dataesr/scientific-paragraphs-categorization huggingface dataset:
To further characterize the impact of Bmal1 deletion on lipid metabolism, we performed RNA-seq on liver, eWAT, and skeletal muscle (quadriceps) samples harvested at Zeitgeber time (ZT)12 in WT-WT, KO-WT, WT-Ob, and KO-Ob mice. We performed a similar differential gene expression analysis, grouping genes in the same models as described for lipids (SI Appendix, Fig. S4A  and Dataset S3). This analysis revealed that the genes differentially expressed between WT and Bmal1 KO irrespective of their leptin status (WT and Ob background) (models 6 and 10) are expectedly involved in the regulation of the circadian clock. However, many genes of these models are also associated with metabolism and the storage of carbohydrate and lipids across all three tissues (SI Appendix, Fig. S5  and Dataset S4). This includes the decreased expression of Dgat2 and Gys2 in the liver (Fig.
S5  and Dataset S4). Accordingly, many liver PPAR-γ target genes involved in lipid droplet formation were increased only in WT-Ob mice. This is the case, for example, for cell-death-inducing DNA Fragmentation Factor subunit alpha (DFFA)-like effector C (Cidec or Fsp27) and Cidea (37), Perilipin 2 (Plin2)  (38) , and Pparg itself (Fig. 4G ). A similar observation is apparent in livers from Bmal1 KO mice fed an HFD (SI Appendix, Fig. S6B ), supporting the decreased steatosis in Bmal1 KO mice under HFD (SI Appendix, Fig. S2I ). Interestingly, the rhythmic activity of the PPAR-γ pathway has been shown to be reprogrammed by HFD and obesity  (39). This result provides evidence that Bmal1 is required for this reprogramming and the development of steatohepatitis by HFD via the transcriptional activation of Cidea and Cidec by PPAR-γ  (37, 40).
Gene Expression Analysis Revealed the Impact of Bmal1 Deletion on the Feminization of Gene Expression. While direct regulation of transcription by BMAL1 likely plays a role in glucose and lipid metabolism, this effect unlikely explains the impact on fibrosis and inflammation. We have previously shown that Bmal1 deletion and obesity cause a feminization of liver gene expression  (20) , a condition known to protect against insulin resistance and NAFLD in rodents  (42) (43) (44) (45). Therefore, we studied sex-dimorphic gene expression in the liver transcriptome of the four genotypes (Dataset S3). A comparison of the differentially expressed sexbiased genes showed that KO-WT, WT-Ob, and KO-Ob mice exhibited a feminization of liver gene expression compared to WT-WT mice (Fig. 5A  and SI Appendix, Fig.
Feminization of Gene Expression Increases during Development of Liver Fibrosis in Humans. Our mouse model experiments pointed to a protective role for feminized liver gene expression associated with perturbed sex hormone concentrations against liver fibrosis. To test whether this is also the case in humans, we decided to next analyze sex-specific liver gene expression in male patients with liver fibrosis for investigating its potential perturbation by the disease. Therefore, we studied samples from patients with fibrosis stage 0 (5 males, 7 females) from the second cohort  (17)  to define the human liver sex-specific transcriptome (Dataset S1). Interestingly, despite the diverse etiologies of liver fibrosis in this cohort, we observed a clear alteration of the sex-biased gene expression during liver fibrosis that correlated with the stage of the disease.
This is particularly pronounced for male-biased genes (Fig. 6 A  and B ). This feminization correlated with a decreased predicted activity of AR and an increased activity of STAT5, the transcription factor that conveys GH signaling (Fig. 6C ). Because the healthy controls of the first cohort were only males (16), we were not able to identify sex-biased gene expression in this dataset. Nevertheless, a motif activity analysis confirmed the decreased predicted activity for AR and the increased predicted activity for STAT5 (Fig. 6D ). Altogether, these results confirmed that simultaneous perturbations of the activity of the circadian clock regulator BMAL1 (Fig. 1  and SI Appendix, Fig. S1E ) and the GH pathway, as well as the feminization of the liver associated with a decreased AR activity, are hallmarks of the progression of liver fibrosis and diseases.
Finally, we wanted to investigate the possibility that the disruption of the circadian clock in hepatocytes initiates these detected endocrine perturbations. Therefore, we generated RNA-seq data from hepatocyte-specific Bmal1 KO mice (Bmal1 HepKO) and performed a similar analysis of differential expression of sex-biased genes in total Bmal1 KO and Bmal1 HepKO mice. In contrast to total Bmal1 KO mice, Bmal1 HepKO mice did not show any change of expression of sex-biased genes (Fig. 6E  and Dataset S5). We rather observed a slight but not significant decrease in the expression of femalebiased genes. An analysis of biological pathways confirmed that the genes exclusively different between total Bmal1 KO and Bmal1 HepKO livers (model 9) were enriched in sex biased functions like lipid, steroids, and drug metabolisms (Dataset S6).
Liver RNA were analyzed by real-time qRT-PCR as previously described  (115). Sequencing libraries were prepared using the TruSeq Stranded RNA Sample Prep Kit v2 (Illumina) with the Ribo-Zero Gold depletion set. Statistical Analysis. Statistical analyses were performed with Prism 6. 0 (Graph-Pad Software, Inc. ) and R  (116). Unless otherwise stated, statistical significance was determined for the comparison of two groups by Student's t test for data following normal distribution. For analyses following a 2 × 2 factorial design, a two-way ANOVA was performed. If the interaction term was significant (P ≤ 0. 05), we performed a Holm-Sidak multiple comparisons test. Differential rhythmicity analyses were performed using dryR  (117). Longitudinal data that have been acquired over several weeks were analyzed using a linear model, and pairwise comparisons were computed using the CRAN package emmeans for each time point.
Multiple testing was corrected with a Benjamini-Krieger-Yekutieli two-stage procedure across all contrasts  (118). Except for box and violin plots, data are represented as mean ± SEM throughout the manuscript. Data Availability. Raw files and technical details about the RNA-seq data have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO)  (119)  and are accessible through GEO Series accession number GSE150102 (Bmal1-KO-Ob/Ob) (120) and GSE190221 (Bmal1 HepKO)  (121). Previously published data were used for this work  (122, 123). All other study data are included in the article and/or supporting information.
ACKNOWLEDGMENTS. We thank  Anabela Dacosta  and Fr ed eric Preitner from the phenotyping facility of the  University of Lausanne  and  Jos e-Luis Sanchez-Garcia  and  Fr ed eric Raymond  from  Nestl e Research  for helping in mouse and RNA-seq experiments, respectively. We would like to acknowledge the  Mouse Pathology Facility of the University of Lausanne  and the  Ecole Polytechnique F ed erale de Lausanne Histology Core Facility  for performing histology. This work was supported by the  European Research Council  (through individual  Starting Grant   ERC-2010-StG-260988  to F. G. ) and the  Swiss NSF  ( P2LAP3_164906  to C. J. ). C. J. and N. H. U. are supported by the  Deutsche Forschungsgemeinschaft Trans-Regio   TRR205  adrenal gland and  CRC1064  chromatindynamics as well as the  Deutsches Zentrum  f€ ur Diabetesforschung.
In this way, their effect on cellular behavior can be systematically tested facilitating the development of an optimal, wellcontrolled microenvironment. Here we employed an in-house-developed, simple, and costeffective photolithography-based technique to fabricate 3D villuslike poly(ethylene glycol) diacrylate (PEGDA)-based hydrogels  [29]  with a controlled mesh size allowing the diffusion of ISC niche biomolecular factors. By developing a custom-made microfluidic device allocating the microstructured hydrogels, we imaged the gradients of a fluorescently labeled model protein formed along the vertical axis of the microstructures using a custom-made light sheet fluorescence microscope (LSFM). [30] We then developed an in silico model matching the experimental data and used the model to predict the gradients of ISC niche factors formed on the microstructures.
Experimental Section Fabrication of Villus-Like Microstructured Hydrogels: The villus-like microstructured hydrogels were fabricated by photolithography, as previously described. [29] Briefly, a prepolymer solution containing PEGDA (6. 5% (w/v), 6 kDa of MW, Sigma-Aldrich), acrylic acid (AA) (0. 3% (w/v), Sigma-Aldrich), and Irgacure D-2959 photoinitiator (1% (w/v), Sigma-Aldrich) in phosphate-buffered saline (PBS) was flown into a chip fabricated with a 1 mm thick polydimethylsiloxane (PDMS) (Sylgard 184, Dow Corning) stencil containing an array of pools of 6. 5 mm diameter. Tracketch polyethylene terephthalate (PET) membranes of 5 μm pore size (Sabeu GmbH & Co. ) were used as substrates for the hydrogels. The microstructured PEGDA-AA hydrogels were fabricated by exposing the pre-polymer solution to UV light under patterned photomasks with transparent windows of 100 μm in diameter, edge to edge spacing of 150 μm and a density of 16 windows mm -2 (designed by Inkscape software and printed on acetate films (CAD/Art Services)).
3, Nikon) (Figure  2A, right ). An achromatic doublet (AC254-200-A-ML, Thorlabs) formed an image onto a Hamamatsu Orca Flash 4. 0 CMOS camera chip. Emission filters of 520/15 (for autofluorescence) and 620/52 (for Texas red) (Semrock) were selected using a motorized filter wheel (FW102C, Thorlabs). To be able to perform in-chip measurements, a special sample holder was designed using FreeCAD software and 3D printed (Hephestos 2, BQ) using poly(lactic acid) (PLA) as printing material (Figure  2A , right). It consisted of a square pool with top open access for the sample insertion and imaging, and two glass windows on the sides for proper light-sheet illumination. The chamber included a glass-covered pedestal (oriented 25°from the optical table) where the microfluidic chip allocating the hydrogel was glued (Loctite).
This pedestal also allowed bright-field illumination with a LED for sample positioning. With this inclination, the sample was illuminated and imaged transversally, to avoid any barrier on the path of the light sheet. Laser scanning for imaging was performed by vertical translation of the sample holder using a motorized stage (PI M-501. 1DG, Physik Instrumente), through a fixed horizontal light-sheet plane. All the components of the microscope were controlled using a custom-made software (LabView). Biomolecular gradients were then formed by filling in the source compartment of the chip through the microfluidic channel through the inlet port while the hydrogel was in the allocated position. Biomolecular gradients formed by diffusing bovine serum albumin (BSA) protein fluorescently labeled with Texas Red (BSA_TxRED, Thermo Fisher) within the PEGDA-AA hydrogel networks were imaged by performing a time-lapse acquisition of a 3D volume of 1.
33×1. 33×2. 3 mm over two hours with a frame interval of 15 min, and a stack step of 5 μm. The source chip compartment was loaded with BSA_TxRED dissolved in PBS-0. 1% Tween20 at a concentration of 10 μg mL -1 and the imaging was started 3 h after loading. To minimize data load, the image by a factor of 4 was binned. Then, the overall lateral pixel size was 2. 6 μm. The image processing was carried out using ImageJ software. [65] First, the stacks were resliced (ImageJ reslice function) to obtain a vertical view of the source compartment, the hydrogel and the sink chip compartment. Then, a plane in the middle of the stack where the radial diffusion could be neglected was chosen. A linear region of interest (ROI) of 10 pixels in size was drawn along the perpendicular axis of the hydrogel. The relative fluorescence intensity (RFI) values along that line were obtained for all the time points acquired.
The pinhole diameter was set to 1 Airy Unit (AU). A z-step of 5 μm was used. The relative coverage was quantified by measuring the cell area after thresholding with ImageJ software the maximum intensity projected F-actin fluorescence images and normalizing to surface of the substrate (base + pillars). For this, a model was employed to estimate the effective surface of the substrate from the projected image (see Supporting Information for the details). In addition, confocal fluorescence microscopy images of Ki67, GFP, Lysozyme, CK20, and FABP1 markers were used to quantify the proliferative cells, stem cells, Paneth cells, differentiated cells and enterocytes, respectively, along the vertical axis of the villus-like microstructures. For this, a custom-made ImageJ macro was used to divide the z confocal stacks to tiles, each comprising one single villus.
Z was divided into five equal segments for data interpretation purposes. Next, the percentage of positive cells with respect to the total number of cells for the five regular Z segments along each villus was estimated. Finally, the fractioned percentage of each segment was computed. To quantify the relative change in the frequency of Ki67 + cells from day one to day three, the mean frequency of Ki67 + cells of day 1 to that of day 3 was subtracted and the value was normalized by the mean frequency of day 1. To quantify the intensity signal along the verti-cal axis of each villus of CK20, GFP and Lyz, another custom-made ImageJ macro was used. Briefly, after dividing each pillar into individual stacks, the ImageJ built-in plot profile function within a drawn ROI surrounding the villus to quantify mean intensity for the marker in question (CK20, GFP or Lyz) and F-acting fluorescent channels was run and the data were exported.
Acknowledgements G. A. and A. A. -L. contributed equally to this work. The authors thank  Rene Fabregas  for the advice on the in-silico model. The authors also thank  Jordi Comelles ,  María García  and  Enara Larrañaga  for the fruitful discussions. Funding for this project was provided by  European Union 's  Horizon 2020 ERC  grant agreements No 647863 (COMIET) and No 884623 (residualCRC), the  CERCA Programme/Generalitat de Catalunya  ( 2017-SGR-1079  and  2017-SGR-698 ),  Laserlab-Europe   EU-H2020  grant agreement No  871124 , the  Spanish Ministry of Economy and Competitiveness (Severo Ochoa Program for Centers of Excellence  in  R&D 2016-2019  and  CEX2019-000910-S ), the  Spanish Ministry of Science  ( PID2020-119917RB-I00 ) and also by the  Fundació Privada Cellex  and the  Fundació Mir-Puig.
Methods Ca. Odinarchaeon LCB_4 genome reassembly. To reassemble the Ca. Odinarchaeon LCB_4 genome (Supplementary Fig. 1a ), its corresponding Illumina reads  21  (BioSample SAMN04386028) were mapped against Asgard archaeal MAGs using Minimap2 (ref. 22 ) v. 2. 2. 17. Mapped reads were extracted and assembled with Unicycler 23 v. 0. 4. 4. Unicycler tested k-mer lengths ranging from 27 to 127; the latter was chosen to perform an assembly with default parameters. This assembly obtained a 1. 406 Mbp contig, which was not predicted as circular despite both of its contig boundaries ending in type I-A CRISPR arrays (Supplementary Fig. 1b ). Additional short (<13 kbp) contigs were not considered part of the main chromosome because they represented mobile elements (with signatures such as differing coverage, circularity, CRISPR spacer hits and/or presence of typical mobile element genes), ribosomal RNA genes from other organisms or CRISPR arrays (the latter two were expected due to the conservation of rRNA gene sequences and CRISPR repeats).
Amplification of control and gap regions was then performed following the parameters shown in Supplementary Tables  5  and 6. Products were separated on a 0. 8% agarose gel in 1× Tris-Borate-EDTA buffer stained with SYBR-Gold and purified using a QIAGEN Spin purification kit according to the manufacturer's instructions. Purified PCR fragments were pooled and used to construct a library with the SQK-LSK109 ligation kit. Sequencing was performed on an Oxford Nanopore MinION Mk1C sequencer using an R9. 4. 1 flow cell. Raw sequence data were basecalled using Guppy v. 4. 2. 2. Reads were separated in 2 bins at 3-9 kbp (subsampled to 30×) and 9-12 kb and processed to obtain consensus sequences using Decona 25 v. 0. 1. 2 (-c 0. 85 -w 6 -i -n 25 -M -r). Both control regions, comprising the rRNA and ribosomal protein operons, were 100% identical to the corresponding nucleotide sequences of the published assembly.
2. 4 and Cas genes were detected and classified through CRISPRcasIdentifier 28 v. 1. 1. 0. Spacer similarity searches were assessed against IMG/VR 29 v. 3 (release 5. 1) and against all available databases on the CRISPRTarget 30 webserver on 26 January 2022. Local spacer searches were performed using BlastN 31 v. 2. 10. 0+ (-task blastn-short) against the Ca. Odinarchaeum assembly, its source metagenome and the nucleotide National Center for Biotechnology Information (NCBI) database. SpacePHARER 9 v5-c2e680a was used to search against the Ca. Odinarchaeum assembly and the 2018 GenBank phage and eukaryotic virus databases facilitated by the software, using as control sequences the eukaryotic virus database (with reversed sequences when using this database as target). WIsH 8 v. 1. 1 was used to predict host sequences of mobile element contigs, using Ca.
Odinarchaeum and all archaeal representative genome sequences from the Genome Taxonomy Database (GTDB)  32  release 202. VirSorter2 (ref. 33 ) v2. 2. 3 was run with default parameters on the mobile element contigs. Proteins were classified into Clusters of Orthologous Groups (COG) families  34   Phylogenetics. Reference pPolB sequences were obtained from Kim et al. 44  and used for Psi-blast 45 v. 2. 10. 0+ against the NR v5 (as of 10 February 2021) database. Sequences with over 70% similarity were removed with CD-Hit 46 v. 4. 7. The remaining sequences were aligned with Mafft-linsi 47 v. 7. 450; columns with over 50% gaps were removed using trimAl 48 v. 1. 4. rev22. Additionally, sequences with over 50% gaps in the trimmed alignment were removed. Maximum-likelihood trees were reconstructed using IQ-TREE 49 v.
based on five best local BlastP 31 v. 2. 10. 0+ hits to the same COG; domain annotation was performed through InterProScan 35 v. 5. 48-83. 0. Mobile element protein annotation was performed using HHsearch 36 v. 3. 3. 0 against Pfam 37 v. 33. 1, Protein Data Bank 38 (16 November 2020), SCOPe 39 (01 March 2017), CDD 40 v. 3. 18 and UniProt 41 vir70 (10 August 2020) viral protein sequence databases. Synteny plots were performed with genoPlotR 42 v. 0. 8. 11. Structural predictions were performed with RoseTTAFold 43 through the Robetta portal.
Acknowledgements We thank  L. Wenzel  for discussions on hybrid assemblies and  R. Staals ,  J. van der Oost  and  I. Zink  for helpful comments on the CRISPR-Cas systems. This research was funded by the  Swedish Research Council  ( International Postdoc  grant no. 2018-00669  to D. T. ), the  European Research Council (ERC)  (consolidator grant no. 817834  to T. J. G. E. ) and a  Wellcome Trust   collaborative award  (no. 203276/Z/16/Z  to T. J. G. E. ). N. R. was supported by a  Leverhulme Research Project Grant  (no. RPG-2019-297 ) and start-up funds from the  Division of Biomedical and Life Sciences, Lancaster University. M. K. was supported by the  Agence Nationale de la Recherche  (no. ANR-20-CE20-0009-02 ) and  Ville de Paris (Emergence(s)  project  MEMREMA ). L. E. received funding from the  ERC  ( ERC Starting Grant  no.
Materials Non-stationary FES-cycling sessions were performed overground on a recumbent tadpole tricycle (Arttrike, Porto Alegre, RS, Brazil). The original crankset was replaced by a power-meter crank (2INPOWER, Rotor Bike Components, Madrid, Spain). Orthotic calf support (HASE ® Bikes, Waltrop, Germany) was fixed to the pedals to keep the ankle joint at 90 • and restrict the leg movement to the sagittal plane. The crank angle was evaluated through an incremental encoder (model LPD3806-360BM-G5-24C). The previously developed constant current topology electrical stimulation device, which is open source and was described in a previously published paper  [9] , was employed during the sessions. Firmware and software applications (Android app) for the algorithm presented in this paper are available as Supplementary Materials.
Data Acquisition A mobile application (Android app) was developed to configure the stimulation parameters and to exchange data wirelessly (Bluetooth) at 10 Hz. The app creates a database for offline processing, which includes data configuration of each channel (ON/OFF, start angle, end angle, frequency, pulse width and amplitude), time and date, reference cadence, actual cadence, and crank angle. It also selects between four different types of cycling modes: (1) manual stimulation; (2) automatic mode without control; (3) automatic mode with control; and (4) RL algorithm enabled. The crank-angle, cadence and power data were also measured by the power meter, which transmitted data via Bluetooth to a personal computer at a sample rate of 50 Hz (manufacturer-specific "fast-mode"). A commercial speedometer was also installed on the tricycle to measure average speed, distance and pedaling time.
Hospitality: A Spatial Approach In addition to "hospitality between whom?", an equally important question underpinning the phenomenon of hospitality, especially when considered in conjunction with security, is: "hospitality where?" For this volume, the question is addressed by paying equal attention to localizations and spatializations of hospitality. 36 Although host-guest relations tend to be primarily shaped by shared or distinct ideals and cultural norms, they are always practiced somewhere, within a concrete spatial context. To understand how initial encounters between hosts and strangers were resolved, we need to study these issues at the local level, paying attention to their spatial components and dynamics and their situated ethics. 37 he individual chapters focus on local, initial encounters along the coasts of the Baltic Sea region, which during the period 1000-1914 meets the criteria of constituting a threshold space writ large.
Captivity in Aarhus and Uppsala: An Overview Aarhus and Uppsala were two of the many towns in the Danish and Swedish realms that were forced to accommodate captive soldiers during the course of the Great Northern War. The archives provide unusually accessible sources on captivity in these two towns, both in a national and an international comparison. The main body of source material consists of, on the one hand, the archives of the local royal administration (Dan. Amt, Swe. Länsstyrelse) and, on the other, those of the local magistrates. These archives provide a diverse range of sources-correspondence between the communal authorities and the crown, minutes of the communal council meetings, court records, and, above all, a diverse collection of prisoner muster rolls. Individually, these sources mostly provide only summary information on the prisoners of war, but compiling the scraps of information in a database proves them to be a rich window into the everyday organization of war captivity.
Acknowledgements. This work is part of the  CIPRHES  project, funded by the  French national research agency ANR  (grant # ANR-20-CE04-0009 ), and a PhD funded by the  French ministry of Environmental Transition (MTE). The stream network shown in maps is from the  BD CARTO database  (IGN, 2021). We would like to thank  Delphine ALLIER  ( BRGM ) for the help in selecting the test dataset and  Jean-Baptise BOISSONNAT  ( INRAE Antony ) and  Benoît GÉNOT  ( INRAE Antony , now at U. R. B. S. ) for the database maintenance. We extend our warmest thanks to  Charles PERRIN  ( INRAE Antony ),  Paul ASTAGNEAU  ( INRAE Antony ) and  François BOURGIN  ( INRAE Antony ) for.