dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, coastal areas are crossed by many vessels (e. g. , recreational boats and artisanal fishing boats <15 m), which are not required to carry AIS. Therefore, AIS data alone would not be adequate to describe coastal marine traffic data. The core areas highlighted by our KDE analysis may be suitable candidates as Important Marine Turtle Areas (IMTAs) due to its role as oceanic foraging area for both large juveniles and adults, which are considered a priority conservation target for their high reproductive value  (Casale et al. , 2012b). Indeed, IMTAs were defined by the IUCN Marine Turtle Specialist Group as "discrete areas within existing marine turtle regional management units that are of particular biological significance for the persistence of sea turtles"  (Bandimere et al. , 2021).
Acknowledgments Logistics during fieldwork were supported by  Filicudi Wildlife Conservation  and the  Marine Protected Area of the islands of Ventotene  and  Santo Stefano , whereas travel to and from the  Aeolian  field site for SH as well as satellite transmission fees were funded by personal research funds of SH made available by the  Stazione Zoologica Anton Dohrn. Special thanks are due to the Director of  Marine Protected Area Antonio Romano  for supporting travel, subsistence, and accommodation at Ventotene island, to  Gianluca Treglia ,  Annarita Matrone , and  Chiara Bruno  for their support in the fieldwork, and to the staff of the SZN marine turtle rescue center for their assistance during rehabilitation and preparation of the release of turtles. The authors are also grateful for the insightful and constructive comments offered by  Dr.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www. datarepository. movebank. org/handle/10255/move. 1099 https:// www. emodnet-humanactivities. eu/view-data. php Some raw data from satellite tracking of turtles are not included in the public database but can be made available upon request.
7J ) with Upadacitinib at different times during sensitization (i. e. , 1-2 vs. 3-4 days post-sensitization) (Fig. 7K ). Mice were then challenged, and cytokine production by WT and Il6r -/-T cells were analyzed in the lung. In HDM LPSsensitized mice, treatment with Upadacitinib on days 1-2 prevented Th2 cell cytokine production by Il6r -/-CD44 hi CD4 + T cells, while treatment with Upadacitinib on days 3-4 had no effect on Th2 cell cytokine production by Il6r -/- CD44 hi CD4 + T cells (Fig. 7L-M ). These data suggest that JAK1 inhibition in recently activated T cells can effectively suppress IL-2 signaling and subsequent Th2 cell lineage commitment. Collectively, our data indicate that IL-6 signaling in allergen-specific T cells is required to prevent bias towards the Th2 cell subset and subsequent allergic inflammation.
Downstream Analysis The R package edgeR  (53)  was used to assess differential expression between groups and to generate gene-by-sample matrices. Volcano plot was generated using R. Comparison of our data to other published data sets was accomplished using Gene Set Enrichment Analysis (GSEA)  (54). Upstream regulator analysis was performed using Ingenuity Pathway Analysis (IPA; Qiagen Digital Insights, Redwood City CA, USA)  (55).
Quantification and Statistical analysis. All plots and histograms were plotted in FlowJo v. 9 and v. 10 software (Treestar). GraphPad Prism (Version 9) was used for data analysis. (A-C) WT and Il6 -/-mice were transferred with OTII cells and i. n sensitized with HDM LPS +OVA. On day 3, OTII cells were sorted from mLN and RNA-seq was performed (three replicates). Volcano plot highlighting the IL-2-driven genes Il2ra and Cish, and the IL-6-driven gene Socs3, upregulated and downregulated in OTII cells from Il6 -/-mice, respectively (FDR <0. 05, ≥2 FC. See Table  S1 ) (A). GSEA plots showing the enrichment of genes in OTII cells from Il6 -/-versus WT.
Acknowledgements We thank  Becca Burnham ,  Uma Mudunuru  and  Thomas S Simpler  for animal husbandry. The work was supported by  UAB  and  NIH  grants  2R01AI116584  to B. L. , and  R01AI150664  and  R01AI162698  to A. B. T. The X-RAD 320 unit was purchased using a  Research Facility Improvement Grant ,  1 G20RR022807-01 , from the  NCRR, NIH. Support for the UAB flow cytometry core was provided by grants  P30 AR048311  and  P30 AI027767.
Characterizations A JEOL 7000 FE SEM and a Hitachi TM3030Plus SEM were used to characterize the enforcements of nano-inclusions/CFs and the composite. A Nikon XT H225 was used to scan the composite samples to carry out 3D characterization. The versatile XT H 225 systems offer a 225 kV microfocus X-ray source with 3 µm focal spot size. XT Software brings the fastest reconstruction of CT data with processing software, such as VolumeGraphics. It can be used for the inspection of internal faults of the plastic parts. The internal structural information of the samples was observed and collected by the compact desktop Bruker micro-CT, 3D X-ray scan system, SkyScan 1272 at NTUA. The system consists of a microfocus sealed X-ray source which operates at 20-100 kV and 10W, an X-ray detector with a maximum resolution of 11 Mp (4032 × 2688 pixels), and a 14-bit cooled CCD fiber optically coupled to a scintillator.
FFF Process Parameters 3D printing filament manufacturing was performed at 230 • C using a single-screw extruder with a water-cooling bath combined with a filament shaper to calibrate a diameter of 1. 75 mm and 4 km long. The extrusion process and the produced spool of PA12_15%GNPs + 1%CNTs can be seen in Figure  1. The filament was evaluated by TGA and DSC, and the technical detail is listed in Table  1. For FFF processability assessment, filament segments of 30 cm were cut and measured with a digital caliper to determine the average diameter of each segment. Filament drying was conducted by a hot-air drying oven at 80 • C for 12 h, and all segments were conditioned prior to testing in a low-humidity enclosure. The Raise 3D Pro 2 Plus FFF system was employed for the fabrication of testing specimens with different process parameters, and the generation of custom Gcode scripts was conducted through 3DOptimizer software (FabControl SIA).
Filament drying was conducted by a hot-air drying oven at 80 °C for 12 h, and all segments were conditioned prior to testing in a low-humidity enclosure. The Raise 3D Pro 2 Plus FFF system was employed for the fabrication of testing specimens with different process parameters, and the generation of custom Gcode scripts was conducted through 3DOptimizer software (FabControl SIA). Firstly, a preliminary extrudability assessment was conducted for three nozzle diameters (0. 4, 0. 6, 0. 8 mm) in order to assess flow consistency and extrusion temperature range. After the nozzle diameter (0. 8 mm) was selected, seven different temperatures above the melting temperature (189 °C in Table  1 ) were tested, from 240 °C to 280 °C, for printing quality examination. Subsequently, sequential interdependence tests were conducted by varying nozzle temperature, printing speed, extrusion multiplier (volumetric flow rate compensation factor), retraction, and thermoplastic strand width and height as seen in Figure  2.
Precise leveling and calibration of the FFF system and first layer settings were conducted prior to each test. Stereomicroscope evaluation (S9 stereomicroscope, Leica Microsystems) was conducted to assess morphology and deposition consistency of thermoplastic strands, aiming to identify parameter sets that eliminated structural defects related to over/under-extrusion and presented high strand uniformity. For FFF processability assessment, filament segments of 30 cm were cut and meas ured with a digital caliper to determine the average diameter of each segment. Filamen drying was conducted by a hot-air drying oven at 80 °C for 12 h, and all segments were conditioned prior to testing in a low-humidity enclosure. The Raise 3D Pro 2 Plus FFF system was employed for the fabrication of testing specimens with different process pa rameters, and the generation of custom Gcode scripts was conducted through 3DOpti mizer software (FabControl SIA).
We use approximately 1800 X-ray active galactic nuclei (AGN) in the eROSITA Final Equatorial-Depth Survey (eFEDS) that span over two orders of magnitude in X-ray luminosity, L X,2-10 keV ≈ 10 43-45 erg s -1 , and compare their star-formation rate (SFR) to that of non-AGN star-forming systems, at 0. 5 < z < 1. 5. To this end, we compiled a reference galaxy catalogue of about 17 000 sources. Both samples have the same photometric coverage, from optical to far-infrared. We construct the spectral energy distributions (SEDs) of all sources and fit them using the CIGALE code, using the same templates and parametric grid for both samples, and accounting for the mass incompleteness and exclude quiescent systems from both datasets. This allows us to compare the SFR of the two populations in a uniform manner, minimising systematic effects.
Based on our analysis, AGN at low and moderate L X (L X,2-10 keV < 10 44 erg s -1 ) have SFR that is lower than, or at most equal to, that of star-forming galaxies, in agreement with previous studies. The large number of luminous X-ray AGN available in the eFEDS dataset enable us to expand our investigations at higher L X to test previous, tentative results. At L X,2-10 keV > 10 44. 2 erg s -1 , the SFR of AGN appears enhanced by ∼30% compared to that of star-forming sources, for systems with stellar mass in the range of 10. 5 < log [M * (M )] < 11. 5, confirming indications found in previous studies. The most massive sources, log [M * (M )] > 11. 5, present a flat SFR norm -L X relation up to L X,2-10 keV ∼ 10 44. 5 erg s -1 , with SFR similar to that of star-forming galaxies. However, at higher L X (L X,2-10 keV ∼ 10 45 erg s -1 ), we find indications that the SFR of these massive AGN hosts may be enhanced compared to that of non-AGN systems.
The datasets are described in detail in Sect. 2. We follow the same methodology applied in the previous works of  Mountrichas et al. (2021c Mountrichas et al. ( , 2022) )  which allows us to compare and complement the results, covering a luminosity baseline of more than 2. 5 orders of magnitude (L X,2-10 keV ∼ 10 42. 5-45 erg s -1 ) in total. Specifically, we construct the spectral energy distributions (SEDs) of all galaxies and fit them using the CIGALE code  (Boquien et al. 2019; Yang et al. 2020 Yang et al. , 2022)). The models, parametric grid, and quality criteria are described in Sect. 3 and are identical to those used in  Mountrichas et al. (2021c Mountrichas et al. ( , 2022 ) so as to avoid systematic effects introduced by different templates and parameter space. Our main goal is to use the large number of luminous AGN available in the eFEDS field and examine whether or not the tentative results presented by  Mountrichas et al.
Lower luminosities have already been examined by  Mountrichas et al. (2022)  using the COSMOS sample. AGN at low redshifts do not contribute to L X,2-10 keV > 10 44 erg s -1. Therefore, we exclude sources below z < 0. 5. Furthermore, in our SED-fitting process, we use the Gaussian Aperture and Photometry (GAAP) data available in the eFEDS X-ray catalogue and the KiDS/VIKING dataset of the galaxy reference sample. GAAP photometry is performed twice, with aperture setting MIN_APER = 0. 7 and 1. 0. A value for each photometric band with the optimal MIN_APER is provided (for the choice of GAAP aperture size, see  Kuijken et al. 2015). GAAP is optimised for calculating photoz which requires colour measurements. In the case of extended and low-redshift sources, total fluxes may be underestimated  (Kuijken et al.
Galaxy reference catalogue To compare the SFR of X-ray AGN with non-AGN systems in a consistent manner, we compiled a galaxy (non-AGN) reference catalogue. We require the same photometric coverage and apply the same SED-fitting analysis in both datasets (see following section). We use the fourth data release catalogue of the KiDS/VIKING imaging survey  (Kuijken et al. 2019) , which has available optical and NIR photometry and photoz measurements for about 100 million galaxies over 1006 square degrees. We restrict the sample to those sources within the eFEDS region (∼65 deg 2 overlap) and apply the same requirements for photometric coverage of the X-ray sample. We also restrict the sample to sources with z > 0. 5 (see previous section). These requirements give us 200 000 galaxies. We cross-match these sources with spectroscopic catalogues from SDSS, WiggleZ, and GAMA which results in 7000 sources with available specz.
For the remaining sources, we use the photoz calculations provided in the KiDS/VIKING dataset. This allows us to significantly increase the size of our reference catalogue, in particular at z > 1, where a large fraction of the X-ray sources lie. Photometric redshift was estimated using the BPZ code  Benitez (2000). Wright et al. (2019)  tested BPZ photoz -using the KiDS/VIKING photometry -against several deep spectroscopic surveys. Based on the analysis of these authors, the accuracy of photoz is found to be σ ∆z/(1+z spec ) = 0. 072. The fraction of outliers (∆z/(1 + z specz ) > 0. 15) is ≈17. 7%. Finally, we cross-match the galaxy reference catalogue with the GAMA-09 A130, page 3 of 14   2000 ) law V-band attenuation A V 0. 2, 0. 3, 0. 4, 0. 5, 0. 6, 0. 7, 0. 8, 0. 9, 1, 1. 5, 2, 2. 5, 3, 3.
CIGALE To measure the (host) galaxy properties of the sources in our datasets, we apply SED fitting, using the CIGALE algorithm  (Boquien et al. 2019; Yang et al. 2020 Yang et al. , 2022)). CIGALE allows inclusion of the X-ray flux in the fitting process and has the ability to account for the extinction of the UV and optical emission in the poles of AGN  (Yang et al. 2020; Mountrichas et al. 2021a,b; Buat et al. 2021 ). For consistency with our previous, similar studies in the Boötes  (Mountrichas et al. 2021c ) and the COSMOS  (Mountrichas et al. 2022 ) fields, we use the same grid used in these works. This minimises any systematic effects that may be introduced due to the different modules and parametric grid used in the SED fitting process. Table  1  presents the templates and the values for the free parameters.
(2021c Mountrichas et al. ( , 2022) )  and in Boötes and COSMOS. However, in these studies, the percentage was ranging from (15-20)% at 0. 5 < z < 1. 5 to (50-60)% at z > 1. 5. The higher percentages we find using the eFEDS dataset can be explained by the different M * distributions in the three samples (e. g. ,  Georgakakis et al. 2017; Aird et al. 2018 ). At 0. 5 < z < 1. 5, galaxies from the COSMOS and Boötes catalogues have mean log [M * (M )] = 10. 5 and 10. 7, respectively. However, galaxies in the eFEDS field at the same redshift interval are more massive, with mean log [M * (M )] = 11. 1. At z > 1. 5, there is also a difference in the mean M * for all three fields. Specifically, log [M * (M )] = 10. 7 and 11. 1 in COSMOS and Boötes, respectively, while mean log [M * (M )] = 11. 4. We split the eFEDS sources in stellar mass bins and confirm that the median value of frac AGN increases with increasing M *.
These percentages appear low compared to those found by  Mountrichas et al. (2022)  in the COSMOS field (∼25% and 10%, for the AGN and galaxies in the reference sample, respectively), and by  Mountrichas et al. (2021c)  for sources in Boötes (∼30% for both datasets). For comparison, Fig. 1  shows the sSFR distributions of the reference catalogues in COSMOS and Boötes for the same redshift intervals. We notice that in both datasets there is a large tail that expands to lower sSFR values. This tail is less prominent in the case of eFEDS sources for galaxies within 0. 5 < z < 1, and is absent for sources in the highest redshift bin. Sources in the Boötes field present the highest fraction of quiescent systems. This could be due to the high mass completeness limits of the Boötes samples that biased these datasets towards systems with low sSFR values.
On the other hand, the brighter luminosities spanned by our X-ray and reference galaxy catalogues compared to the datasets in the COS-MOS field may bias our samples against sources with low sSFR A130, page 6 of 14 0:5 0:6 0:7 0:8 0:9 1:0 1:1 1:2 1:3 1: We explored other possible methods to exclude quiescent systems. Alternatively, we identified as quiescent those sources that have sSFR 1 dex below the mean value at each redshift range (e. g. ,  Salim et al. 2018). Following this approach, we exclude sources with log sSFR < -1. 4 Gyr -1 and log sSFR < -1. 15 Gyr -1 , at 0. 5 < z < 1. 0 and 1. 0 < z < 1. 5, respectively. Regarding the low redshift interval, the sSFR value is almost the same as that used in our analysis above. At the high redshift range, the indicated sSFR value excludes almost none of the quiescent systems.
We then tried a more strict definition for selecting quiescent systems. We excluded those sources that have sSFR values 0. 3 dex below the mean value at each redshift range. The results from following these approaches are presented in Appendix A. We find that our results do not change (same trends are observed) regardless of how quiescent systems are defined. Following the stricter definition, SFR norm values are slightly higher, namely by only 6% on average, and well within the quoted errors of the two measurements. This effect is discussed further in the following section. Based on these results, in the following analysis, we exclude quiescent systems from our datasets using the location of the low, secondary peaks of the sSFR distributions. The numbers of sources in our final samples are shown in Table  2.
Comparison of the SFR of X-ray AGN with that of star-forming non-AGN systems as a function of L X To compare the SFR of X-ray AGN with that of star-forming systems from the reference galaxy catalogue, we use the SFR norm parameter. SFR norm is defined as the ratio of the SFR of galaxies that host AGN to the SFR of star-forming galaxies. To calculate this, we use the SFRs that CIGALE has calculated for the 1763 X-ray sources and the 17 305 sources in the reference galaxy. This approach has certain merits. Both samples have the same photometric coverage, the same method (SED fitting) has been applied for the estimation of (host) galaxy properties (SFR, M * ), the same grid has been used for both datasets, and quiescent systems have been excluded following the same criteria for both samples. These similarities minimise a number of systematic effects that may have affected the results of previous studies that used analytical expressions from the literature for the estimation of the SFR of star-forming galaxies (e.
This is in agreement with previous studies  (Mullaney et al. 2015; Mountrichas et al. 2021c Mountrichas et al. , 2022)). Based on this result, in Fig. 4 , we plot SFR norm -L X in the total redshift range spanned by our datasets, that is, 0. 5 < z < 1. 5. Our goal is to examine the SFR of X-ray AGN relative to the SFR of star-forming galaxies as a function of AGN activity, that is, Xray luminosity. Figure  4  also shows the results from the Boötes (0. 5 < z < 2. 0;  Mountrichas et al. 2021c ) and the COSMOS (0. 5 < z < 2. 5;  Mountrichas et al. 2022)  fields. At low to intermediate X-ray luminosities (L X,2-10 keV < 10 43. 5 erg s -1 ), our measurements are in very good agreement with those from COSMOS. Both results show that, in this L X regime, SFR norm values are systematically below the dashed line, that is, the SFR of AGN is lower compared to that of star-forming galaxies.
At higher L X , we observe an increase in the SFR norm values, which is mild but in agreement with that found in previous studies  (Mountrichas et al. 2021c (Mountrichas et al. , 2022)). The trends found by our analysis are consistent with those from previous studies. Masoura et al. (2021)  found a strong dependence of SFR on L X using X-ray AGN from the A130, page 8 of 14 XMM-XXL dataset. Although our results do not present such a strong evolution of SFR norm with L X , the overall trends are similar. Specifically, at low to moderate luminosities, the SFRs of AGN tend to be lower than or consistent with those of non-AGN systems, while at higher L X the SFR of X-ray AGN appears enhanced compared to that of SF galaxies. We also note that  Masoura et al. (2021)  calculated SFR norm using the analytical expression of  Schreiber et al.
The top left panel of Fig. 5  presents SFR norm versus L X for galaxies with 10. 0 < log [M * (M )] < 10. 5. In agreement with the results in the COSMOS field  (Mountrichas et al. 2022) , the SFR of galaxies that host AGN is consistent with the SFR of starforming galaxies (dashed line). There are no results from Boötes in this stellar mass range because of the mass completeness limits of the Boötes sample. The eFEDS and COSMOS datasets do not provide us with enough AGN at L X,2-10 keV > 10 44 erg s -1 in this M * regime to examine whether or not the SFR of X-ray AGN changes compared to star-forming galaxies at higher L X. In the top right panel of Fig. 5 , we plot SFR norm as a function of L X for AGN that live in galaxies with stellar mass in the range 10. 5 < log [M * (M )] < 11. 0. We also plot the measurements from the COSMOS and Boötes fields.
For the latter, the M * interval is slightly different because of the high mass completeness values of this dataset. At low to intermediate luminosities (L X,2-10 keV < 10 44. erg s -1 ), our results are in agreement with those from previous studies and in particular with those from the COSMOS dataset, which has larger size compared to the Boötes sample in this luminosity interval. At L X,2-10 keV ≈ 10 44. 2 erg s -1 , our measurements confirm the results from the previous studies for increased SFR norm. More importantly, the eFEDS dataset extends this trend at higher X-ray luminosities, that is, up to L X,2-10 keV ≈ 10 44. 6 erg s -1. Based on our results, X-ray AGN that live in galaxies with 10. 5 < log [M * (M )] < 11. 0 at L X,2-10 keV > 10 44 erg s -1 have enhanced SFR compared to starforming galaxies by ∼30%, with a statistical significance of ≈2σ.
Of the 47 X-ray sources in Boötes, 32 (68%) are X-ray obscured (N H > 21. 5 cm -2 ) compared to 10/31 (32%) of the eFEDS AGN. However, we do not find a tendency for lower SFR norm values for the obscured sources in either of the two fields. This is also true if we increase the N H value used for the X-ray classification (N H > 22 cm -2 ). Similar results are found when we A130, page 9 of 14 Fig. 5. SFR norm vs. X-ray luminosity for four stellar-mass bins. We complement our results in the eFEDS field (red squares) with those using the COSMOS dataset  (Mountrichas et al. 2022 ) and those from the Boötes sample  (Mountrichas et al. 2021c ). The dashed horizontal line indicates the SFR norm value (=1) for which the SFR of AGN is equal to the SFR of star-forming galaxies. Errors are calculated using bootstrap resampling.
In some cases, the error bars associated to the COSMOS dataset are smaller than those associated to eFEDS. This is due to the finer binning grid used for the eFEDS sample. At low luminosities (L X,2-10 keV < 10 44 erg s -1 ), the results from the three studies are in very good agreement. At high luminosities (L X,2-10 keV > 10 44 erg s -1 ), our measurements from the eFEDS field confirm the tentative results from the Boötes field, which suggest that AGN host-galaxies with 10. 5 < log [M * (M )] < 11. 5 present an approximately 30% higher SFR than star-forming galaxies. In the most massive systems, log [M * (M )] > 11. 5, our calculations indicate that, in the most luminous systems (L X,2-10 keV ∼ 10 45 erg s -1 ), the SFR of AGN hosts could be enhanced compared to that of MS galaxies. The two dotted points show the results when we split the highest L X bin from the eFEDs field into two using an L X cut at L X,2-10 keV = 10 44.