dataesr/scientific-paragraphs-categorization huggingface dataset:
The PCA was performed in MATLAB. The results of the PCA are displayed using a biplot of the principal component coefficients. The axes of the biplot represent the principal components, the observations are shown as red dots, and the original variables are plotted in the PC space as vectors. Three random network models were used. The first generated random networks that did not preserve the degree distribution, using the function "makerandCIJ_und" from the Brain Connectivity Toolbox  (Rubinov & Sporns, 2010). The second rewired the edges while preserving the degree distribution of the networks, using the function "randmio_und connected" from the Brain Connectivity Toolbox  (Rubinov & Sporns, 2010) , with a rewiring parameter of 15. Note that increasing the rewiring parameter to 25 did not change the resulting motif fingerprint.
In the third random model the wavelet coefficients were scrambled, as proposed by  Zalesky et al. (2012) , using the Matlab function "randperm" to obtain random permutations. For the latter two null models, 100 randomized networks were calculated for each of the 100 real fMRI networks (giving 10,000 networks in total). The global efficiency, assortativity, modularity, and transitivity of the networks were calculated using the Brain Connectivity Toolbox  (Rubinov & Sporns, 2010). Note that transitivity is calculated as the ratio of triangles to triplets in the network (sometimes known as clustering). Modularity is calculated as the maximized modularity Q obtained by Newman's spectral community detection  (Newman, 2006). The total Euclidean distance is a sum of the Euclidean distance of all edges in the thresholded network, where the Euclidean distance of the functional edge between two brain regions is approximated as the distance between the centroids of the brain regions.
2016). This acoustic observatory, called OHASISBIO (Observatoire Hydro Acoustique de la SISmicité et de la BIOdiversité), now comprises 7 yr of acoustic records at 6 sites spanning an area of 9 000 000 km 2. The analysis of this dataset shed light on the seasonal and geographic distribution of Antarctic blue whales in the southern Indian Ocean from 2010 to 2016  (Leroy et al. 2016). In this paper, we extend this analysis to pygmy blue whales and fin whales, based on coarser power spectral density (PSD) analyses.
Data acquisition The acoustic data were collected by a hydrophone network in the framework of the OHASISBIO hydro acoustic experiment  (Royer 2009). This network, initially deployed in December 2009 at 5 sites in the southern Indian Ocean, was designed to monitor low-frequency sounds produced by seismic and volcanic events  (Royer et al. 2015 , Tsang-Hin-Sun et al. 2016) , and by large baleen whales. Instruments were deployed south of La Reunion Island in the Madagascar Basin (MAD), northeast of the St Paul and Amsterdam plateau (NEAMS), mid-way be tween the Kerguelen and Amsterdam islands (SWAMS), north of Crozet Island (NCRO) and west of Kerguelen Island (WKER) (Fig. 1 ). In 2014, a new site was established in the northern Crozet Basin (SSEIR). All moorings were recovered and redeployed annually, except in January 2016 when poor weather conditions prevented the recovery of the hydro phones at the WKER and NEAMS sites.
Instead, a new mooring was deployed at the WKER site, and a provisional mooring was deployed at 35°S, 079°E (NSPA). Each mooring consists of an expandable anchor, an acous tic release, and an au tonomous hydrophone programmed to record acoustic waves continuously at a rate of 240 Hz with a 24-bit resolution and a sensitivity of -163. 5 dB re: 1V µPa -1 (see D  'Eu et al. 2012  for instrumental details). Hydrophones are deployed in the axis of the sound fixing and ranging (SOFAR) channel, from 500 to 1300 m below sea surface de pending on the site. Data are retrieved every year during the annual voyages of the R/V 'Marion Du fresne' to the French Southern and Antarctic Territories. The database comprises nearly continuous acoustic data over 7 yr  (2010) (2011) (2012) (2013) (2014) (2015) (2016) , barring a few months or years depending on the site, due to battery failures or instrument losses (see  Leroy et al.
2016  for more details). Data recorded at the NCRO site in 2010 and 2013 were excluded from the analyses since those records were hindered by high noise levels likely due to the strumming of the mooring line. Deployment periods are summarized in Fig. 2  and listed in Table  S1  in the Supplement at www. int-res. com/ articles/ suppl/ n037 p289 _ supp. pdf.
The upper note produced by Antarctic fin whales varies geographically; this pulse occurs at 89 Hz off west Antarctica and at 99 Hz off east Antarctica  (Širovicet al. 2009 ). Long-term spectral averages of our data show that, in the southern Indian Ocean, fin whales also produce a pulse at ~99 Hz. One way to assess the broad-scale presence of vocalizing whales is to compare the acoustic power in the frequency bands of their vocalizations to the acoustic power in adjacent frequency bands  (Gedamke et al. 2007 (Gedamke et al. , Širovicet al. 2009)). This method, known as PSD analysis, is commonly used to determine the seasonal presence of blue and fin whales in large acoustic datasets  (Curtis et al. 1999 , Širovicet al. 2004 , 2009 , Gavrilov et al. 2012 , Nieukirk et al. 2012 , Leroy et al.
3a ). Note that the selected frequency band for analyzing the presence of Antarctic blue whale calls is extremely narrow and focuses on Z-call unit A, hence avoiding any energy from other calls in the 18-28 Hz frequency range (e. g. fin whale 20 Hz pulses, Madagascar pygmy blue whale 25 Hz unit). In a previous analysis  (Leroy et al. 2016) , the geographic and seasonal distribution of Antarctic blue whales was inferred from individual calls, automatically detected using a subspace projection algorithm  (Socheleau et al. 2015). Comparing these results provides a way to cross-validate the 2 approaches. The PSD for each acoustic file (~6 h 28) was computed using the P-welch Matlab function, over 300 s windows, with 50% overlap and a frequency resolution of 0. 0018 Hz. The averaged power (in dB Hz -1 re: 1 µPa) was then calculated (1) in the frequency range that encompassed each unit selected to characterize the vocal signature of each species or subspecies (Fig.
Methods In this multicohort study, we used data from seven cohort studies in the IPD-Work consortium, initiated between 1985 and 2002 in Finland, France, Sweden, and the UK, to examine the association between work stress and mortality. Work stress was denoted as job strain or effort-reward imbalance at work. We extracted individual-level data on prevalent cardiometabolic diseases (coronary heart disease, stroke, or diabetes [without differentiation by diabetes type]) at baseline. Work stressors, socioeconomic status, and conventional and lifestyle risk factors (systolic and diastolic blood pressure, total cholesterol, smoking status, BMI, physical activity, and alcohol consumption) were also assessed at baseline. Mortality data, including date and cause of death, were obtained from national death registries.
Study population Established in 2008, the objective of the IPD-Work Consortium 1,10 is to provide a large-scale harmonised database for the longitudinal estimation of associations between predefined psychosocial working conditions and chronic disease outcomes. The participating studies comply with the Declaration of Helsinki and were approved by local ethics review boards. Informed consent was obtained from all participants. Of the 12 original studies in the IPD-Work Consortium, 1 seven independent cohort studies, initiated between 1985 and 2002 in Finland (FPS, HeSSup, Still Working), France (GAZEL), Sweden (WOLF S, WOLF N) and the UK (Whitehall II), had data relevant to the present research. From each cohort study, eligible participants were those who were employed at the time of the baseline assessment, had data for age, sex, job strain, effort-reward imbalance at work, and prevalent cardiovascular disease and diabetes, and were being followed up for mortality.
Research in context Evidence before this study Work stressors, such as job strain and effort-reward imbalance at work, are common sources of stress in adulthood. Work stressors have been examined as risk factors for cardiometabolic disease, such as coronary heart disease, stroke, and diabetes, but few studies are available on their role as prognostic factors for these diseases. We searched PubMed and Embase databases from inception up to Feb 1, 2018 using the search terms: "work stress", "job stress", "job strain", "effort-reward imbalance", and "mortality", without language restrictions. We identified no large-scale studies comparing the association between work stressors and mortality in people with and without cardiometabolic disease.
Work stress Analyses were based on two indicators of work stress: job demand-control (ie, job strain) and effort-reward imbalance at work. Reports from the IPD-Work consortium are based on predefined, harmonised, and validated definitions of work stress. The psychometric properties of these data were published before the extraction of outcome data. 18, 19 Job strain, referring to a combination of high demands and low control at work, was measured with sets of questions from the validated Job Content Questionnaire and Demand-Control Questionnaire, which were included in the baseline self-report questionnaire of all of seven studies. 18 Using both questionnaires, we defined high job demands as having a job-demand score that was greater than the study-specific median score; similarly, we defined low job control as having a job control score that was lower than the study-specific median score.
21 We identified history of stroke using selfreported doctor-diagnosed events, event tracing, and linkage to national hospital admission records (ICD-10 codes I60, I61, I63, I64). 10  Prevalent diabetes was defined with information from any of the following data sources: hospital admission records with ICD-10 diagnoses (E10, E11; all studies apart from Whitehall II), antidiabetes drug reimbursements (only FPS, Still Working, and HeSSup),  3  or 2 h oral glucose tolerance test (WHO criteria) complemented by self-report of diabetes diagnosis and medication (Whitehall II). 22.
Statistical analysis Means and SDs were calculated according to cardiometabolic disease status at baseline (prevalent coronary heart disease, stroke, or diabetes vs none of these diseases). Each participant was followed up from the date of the assessment of their work stressors and prevalent cardiometabolic disease to the earliest event out of death, loss to follow-up, or end of follow-up (maximum 20 years). We computed time to death to obtain age-adjusted incidence rates per 10 000 person-years in men and women in the pooled dataset. We used Cox proportional hazards regression to study the associations    Robustness of the association between work stressors and mortality in subgroups was tested in analyses stratified by number of lifestyle risk factors (zero, one, or two or more from current smoking, physical inactivity, obesity, and high alcohol consumption).
Results 105 284 people were recruited into the seven studies between 1985 and 2002. Of the eligible population, 102 663 participants had data on prevalent cardiometabolic disease, at least one of the work stressors (job strain or effort-reward imbalance), and mortality, and were therefore included in this study (figure  1 ). Characteristics were similar between the eligible and included populations in terms of the proportion of men (43•5% in eligible vs 43•4% in enrolled participants), mean age (44•0 years vs 43•9 years), and proportion of participants of low-socioeconomic status (26•2% vs 25•8%). Mean follow-up for mortality was 13•9 years (SD 3•9). During 1 423 753 person-years at risk, we identified 3841 deaths, of which 397 were among the 3441 individuals with cardiometabolic disease at baseline.
Stage 1: sepal initiation, stage 2: petal initiation, stage 3: stamen initiation. Data were extracted from RNA-seq analyses. Y-axis is labelled in FPKM. subfamily contains a single homolog for AtTOE1 (RcHm5g0061501) and a gene (RcHm1g0364341) that groups in a branch with Arabidopsis TOE2, SMZ (SCHLAFMUTZE) and SNZ (SCHNARCHZAPFEN). The higher divergence of this last branch from the rest of the tree is likely due to the presence of a non-functional second AP2 DNA-binding domain, that could have accumulated more mutations and putatively acquired a new function  12, 39. Phylogenetic analyses, using AP2 domains, revealed no direct orthologue of RcAP2L (RcHm3g0468481) in Arabidopsis genome. Interestingly, RcAP2L appears to group with the Petunia PhBEN and PhBOB gene. PhBEN was reported to repress the expression of the C-function genes in the perianth, and together with PhBOB, it is required for organ growth in the second whorl  12.
RcAP2L is part of the TOE-subfamily (observed for both the truncated RcHm3g0468481 and the wild-type RcHt_S350. 36 version). The inserted sequence is repeated in the rose genome and corresponds to a transposable element (TE) belonging to the Gypsy LTR retrotransposon family (Fig. 1b ). Sequence alignment also showed that the two LTRs of the inserted TE are 100% identical on their whole length, indicating a recent insertion  40. The inserted TE contains an open reading frame of 5,535 pb and DANTE software predicted the presence of at least 5 retroviral sequences coding for the structural protein GAG, a protease, a reverse transcriptase, a H-Ribonuclease and an integrase. We found 17 complete copies from this TE family in the rose genome, and 66 solo-LTRs, making it moderately repeated. The TE insertion in RcAP2L creates a new splicing acceptor site that is predicted to lead to a fusion of the 8 th exon of RcAP2L with a sequence from the 5′ LTR from the TE.
Markers that had unique match were kept and used to define the corresponding physical region on the rose genome sequence. Genes within this interval were analyzed using Blast and Pfam web interface  50, 51. Analysis of the presence of the TE element in RcAP2L was also performed using the available genome sequences of rose cultivars  27. Single reads from resequenced genomes of the different rose cultivars  27  were trimmed using cutadapt  52  and custom Perl scripts. They were cut to an homogeneous length of 100 bp and aligned on the reference rose genome using bwa software  53  allowing up to two mismatches on the whole length of the read  (end-to-end alignment). Reads overlapping genomic positions of interest over at least 15 bp on each side were counted. Read counts were normalized on the library size for each genotype.
psRNATarget webserver interface was used to detect miR targets  54. Haplotype identification and comparison. Sequence analysis was performed using the high-quality genome assembly of homozygous R. chinensis 'Old Blush'  27. The two distinct haplotypes within the double flower interval were retrieved from the heterozygous genome assembly  27. Blastn  55  and gene synteny were used to confirm alleles sequences. The water program from Emboss suite  56  was used to obtain optimal end-to-end alignments between allelic regions and identify polymorphisms. Gene sequence analysis. Gene models were recovered from the rose reference genome sequence annotation  27. Splicing site predictions and untranslated region (UTR) boundaries were manually adjusted based on cloned cDNA sequences and RNA-seq data.
Transposable element annotation. To identify repeated regions, the genomic sequence of RcAP2L neighbourhood was cut into 47 bp overlapping k-mers, and the number of occurrences of each k-mer was counted in the 375 Gb-dataset of genomic reads used to assemble the Rosa chinensis heterozygous rose genome sequence  27. These occurrence counts were plotted along the sequence (Fig. 1b ) and compared to the mean occurrence counts for homozygous and heterozygous regions. Automatic transposable element (TE) annotations from the rose genome  27  were used as a starting point, and manually curated. The boundaries of the two long terminal repeats (LTRs) were accurately identified using a graphical dotplot program  58  and LTR sequences were compared using bl2seq alignment  55. Open reading frames were predicted in the TE internal region using Pfam software  50  and protein domains were annotated by similarity search using DANTE (http://repeatexplorer.
These read pairs were remapped on the whole genome with Tophat allowing up to 5 multimatches and secondary alignments. Based on the number of matches and the alignment scores, read pairs were sorted into four categories: (i) specific to RcAP2L WT , (ii) specific to RcAP2L ∆172 , (iii) coming indiscriminately from RcAP2L WT or RcAP2L ∆172 , and (iv) coming indiscriminately from RcAP2L WT , RcAP2L ∆172 or other loci in the genome (hereafter called non-specific read pairs). Read pairs that Tophat could match on the extracted genomic sequences of RcAP2L alleles, but not on the whole genome, were put in category (iv). This case is expected for reads originating from repeated sequences. The read pairs from each category were mapped on the predicted transcripts using Bowtie2 version 2. 3. 4. 1  61.
Coverage at each position was computed using Samtools version 1. 5  62. Normalization was done using the library sizes (custom Perl scripts), before adding up the coverage values from all libraries. Reads from categories (iii) and (iv) were spread between the two alleles according to the ratio (i)/(ii), computed on a sliding window of width 241 bp. After ensuring that sequence polymorphism between RcAP2L WT and RcAP2L ∆172 transcripts was sufficient to estimate independently their expression level, we used Tophat version 2. 1. 1  60  on the annotated Rosa chinensis heterozygous genome  27  , with corrected annotations for RcAP2L alleles, and we normalized read counts using DESeq. 2 version 1. 2. 0 63.
Phylogenetic analysis. EuAP2 family members were identified by using the Arabidopsis thaliana AP2 protein as a Blast query against rose and strawberry predicted proteins  27. Sequences were aligned using ClustalW  64  and BioEdit software  65. Where applicable, gene annotation was corrected manually. Neighbor-Joining tree based on the aligned AP2 DNA binding domains of the euAP2 members from Rosa chinensis (RcHm and RcHt)  27  , Fragaria vesca (Fv), Petunia  12  , Solanum lycopersicum, Arabidopsis thaliana, Capsella rubella, Medicago truncatula, Vitis vinifera and Prunus persica. Sequences were downloaded from the Phytozome website (https://phytozome. jgi. doe. gov/pz/portal. html). The aligned regions containing the two AP2 domains (Supplemental Data File 1) were selected for phylogenetic analysis.
Neighbor-Joining tree was computed with Treecon software 66 using the following parameters (1) Distance estimation options: Tajima and Nei 67 ; Distance calculations; insertions and deletion not taken into account; Alignment positions: all; Bootstrap analysis: yes, 2000 samples. (2) Infer tree topology options: Neighbor-joining; Bootstrap analysis: yes. (3) Root unrooted trees options: outgroup option: single sequence (forced); bootstrap analysis: yes. Tree was rooted using the Arabidopsis ANT protein.
The region of RcAP2L ∆172 homologous to exons 9 and 10 exhibits a negligible expression level (highest value ≤ 2). (e) Expression of wild-type allele RcAP2L WT (green) and RcAP2L ∆172 allele (orange) early on in flower formation. Stage 1: sepal initiation, stage 2: petal initiation, stage 3: stamen initiation. Data were extracted from RNA-seq analyses. Y-axis is labelled in FPKM.
57tative functions for genes flanking RcAP2L were inferred from Arabidopsis best blast hit. Unknown protein domains were identified using InterProScan software version 5. 27. -66. 057, Pfam database version 31. 0 50 and manual annotation. miR172 putative binding sites were predicted using a local instance of WMD3 software (Ossowski Stephan, Fitz Joffrey, Schwab Rebecca, Riester Markus and Weigel Detlef, personal communication).
Acknowledgements We thank  Judit Szécsi  ( ENS de Lyon ) and  Philippe Vergne  ( ENS de Lyon ) for useful discussions and for critical reading of the manuscript. We thank  Alexis Lacroix ,  Patrice Bolland  and  Justin Berger  ( ENS de Lyon ), and the  "Lyon Botanical Garden-France " for providing plant material. We gratefully acknowledge support from the  Pôle Scientifique de Modélisation Numérique of the ENS de Lyon  for the computing resources. We thank  Loïs Taulelle  ( PSMN ) for his help with computing clusters and  Emmanuel Quemener  ( Centre Blaise Pascal, ENS de Lyon ) for setting up ad-hoc servers when needed. This work was supported by funds from the  French National Institute of Agronomic Research (INRA) , from the  Ecole Normale Supérieure-Lyon-France , and from  French National Research Agency program DODO  ( ANR-16CE20-0024-03 ).
In addition to the regular tree attributes measured in practical field inventories, e. g. , the diameter at breast height (1. 3 m, DBH) and tree height, more detailed tree attributes, such as the stem curve or taper curve (stem diameter as a function of height) that reveals the wood productivity and quality yet difficult to acquire non-destructively in the field, can be derived from TLS with high degrees of accuracy and cost efficiency  (Liang et al. , 2014b). Tremendous efforts have been put into research to investigate the automated interpretation of TLS data and to establish best practices for using TLS. In the past 20 years, significant progress has been made in deriving tree-and stand-level attributes from TLS data to depict forest productivity, evolution and ecological functions. Early studies around the year 2000  (Erikson and Karin, 2003; Lovell et al.
For example, as a fundamental criterion of TLS-based forest in situ observation, the percentage of detected trees from multi-scan TLS data ranged from 20% to 100% at the plot level as reported in previous research  (Maas et al. , 2008; Strahler et al. , 2008; Brolly and Kiraly, 2009; Murphy et al. , 2010; Lovell et al. , 2011; Yao et al. , 2011; Liang et al. , 2012; Lindberg et al. , 2012; Astrup et al. , 2014; Olofsson et al. , 2014). Considering the diversity of the elementary components in the reported studies, such literature-based statistics do not reflect the capability and the overall performance of TLS due to the lack of a common frame of reference. A proper understanding of the performance of TLS for forest in situ inventory can only be achieved when certain conditions are satisfied: that identical TLS data are processed; that common plot-and tree-level forest attribute are extracted; and that, the results from the algorithms are evaluated with reliable reference information utilizing standardized evaluation procedures.
Under such conditions, all the algorithms are projected to a unique frame of reference, and an assessment of the status quo of the TLS-based forest inventory can be conducted by comparing the attribute extraction results of different algorithms. As such, an international benchmarking study of TLS in forest inventories (TLS benchmarking) was launched in 2014 by the European Spatial Data Research Organization (EuroSDR) and partly funded by the European Community's Seventh Framework Programme Project Advanced_SAR. The TLS benchmarking aims to clarify the potential and current status of the TLS application in field inventories by evaluating methodologies on the basis of a standard evaluation procedure and a common dataset, thereby orienting further research and developments. As the project leader and coordinator, the Finnish Geospatial Research Institute (FGI) conducted the TLS and reference data acquisition, defined a series of plot-and tree-level attributes as standardized criteria, developed a standard and fully automated evaluation procedure, evaluated the performances of the algorithms, and benchmarked the results.