dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of multiple spermatocyst stages in each sample and overlap in spermatogenic cohorts combined with the natural variation between samples provided coverage over the complete range of spermatogenic stages without the collection of a time series. These samples were re-assayed at higher magnification (600 × and 1000x) under oil immersion using an Olympus BH-2 compound microscope to identify characteristics that would indicate late-stage spermatozoa such as the presence of tails and narrowed, conical sperm nuclei  13. Due to meiotic divisions during spermatogenesis, the nuclear diameter of individual spermatocytes decreased as spermatogenesis progressed  12. Images of spermatocysts were captured using an Olympus BH2 compound microscope fitted with an InfinityPro1 camera and InfinityCapture software.
Acknowledgements The authors thank the captains, crew, and scientists aboard the sample collection cruises:  Captain Dennis Rogers  and crew of the  MV Alaskan Legend  and  MV Northern Song ,  Captain Dan Foley  and crew of the  FV Steller, Ocean Science Services  and the captain and crew of the  FV Alaska Provider ,  Captain Jim deLaBruere  and crew of the  Alaska Department of Fish and Game RV Medeia, Pelagic Research Services  and the captain and crew of the  RV Dorado Discovery , and the captain and crew of the  MV Norseman II. Also , the authors thank  Michele Masuda , who provided the map of the study sites shown in Figure 1. Many thanks also to  Kevin Eckelbarger  and  Kelly Edwards  for advice on ultrastructural preparation and interpretation, and  Laura Robinson  for helpful comments on an earlier draft of the manuscript.
Cell count. For analyzing brain cell proliferation following intracerebroventricular injection of HDLs, brains were fixed and processed for PCNA immunostaining. The PCNA positive area was counted in blind conditions on three vibratome sections/fish for each condition (PBS; rHDLs, plHDLs). Similarly, for analyzing the number of microglia and proliferative cells in the ventricular layer at 2 dpl and 5 dpl, respectively, three vibratome sections per fish were selected in the control and lesioned telencephalon. Images were analyzed using the ImageJ software considering the area stained (along the ventricular layer for PCNA and in the brain parenchyma for l-plastin), and after adjusting the appropriate parameters (threshold, binary, and watershed). These analyses were done in blind conditions.
3E-G ). Taken together, such data strongly argue for scarb1 expression in NSCs. To ascertain this hypothesis, fluorescent scarb1 ISH was performed, followed by Aromatase B and HuC/D immunohistochemistry, allowing us to label NSCs (radial glia) and neurons, respectively  37, 69. As shown in the dorsomedial telencephalon and in the regions surrounding the posterior recess of the hypothalamus, scarb1-positive cells corresponded to both HuC/D-positive neurons and AroB-positive neural stem cells (Fig. 4 ). These results were also confirmed by performing fluorescence ISH using the anti-digoxigenin antibody conjugated with horseradish peroxidase (data not shown). Taken together, these observations strongly argue for a possible role of scarb1 and HDL signaling in the control of NSC activity. Peripheral HDLs reach the brain microvasculature and the damaged brain tissue.
Acknowledgements We thank  Cynthia Planesse  for excellent technical support with the fish facility, as well as  Dr M. Redd  for kindly providing the l-plastin antibody and  F. Brion  for the Aromatase B antibody. We thank  Uwe Strähle  for sharing the RNA sequencing data from 58 and  Luisa Lübke  for her help in intracerebroventricular injection. We thank  Christian Lefebvre d'Hellencourt  for advice and discussion.
The ice thickness redistribution and thermodynamic schemes are outlined in the appendix of  Rampal et al. (2019). All output variables are interpolated using a conservative scheme from the moving Lagrangian model mesh onto a fixed and regular Eulerian grid and are averaged on a daily basis to match the temporal resolution of the observations (see Sect. 2. 2). The model set-up covers the central Arctic Ocean, with open boundaries at the Bering Strait, the Canadian Arctic Archipelago, Greenland, and the Barents and Kara seas (see Fig. 1 ). The model's triangular mesh is built on the 6. 25 km resolution grid of the lead-fraction dataset  (Ivanova et al. , 2016; see Sect. 3 ) such that the two have the same coastlines and comparable resolutions. This is done to simplify the comparison of the two.
In all other respects the model set-up is the same as that of  Rampal et al. (2019) : it is forced using the ocean state from the TOPAZ4 oceanic reanalysis  (Sakov et al. , 2012)  and the atmospheric state of the Arctic System Reanalysis 1   (Bromwich et al. , 2016). The model is initialised with sea-ice concentration and thickness from the OSI SAF climate data record  (Tonboe et al. , 2016)  and ICESat 2  (Kwok et al. , 2009)  datasets respectively. We use results from the TOPAZ4 reanalysis to fill in gaps in the ICESat thickness. The initial snow thickness is set based on the  Warren et al. (1999)  climatology and ice age, using half the climatological snow thickness over first-year ice. We start the model run on 15 November 2006, restricting our analysis to the winter months of January, February, and March (JFM) 2007 so as not to influence the results by the very different heat fluxes and lead fractions seen during the freeze-up and melt periods.
Observational data We analyse the observed lead fraction from  Ivanova et al. (2016) , as well as using it to evaluate the model results. This product improves on the original product from  Bröhan and Kaleschke (2014)  by correcting an overestimation of the lead fraction by a simple adjustment of the upper tie point used in the method. This product is based on passive microwave observations of the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) and is a daily, lightand cloud-independent, pan-Arctic dataset, available from November to April for the period 2002-2011. The dataset resolution is 6. 25 km, and the method allows the detection of leads wider than 3 km, meaning that a substantial number of smaller leads are undetected in this product. The data show the area fraction of each grid cell that is covered by leads filled with open water, thin ice, or a mixture thereof.
We compare the fraction of the pixel area covered with open water or thin ice within a lead as estimated from passive microwave satellite data to the modelled fraction of open water and newly formed ice thinner than 0. 098 m in neXtSIM. Our analysis shows that the agreement between the two is very good and improves when we take into account the fact that small leads are under-represented in the observations. This under-representation of small leads is most likely the largest source of uncertainty in the present comparison. It is not clear whether the model needs further tuning or enhancement at this point. The next step would be to compare neXtSIM with other lead-fraction datasets at higher resolution, e. g. from synthetic aperture radar (SAR) images or the Moderate Resolution Imaging Spectroradiometer (MODIS; see  Willmes and Heinemann, 2015).
Acknowledgements. The authors would like to thank  Nils Hutter  and the anonymous second reviewer for reviewing the paper and making helpful suggestions and comments, as well as the editor  Petra Heil  and the editorial staff at The Cryosphere for their assistance. Financial support. This research has been supported by  Norges Forskningsråd  (grant nos. 263044  and  276730 ) and the  Bjerknes Centre for Climate Research  (through the  AOI  project). Review statement. This paper was edited by  Petra Heil  and reviewed by  Nils Hutter  and one anonymous referee.
For this current study, we collected 3,284 lung samples from 30 rodent and insectivore (shrew, tree shrew, and short-tailed gymnure) species in 12 provinces of Thailand, three provinces of Lao PDR, and three provinces of Cambodia over a 12-year   period (2006-2018)  for a meta-transcriptomic analysis. The virome data obtained revealed an overview of the viral community present in the lungs of these small mammalian species across the Indochina Peninsula. The description of the host and geographical bias of these diverse viral populations and characterization of novel viruses in multiple viral communities, allowed us to identify the ecological and genetic imprints of known human and animal viral pathogens, including HanVs, phleboviruses (PhleVs), AreVs, hepaciviruses, pegiviruses and arteriviruses (ArteVs) in these regions.
66% of the total samples. The genus Bandicota accounted for 18. 30% and Mus accounted for 14. 92% of the total samples. These rodent species are ecologically closely associated with humans, arthropod vectors, and other animals in these areas. Meta-transcriptomic analysis and overview of the virome Due to the repeated sampling of some species in the same location, the 3,284 isolated lung total RNA samples were combined into 233 pools with equal quantity. To determine whether viral RNA was present in each pool, an rRNA excluded RNA library was constructed and processed for NGS-based metatranscriptomic analysis. A total of 262. 38 GB of nucleotide data was obtained. Reads that were classi ed as cellular organisms (including bacteria, archaea, and eukaryotes) and reads with no signi cant homology to any amino acid sequence in the NCBI NR database were removed.
The remaining 495,579 reads were used to identify their best-matched hit with viral proteins available in the NCBI NR database (Table  S2 ). Due to the presence of numerous transcripts from the hosts and other cellular organisms, most pools had low levels of viral RNA. To analyze the virome, virus-associated reads were classi ed into a group of unclassi ed RNA viruses and 98 known families under the double-stranded (ds) RNA viruses, reverse-transcribing viruses, singlestranded (ss) RNA viruses, dsDNA viruses, and ssDNA viruses. By further characterization of dietary habits and other host traits, non-vertebrate associated viral reads and retrotransposon related sequence reads that have previously been described  [23, 29]  were removed. The remaining 406,869 viral reads (approximately 82. 1% of the total viral hits) were then assigned into 24 mammal-related viral families and a group of unclassi ed RNA viruses.
This might suggest that the Hepadnaviridae and Orthomyxoviridae viruses were of low viral load or spurious sequence similarities. In addition to the family assigned reads, a substantial number of viral reads were for unclassi ed RNA viruses in the realm Riboviria, including diverse Chuviridae-, Nodaviridae-, or Totiviridaerelated viruses. Although most viral hits (98. 64%) were assigned into the RNA virus group, a small number of DNA viral reads were found in sequence data due to their corresponding RNA transcripts being detected. Due to their low number, we did not perform any further analyses of these DNA viruses. Based on the virome data provided by these meta-transcriptomes, the prevalence and diversity of viruses in families including pathogens that are known to cause human and animal infection or are novel in rodent were then con rmed by PCR screening of individual lung samples.
All HanVs identi ed here were assigned to the Murinae-related phylogroup III, clustered together, and formed a separate clade associated with the clade of Anjozorobe HanVs that are closely related to the lineage of the Seoul virus strains. Although the exact relationship between these HanVs and Thailand virus cannot be resolved, due to the absence of complete L sequences from the Thailand viruses in GenBank, we propose that a single lineage of the species Thailand orthohantavirus  [46]  includes all of the HanV strains detected in Thailand from the M, S, and partial L based alignments and our phylogenetic analysis. This suggested that these HanVs have circulated in diverse Thai provinces for several decades. PhleVs. Similar to HanVs, viruses of the genus Phlebovirus are also linear segmented negative-stranded RNA viruses that belong to the family Phenuiviridae of the order Bunyavirales  [47].
Conclusions These ndings, combined with our previous viral surveys from rodents, bats, and mosquitoes, and the online viral databases for these animals (DBatVir, DRodVir, and DMosVir, http://www. mgc. ac. cn/)  [27] [28] [29] [91] [92] [93] , greatly increase our knowledge of the viral community in wildlife and arthropod vectors in densely populated countries of East and Southeast Asia. Continued efforts in viral surveillance among wildlife hosts will reveal greater diversity of viral lineages, as recently hypothesized globally  [35] , and provide evidence to mitigate the risk of potential zoonotic disease emergence, as well as build the regional and global capacity for effective prevention and response to EIDs  [22].
Each line was separated from others by at least 1 km (different habitats) or 3 km (same habitat). Villages and isolated houses, which correspond to a fourth habitat category, human settlement, were also sampled opportunistically using cage-traps distributed to residents  [41]. The locations of sampling sites were recorded by place name and GPS coordinates with latitude and longitude. The identity of the captured species was initially determined by morphology by experienced eld biologists and subsequently con rmed by barcoding of mitochondrial cytochrome b, mitochondrial cytochrome oxidase I, or 16sRNA sequences  [38, 94]. Complete data on the animals used as reference specimens for the barcoding assignment is available on the "Barcoding Tool/RodentSEA" section of the CERoPath project web site (www.
For sequencing, each pool was labeled with a speci c index using NEBNext Multiplex Oligos for Illumina (NEB), and then processed for next-generation sequencing (NGS) using an Illumina HiSeq2500 sequencer (Illumina) using the 125 bp paired-end method. Raw sequence reads were ltered using previously described criteria to obtain valid sequences, and reads with no call sites, sequencing adaptor reads, duplicate reads, and low-complexity reads were removed  [29, 95]. Taxonomic assignment and viral RNA quantification. Sequence-similarity-based taxonomic assignment was conducted as previously described  [29]. Brie y, sequence reads were evaluated for viral origin by conducting alignments with the NCBI protein database (NR), using BLASTx (-e 1e-5 -F T). To detect potential viruses with remote similarities, reads with no hits in the NCBI NR database were further assembled using the software Trinity (v.
Funding This work was supported by the  National S&T Major Project  " China Mega-Project for Infectious Disease " (Grant No. 2018ZX10101001 ), the  National Natural Science Foundation of China  (Grant No. 81772228 ), the  CAMS Innovation Fund for Medical Sciences  (Grant Nos. 2016-I2M-1-014  and  2017-I2M-B&R-12 ), and the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  [Award No. R01AI110964 ]. Field work studies were supported by  ANR   07   BDIV 012 CERoPath project (Community ecology of rodents and their pathogens in a changing environment in Southeast Asia ),  ANR   11 CPEL 002  BiodivHealthSEAproject (Local impacts and perceptions of global changes: biodiversity, health and zoonoses in Southeast Asia) and  ANR-17-CE35-0003-02  FutureHealthSEA (Predictive scenarios of health in Southeast Asia).
Statistical approach Using an information-theoretic approach, we built two linear mixed model candidate sets to determine a statistically relevant spatial extent for land cover extraction, as well as which spatial and environmental parameters are associated with blood THg variation in tidal marsh sparrows among marsh sites. We selected independent variables based on a set of a priori hypotheses  (Burnham and Anderson 2002)  derived from known dynamics of Hg in the environment, and scaled these variables (µ = 0, sd = 1) because raw values occurred at different orders of magnitude. We constructed models in R (v. 3. 6. 2; R Core Team 2019) using the package ''lmerTest''  (Kuznetsova et al. 2017) , and fit models with maximum likelihood estimations  (Zuur et al. 2009). Model selection was performed using second-order Akaike's Information Criterion for small-sample sizes (AIC C ;  Burnham and Anderson 2002).
We inspected the distribution of the blood THg concentrations for normality using quantile-quantile plots and a Shapiro-Wilk normality test, and rejected the null hypothesis that the data were normally distributed (W = 0. 881, p < 0. 001). To achieve normality, we used a natural log-transformation (W = 0. 979, p = 0. 054). Four high outliers were present prior to transformation, but were included in the analysis because we had no reason to believe they were erroneous. Following transformation, there was no evidence that homogeneity of variance, normality of residuals, and normality of random effects assumptions were violated, based on visually inspecting quantile-quantile plots and plotted residuals versus predicted values  (Zuur et al. 2009 ). To populate the full linear model, we extracted 30 x 30 m resolution land cover data (percent of total area) around each sampling point from the National Land Cover Database 2016 CONUS data (NLCD,  Yang et al.
2018 ) and lumped land cover types into 3 relevant categories: wetland (NLCD categories 90 and 95 combined), forest (NLCD categories 41, 42, 43, and 52 combined), and developed land  (NLCD categories 21, 22, 23, and 24 combined). We also included cumulative average annual precipitation data around each sampling point at a 30-second resolution sourced from WorldClim Version 2. 0 Bioclimatic variable "BIO12" using the R package "raster"  (Hijmans 2018)  to account for potential differences in deposition rates throughout the sampling range. We included species as a fixed effect because the uptake of MeHg occurs mainly through diet; and the interspecific foraging differences and relative size differences between species  (Woolfenden 1968 ) could influence MeHg uptake as measured in the blood. To account for geographic variation in Hg deposition, prey availability, marsh hydrology, and other spatially variable conditions, we initially included latitude and longitude as fixed effects, and included marsh site as a random intercept in all models.
When investigating appropriate spatial scales for the analysis (see below), we discovered that some of our variables were highly correlated at larger spatial scales, as indicated by variation inflation factors (VIF > 3) and correlation coefficients ( R 2 > 0. 7) using R package "usdm"  (Hair et al. 2010; Naimi et al. 2014 ). After removing latitude, longitude, and percent area of wetlands from the full model, there was no evidence of multicollinearity at any of the spatial scales we evaluated. To test for correlations between tidal marsh sparrow THg concentrations and landscape characteristics surrounding sampling points, we attempted to define a statistically relevant spatial extent, or "scale of effect," for the independent variables. To accomplish this, we compared linear mixed models of identical structure, but with spatial attributes extracted within different buffer radii  (Jackson and Fahrig 2012).
This work was supported by the  M. Savoca & L. Orrok Undergraduate Research Endowment  at the  Cornell Lab of Ornithology ; Ann S. &  Robert R. Morley Student Research Grant at  Cornell University ;  NSF   EPSCoR Track -2  Award # 1826777 ;  National Fish and Wildlife Foundation  and  US Fish and Wildlife Service Award  # F18AC00046. Conflicts of interest : The authors declare that they have no conflict of interest. Ethics approval (include appropriate approvals or waivers) : Sampling was conducted under approved animal care protocols from the  State University of New York: College of Environmental Science and Forestry  ( 180201 ),  University of Connecticut  ( A17 -011), and  University of Maine  ( A2018-03-03 ). Consent to participate (include appropriate statements) : Not applicable. Consent for publication (include appropriate statements) : Not applicable.
Identification of the most time-consuming submodel in FLORSYS (step 1) The computing time for each FLORSYS submodel was registered for a simulation with diverse crops and cultural practices over 13 years corresponding to a cropping system trial  (Colbach et al. , 2016a). Code profiling of the C++ source code of FLORSYS showed that the 3D radiation interception submodel was by far the most time-consuming submodel. This submodel predicts the photosynthetically active radiation (PAR) intercepted by each plant of the crop:weed canopy volume discretised into voxels (3D pixels). It used 57%, 64% and 99% of total simulation time with a voxel edge size of 7, 4 and 1 cm, respectively. The second most time-consuming submodel was the germination/emergence submodel which used 20%, 7% and 0. 04% of the computation time for the three voxel edge sizes.
The large-range experimental design via Latin Hypercube Sampling, LHS  (McKay et al. , 2000) , created in the previous section was used to estimate both indices. PCE-OLS indices were similar to Sobol indices computed on the same dataset (supplementary material online S2 section 2). The largest difference was of 0. 13 for the total effect of the voxel on the radiation intercepted by the target plant (rPARiplant). The ranking of the inputs was the same with both methods. We thus preferred PCE in the following steps since it both estimates sensitivity indices and fits a metamodel, which is needed to simplify the radiation interception submodel.
g. INTERCOM: Kropff and Spitters, 1992; Kropff et al. , 1992)  as well as in intercrop models  (Gaudio et al. , 2019). However, these models only work for homogeneous 2-species canopies and cannot grasp the complexity of heterogeneous multispecies crop-weed canopies (in terms of location, emergence timing and morphology), as shown by comparisons of simulations with such models to independent field observations  (Debaeke et al. , 1997; Deen et al. , 2003). Consequently, the recent trend in crop-weed competition modelling goes towards more complexity rather than less  (Renton, 2013). A recent review of multispecies canopy models even concluded that the FLORSYS approach was a good compromise between simplicity and accounting for canopy heterogeneity  (Gaudio et al. , 2019). There are also technical solutions for speeding up simulations, for instance parallelising the execution of the source code or using graphical processing units.
Unfortunately, these solutions make it difficult to maintain a unique source code for any type of computer or server. A "portable" solution is to run multiple FLORSYS clones simultaneously on a single computer or server, either manually or automatically via scripts. The speed gain then depends on the number of logical processors of the computer. Another avenue is similar to the large-voxel solution, i. e. reduce the size of the simulated field sample. This solution was already assessed in a previous paper  (Colbach et al. , 2016b)  where we determined the minimum acceptable size. Again, the more complex (e. g. , many species, large interrows), the larger the field sample needs to be. So, there are several avenues for speeding up a complex model (Table  6 ). The best choice depends on the objective and situation of use, and several solutions can be combined for an even better result.