dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Lord Peter Melchett  (Policy Director of the  Soil Association, Bristol, UK , and an organic farmer),  Professor Dr Juha Helenius  ( Professor of Agroecology, University of Helsinki, Finland ) and  Teresa Jordon  ( Nafferton Ecological Farming Group office manager, Newcastle University ) for critically reviewing/editing the manuscript. Lord Peter Melchett , the Policy Director of the Soil Association, was invited to critically review the manuscript to (1) ensure that the authors had covered all the information/publications available to the main UK Organic Farming sector body and (2) obtain feedback on the main results and conclusions reported in the article. Lord Melchett did not suggest any major revisions to the manuscript. The authors are grateful to the  European Community  for funding through financial participation under the  Sixth Framework Programme for Research, Technological Development and Demonstration Activities  for the  Integrated  Project  QUALI-TYLOWINPUTFOOD, FP6-FOOD-CT-2003-506358.
They are also grateful to the  Sheepdrove Trust  for the 'Meta-analyses of data on composition of organic and conventional foods' for providing financial and technical support. The Sheepdrove Trust supports independent R&D underpinning the development of organic and sustainable farming and food systems. Financial support was provided by the Trust without conditions, and the Trust had no influence on the design and management of the research project and the preparation of publications from the project. The authors' contributions are as follows: M. B. (an animal and food scientist) designed the database, carried out many of the meta-analyses and contributed to the writing of the manuscript; D. S. -T. (a nutritionist) carried out a major part of the literature search and extraction and contributed to the writing of the manuscript; N.
Background: Advances in high-throughput sequencing have led to the discovery of widespread transcription of natural antisense transcripts (NATs) in a large number of organisms, where these transcripts have been shown to play important roles in the regulation of gene expression. Likewise, the existence of NATs has been observed in Plasmodium but our understanding towards their genome-wide distribution remains incomplete due to the limited depth and uncertainties in the level of strand specificity of previous datasets. Results: To gain insights into the genome-wide distribution of NATs in P. falciparum, we performed RNA-ligation based strand-specific RNA sequencing at unprecedented depth. Our data indicate that 78. 3% of the genome is transcribed during blood-stage development. Moreover, our analysis reveals significant levels of antisense transcription from at least 24% of protein-coding genes and that while expression levels of NATs change during the intraerythrocytic developmental cycle (IDC), they do not correlate with the corresponding mRNA levels.
While this bias was not apparent for all genes, it was highly reproducible for individual genes and distinctly more pronounced in large genes. Therefore, to obtain more uniform sequencing coverage, we prepared all subsequent libraries from polyA-enriched RNA. Using the RNA-ligation-based protocol, we prepared sequencing libraries from RNA extracted 10 h, 20 h, 30 h and 40 h post infection (p. i. ) (n = 4), as well as from cytoplasmic and nucleic RNA of parasites harvested at 20 h and 30 h p. i. (n = 4). Combining the data from these libraries with the data from libraries prepared for protocol development (n = 3), our dataset consists of ~600 million mapped strand-specific reads derived from 11 libraries (Figure  2 ).
A recent analysis had compared amplification efficiencies of different polymerases for genomes ranging in GC-content from 67. 7% (Boretella pertussis) to 19. 3% (P. falciparum) and found KAPA HiFi (Kapabiosystems) to provide the most consistent results for AT-rich genomes  [19]. B) Meta-gene coverage plots for libraries prepared from polyA-enriched and Tex-treated RNA. X-axis: scaled gene body, y-axis: scaled coverage. incomplete due to the limited depth or uncertainties in the level of strand-specificity of the published datasets. Using strand-specific RNA-Seq data from ~30 million mapped reads, López-Barragán et al. observed NAT transcription in 312 coding genes  [7]. To gain more insight into the genome-wide distribution of NATs, we took advantage of the unprecedented depth of our dataset (~600 million mapped reads, Figure  2 ) and combined the mapping results of 11 libraries to generate a coverage map.
We also re-analyzed the López-Barragán et al. dataset (pooled from 4 strand-specific libraries in  [7] ) in parallel for comparison of coverage and level of strand-specificity. Using the combined data from 11 libraries, we detected transcription of 78. 3% of the Plasmodium genome (≥5-fold coverage, Additional file 1: Figure  S1 ). Keeping all parameters constant, re-analysis of the López-Barragán et al. dataset indicated transcription of ~39. 5% of the Plasmodium genome. Most of the transcribed genomic positions detected in the López-Barragán et al. dataset were also detected in this study (Additional file 2: Figure  S2A ), suggesting both datasets are consistent and that our dataset represents a substantially deeper coverage. Next, we tried to determine the level of strandspecificity of our datasets compared to that generated by López-Barragán et al.
Previously, a globally positive correlation between sense and antisense transcripts has been used as an indicator for potential presence of artifactual antisense transcripts  [22, 23]. This approach is based on the fact that the observed antisense transcripts could be derived from the sense transcript during library preparation, e. g. incomplete second-strand cDNA digestion in dUTP method  [7] , spurious synthesis of second-strand cDNA  [22] , or other unidentified sources of artifacts. While the correlation in our dataset was close to 0 (Pearson's correlation = 0. 057, P < 0. 01), we observed a strong positive correlation for the López-Barragán et al. dataset (Pearson's correlation = 0. 82, P < 0. 01, Additional file 2: Figure  S2B ), suggesting a relatively higher level of strand-specificity of our dataset.
Likewise, the ratios of sense to antisense reads differ significantly between our (1 antisense to 328 sense reads) and the López-Barragán et al. dataset  (1 to 11. 25). The relatively lower level of strand-specificity in López-Barragán et al. dataset maybe due to the imperfect second-strand cDNA digestion in dUTP method as the authors mentioned  [7] , comparing to the RNA ligation method used in this study. López-Barragán et al. thus avoided false positives in NAT identification by applying stringent cutoffs on both proportion of antisense read (>70%) and antisense read number (>150)  [7] , which may have underestimated the prevalence of NATs. To estimate the prevalence of NATs in Plasmodium, we established two sets of thresholds with different stringency (see Methods for details). Our data indicate that between ~24% (n = 1247, stringent thresholds) and ~45% (n = 2389, relaxed thresholds) of all coding genes (n = 5284) are overlapping with NATs (Additional file 3: Table  S1 ).
Using the software package EdgeR, we observed significant changes (FDR <0. 05) in  antisense transcript levels for 357 of the 1247 genes with NATs in at least 1 of the 6 non-redundant time point pair comparisons. For 125 genes with NATs we observed significant changes in antisense transcript levels in at least 2 of the 6 non-redundant time point pair comparisons (see Methods for details). These findings suggest that at least ~10% of NATs are regulated during the IDC (Additional file 3: Table  S1  and Additional file 4: Tables  S2 ). Subsequently, we investigated whether the changes in sense and antisense transcript levels between times points are correlated and observed no global correlation (Additional file 5: Figure  S3 ). Thus, our data suggest that transcription of NATs does not exert a globally positive or negative effect on the transcription of its sense counterpart.
We did not observe a significant enrichment of antisense reads in these genes (n = 198) in the nucleus against both the total and the cytosolic fractions at 20 h p. i. , while ncRNA from telomere-associated repeated elements were enriched in the nuclear fraction (Additional file 7: Figure  S4 ). A final mechanism we considered was antisensemediated translational inhibition in which case overexpression of antisense transcripts leads to a reduction of proteins but not to reduction of sense RNA levels  [31]. To this end, we correlated our RNA-Seq data with previously published stage-specific proteomic datasets  [32]. As observed by Le Roch et al. , we saw a positive correlation between mRNA abundance (in RPKM (reads per kilobase per million)) and protein abundance (in peptide counts per kilobase), thus an increase in sense RPKM correlates with an increase in peptide counts per kilobase for a given gene (Pearson's correlation = 0.
Mapping of sense and antisense polyadenylation sites The sequencing depth of our dataset combined with the use of a polymerase better suited for the amplification of extremely AT-rich regions than previously used enzymes (Additional file 9: Figure  S6  and  [19] ), allowed us to map for the first time polyadenylation sites (PAS) for P. falciparum. Despite the importance of 3′-UTRs in the regulation of gene expression, the genome-wide characterization of 3′-UTRs in Plasmodium lags far behind that of coding sequences. To determine PASs and to address the question of whether the observed antisense transcripts are polyadenylated, we used the combined RNA-Seq data from all 11 libraries to map PASs on a genome-wide scale. Due to the high abundance of coding mRNAs compared to antisense RNA (Additional file 3: Table  S1 ), we expected most of the identified PASs to correspond to coding mRNAs and only a small fraction to correspond to NATs.
Evidence of antisense spliced junctions Based on the splicing junctions identified from our datasets using HMMSplicer  [37] , we found evidence for antisense spliced junctions in 123 of the 1247 genes with NATs (Additional file 11: Table  S5 ). Thus, at least a portion of the observed NATs (~10%) is spliced. Furthermore, we found 55 of the 123 antisense junction clusters to overlap with sense junctions on mRNA (Additional file 11: Table  S5 ). These findings are consistent with a previous study, which has identified antisense introns and antisense junctions overlapping with sense junctions, using a similar approach  [6]. These observations further validate antisense transcription in P. falciparum to be a widespread phenomenon.
While polyA-enrichment harbors the intrinsic problem of selecting against nonpolyadenylated transcripts, for the following reasons we believe that our libraries contain at least some non-polyadenylated transcripts: 1) PolyA-enrichment is generally not 100% efficient and indeed, tRNA and rRNA sequencing reads were present in our libraries. 2) The Plasmodium genome is extremely AT-rich and long stretches of As exist even within genes. Thus it is likely that polyA-enrichment can also enrich for nonpolyadenylated transcripts as long as they have internal stretches of As. Nevertheless, most likely our dataset lacks weakly transcribed RNA without polyA-tails. Our analysis revealed transcription from almost 80% of the Plasmodium genome and exposed antisense transcription for 24% of genes many of which are developmentally regulated.
Conclusions Intergenic and non-coding regions of the P. falciparum genome have been particularly understudied due to high AT-content, typically ranging between 85 and 95%. Using a protocol that permits the generation of highly strand-specific RNA-Seq libraries and KAPA HiFi DNA polymerase, a polymerase that amplifies even extremely AT-rich DNA, we generated a comprehensive transcriptome dataset for P. falciparum, containing great detail about the origin of antisense RNA. Given the large number of independently regulated NATs, our work suggests that ncRNA in P. falciparum is of biological significance and not merely a consequence of noisy transcriptional regulation. Importantly, our high coverage and long sequence reads allow us to provide for the first time an extensive list of polyadenylation sites for blood stage parasites.
Mapping of sequence reads First, reads longer than 50 nt flagged with Illumina's low quality flag "B" were removed from all datasets. Then, we removed the 4 custom index nucleotides at the 5′-end and the low quality nucleotides at the 3′-end of the reads. The P. falciparum 3D7 genome and its gene model annotations (version 3 on January 2012) were downloaded from Sanger FTP (ftp://ftp. sanger. ac. uk/ pub/pathogens/Plasmodium/falciparum/). Unless specified otherwise, the reads in all datasets were mapped onto the reference genome using Bowtie 0. 12. 8 (parameters "-n 2 -k 1 -m 50 -best"), with maximum 2 mismatches and multiple hits (maximum 50) distributed to the best-aligned location  [49]. To map the splicing junctions, the unaligned reads from Bowtie were mapped using HMMSplicer (parameters "-w 4 -j 10 -k 3000 -ja 10 -e 2 -m 500 -n 700 -d True").
It should be noted that HMMSplicer was specifically developed to work with P. falciparum RNA-Seq data  [37]. Read pileups were generated for each library and all read pileups (n = 11) were pooled using custom scripts to generate a coverage map for defining the genomic distribution of NATs. Strand-specific fastq data of López-Barragán et al. [7]  were downloaded from Short Read Archive of NCBI (http://www. ncbi. nlm. nih. gov/sra/) under the accession of SRR364836, SRR364841, SRR364842 and SRR364846. Second reads of the pairs in these datasets were reverse complemented and all reads were processed in the same way as our datasets (mentioned above).
tugraz. at/). Briefly, the order of genes was sorted based on the changes of sense RKPM (in log2 scale) across the 4 time points, and a heat map was generated using both sense and antisense RKPM while keeping the same gene order. The process was then repeated using antisense RKPM for gene sorting. The patterns of these heat maps were then visually inspected as described in the main text. To identify transcripts that are developmentally regulated, we used EdgeR  [51]  to screen for genes that are differentially expressed across the IDC. Briefly, each gene was assigned to have a sense and antisense transcript of the same length (i. e. coding region), which were then treated as independent transcripts in the EdgeR analyses. The read counts on these transcripts among the 4 time points were paired into 6 nonredundant pairs and compared using EdgeR with the biological coefficient of variation set to 0.
Identifying the polyadenylation sites from reads The criteria used here are primarily based on Lee et al. [33]. Briefly, reads containing 15 or more consecutive "A" at their 3′-end were selected from all datasets and redundant reads within the same library were discarded. These non-redundant reads were pooled. These reads potentially contain the sequence of polyA tails. The A stretches at the end were trimmed and the reads with minimal 18 nt after trimming were mapped to the reference genome using Bowtie with parameters "-n 2 -k 1 -m 50 -1 30". To distinguish polyA tracks of true polyadenylation from polyA tracks of internal polyA stretches on the mRNAs themselves (i. e. false positives), we analyzed the base compositions surrounding the end of the mapped reads and discarded those that might not represent true polyadenylation.
Acknowledgments We thank  Stan Gorski ,  Nicholas Malmquist  and  Annette Windisch  for valuable discussions and critical reading of the manuscript and  Nikolay Kolev  for much appreciated advice on generating RNA-Seq libraries. TNS was supported by the  Human Frontier Science Program  and an  EMBO  long-term fellowship. CCH was supported by the  French National Research Agency  ( ANR-2010-GENM-011-01 , GENAMIBE). The project was supported by  European Commission   FP7 ERC  advanced grant (PlasmoEscape  250320 ),  EVIMalaR  and  ANR  grant  MalNucArch  ( ANR-09-BLAN-0274 ).
25 kHz (or a velocity channel width of 0. 4 km s - 1  ). Each source was observed for 2 min on-source integration. The NH 3 (1,1) and (2,2) observations were reduced using the ATNF Spectral Analysis Package 1 (asap) and the graphical interface developed by Cormac Purcell 2  (Quick-Data. py). The NH 3 (3,3), if detected, will be used in future work to constrain the temperatures with large uncertainties. The individual on-off scans were subtracted to remove sky emission and visually inspected to identify and remove poor data. Where necessary a low-order polynomial was fitted to the channels free of emission and subtracted to remove baseline anomalies. The remaining scans were combined to produce a single spectrum for both transitions at each observed position. Hanning smoothing was used to improve the signal to noise of these spectra, which reduced the spectral resolution by a factor of 2.
Cool dust from the Herschel Space Observatory The G333 GMC was observed by Herschel  (Pilbratt et al. 2010)  with the PACS  (Poglitsch et al. 2010)  and SPIRE  (Griffin et al. 2010)  instruments in Parallel mode at 70, 160, 250, 350 and 500 µm as part of the Open Time Key Programme "Hi-Gal" (PI: S. Molinari;  Molinari et al. 2010). The observations comprised of a scan and cross-scan of two 2 • fields (field 332. 0 with obsid: 1342204046/47 and field 334. 0 with obsid: 1342204054/55) taken at a scan speed of 60 arcsec s -1. These observations were reduced in the Herschel Interactive Processing Environment (HIPE; Ott 2010) to Level 1 (basic calibrated data product), then exported to the scanamorphos map-making software to create a mosaic of the G333 GMC  (Roussel 2013). The pixel size of the final mosaic was 6.
ACKNOWLEDGMENTS We would like to thank the anonymous referee for their comments that have greatly improved this paper. Partial financial support for this work was provided by the  Australian Research Council. JPM is supported by  Spanish  grant  AYA 2011-26202. JSU was supported by a  CSIRO   OCE postdoctoral grant. NL is supported by a  CONICYT/FONDECYT postdoctorado , under project no. 3130540. NL acknowledges partial support from the  ALMA-CONICYT Fund  for the Development of  Chilean Astronomy Project 31090013, Center of Excellence in Astrophysics and Associated Technologies  ( PFB 06 ) and  Centro de Astrofísica FONDAP 15010003. The Tidbinbilla 70-m Radio Telescope is part of the  NASA Deep Space Network  and is operated by  CSIRO Astronomy and Space Science. Herschel  is an  ESA  space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from  NASA.
This research has made use of  NASA 's Astrophysics Data System. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the  NASA/ IPAC Infrared Science Archive , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This work is based in part on archival data obtained with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. Support for this work was provided by an award issued by  JPL/Caltech. This paper made use of information from the Red MSX Source survey database at www. ast. leeds. ac. uk/RMS which was constructed with support from the  Science and Technology Facilities Council  of the UK.
Ecological trait data are essential for understanding the broad-scale distribution of biodiversity and its response to global change. For animals, diet represents a fundamental aspect of species' evolutionary adaptations, ecological and functional roles, and trophic interactions. However, the importance of diet for macroevolutionary and macroecological dynamics remains little explored, partly because of the lack of comprehensive trait datasets. We compiled and evaluated a comprehensive global dataset of diet preferences of mammals ("Mam-malDIET"). Diet information was digitized from two global and cladewide data sources and errors of data entry by multiple data recorders were assessed. We then developed a hierarchical extrapolation procedure to fill-in diet information for species with missing information.
Missing data were extrapolated with information from other taxonomic levels (genus, other species within the same genus, or family) and this extrapolation was subsequently validated both internally (with a jack-knife approach applied to the compiled species-level diet data) and externally (using independent species-level diet information from a comprehensive continentwide data source). Finally, we grouped mammal species into trophic levels and dietary guilds, and their species richness as well as their proportion of total richness were mapped at a global scale for those diet categories with good validation results. The success rate of correctly digitizing data was 94%, indicating that the consistency in data entry among multiple recorders was high. Data sources provided species-level diet information for a total of 2033 species (38% of all 5364 terrestrial mammal species, based on the IUCN taxonomy).
Introduction With the emergence of the macroecological research field  (Brown and Maurer 1989) , an increasing interest has developed in compiling comprehensive data on the geographic distribution of life on Earth. For instance, broadscale datasets on species distributions, phylogenies, and ecological or life-history traits are now increasingly becoming electronically available, at least for some vertebrate groups such as birds and mammals  (Bininda-Emonds et al. 2008; Jones et al. 2009; BirdLife International & NatureServe 2011; Jetz et al. 2012; IUCN 2013 ). However, compiling ecological trait data for species-rich clades is challenging and time-consuming, and many individual researchers lack the resources and time to compile such comprehensive datasets. Moreover, ecological trait data are often incomplete, even for well-known and wellstudied clades  (Jones et al.
Hence, the importance of diet for macroevolutionary and macroecological dynamics and the structure and functioning of ecosystems worldwide remains little explored. Mammals are a diverse vertebrate group whose species have colonized nearly all parts of the world. Mammalian species show a wide range of diet preferences (Fig. 1 ) which is partly related to their dental diversity  (Price et al. 2012). Data on global species distributions  (IUCN 2013 ) and phylogenetic relationships (e. g. ,  Bininda-Emonds et al. 2008; Fritz et al. 2009 ) of mammals have recently become available and numerous ecological adaptations and life-history traits have been described in the literature (e. g. ,  Nowak 1999; Smith et al. 2003; Jones et al. 2009; Qian et al. 2009; IUCN 2013 ). However, current datasets on ecological traits of mammals are incomplete and do not provide data for all mammals worldwide (e.
g. ,  Smith et al. 2003; Jones et al. 2009; Safi et al. 2011; Price et al. 2012). Available datasets on diet preferences of mammals are either restricted to small subsets of species (e. g. ,  Cantalapiedra et al. 2014)  or cover around 30-40% of the species (e. g. ,  Jones et al. 2009; Price et al. 2012 ) and typically only allow categorizing species into three predefined trophic levels (carnivores, omnivores, herbivores). This forces researchers to limit their investigation to the best-known subset of taxa and to a few broad diet adaptations. Moreover, the deletion of missing values (or the use of incomplete datasets) reduces the power of statistical inference and might increase estimation bias  (Nakagawa and Freckleton 2008). Hence, available data on key mammalian traits such as diet require additional efforts to achieve broader taxonomic coverage and finer ecological detail.