dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus the DGVM forest area and forest area change over time is not consistent with the FAO's forest area data used for the bookkeeping model to calculate emissions from land-use change. Similarly, modelspecific assumptions are applied to convert deforested biomass or deforested area, and other forest product pools, into carbon in some models. DGVMs typically classify vegetation in broad plant functional types (PFT) and use average characteristics of each PFT within rather coarse resolution gridcells (0. 5° or coarser). Not all TRENDY models simulate wood harvest or fire, and most do not simulate forest age-class distributions (see Tab. SI 7). In this study, we used the TRENDY data to assess the impact of indirect effects in managed forest land (excluding land-use change and harvest, already captured in the bookkeeping model).
For developed countries (i. e. , the main focus of our study), the analysis of countries' GHGIs indicates that, when emissions associated with land-use changes are excluded, forest NBP is slightly greater (by 10%) than total NBP (including "cropland", "grassland", "wetland" etc. ). Overall, this suggests that at large scale non-forest NBP is likely to be small relative to forest NBP. We assumed primary and secondary forest as defined in the land-use harmonization dataset (LUH2-v2h, http://luh. umd. edu/data. shtml) to be conceptually comparable, respectively, to unmanaged and managed forest. "Secondary" in the LUH2-v2h datasets refers to land previously disturbed by human activities (post-850 AD) and recovering. We therefore extracted the fraction of primary and secondary forest area per grid cell from the LUH2-v2hn dataset.
Finally, the forest NBP provided by the different DGVMs was separated into fractions originating from secondary and primary forests using the LUH2-v2h area fractions. Grid-cells that have no forests during the period 1990-2014 in LUH2-v2h dataset were excluded from the analysis. This approach implicitly assumes that within each grid cell the response of primary and secondary forests to environmental change is approximately the same. To our knowledge, there is no scientific evidence supporting other assumptions. The approach above would be improved if DGVMs were to provide more disaggregated outputs (NBP from primary and secondary forests in each gridcell), or if more sophisticated approaches are developed to separate ex-post forest NBP from total NBP. Models that   22, 24  from nine models: JSBACH 40 , CLM4.
8  , (see Table  1  and SI section 3). 1 "managed forest" includes the area for which countries report net emissions to UNFCCC. "Secondary forest" (considered here conceptually comparable to "managed forest") refers to area classified as forest in the period analyzed and subject to some human disturbance in the past, according to the bookkeeping model  9  or to the analysis of DGVMs (using the LUH2-v2h dataset, see From bookkeeping model 9 , DGVMs, and country GHGIs (see Methods). "Managed forest", "Secondary forest" and the grey column in panel (c) are estimated as in Fig. 4. While our analysis does not include all developing countries, it covers about 80% of the FAO-FRA's global "secondary forest" area. Whiskers express +/-1 SD.
Acknowledgments: The authors thank  Julia Pongratz  for discussing an early stage of the analysis,  Vladimir Korotkov  for checking our analysis on Russia, and  Grant M. Domke  for checking our analysis on USA. J. H. was supported by  EU   FP7  through project  LUC4C  ( GA603542 ) and the  UK NERC  project  GGRiLS-GAP. G. G. was supported by the  Administrative Arrangement nº340203/2016/742550/SER/CLIMA. A3. Atul K. Jain  was supported by  NSF  ( AGS 12-43071 ) and  DOE  ( DE-SC0016323 ). Julia Nabel  was supported by the  German Research Foundation's Emmy Noether Programme  (grant no. PO1751/1-1 ). G. G. , J. H. , G. P. P. and L. P. received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776810  (VERIFY).
Results Pre-processing treatment of reads and assembly construction. Illumina HiSeq. 2500 sequencing of red porgy and common pandora samples yielded 685,707,304 (342,853,652 read pairs) and 644,189,592 (322,094,796 read pairs) reads, respectively. The raw sequence data have been submitted to the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (BioProject ID: PRJNA395994). For red porgy, pre-processing of reads resulted in 247,712,854 high-quality reads used for the initial transcriptome assembly, which consisted of 295,367 putative transcripts (N50: 1,961 nucleotides, mean length: 928 nucleotides) and showed significant similarity to 23,122 out of 26,754 European sea bass genes. After transcript quality assessment, our initial dataset was limited to 98,012 contigs (N50: 2,978; mean length: 1,587 nucleotides) corresponding to 67,132 genes and was significantly similar to 20,130 European sea bass genes.
Filtering resulted in elimination of 197,355 possibly spurious transcripts and this restricted dataset constituted the final transcriptome with only slightly reduced numbers of hits to the unique genes of European sea bass. For common pandora, filtering of reads resulted in 232,768,947 high-quality reads that were used to construct an initial assembly of 396,201 putative transcripts (N50: 1,529 nucleotides, mean length: 824 nucleotides). Additional editing excluded 254,892 possibly spurious transcripts, thus limiting our dataset to 141,309 contigs (N50: 2,450; mean length: 1,254 nucleotides), corresponding to 98,211 genes. On this occasion, the unfiltered transcript dataset showed significant similarity to 23,631 European sea bass genes out of 26,754, while the filtered assembly showed significant similarity to 21,028 genes.
Following the same reasoning, the restricted dataset constituted the final transcriptome (Fig. 1 ). To evaluate the accuracy of the assembled sequences (transcripts), all the usable sequencing reads were aligned onto the transcripts using Bowtie2  51. A high percentage of red porgy and common pandora reads (84% and 81%, respectively) were successfully back-mapped on each assembly. Similarities between assemblies and UniProtKB/ SwissProt database disclosed 21,217 and 21,251 contigs for red porgy and common pandora, respectively, with coverage higher than 80%. In addition, BUSCO run (Supplementary Table  ST1 ) indicated that the assemblies were nearly complete with 86% and 82% complete matches of vertebrate orthologs, found in red porgy and common pandora assemblies, respectively. Transcriptome annotation.
Targeted search of candidates genes for sex determination/differentiation. To identify genes that play a role in reproduction and more specifically in sex determination and differentiation of fish, we searched the annotated brain and gonad datasets for these GO categories and checked if they were differentially expressed. In particular, DE genes related to broad terms of biological processes, such as "reproduction" and "reproductive process" were searched in gonads and brains of both sparids. A large number of genes related to "reproduction" and a few to "reproductive process" were found in the gonads of both species. In the common pandora, 492 female-biased and 717 male-biased genes were found under the "reproduction" GO term, while thirteen female-biased and eleven male-biased genes were found under the "reproductive process" GO term.
fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:45 CROP:99'). Finally, low-complexity sequences (threshold entropy value of 30) and poly A/T 5′ tail (minimum of 5 A/T) trimming was performed using PrinSeq  137. Lastly, the reads of each pair of all the libraries were combined using custom Perl scripts. Transcriptome assembly construction. The de novo assemblies for red porgy and common pandora were performed using Trinity package 138 (version 2. 0. 6) with default parameters (default kmer 25, min length 200 nucleotides). To increase transcriptome coverage, de novo assemblies were carried out on a pool of the pre-processed reads from all samples. To assess the assembled transcripts and exclude the spurious ones, we pooled all filtered reads and mapped them to the selected assemblies (Pagrus_Trinity_assembly.
fasta and Pagellus_Trinity_assembly. fasta) with Bowtie2 51 within RSEM (version 1. 2. 19)  139  , using the script available in trinity utilities align_and_estimate_abundance. pl. Putative transcripts with less than 1% of a locus reads mapped to that particular isoform (IsoPct <1) were excluded (as proposed in)  140. The same was done for those with Fragments Per Kilobase of transcript per Million mapped read (FPKM) values less than 0. 3. The choice of FPKM threshold was based on BLASTn similarity searches (e -value threshold 10 -10 ) against European sea bass cDNA sequences (http://seabass. mpipz. mpg. de). We first contacted a blast search of the unfiltered assemblies against the European sea bass cDNA sequences and then repeated the blast and tested three differently filtered assemblies (a) FPKM_cutoff = 0.
3, isopct_cutoff = 1. 0, (b) FPKM_cutoff = 0. 5, isopct_cutoff = 1. 0 and (c) FPKM_cutoff = 1, isopct_cutoff = 1. 0. To assess the assembled transcripts quality, we followed three methods: (a) pooling all the reads and mapping them to the assembly using Bowtie2 51 and Samtools 141 to estimate the percentage of mapped reads, (b) examining the similarity between the Trinity assembly and swissprot database using blastx search and (c) quantifying the completeness of the transcriptome assembly and determining how many of the 3,023 genes for vertebrates (OrthoDBv8 was used from BUSCO) were present in our two species transcriptomes, using BUSCO (Benchmarking Universal Single-Copy Orthologs) software  142  that searches for highly conserved, near-universal, single copy orthologs. All BLAST 143 searches were performed in parallel using NOBLAST program  144.
Transcriptomes functional annotation. For the annotation of the assembled transcripts, we conducted a BLASTx similarity search against UniProtKB/SwissProt database 52 (e-value threshold 10 -5 ) keeping the top 20 hits. BLASTx was done in parallel using NOBLAST  144. For gene ontology (GO) mapping, Blast2GO  54  was used locally to recover all the GO terms associated to the hits obtained by the blast search. After the mapping step, results were subjected to GO annotation, a process of selecting GO terms and assigning them to the query sequences for describing biological processes, molecular functions and cellular components. Gene ontology graphs were generated in "level 2" for GO plot visualization and pie charts for the whole transcriptome profiling. Further, brains and gonads of each species were analyzed separately, using in-house R scripts.
The Blast2GO output file was input into the BGI WEGO program  55  and GO annotations were plotted. The sequences were additionally annotated using InterPro  145  ; a local InterProScan (version 5, InterPro release 48. 0)  53  was run in parallel (splitting the query set) on the longest open reading frame (ORF) of the contigs using a custom Perl script based on the EMBOSS program "getorf "  146. GO terms corresponding to these InterPro domains, were merged with the already existing GO terms derived from the blastx against UniProt. The GO terms were modulated using the annotation augmentation tool ANNEX  147  followed by GOSlim  148. Enzyme classification (EC) codes were obtained through the direct mapping of GO terms to the corresponding enzyme codes. Sequences having EC numbers were further characterized by Kyoto Encyclopedia of Genes and Genomes (KEGG)  149  metabolic pathway annotations using custom Perl/R scripts.
Differential expression analysis. Differential expression analysis was performed at the "gene" (component/unigene) level by pairwise comparisons between male and female for brain and gonad samples. The de novo transcriptome assembly of each species served as a reference for read mapping. For the quantification, the cleaned reads of gonads and brains (not larvae) of each sample of a species were aligned to the species' transcriptome assembly with Bowtie2 51 and abundance was estimated with RSEM, as implemented in the trinity script align_and_estimate_abundance. pl. The estimated expected counts for each sample, when the sum of counts in each row >15, were extracted  150. Loci with less than fifteen reads mapped on them, were excluded prior to differential expression analysis to improve the statistical power.
Count data were imported in R (version 3. 2. 0) and the analysis of differential expression conducted in DESeq. 2 (version: 1. 8. 1)  151  , an R bioconductor package  152. Samples of both species were grouped according to sex and expression was compared separately for brains and gonads, following the developers' manual (false discovery rate, FDR, threshold of 0. 05). Principal component analysis (PCA)  153  and the heatmap. 2 function in the gplots package  154  were used to visualize global similarities and differences among either the brain or the gonadal samples. The global expression analysis conducted with sex and tissue being the (design/variable/intgroup) factors influencing the counts in a multifactor analysis. Each species global (brains and gonads) PCA plot was produced including all the genes in the filtered (sum of counts in each row >15) count matrix.
Genes with an adjusted p value less than 0. 05 were considered as significantly differentially expressed. Functional GO and pathway enrichment analysis. Functional-enrichment analysis was performed to identify GO terms and metabolic pathways significantly enriched in differentially expressed genes in brains and gonads, in the two sparids. In each species, male and female up-regulated gonad and brain gene sets were tested for enriched GO terms, in a comparison of female versus male (female/test-set vs male/reference-set). Particularly for gonads, we restricted the gene-set to only those with |log 2 FC| >2. We also conducted enrichment analysis for common (between species) male-biased and female-biased gene set using both 'red porgy' and 'common pandora' total datasets as reference set of the annotated transcripts.
All enrichment analyses were implemented in BLAST2GO with default settings enabling for a two-tailed test and reducing the output table to the most specific terms with FDR = 0. 05. Targeted search for sex-related candidate genes. To identify transcripts related with the GO terms "reproduction" (GO:0000003), "reproductive process" (GO:0022414), "sex determination" (GO:0007530) and "sex differentiation" (GO:0007548), both annotated transcriptomes were searched for the presence of these GO terms and the retrieved genes were checked for whether they exhibit a male or female-biased expression in brains and gonads. Genes known to be involved in sex determination/differentiation in vertebrates and other fish taxa were downloaded for tilapia, medaka and spotted gar Lepisosteus oculatus from Ensembl (release 84)  155  , resulting in a set of 114 candidate genes.
Acknowledgements Financial support for this study has been provided by the  Ministry of Education and Religious Affairs , under the Call "ARISTEIA I" of the National Strategic Reference Framework 2007-2013 (SPARCOMP, # 36 ), cofunded by the  EU  and the  Hellenic Republic  through the  European Social Fund. The authors wish to thank  Mrs. Margaret Eleftheriou  for proof-reading and English editing of the manuscript. We would also like to thank  Haris Apostolidis ,  Constantinos Vatikiotis  and  Irini Sigelaki  for their valuable help in sampling.
To assess the effect of blocking the EP2 receptor, in vivo, on microglial phagocytosis, we injected pHrodo-myelin into the brain (corpus callosum) of WT mice, together with vehicle or the EP2 receptor antagonist (PF-04418948). We assessed brain tissue three days after injection with immunofluorescence and confocal microscopy. In the corpus callosum of mice injected with pHrodo-myelin and vehicle, the area containing pHrodo-myelin is mainly populated with CD11b+, Tmem119-negative cells (MDMs) (Fig  4C ). In mice injected with pHrodo-myelin and EP2 antagonist, there is a significant increase in Tmem119+ microglial cells in the area plot generated with Miru software (Pearson correlation threshold, r � 0. 86). Nodes in panel c represent transcripts (probe sets), and edges (connecting lines) represent the degree of correlation in expression between them.
Reverse transcription and cDNA generation were performed using random hexaprimers (Roche) and the Moloney murine leukemia virus-RT enzyme (Invitrogen, Carlsbad, CA) at 42 ˚C. PCR reaction cycling was performed according to the ABI PRISM 7000 Sequence Detection System default temperature settings (two minutes at 50 ˚C, 10 minutes at 95 ˚C, followed by 40 cycles of 15 seconds at 95 ˚C, one minute at 60 ˚C). TaqMan quantitative real-time PCR was used to measure mRNA expression levels for all mRNAs. Relative gene expression data were calculated according to the 2 -ΔΔCt method.
0 NA), a quad HyD external detector array, and a Mai Tai HP DeepSee Laser (Spectra-Physics, Santa Clara, CA) tuned to 905 nm. Three-dimensional time-lapse movies were captured in z-stacks of 15-30 planes (3-μm step size) at 1-2-minute intervals. Signal contrast was enhanced by averaging 10-12 video frames per plane in resonance scanning mode. Three-dimensional time-lapse movies were imported for analysis into Imaris software (Bitplane). "Jellyfish" microglia were quantified by counting cells that had a process that was 20 μm or more in diameter at any time within about two hours of imaging immediately following mTBI. This number was then divided by the area of reactive microglia for each mouse.
pHrodolabeled myelin was detected in the PE channel. For analysis of cells from in vivo experiments, spinal cord tissue was harvested and transferred to a 15-mL Dounce homogenizer on ice and homogenized with 20× passes of a large clearance pestle. Tissue was resuspended in 70% and overlaid 30% isotonic Percoll. Following centrifugation (900g; 25 minutes), cells at the Percoll interface were collected, washed, and resuspended. Cells were blocked with FC-receptor blocked (1:200; BD Bioscience, Mississauga, ON) and stained with CD45-PE-Cy7, CD11b-V450, Ly6G-APC, or FITC, Ly6C-APC-Cy7 (all 1:200; BD Bioscience, Mississauga, ON). CCR2-RFP was detected in the PE channel. Cells were acquired using a BD FACS Canto II and analyzed using FlowJo software. For fluorescently activated cell sorting (FACS), a BD FACSAria Fusion (BD Bioscience, Mississauga, ON) was used to isolate CD11b +ve /CD45 lo /Ly6G -ve /Ly6C -ve microglial cells, which were directly sorted in Trizol for subsequent RNA extraction.
Computational analysis and bioinformatics Microarray data sets were normalized by the robust multiarray averaging (RMA) method in Affymetrix Expression Console (Affymetrix). To assess whether there were transcripts differentially expressed between activated microglia and activated microglia in the presence of macrophages, normalized data sets were compared in Affymetrix Transcriptome Analysis Console (TAC) Software. To assess whether there were transcripts differentially expressed between LPS-stimulated microglia and LPS-stimulated microglia in the presence of macrophages, normalized data sets were compared by ANOVA with a p < 0. 05 cutoff. Differentially regulated gene lists were then assessed with the IPA software tool (Qiagen). To investigate gene coexpression relationships between groups, a pairwise transcript-to-transcript matrix was calculated in Miru from the set of differentially expressed transcripts using a Pearson correlation threshold r = 0.
All images were visualized using a confocal laser scanning microscope (FluoView FV1000; Olympus) using FV10-ASW 3. 0 software (Olympus) and prepared with ImageJ. Histochemical staining with Luxol fast blue was used to assess myelin loss 28 days after injury. Myelin was quantified as a measure of Luxol fast blue (mean gray value, ImageJ) across the whole cross section, measured at 200-μm intervals over the 2-mm length of the cord.
Quantification of immunofluorescence data In spinal cord sections 28 days after injury, single plane images at a depth of 8 μm were acquired at two sites either side of the midline and included white and gray matter, as depicted in Fig 4. Images were taken 600 μm caudal to the lesion epicenter to avoid overt tissue cavities but include putative areas of MDM and microglia cell interactions. Images were acquired and analyses performed whilst blinded to genotype. Area of fluorescence and fluorescence intensity (as measured by Integrated Density [IntDen], which is the product of area and mean gray value) were quantified with ImageJ. For quantification of pHrodo-myelin injected mice, images were acquired from three consecutive slices around the injection site and analyses performed whilst blinded to treatment.
We present BeechCOSTe52; a database of European beech (Fagus sylvatica) phenotypic measurements for several traits related to fitness measured in genetic trials planted across Europe. The dataset was compiled and harmonized during the COST-Action E52 (2006-2010), and subsequently cross-validated to ensure consistency of measurement data among trials and provenances. Phenotypic traits (height, diameter at breast height, basal diameter, mortality, phenology of spring bud burst and autumn-leaf discoloration) were recorded in 38 trial sites where 217 provenances covering the entire distribution of European beech were established in two consecutive series (1993/95 and 1996/98). The recorded data refer to 862,095 measurements of the same trees aged from 2 to 15 years old over multiple years. This dataset captures the considerable genetic and phenotypic intra-specific variation present in European beech and should be of interest to researchers from several disciplines including quantitative genetics, ecology, biogeography, macroecology, adaptive management of forests and bioeconomy.
Here, we present a consolidated set of phenotypic data from genetic trials of Fagus sylvatica across Europe compiled by the COST Action E52 (2006-2010). There have been several efforts to make use of phenotypic data from forestry trials of various species at the European level e. g. to compile metadata and to produce standardised protocols (http://www. trees4future. eu/ and http://www. trees4future. eu/transnational-accesses/treebreedex. html). However, raw data is seldom published, and initiatives to compile old genetic trials tend only to be promoted at the national level; e. g. GENFORED in Spain (www. genfored. es) and PLANTACOMP in France  5. The BeechCOSTe52 dataset (Data Citation 1) provides a high density map of phenotypic data from 38 genetic trials across Europe, even including trials established outside the range of the species.
The wide coverage provided by this dataset makes it a singular resource that can help us to understand the entire gradient of climatic tolerance of European beech (Fig. 1 ). Likewise, the origin of the 217 provenances planted covers the entire range of the species distribution, which is important for understanding the overall adaptive capacity and the tolerance limits of the species (Fig. 1 ). Genetic trials have historically been used in forestry to test which reproductive material to select (http://www. euforgen. org), but the utility of genetic trials in general and of this database in particular go beyond this traditional use  6. The most immediate use of the BeechCOSTe52 database is in understanding of trade-offs between traits from an ecological perspective. Comparable trials over a larger geographical areas covering the range limits of a species can be helpful in assessing local adaptation in populations of trees that have grown nearby during the recent past.