dataesr/scientific-paragraphs-categorization huggingface dataset:
The α grain sizes were determined based on the reconstructed EBSD data, using the Channel 5 software. Firstly, the grain boundaries were identified under the criterion of that the disorientation angle between neighboring pixels is larger than 15°. Then, the diameter of a circle having equivalent area to that of the grain is defined as the grain size  [44]. To ensure a statistical relevance, the grain sizes and the area fractions of grains of interests were determined based on 2 to 4 EBSD maps each covering an area of 1. 6 × 1. 2 mm. Due to the unavoidable phase transformation from the high-temperature α to the γ lamellae that modified the local microstructural constituent, it is difficult to examine the dislocations in the deformed α phase by means of transmission electron microscopy (TEM).
Acknowledgements This work was supported by the  National Natural Science Foundation of China  under grant No. 51571162 , and the  Research Found of the State Key Laboratory of Solidification Processing (NWPU) ,  China  under grant No. 2020-TZ-03. The first author Fengming Qiang is grateful to the  China Scholarship Council  for the support to her PhD study in France. The authors acknowledge the inventors of the Merengue 2 for the reconstruction of hightemperature α phase in the present work.
Dew condensation is the result of cooling by radiative deficit between a substrate and the atmosphere. Dew yield can be enhanced in hollow structures like hollow cones where the influence of wind is lowered. Corrugation increases the local tilt angle with horizontal and makes dew drop grow faster on edges, then drops detach sooner and act as natural wipers. However, corrugation increases the heat exchange with surrounding local air, which may reduce cooling. In order to evaluate these effects on cooling and dew yield, a corrugated, W-shaped hollow cone is compared to the same, smooth structure (S-cone) by Computational Fluid Dynamics. Two softwares were used: Ansys CFX for a pre-study concerning detailed aerodynamics where the computational domain is modeled to obtain a fully developed wind profile assuming an unobstructed inlet and COMSOL Multiphysics for aerodynamics coupled with heat flux, including radiative exchange surface-to-sky and surface-to-surface.
Two softwares were used: Ansys CFX for detailed aerodynamics where the computational domain is modeled to obtain a fully developed wind profile and COMSOL Multiphysics aiming to detail the heat fluxes, including radiative exchange surface-to-sky and surface-to-surface. Since neither Ansys CFX nor COMSOL simulation models include dew formation, an evaluation of dew yield was performed by calculating with COMSOL the local temperatures, which can be related to the dew yield per surface area within some assumptions that will be discussed. The Ansys CFX was utilised to analyse the proposed design of the cones and test the aerodynamic effects on the airflow velocity over the inner surfaces of the condenser. The metrics used for the aerodynamic performance assessment were based on minimising the air speed as well as maintaining uniform levels of air velocity on the condensing surface.
Ansys CFX simulation As already noted, the Ansys CFX code version 18 is utilised to analyse the proposed design of the cones and test the aerodynamic effects on the airflow velocity over the inner surfaces of the condenser. turbulence model is used to simulate mean flow characteristics for turbulent flow conditions. In order to capture the aerodynamic effect of the installation, the geometry includes the lower stand (trunk of the tree) and all the details of the supporting structure that could affect the airflow in the main areas of interest. The computational size of the domain was developed in line with the recommendations for the assessment of wind conditions around buildings  (Mochida et al, 2006). The inlet, the sides and the upper boundary were set at 5 times the cone height H whilst the outflow boundary was set at 15H behind the cone ensuring an appropriate development of the wakes produced by the interaction of the flow with the geometry itself.
COMSOL Multiphysics simulation The commercial software, COMSOL Multiphysics, was used to analyze the heat transfer and fluid flow taking place around the cones, also using the turbulence model to simulate the mean flow characteristics for turbulent flow conditions. The goal of this modeling effort is to predict the temperature of the condenser surfaces for different values of air velocity. In the fluid domains, the continuity equations as well the Navier-Stokes and the energy conservation equations have to be solved simultaneously. The cones structure is made of Styrofoam to ensure thermal isolation. The simulation of the dew condenser heat exchange takes in account the following processes: (i) Thermal behavior of the radiative material and the insulation material, including its thermal conductivity, thermal expansion, density, heat capacity and emissivity in the atmospheric window whenever known (see Table  1 ).
C. Estimates of line friction parameter Experiments of a droplet spreading on a flat OSTE surface are modelled numerically to determine the line friction parameter. The line friction parameter is determined by fitting the spreading curve with the experiments. Spreading of a droplet on a flat surface is experimentally observed with a high-speed camera at a frame rate of 52000 s -1 and the spreading radius and the spreading time are recorded. To enhance sensitivity to the line friction parameter  [57] , we reduce the initial radius to 0. 4 mm. Navier-Stokes-Cahn-Hilliard equations are solved using in-house software "FemLego" to obtain the spreading radius for different values of µ f. FemLego is an adaptive finite element toolbox where weak formulation of partial differential equations is defined on a MAPLE worksheet  [63].
(2018a). SCUBA-2 data show compact sources with high column densities and can thus be used to find dense cores in the fields. Owing to its resolution of ∼14 , SCUBA-2 can detect dense cores with size of ∼0. 1 pc to within 2 kpc. As noise ranged from 6-10 mJy beam -1 in the central 3 to 10-30 mJy beam -1 out to 12  (Liu et al. 2018a) , we assume an uncertainty of 10 mJy beam -1 for the SCUBA-2 data, as in  Juvela et al. (2018b).
6 at 70 µm to 35. 4 at 500 µm. Unlike the SPIRE fields used, the PACS fields do not have a constant FWHM size due to different scanning velocities; a faster scan results in elongation of the effective beam. Herschel data were collected from the Herschel Science Archive 3. The data were reduced by the official pipeline to level 2. 5 or 3, and the SPIRE maps include intensity zero-point corrections, which are obtained from comparison to Planck measurements. The PACS images are not zero point corrected by the pipeline. All data were converted to units of MJy sr -1. Extended emission is visible in the SPIRE observations (Fig. 2b ), which are useful for determining cloud characteristics such as temperature and column density. Hotter protostellar sources are visible at shorter wavelengths, and 70 or 100 µm detection are a strong indicator of ongoing SF.
The SCUBA-2 measurements were used only in clump analysis, where we compared the fluxes to MBB fits made to the Herschel data. Shorter wavelength PACS data are not used for analysis of clumps because PACS data exist only for 45 out of the 53 fields. The shorter wavelengths are sensitive to temperature variations and therefore their inclusion could also increase bias in the derived physical quantities (i. e. , lead to higher dust temperature and lower column density estimates)  (Shetty et al. 2009a; Juvela & Ysard 2012). We compare the results of SED fits including PACS and SCUBA-2 data with those that only use SPIRE data in Appendix B. A least-squares fit of the MBB function was performed on each pixel. Based on the fits, we made maps of the dust color temperature T, intensity I 250 , and the optical depth τ 250 at 250 µm.
Clump properties Clump properties were extracted using Starlink's EXTRACT-CLUMPS algorithm from the CUPID package using 850 µm data as well. Up to 15 clump properties were derived, either directly with EXTRACTCLUMPS or extracted from Herschel maps using those parameters. The characteristics are listed in Table  1. The 850 µm clump maps are at the resolution of the SCUBA-2 data, 14. 6 , and the temperature and column density maps at the resolution of the SPIRE 500 µm data, 35. 4. Central coordinates, position angle, and angular sizes of the major and minor axes of the clump are given by the EXTRACT-CLUMPS routine. Average values of dust temperature T , surface brightness I 250 , and column density N(H 2 ) over the clump area were calculated using the Herschel SPIRE maps. The source flux density F ν is calculated by multiplying the average 850 µm surface brightness with the clump solid angle, F 850 = I 850 Ω.
Catalogs used We searched for potential YSOs from several catalogs. We used AKARI and Herschel point source catalogs, the YSO candidate catalog of  Marton et al. (2016) , and the Spitzer catalog of  Gutermuth & Heyer (2015). As coverage between catalogs varies, the fields covered by each catalog are listed in Table  C. 1. The Akari/IRC point-source catalog (PSC) 6  is based on the 9 µm and 18 µm observations of the AKARI satellite  (Kataza et al. 2010 ) and the AKARI/FIS bright-source catalog (BSC) data from 65-160 µm observations  (Yamamura et al. 2010 ). The Herschel PACS point source catalogs (hereafter PACS catalogs) include point sources at 70, 100, and 160 µm, as well as extended source and rejected source catalogs  (Marton et al. 2017). For YSO detection, we used the 70 and 100 µm catalogs.
The YSO candidates in the  Marton et al. (2016)  catalog are based on source classification using the WISE (Wide-field IR Survey Explorer) satellite  (Wright et al. 2010 ) data and the 2MASS (Two Micron All-Sky Survey) catalog  (Skrutskie et al. 2006). The Marton catalog is divided into two sections: Class I and II YSO candidates, and Class III and more evolved YSO candidates. We used both of these. The Gutermuth catalog was created using the MIPSGAL 24 µm Galactic Plane Survey matched with archival data from the 2MASS, GLIMPSE, and WISE surveys  (Gutermuth & Heyer 2015). We searched the catalog for those sources with spectra matching those of Class I and II YSOs. Sources from different catalogs were merged if their separations were smaller than 2. The detection limit of the Herschel catalogs depends on the wider environment of the field, but also on the mass of the protostar.
47 , for the temperature range 10-30 K. dation grant AST-1715876. CWL is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2019R1A2C1010851). Tie Liu acknowledges the support from the international partnership program of Chinese academy of sciences through grant No. 114231KYSB20200009 and the support from National Natural Science Foundation of China (NSFC) through grant NSFC No. 12073061. The James Clerk Maxwell Telescope is operated by the East Asian Observatory on behalf of The National Astronomical Observatory of Japan; Academia Sinica Institute of Astronomy and Astrophysics; the Korea Astronomy and Space Science Institute; Center for Astronomical Mega-Science (as well as the National Key R&D Program of China with No.
Acknowledgements. We thank the anonymous referee for their helpful comments. E. M. and M. J. acknowledge the support of the  Academy of Finland  Grant No. 285769. E. M. is funded by the  University of Helsinki doctoral school in particle physics and universe sciences (PAPU). L. B. acknowledges support from  CONICYT  project  Basal   AFB-170002. J. H. thanks the  National Natural Science Foundation of China  under grant Nos. 11873086  and  U1631237  and support by the  Yunnan Province of China  (No. 2017HC018 ). This work is sponsored in part by the  Chinese Academy of Sciences (CAS)  through a grant to the  CAS South America Center for Astronomy (CASSACA) in Santiago, Chile. P. S. was financially supported by a  Grant-in-Aid for Scientific Research  (KAKENHI Number  18H01259 ) of  Japan Society for the Promotion of Science (JSPS).
Hydrographic data including salinity, photosynthetically active radiation (PAR) and temperature were recorded using an SBE 25 plus and an SBE 43 CTD (Sea Bird Scientific, United States). Samples for quantification of chlorophyll a (Chla) and dissolved inorganic nutrients were collected from 1 L seawater filtered through 0. 7 µm glass fiber filters (Whatman GF/F, MilliporeSigma, MA, United States). For Chla determination, 200 ml of seawater was filtered (GF/F Whatman glass fiber filters, 0. 7 µm nominal pore size) in triplicate and immediately frozen (-20 • C) until later analysis via fluorometry (Turner Design TD-700), using acetone: water (90:10% v/v) for pigment extraction according to standard procedures  (Parsons, 2013). Samples for nutrient analysis were filtered through GF/F filters and frozen at -20 • C before analysis by spectrophotometry at the Centro de Investigación en Ecosistemas de la Patagonia (CIEP; Coyhaique, Chile) according to  Strickland and Parsons method (Strickland and Parsons, 1968 ).
Sequence Analyses All the reads that had a mismatch with the 16S rRNA primers contained ambiguous nucleotides (N) or were <300 bp long beyond the forward primer were removed. In addition, a stringent quality trimming criterion was applied to remove reads that had ≥10% of bases with Phred values <27. This procedure is recommended to ensure that when clustering at 97% or more, the influence of erroneous reads is minimized  (Huse et al. , 2010; Kunin et al. , 2010). The sequences were then de-replicated and clustered at a 99% sequence similarity threshold using UCLUST  (Edgar, 2010)  for de novo OTU picking. Representative sequences were classified against the SILVA v. 128 database  (Quast et al. , 2013). Sequence data analyses were conducted with Pyrotagger V. 01 pipeline  (Kunin and Hugenholtz, 2010).
Statistical Analyses The OTU sequences abundance table was transformed with an Hellinger transformation  (Legendre and Gallagher, 2001)  with the Vegan package  (Oksanen et al. , 2019)  in R 3. 5. 3 (Team, R. C. 2018). An MDS based on Bray-Curtis similarity was conducted to visualize similarities in community composition between samples with the "vegan" package. Significant differences in community structure among the different variables were tested with PERMANOVA with the adonis function. Indicator species analysis was conducted using the multipatt function of the "indicspecies" package in R  (De Cáceres et al. , 2012). Finally, Pearson correlation coefficient was calculated to analyze associations between abundance of indicator species and environmental variables.
Overall DNA and RNA Community Composition We obtained a total of 2,722,567 16S rRNA genes and transcripts sequences of Bacteria and Archaea, from 78 samples taken during July 2017 and July 2018 in both fjords. A subset of the data was used to include only samples from which both DNA and RNA were successfully amplified (Supplementary Table  1 ). A total of 1,864,183 16S rRNA genes and transcripts sequences remained, divided between 44,006 different OTUs, of which 39,932 were assigned to Bacteria and 4,074 to Archaea. We compared the microbial community composition among fjords and years based on both the active fraction of the community (16S rRNA transcripts) and the total community (16S rRNA genes; Figure  3 ). For both fjords, the RNA fraction was different from the DNA fraction on the nonmetric multidimensional scaling (NMDS) plot (PERMANOVA, p = 0.
, 2012; Luo et al. , 2014; Pedneault et al. , 2014; Tolar et al. , 2016). Additionally, MGI is an ammonia oxidizer archaeon with high substrate affinity (Lehtovirta-Morley, 2018), which allows it to proliferate rapidly when ammonia concentrations increase. Furthermore, a high proportion of the OTUs indicator species for July 2018 was related to the non-photosynthetically activity such as OTU1238 (Alcanivorax/Oceanospirillaceae), OTU41001/29078 (SAR406), OTU27341 (Pseudoalteromonadaceae), MGI and Thermoplasmatales OTUs, among others. Some of these species, such as SAR406, Pseudoalteromonadaceae and Oceanospirillaceae, are described to participate in sulfur and nitrogen biogeochemical cycles  (Wright et al. , 2014; Delmont et al. , 2018). Finally, in our dataset, the rDNA community did not reveal the clear differences seen with rRNA data.
FUNDING This research was supported by the  National Agency for Research and Development (ANID)  through project  FONDAP-IDEAL , grant number  15150003 , and a  CONICYT Ph. D. grant to CM-M. This study was partially funded by  FONDECYT  project  1180954  and by  COPAS Sur-Austral   ANID PIA APOYO CCTE AFB170006. This study was developed in the frame of the  French-Chilean  project  LIA-MORFUN  and the current LIA- MAST  project (CNRS).
The objective lens used was a HCX PL APO CS 100 × 1. 40 (oil immersion). Image acquisition was performed in sequential mode with the following excitation and emission wavelengths: (1) λ ex1 = 488 nm and λ em1 = 490-555 nm; (2) λ ex2 = 488 nm and λ em2 = 560-600 nm. The images were processed and exported using LAS X software, version 3. 4. 2. 18368. Three independent repetitions were performed, and approximately 20 representative images were acquired for each repetition. Image sequences were acquired at frequency f = 2 Hz in order to investigate the movements of LGG in the milk over time. The bacterial dynamic was illustrated by calculating the cumulative area covered by the bacteria as a function of time. For each image, the area of all the bacteria was measured first by applying a threshold function and then an edge-detection filter.
In addition, a few plant families of similar structure dominate grasslands worldwide, which makes it easier to compare results across global grasslands. In our analyses, we explicitly ask (1) if the strength of nutrient effects on plant biomass and diversity changes over time, (2) if nutrient-induced diversity loss reduces the positive effects of nutrient addition on plant biomass, and (3) whether the nutrient-induced accumulation of dead biomass reduces the positive effects of nutrient addition on plant biomass? METHODS Study System and experimental design. This work was conducted within the Nutrient Network Distributed Experiment, a globally replicated nutrient and herbivore manipulation experiment (NutNet; www. nutnet. org) (Borer et al. 2014a , Borer et al. 2017). For this study, we used data from 47 sites that had been receiving nutrient addition for 5-11 years (Appendix S1: Appendix S1: Table  S7 ).
Author Manuscript 19399170, 2021, 2, Downloaded from https://esajournals. onlinelibrary. wiley. com/doi/10. 1002/ecy. 3218 by Queensland University Of Technology, Wiley Online Library on [14/02/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Each site was set up as a completely randomized blocked design typically with 3 replicates (ranged from 1-6). The experimental unit was a 5 x 5 m plot. Data Collection. Starting in the year prior to fertilization, we annually sampled aboveground plant biomass and plant community composition. Biomass was sampled by clipping all aboveground biomass (live and dead) in two 0. 1 m x 1 m strips, sorting current year's biomass (live biomass) from previous year's biomass (dead biomass), drying the biomass to a constant mass at 60 o C, and weighing it to the nearest 0.
In our regression of site level characteristics that may predict the change in nutrient effects over time, many of our predictor variables co-varied (e. g. , pre-treatment live biomass, soil N, soil C:N, and water availability index). To account for the potential for multiple models that had similar explanatory power due to this correlation, we used a multi-model approach, as described in Grueber et al. (  2011 ) using the dredge and model. avg functions in the MuMIn library. We standardized the input variables using the arm library. In the model averaging, we included all models within 4 AIC c units of the best model. We ran these models with and without soils data, as we were missing soils data at some sites. All analyses were conducted in R (v. 3. 4. 1; R Foundation for Statistical Computing, Vienna, Austria).
ACKNOWLEDGMENTS Author Manuscript 19399170, 2021, 2, Downloaded from https://esajournals. onlinelibrary. wiley. com/doi/10. 1002/ecy. 3218by  Queensland University Of Technology, Wiley Online Library on  [ 14/02/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License This article is protected by copyright. All rights reserved This work was generated using data from the  Nutrient Network  (http://www. nutnet. org)experiment, funded at the site scale by individual researchers. Coordination and data management have been supported by funding to  E. Borer  and  E. Seabloom  from the  National Science Foundation Research Coordination Network  ( NSF-DEB-1042132 ) and  Long Term Ecological Research  ( NSF-DEB-1234162 & DEB-1831944  to Cedar Creek LTER) programs, and the  University of Minnesota 's Institute on the Environment ( DG-0001-13 ).
availability index (Mean Annual Precipitation/Potential Evapotranspiration), mean annual precipitation, mean annual temperature, mean diurnal temperature range, mean annual temperature range, and temperature in the wettest quarter. The plot-level soil predictors were pre-treatment soil pH, total base cations (sum of Ca, Mg, and K), % soil N, and the soil C:N ratio. Note that % soil N, % soil organic matter, and % soil organic C are all highly correlated (r DATA AVAILABILITY Data are publicly available on the Environmental Data Initiative platform  (Seabloom et al. 2020 ): https://doi. org/10. 6073/pasta/d78e6e48df246a03129c85a6c1da00d6.
1%, the membranes were incubated with HRPlabeled goat anti-rabbit antibody (1:10,000) or HRP-labeled goat anti-mouse antibody (1:10,000). The protein bands were revealed by using enhanced chemiluminescence (Santa Cruz, Heidelberg, Germany). The immune complexes were visualized on the Amersham imager. Densitometric analysis of bands was performed by Image J software, and the data were normalized to total protein.
The inhibitory effect was more pronounced with juglone-treated cells, the chemotaxis was completely inhibited at 30 µM for some donors. Juglone and PiB did not affect cell viability even at the highest concentration used (data not shown). These results suggest that Pin1 controls fMLP-induced neutrophil chemotaxis and recruitment to the inflammatory site. were incubated in the absence or presence of different concentrations of juglone or PiB and stimulated by fMLP (10 -7 M) for 1 min. Neutrophils were lysed and Pin1 activity was measured using the Suc-AEPF-p-nitroaniline (pNA) peptide-Glu. The increase of pNA by chemotrypsin was detected at 390 nm. Ctrl corresponds to resting neutrophils. Data are means ± S. E. M. of 3 separate experiments (* p < 0. 05, ** p < 0. 001, *** p < 0. 0001 juglone and PiB as compared to control).
(C) Equal amounts of supernatant samples and neutrophil pellets were subjected to SDS-PAGE followed by immunoblot analysis with anti-MPO antibody. A representative Western blot of 4 experiments is shown. (D) Western blot results were quantified and expressed in arbitrary units (AU). Values are means ± S. E. M. n = 4 (* p < 0. 05, ** p < 0. 01, *** p < 0. 001). In order to address the question if Pin1 is also involved in fMLP-induced specific granule release, we studied the release of NGAL a marker of specific granules detecting it by Western Blot using a specific antibody. Results show that in combination with cytochalasin B, fMLP induced release of NGAL in the extracellular media and that juglone and PiB inhibited NGAL protein release from specific granules (Figure  5 ). These data corroborate with the previous results and suggest that Pin1 controls fMLP-induced neutrophil degranulation of specific granules.