dataesr/scientific-paragraphs-categorization huggingface dataset:
A. rohweri" using IMNGS at 97% and 99% similarity thresholds (Supplementary Table  S14 ). While metadata for the SRA database is less detailed than the EMP database, these results indicated that sequences identified as "Ca. A. rohweri" were found exclusively in marine metagenomes, seven of which were coral metagenomes. Although there were 12,665 marine metagenomes (including seawater, marine sediment, and plankton metagenomic samples) and 1855 coral metagenomes queried, "Ca. A. rohweri" was only found in 21 samples. This is likely due to our use of the full-length 16S rRNA sequence for this analysis, which is more informative than an 100 -bp fragment. Together these data suggest that the proposed genus "Ca. Aquarickettsia" broadly associates with corals and with many members of the non-bilaterian metazoan phyla (Placozoa, Porifera, Cnidaria, and Ctenophora), as well as the even more ancient protists.
Acknowledgements We would like to thank  Drs. Steve Giovannoni  and  Luis Bolanos  for their insight into this paper. Further we thank all of the people involved in the Global Coral Microbiome Project, primarily  Drs. Monica Medina ,  Joe Pollock , and  Jesse Zaneveld. This work was funded by a  National Science Foundation  DOB grant # 1442306  to RVT. JGK acknowledges funding through the  National Science Foundation Graduate Research Fellowship Program  (No. 2018260750 ). GW acknowledges funding through the  LMU Munich's Institutional Strategy LMUexcellent  within the framework of the  German Excellence Initiative  and the  European Union 's  Horizon 2020 Marie Skłodowska-Curie Innovative Training Network IGNITE  (No. 764840 ). GW also acknowledges the  Leibniz Supercomputing Center of the Bavarian Academy of Sciences and Humanities (www.
Model description iLOVECLIM iLOVECLIM is an earth system model of intermediate complexity (EMIC). This class of climate models arose as an intermediate between the fully integrated general circulation models and simplified conceptual climate models  (Claussen et al. , 2002). As a result, EMICs have the ability to simulate hundreds of thousands of years at a reasonable computational cost but also represent most of the natural earth system, particularly incorporating the components with slow feedback effects, such as ice sheets, vegetation and permafrost. iLOVECLIM is a 'code fork' of LOVECLIM 1. 2  (Goosse et al. , 2010) , from which most of the physical climate components have been retained. Namely, these are the atmosphere, ocean and vegetation. Other components of iLOVECLIM were not activated here, including ice sheets, carbon cycle and water isotopes.
There is not yet groundwater hydrology implemented into VAMPERS so no water/convective heat is transferred between subsurface layers. Rather, the subsurface is assumed to be saturated, where the water content of each layer equals its void space (porosity). For grid cells with a presumed sandy lithology, porosity decreases with depth based on a common depth-porosity equation (e. g. Athy, 1930). For bedrock lithologies, porosity is assumed to be very low (0. 1) and remains the same throughout depth. Equations and parameter sensitivity tests associated with VAMPERS are described in  Kitover et al. (2013). To match the minimalism of the land surface within ECBilt, VAMPERS only uses porosity and geothermal heat values to vary between each ECBilt grid cell. These values are derived from individual databases that are based on a reclassified version of the Global Lithological Map Database  (Hartmann and Moosdorf, 2012)  and median geothermal heat flux values previously derived from a combination of measurements, modeling and correlation estimates  (Davies, 2013).
Furthermore, they demonstrate that the reaction rate of the sodium alanate required to provide the hydrogen to the fuel cell stack was reached for a temperature of 160°C and for a pressure of 1. 7  [bar]. Some other important studies considering active systems are suggested in  [13, 15]. The authors propose the use of heat exchangers for embedded and stationary applications. Several simulation works have also shown important results for coupling and heat transfer. For instance, Pfeifer et al. in  [3]  simulate the heat of a high temperature PEM fuel cell and an alanate hydrogen storage tank by using the software package gPROMS. The results show that the initial temperature has a considerable influence on the operation time, due to the possible break-down of hydrogen pressure in the tank. The simulation has been validated with a small tank.
Simulation and discussion The inlet temperature of the heat transfer circuit is used as a reference for controlling the flow rate of the cold circuit of the exchanger. Driven by the servomotor, the valve will regulate the flow of the secondary circuit of the exchanger which will condition the heat exchange and the input temperature of the hot circuit in the tank. The simulation block diagram is executed in MATLAB/Simulink, depicted in Figure  6. The response of the system is illustrated in the figures 8 and 9. Figure  9 (a) depicts the time-dependent changes of hydrogen to metal (H/M) ratio. At the beginning, the hydrogen discharge rate of the tank drops without the addition of heat to the hydride tank. This is due to the presence of gaseous hydrogen present. During this phase, the temperature of the metal bed decreases abruptly, followed by an increase to reach the equilibrium temperature, as shown in figure  (9(d) ).
A B D C Figure 5. Overlap Length Correlations (A) The maximum (peak) lengths and the final lengths of antiparallel microtubule overlaps in self-organized minimal midzone bundles were extracted from time courses of overlap length. For each combination of PRC1 and KIF4A concentrations (datasets as used for Figures  4C  and 4D ), the mean peak overlap length and mean final overlap length show a positive correlation. (B) Plot of the mean peak overlap length against the mean PRC1/KIF4A intensity ratio measured in the overlap at the time of its maximum length, also demonstrating a positive correlation. Errors are SD. (C and D) Mean PRC1/tubulin (C) and mean KIF4A/tubulin (D) total fluorescence intensity ratios in the overlap region versus the mean end overlap length, calculated from the same datasets as used for (A) and (B).
5 min. The time interval between images was 4. 5 s for data in Figures  2  and S2 , and 18 s for all other time course data. One time-course per sample was recorded. After recording time courses of minimal midzone self-organization, several additional images per sample were recorded outside the area used for imaging the time course. These data were used to investigate the dependence of the end state of self-organization on the KIF4A and PRC1 concentrations (Figures  4, 5C, 5D,  and S6 ). Imaging of single dynamic microtubules started $2 min after adding the protein mixture to the flow chamber. Experiments were recorded over 12. 5 min, with a time interval of 1. 5 s per frame.
The 'Analyze Particles' plugin was run to further refine the position of all overlap regions in each frame. The 'DropletTracker2 0 plugin (https://github. com/ottobonn/ DropletTracker) was used to link corresponding overlap regions between each frame. Overlaps were rejected if tracked for less than 50 or 200 image frames for videos recorded with 18 s or 4. 5 s time intervals between image frames, respectively, or if the overlaps were within 130 pixels of the edge of the image. For each remaining overlap, the central coordinates and orientation were used to obtain an intensity profile along the length of the bundle, averaged over a width of 25 pixels, in each frame for each fluorescence channel. A composite kymograph was generated from these intensity profiles.
Time course data from 17 overlaps produced in the presence of 20 nM PRC1-Alexa546, 50 nM KIF4A-mBFP and 12. 5 mM Alexa647-tubulin were used to calculate the average time course of overlap length and total protein intensities in the overlap region. Peak overlap lengths and their corresponding properties were determined from a 90 s moving average of the raw time lapse data for each overlap. For the statistical analysis of final antiparallel microtubule overlap properties, overlap lengths and total fluorescence intensities were extracted from images taken $40 min after initiation of microtubule nucleation for more than 93 overlaps per condition. The investigated conditions were 12. 5 mM Alexa647-tubulin and the following ten combinations of PRC1-Alexa546 / KIF4A-mBFP concentrations (in nM/nM): 5/ 5, 5/10, 5/50, 10/5, 10/10, 10/50, 20/5, 20/10, 20/50, 50/50.
ACKNOWLEDGMENTS We thank  Julian Gannon  for cloning the PRC1-GFP expression plasmid and for protein expressions and  Jamie Rickman  and  Jayant Asthana  for helpful discussions. This work was supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001163 ), the  UK Medical Research Council  ( FC001163 ), and the  Wellcome Trust  ( FC001163 ). T. S. acknowledges support from the  European Research Council  (advanced grant, project  323042 ). F. N. was supported by the  Gatsby Charitable Foundation  (https://www. gatsby. org. uk/). M. L. R. is a fellow of the European Union's  Horizon 2020 Innovative Training Network DivIDE  ( Marie Sk1odowska-Curie  grant agreement no. 675737 ). F. J. F. was supported by an  EMBO  fellowship ( ALTF 219-2011 ).
OriginLab. com Python for data analysis CWI, the Netherlands https://www. python. org/ Cytosim [30] https://github. com/nedelec/cytosim Other Ni-TED resin Macherey-Nagel Cat#: 745200. 12 StrepTrap HP column GE Healthcare Cat#: 28907547 PD-10 desalting column GE Healthcare Cat#: 17-0851-01 Superose 6 10/30 column GE Healthcare Cat#: 29-0915-96 Vivaspin concentrator Sartorius Cat#: VS15RH21.
Calculations and Statistical Analyses Egg mass (EM) was calculated by multiplying percentage hen-day EP by the average EW in grams as described by  North and Bell (1990). Feed conversion ratio was calculated by dividing daily FI per hen (g/hen/day) by EM (g/egg). For each of the variables (BW, FI, EP, EW, EM, FCR), presented data are biweekly for layer I phase (19 to 28 wk of age) and 4-weekly for layer II phase (29 to 48 wk of age). The individual egg component weight was expressed as percentage of total whole EW. In addition, egg shell was expressed per unit of surface area to derive shell index as described by  Ahmed et al. (2005)  citing  Sauveur (1988). Apparent retention of components, apparent metabolizable energy (AME), and AME corrected for nitrogen (AMEn) were calculated according to  Kiarie et al.
(2014). Data were analyzed via one-way ANOVA using the General Linear Models procedure of SAS (SAS Inst. Inc. , Cary, NC). The model had diet as a fixed effect and hens in replicate-cages (cage was the experimental unit) as the random effect. Linear and quadratic effect of the probiotics among treatments was analyzed by using contrast coefficients based on unequally spaced dose of supplementation of the probiotics generated using the interactive matrix language procedure of SAS. Differences were considered significant at P ≤ 0. 05 and trends (0. 05 ≥ P ≤ 0. 10) were discussed.
ACKNOWLEDGMENTS Funding from  Ontario Agri-Food Innovation Alliance (Ontario, Canada)  and  Adisseo USA Inc. Alpharetta, Georgia (USA)  is highly appreciated. The authors also acknowledge technical assistance obtained during the course of the study from members of the  Monogastric Nutrition Lab. , in particular  C. Voth  ( Dept. of Animal Biosciences, University of Guelph, Ontario, Canada ), and staff members at  Arkell Research Centre (University of Guelph, Ontario, Canada ).
Removal of one base pair from the DP2 stem restores the canonical UNCG loop structure Following this observation, we engineered a CP LC ribozyme construct (CP LC DP2ΔUA) in which one base pair was removed from the DP2 stem (Fig. 1 ). This RNA crystallized under the same conditions and in identical space group P2 1 2 1 2 1 with each parameter expanded by <4% with respect to the parent CP LC ribozyme construct (Table  1 ; PDB: 6gyv). The crystal structure of the CP LC DP2ΔUA construct reveals that the canonical structure of the UUCG loop capping the DP2 stem has been restored (Fig. 2B ) and displays the typical Z-turn loop structural characteristics reported in D'Ascenzo et al. (  2017 ). It appears that the removal of one base pair from the DP2 stem restores enough space so that residues U1 and G4 from the loop can stack within the helix and form the trans base pair between their Watson-Crick and sugar edges  (Leontis and Westhof 2001) , respectively.
thermophilus ribosome, and require additional data to be confirmed. The NGNN tetraloop from the double-stranded RNA substrate of the yeast Rnt1p (RNase III family homologous to human drosha and dicer) also adopts the squashed conformation from cluster 41 in the crystal structure of the complex  (Liang et al. 2014; Song et al. 2017). This tetraloop (AGUC in pdb file 1k6g) adopts a regular A B FIGURE 2. Secondary structures and conformations of the DP2 stem-loops. (A) The UUCG tetraloop of the CP LC ribozyme presents a squashed conformation (PDB: 6gyv). Squashing results from the proximity between the backbone of residues U2 and C3 and the last base pair of the stem (nucleotides with filled rings), which expels the residues from the loop toward the solvent. The helical conformation is interrupted at the level of U1 and restored only after G4.
(B) The loop from the CP DP2ΔUA ribozyme adopts the canonical UUCG conformation with a maintained helical continuity at U1 and G4 (PDB: 6g7z). The Z-turn allows reorientation of the RNA chain toward the second strand of the stem. On each panel, the tetraloop is surrounded by a maximum likelihood map 2mF obs -DF calc contoured at 1. 5 σ. conformation in the free form of the RNA  (Lebars et al. 2001) , in the sense that the first and fourth residues from the loop form a base pair, which stacks upon the last base pair of the stem. Nevertheless, the kink in the backbone is mediated at the phosphate group between residues 2 and 3, a situation neither typical of U-turn nor Z-turn loops. Mapping the NMR distance constraints from  Lebars et al. (2001)  onto a Z-turn loop points to only two which cannot be accommodated between the 2 ′ and 3 ′ hydrogen atoms from residue L4 and the H6 atom from L3.
ACKNOWLEDGMENTS B. M. is a USIAS recipient for the exchange program between  University of Strasbourg (France)  and  Sophia University (Tokyo, Japan ). This research is supported by the  Centre National de la Recherche Scientifique  ( LABEX ANR-11-LABX-0057_MITOCROSS ), the  University of Strasbourg. M. C. acknowledges the  French agency ANR  (grant  ANR-10-BLAN-1502 ) and the  BIG Lidex program  for funding.
Data analysis Representative loops from the various clusters presented in the Bottaro and Lindorff-Larsen study  (Bottaro and Lindorff-Larsen 2017)  were superimposed to the squashed loop of the LC ribozyme to pick up clusters with similar folds. Torsion angles were determined with x3DNA-DSSR  (Lu and Olson 2003). Figures, superimpositions and RMSD calculations were made in PyMol  (Schrodinger 2010 ).
IL-38 Is an Endogenous Suppressor of Psoriasiform Skin Inflammation By analyzing IL-38 transcripts using in situ hybridization, we observed predominantly epidermal expression of IL-38 in mouse skin (Figure  1A ). As expected, IL-38 was absent in the skin of  mice  (Figures 1A and 1B). In wildtype (WT) mice subjected to IMQ-induced psoriasis, IL-38 expression was continuously reduced until day 5, while expression increased again on day 6, when IMQ-induced psoriasis resolves  (Figures 1C and 1D). The inverse pattern of IL-38 expression and skin inflammation, which was also apparent in a publicly available dataset of human psoriasis patients  (Li et al. , 2014a ) (Figure  S1 ), suggested an impact of IL-38 in IMQinduced psoriasis. Indeed, clinical PASI scores suggested a delayed resolution of skin inflammation in IL-38 KO mice, as indicated by increased inflammation and skin stiffness, as well as prolonged scaling from day 5 onward (Figure  1E ).
Data were analyzed using FlowJo Vx (TreeStar). All antibodies and secondary reagents were titrated to determine optimal concentrations. Comp-Beads (BD) were used for single-color compensation to create multicolor compensation matrices. For gating, fluorescence minus one controls were used. The instrument calibration was controlled daily using Cytometer Setup and Tracking beads (BD Biosciences). For characterization of immune cell subsets in skin the following antibodies were used: anti-CD3-PE-CF594, anti-CD4-BV711, anti-CD8-BV650, anti-CD11b-BV605, anti-CD11c-AlexaFluor700, anti-CD19-APC-H7, anti-CD326-BV711, anti-Ly6C-Per-CP-Cy5. 5, anti-NK1. 1 BV510 (BD Biosciences), anti-CD45-Vio-Blu, anti-MHC-II-APC (Miltenyi Biotec), anti-CD-90-PE, anti-F4/80-PE-Cy7, anti-GITR-FITC, anti-gd TCR APC, and anti-Ly6G-APC-Cy7 (BioLegend).
Cytometric Bead Array To determine cytokine levels in cell culture supernatants or murine tissues (in skin and tumors as described before  (Weichand et al. , 2017) ), human IL-17A, or murine IL-17A, IL-1b, IL-6, IL-23, and IFN-g Cytometric Bead Array Flex Sets (BD Biosciences) were used. Samples were acquired with a LSR Fortessa flow cytometer (BD Biosciences) and data was analyzed using BD Biosciences FCAP software (V3. 0).
ACKNOWLEDGMENTS The authors thank  Margarethe Mijatovic  and  Praveen Mathoor  for excellent technical assistance. We are grateful to  Johannes Buyel ,  Dirk Scheffler ,  Robin Kastilan , and  Michael Limberg  for large-scale protein production at the  Fraunhofer Institute IME, Aachen, Germany. The authors are supported by the  Else Kroner-Fresenius Foundation (EKFS) ,  Deutsche Krebshilfe  ( 70112451 ), the Landesoffensive zur Entwicklung wissenschaftlich-o ¨konomischer Exzellenz (LOEWE;  LOEWE Center for Translational Medicine and Pharmacology) , and  Deutsche Forschungsgemeinschaft (DFG ;  GRK2336 ,  SFB1039 , and  FOR2438 ). Y. H. was supported by the  China Scholarship Council (CSC).
Material and Methods: In this study, 54 patients (mean age 75. 80 ± 14. 29 years) with neovascular AMD (type 1 and type 2 CNV) were included retrospectively and separated into two groups (Group 1 -24 images; Group 2-30 images), according to the lesion topology. All patients underwent a 3x3 mm OCTA examination (AngioVue, Optovue, Freemont, California). The proposed algorithm is based on segmentation and enhancement methods including Frangi filter, Gabor wavelets and Fuzzy-C-Means Classification. Our results were compared to the manual quantifications given by the embedded quantification software "AngioAnalytics". Results: Automated CNV segmentation and quantification of three neovascular AMD biomarkers: the total vascular area (TVA), the total area (TA) and the vascular density (VD) were possible in all cases.
Generally, vessel segmentation methods include two steps  [14]. The first one is an enhancement of vascular structures mainly based on matched filter, Frangi filter or Gabor Wavelet filter  [15]. The second step leads to the separation of tubular components of the image into vessel and non-vessel structures. Following this scheme, our method is based on the enhancement of vessels located in the choroidal neovascularization lesion using the Frangi filter. Our database images are degraded by an intensive impulse noise, Frangi filter is thus the most suitable for the CNV enhancement because it is less sensitive to noise than matched filters and Gabor wavelets. In addition, it is faster and more efficient for emphasizing vessels of different sizes. Its principle is to detect geometrical structures in the image and to enhance them at different scales using Gaussian kernels  [16].
All the patients underwent a 3x3 mm OCTA examination (AngioVue, Optovue, Freemont, CA). The OCTA flow images size was of 304 x 304 with a pixel size of 9. 87 x 9. 87 μm. This definition allowed an accurate visualization of small vessels. The outer retinal slab with projection artifact removal was extracted when the image quality assessed using the Strength Signal Index SSI was greater than or equal to 6 (SSI ≥ 6) and manual editing of the outer retinal segmentation was performed if the software segmentation was not satisfying. Manual CNV delineation and quantification was obtained for all patients by an expert reader (AM). Included patients were further separated into two groups according to the CNV's topology on the images. Group 1 consisted of densely packed high flow networks, with no distinguishable branching (Figure  1 , Panel A).
Group 2 consisted of a high flow neovascular network with distinguishable individual branching pattern (Figure  1 , Panel B). A distinct algorithm was developed for each group. Two common steps preceded each algorithm: firstly, contrast enhancement in addition to a classic median filtering was employed, so as to separate the lesions from noise and to clearly distinguish the CNV contours; secondly, clinician interaction was solicited to select one or more markers on the flow image to indicate the lesion location. The proposed method was compared with manual delineation of the CNV and quantitative analysis with the embedded software (AngioAnalytics).
III. Results Fifty-four eyes of 54 patients were included in this study, ranging from 26 to 91 years (mean age: 75. 80 years ± 14. 29 SD), with neovascular AMD. The overall studied population was divided into Group 1 -consisting of 24 eyes-and Group 2 -consisting of 30 eyes-. We proposed a different segmentation algorithm for each group. MATLAB R2017a (The MathWorks, Inc. , Natick, Massachusetts, United States) was used for the software implementation. Automated versus manual quantitative analysis using AngioAnalytics is summarized in Table  1. The mean value of the measured total vascular area (TVA) for Group 1 is of 0. 402 ± 0. 380 mm 2 using the automated algorithm, against an average value of 0. 494 ± 0. 469 mm 2 estimated by AngioAnalytics. Similarly, the TVA average value measured automatically for Group 2 is of 0.
We tested the two algorithms on 54 OCTA flow images of 54 patients with type 1 and 2 CNV (24 flow images in Group 1 and 30 flow images in Group 2). Furthermore, we showed that our algorithms were able to extract and quantify different shapes and types of CNV. When comparing the biomarkers (TVA, TA and VD) using our proposed methods, to the tool embedded in the AngioVue Software "AngioAnalytics" (after the manual delineation of the CNV by one expert), the correlation coefficients by linear regression were very high for TVA and TA measurements of both groups (Group 1: TVA CC = 0. 989, TA CC = 0. 994; Group 2: TVA CC = 0. 987, TA CC = 0. 994) and lower for VD measurements (Group 1: VD CC = 0. 859; Group 2: VD CC = 0. 836). We reported in table 2 the Bland and Altman biases for the inter-methods agreement for each biomarker.
Furthermore, the quantification performed in this study is based on two-dimensional flow images, advanced studies on threedimensional data can so be performed to obtain volumetric measurements. Finally, it is notable to highlight our main contributions in relation to the existing few works  [1]  and  [9]. In the present study, a larger number of OCTA flow images was used and was separated according to the CNV topology into two groups, in order to preserve vascular details and have a correct quantification of the CNV. Three biomarkers of neovascular AMD were quantified by the same algorithm for each group. In addition, the results were compared to those obtained from AngioAnalytics which is the embedded algorithm, widely used in the current clinical routine. The performances of our proposed segmentation algorithms for the total area segmentation are compared in Table  3  with the saliency method and the DEC P systems proposed in  [1]  and  [9]  respectively.