version 9. 1; SAS Institute, Cary, NC]) separately for the main factors "modulator" (ie, age, sex, noise, light, sleep pressure) and the "sleep cycle number" as a repeated/within-subject factor (ie, cycle 1-4).
Acknowledgments We would like to thank all the study assistants and the laboratory technicians  Claudia Renz ,  Marie-France Dattler  and  Giovanni Balestrieri  for their valuable support in carrying out the studies. We would also like to thank all the former PhD students who worked as investigators in the reported studies and who are now working in clinics, NGOs or commercial companies:  Vera Knoblauch ,  Katharina Blatter , the late  Jakub Späti ,  Sylvia Frey ,  Angelina Birchler-Pedross ,  Stephanie Vollenweider ,  Doreen Anders ,  Britta Gompper ,  Vivien Bromundt ,  Micheline Maire ,  Franziska Rudzik , and  Laurie Thiesse. Finally , we would like to acknowledge the valuable support of  Prof Anna Wirz-Justice  and  Kurt Kräuchi.
Step 0: Data acquisition and generation of random debris flows scenarios Several input data should be acquired for launching simulations. Indeed, the model requires knowledge concerning the (i) geometry of the retention dam (shape, dimensions, openings), (ii) characteristics of the debris basin (storage capacity, deposition slope, stage-volume capacity curve) and (iii) debris flows characteristics (return periods, inlet hydrographs, size and number of transported boulders). Some of these data can be extracted from the historical database and topographical surveys while others could be missing and have to be assumed or obtained by modeling. In debris basins, deposition does not occur horizontally. The deposition slope depends on the sediment concentration and on the type of sediment transport.
Overall computational analysis A code for solving all previous steps was written in R to model the time-dependent evolution of the volume stored in the debris basin. For a given structure, it first loads the structure features (Fig. 2a ): (i) retention dam geometry and (ii) debris basin stage-volume curve. It secondly loads the boulder counting for the reference volume (Fig. 4 ) and computes the associated probability 𝑝 𝑗. It then takes as an input the volumes of the debris flow events involved in a scenario. For each event, it goes through the following main steps: -Step 1 Defining an input vector that involves the volume of the debris flow event 𝑉 𝑒𝑣𝑒𝑛𝑡 , peak inlet discharge 𝑄 𝑝𝑒𝑎𝑘 based on Eq. (  1 ), time to peak 𝑡 𝑝𝑒𝑎𝑘 , deposition slope, initial volume stored in the basin 𝑉 𝑖𝑛𝑖𝑡 and initial lateral and vertical jamming rates of the dam's openings.
-Step 7: Estimating the volume stored in the basin 𝑉 𝑏 𝑚 3 using Eq. (11). In order to model the evolution of indicators over time, steps 4-7 are performed at each time step over the whole duration of a debris flow event. The main outputs obtained after running the code are the time series of the inlet discharge 𝑄 𝑖𝑛𝑙𝑒𝑡 (𝑡) (m 3 ∕s), outlet discharge 𝑄 𝑜𝑢𝑡𝑙𝑒𝑡 (𝑡) (m 3 ∕s), lateral and horizontal jamming rates (%) of the dam's openings, flow level at the dam 𝑍(𝑡) (m. a. s. l -meters above sea level) and the cumulative volume stored in the basin 𝑉 𝑏 (𝑡) (m 3 ). Since the objective of the approach is to analyze the behavior of the retention dam over 50 years, the generated debris flow events involved within a scenario should be modeled one after another. In other words, the outputs provided after running event 𝑛 in terms of filling state and jamming rates will be the initial inputs provided for analyzing event 𝑛 + 1.
Random debris flow scenarios Data concerning the average number of torrential floods that occur during every month of the year is extracted from the database of the system's managers (ONF-RTM, i. e. , French torrent control service). Fig. 7  shows the monthly distribution of all recorded floods in the Claret and the corresponding cumulative distribution function CDF  [41]. It can be noticed that almost all of the events (87%) occur between June and September due to summer rainstorms, which are often very intense. Morel et al. [42]  gathered data concerning the volumes of past events recorded from 1992 until 2019, which enabled to fit a generalized Pareto distribution (GPD) to build a ''Frequency-Magnitude'' curve of the Claret catchment on an observation period of barely 27 years (Fig. 8 ).
The blue curve forms a continuous representation (GPD fitting) derived from historical databases, capturing the input volumes of debris flow events. In contrast, the green curves are constructed from seven discrete data points, each representing the output volume of a specific event generated by the developed model. These discrete points were then connected to create a smooth curve using a linear model implemented in R. From the obtained results, it is revealed that the higher 𝑉 𝑒𝑣𝑒𝑛𝑡 is, the higher 𝑉 𝑜𝑢𝑡 is (all curves are increasing to the right, i. e. , towards higher return period). In addition, the higher 𝑉 𝑖𝑛𝑖𝑡 is, the higher 𝑉 𝑜𝑢𝑡 is (curves are sorted by green shading). Since 𝑉 𝑜𝑢𝑡 is the volume escaping the retention dam and transported to the downstream where vulnerable exposed issues are located, Fig.
Sensitivity analysis: Uncertainties affecting maintenance decisions Although the modeling and analysis of the Claret retention system are based on real data, the data used could still be imperfect, invaded by uncertainties or based on expert assessments. Therefore, it is worth exploring different situations under a given set of assumptions. In this section, a simple sensitivity analysis is performed considering different parameters that could affect maintenance decisions. The objective is not only to test the robustness of our analysis but also to improve decision-making in case something changes in the future (e. g. , easier maintenance operations, more expensive operations). Three different parameters are chosen for this purpose: frequency of inspection, maintenance duration and maintenance costs.
Preliminary database searching suggested that some nematodes have, like C. elegans, a number of GH20 subfamily 1 genes; for instance, Oesophagostomum dentatum, a clade V nematode like C. elegans, has at least HEX-2, -3 and -5 orthologues, whereas Trichinella spiralis and Trichuris suis (both clade I nematodes) appear to have only one subfamily 1 enzyme. On the other hand, O. dentatum lacks N-glycans with terminal GalNAc residues  (23)  and wildtype C. elegans has very few  (21)  ; both species rather have chito-oligomer-based antennae for their most complex N-glycans. In contrast, T. suis  (24)  and T. spiralis  (25)  are rich in N-glycans containing terminal GalNAc motifs, which may indicate a difference in the hexosaminidase-dependent processing between clade I and V species. Therefore, a thorough phylogenetic analysis was performed and the GH20 subfamily 1 candidate enzyme from T.
elegans, the earliest separation occurs between HEX-4 and HEX-5 (Figure  2  and Supplementary Figure  1 ), indicating their specific ability to only remove GalNAc. Later, HEX-3 and HEX-2 evolved and are capable of removing both GalNAc and GlcNAc from glycan structures in vitro  (2). The presence of numerous enzymes with similar functions indicates a significant amount of evolutionary pressure or possibly diverse applications for these enzymes. In general, the degree of relatedness within the GH20 clades correlates well with the proposed phylogeny of nematode species (e. g. , filarial sequences are grouped together). Additionally, a subclade consisting of Trichuris spp. and Trichinella spp. representatives was identified in which only one hexosaminidase homologue per species, annotated as HEXD, was detected in the database.
Phylogenetic analyses: Eukaryotic tree: To find all eukaryotic sequences which belong to the GH20 hexosaminidase family, the Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) was used  (44). Two protein families IPR015883 (subfamily 1) and IPR025705 (subfamily 2) were found and all eucaryotic data was downloaded and stored on a local disk. The data has been processed to decrease the number of input sequences -any sequences below 300 or over 750 amino acids were removed. Next, sequences were used to build an alignment with MAFFT  (45)  and then the TrimAl tool was used for alignment trimming  (46). Thereafter, the data was used as input to calculate a new phylogeny tree using the FastTree tool  (47)  on the local computer and visualized with iTOL  (48). Nematode tree: Characterised GH20 sequences from C.
elegans were taken from the Wormbase database (gene names, HEX-1 -CE07499; HEX-2 -CE36785; HEX-3 -CE41720; HEX-4 -CE46668; HEX-5 -CE53609) and used as a query the whole Nematode proteome (NCBI 11. 01. 2023) using the hidden Markov Models algorithm from phmmer. All found sequences were used to build an alignment with MAFFT  (45)  and subsequently the final approximately-maximum-likelihood phylogenetic tree was built with IQ-tree  (49). The resulting phylogeny tree was limited to one homologue per species and visualized with iTOL  (48).
Cloning and purification: The T. suis HEX-2 open reading frame sequence, excluding the region encoding the cytosolic, transmembrane and stem domains (residues 1-84), was synthesized by GenScript, based on the sequence with NCBI database ID KFD67184. The hexosaminidase sequence was cloned into the pPICZαA plasmid (Invitrogen) without the native stop codon using Gibson Assembly® Cloning Kit (primers: Forward/Long: 5'-AGAGAGGCTGAAGCTGAATTCACGATGAAAGTGTATCGATGGCGA-3', Forward/Short: 5'-AGAGAGGCTGAAGCTGAATTCACGGTGTTTATTCCGAAACGT-3', Reverse: 5'-GACGGCACGCGTCGTATCGATAG-3'). Ligation products were transformed into NEB 5-alpha competent Escherichia coli (NEB #C2987) prior to selection on zeocin. The sequenced expression vectors were linearised and transformed into P. pastoris (GS115 strain), colonies were selected on zeocin and expression performed with methanol induction at 30 °C as previously described  (12).
The XDS  (55)  and XDSme  (56)  were used to process the data and further steps were performed with CCP4, version 8. 25-27  (57). As crystal diffracted anisotropically, data was processed using the STARANISO server and the aimless CCP4 program. The structure of hexosaminidase was solved by molecular replacement using AlphaFold  (58)  as a search model in PHASER  (59). Iterated maximum likelihood refinement and manual building of the resulting electron density maps were respectively performed using REFMAC 5. 8  (60)  and Coot  (61). Five percent of the reflections were used for cross-validation analysis, and the behavior of the Rfree was employed to monitor the refinement strategy. Water molecules were added using Coot and subsequently manually inspected. The coordinates were deposited in the Protein Data Bank (PDB) under code 8QK1.
Acknowledgements: This work was funded by the FWF-funded BioTOP doctoral programme [Austrian Science Fund W 1224] and by an FWF grant [P29466] to I. B. H. W. ; I. A. is funded by the "Top Vet Science" Program of the University of Veterinary Medicine Vienna. We acknowledge the synchrotron SOLEIL (Saint Aubin, France) for access to beamlines Proxima 1 (Proposal Number 20210859) and for the technical support of Pierre Legrand. We would like to thank Valérie Chazalet and Emilie Gillon for their technical help during Z. D. 's stay at CERMAV.
e. it takes values from 1 to k , expressed by choos- ing the domain D(X i ) = {1,. , k} for each X i. Finally, we define the constraint set consisting of one inequality constraint NotEquals between the variables X i and X j for every edge (i, j) ∈ E. Fig. 1  The running example: graph coloring. A Example input graph. B One valid coloring with 4 colors, corresponding to an assignment of variables to colors (domain values) that satisfies all the inequality constraints along the edges. In our example extension, which minimizes the feature counting the different colors in each of its four cycles of length 4 , (v 2 , v 3 , v 5 , v 6 ) , (v 2 , v 5 , v 7 , v 8 ) , (v 5 , v 6 , v 7 , v 8 ) and v 5 , v 6 , v 8 , v 9 , this coloring is not optimal (e. g. recolor v 3 ) 1 https:// gitlab. inria. fr/ amibio/ RNARe dPrint.
Funding Open access funding provided by University of Vienna. This work is supported by the French national research agency ANR: grant ANR-21-CE45-0034-01 "INSSANE", as well as the Austrian Science Fund (FWF), grant no. I 4520. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 10102967.
Acknowledgments The authors would like to thank the following people; for reagents and fly stocks:  Claire Thomas ,  Debbie Andrew ,  Daniel St. Johnston ,  Magali Suzanne ,  Alexandre Djiane ,  Markus Affolter ; for use of the otracks software:  Guy Blanchard. This work was supported by the  Medical Research Council , as part of  United Kingdom Research and Innovation  (also known as  UK Research and Innovation)  [MRC file reference number  U105178780 ]. For the purpose of open access, the  MRC Laboratory of Molecular Biology  has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising'.
XRD patterns were recorded for manufactured pellet and both powders with a Panalytical X'Pert Pro-diffractometer equipped with an X'Celerator detector and using Cu-K α radiation (λ CuK α = 1. 5405 Å) under 40 kV voltage and 30 mA current. Data were collected at room temperature under ambient atmosphere, over the 2θ angular range 5 • -90 • with a step width of 0. 0084 • (2θ) and a counter time of 5 s per step. For a better resolution, XRD patterns of L-PBF pellet were recorded in the 20-40 • (2θ) domain with the same step width and a counter time of 20 s. All data were processed in DIFFRAC. EVA software using JCPDF database for peaks identification.