dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the anonymous reviewers who have dedicated their time and effort to make considerable improvements on the manuscript. This work has been conducted as part of a PhD thesis project supported by the  Agricultural Transformation by Innovation (AGTRAIN) Erasmus Mundus Joint Doctorate Programme , funded by the  EACEA (Education, Audiovisual and Culture Executive Agency)  of the  European Commission. The technical resources provided by the  CGIAR Research Programmes on Climate Change, Agriculture and Food Security (CCAFS) 's  Low Emissions Development Flagship , and the  Standard Assessment of Mitigation Potential and Livelihoods in Smallholder Systems  (SAMPLES) program have been crucial for the achievement of our objectives. CCAFS also acknowledges the  CGIAR Fund Council, Australia (ACIAR) ,  Irish Aid , the  European Union , the  International Fund for Agricultural Development (IFAD) ,  The Netherlands, New Zealand, Switzerland , the  UK, USAID and Thailand.
Diversification rate analyses A lineage through time (LTT) plot for the entire genus was generated with the R package paleotree 2. 7  (Bapst, 2012). One thousand random post-burnin trees from the BEAST inference analysis by  Cruaud et al. (2012)  were used as input. A median curve with the 95% confidence interval is displayed. A Bayesian Analysis of Macro-evolutionary Mixtures (BAMM 2. 5)  (Rabosky et al. , 2014)  was used to infer shifts in speciation and extinction rates across the phylogenetic tree. To account for non-random incomplete taxon sampling, total diversity and sampling fractions for all clades (see Supplementary data file S1) within figs were obtained from the literature  (Berg and Corner, 2005; Rønsted et al. , 2008a). The MCMC analysis was run for 1 million generations with sampling every 1000 generations and, after checking for convergence with the R package coda 0.
19-1  (Plummer et al. , 2006) , the first 10% was discarded as burn-in. We used the R package BAMMtools 2. 1. 6 (Rabosky et al. , 2014)  and ape 4. 0  (Paradis et al. , 2004)  to summarize rates over each branch of the phylogenetic tree (visualized in the so-called 'phylorate' plot), to plot the 95% credible shift set (CSS) with sampling frequencies of the different shift configurations, and to obtain the shift configuration with the maximum a posteriori (MAP) probability. Problems with estimating extinction rates have been reported for earlier versions of BAMM  (Moore et al. , 2016)  and although they have been accounted for in the latest version, we decided to include a ME-DUSA analysis as a second method to infer diversification rate shifts  (Alfaro et al. , 2009). MEDUSA requires species richness to be assigned to unresolved clades and we therefore pruned the original dated phylogenetic tree of  Cruaud et al.
State-dependent diversification rate analyses State-dependent diversification rate analyses were performed with the Binary-State Speciation and Extinction (BiSSE) model  (Maddison et al. , 2007)  and the MultiState Speciation and Extinction (MuSSE) model, as implemented in the R package diversitree 0. 9-9  (Fitzjohn, 2012). The influence of two traits on the diversification rates was analysed using the BiSSE model: 1) pollination mode with the states active versus passive pollination, and 2) monoecious versus (gyno)dioecious plants. The influence of habit was analysed using the MuSSE model and four different states were defined: shrub or small tree (< 30 m, understory), climber, large tree (≥30 m, canopy), and hemiepiphyte. Information on the trait states for each taxon was extracted from the literature (Fig.
Introduction: Stereotactic hypofractionated radiotherapy is an effective treatment for brain metastases in oligometastatic patients. Its planning is however time-consuming because of the number of organs at risk to be manually segmented. This study evaluates 2 automated segmentation commercial software. Methods: Patients were scanned in the treatment position. The computed tomography scan was registered on a magnetic resonance imaging and volumes were manually segmented by a clinician. Then 2 automated segmentations were performed (with iPlan and Smart Segmentation). RT STRUCT files were compared with Aquilab's Artistruct segment comparison module. We selected common segmented volume ratio as the main judging criterion. Secondary criteria were Dice-Sørensen coefficients, overlap ratio, and additional segmented volume.
Data Creation This study was conducted on the treatment planning computed tomography (CT) scans of 20 consecutive patients referred to our institution for brain metastases. All patients treated in our institution are informed on admission about the potential use of their collected data for future research. Need for written informed consent from the participants was waived by our institutional review board, but oral consent was required in accordance with French laws (data, data collection, and freedom law, CNIL, January, 6, 1978). For this study, no personal data were collected beyond age and sex and all patient's data were anonymized by the physicians in charge of the patients before being used for analysis. The patients were treated on a CyberKnife M6 system (Accuray, Sunnyvale, California) either exclusively or as an adjuvant postsurgery strategy.
Each patient was positioned (head-first supine position, arms resting on each side of the body). A thermoformed contention mask was modeled. They were then scanned on a LightSpeed CT (GE Healthcare, Little Chalfont, United Kingdom): helical acquisition, contiguous slices of 1. 25 mm, 512 Â 512 Â 512 matrix, 120 kV, Tube Current -Exposure Time Product > 350 mA. s speed: 3. 75, mode: 0. 75, no contrast injection, software version 0. 7MW11. 10. The obtained digital imaging and communications in medicine (DICOM) data were used as samples for analysis. Manual segmentation was performed after registration of the CT scan with a 3-D, T1-weighted, contrast-enhanced magnetic resonance imaging (MRI; most often using the diagnostic MRI performed outside of our institution, voxels of 0. 25-1. 25 mm 3 ).
Structures were manually segmented by a first clinician and subsequently verified and edited by a second clinician on Eclipse V. 13 (Varian Medical Systems, Palo Alto, California). The 2 commercial treatment planning systems used for this study were iPlan RT Image v. 4. 1. 1 (Brainlab AG, Munich, Germany) and Eclipse v. 13's Smart Segmentation. The former uses a library of atlases which are included in the software at the time of purchase. The latter lets the clinician choose from a series of example cases and increment the library with new cases. 11 This potentially allows an automated segmentation that is closer to that of the clinician who created the library. As the Smart Segmentation software did not include a brain expert case, a new atlas was created using a CT scan and an MRI from an archived patient data set.
The patient selected for the atlas had no brain lesion, so as to avoid skewing automated segmentation results. The newly created atlas was then used for automatic segmentation on each CT scan. The images from each patient's planning CT scan and MRI were also transferred on an iPlan planning console where an automated segmentation was also performed, using the MRI images and then resampled on the CT scan. Automated segmentation was performed using the full width of Hounsfield units, as the width and level did not impact iPlan automated segment but impacted Smart Segmentation results. We also ensured that repeated segmentation attempts yielded the same results, both on the index case used for creating the atlas and the new cases (data not shown). As the aim of the study was to perform an evaluation of each software in replacing the clinician, no editing was made to the segmentation (Figure  1 ).
Statistical Analysis Computed tomography scan, MRI images, and structures were then exported as DICOM files (DICOM image and DICOM for radiation therapy-DICOM RT STRUCT). Each automated set was compared to the gold standard set (manual segmentation) via commercial software Artiview 4 (Aquilab, Loos Les Lille, France), which was used as a segment comparison module. The module allows comparison of DICOM RT STRUCT files and is able to compute each of the following indices (with A being the reference volume and B the automated volume): -Dice-Sørensen coefficient (DSC): DSC ¼ 2 (A \ B)/(A þ B) , where 0. 7 and above is considered good. 12 -Overlap ratio (OR): OR ¼ (A \ B)/(A U B), much like the DSC, it should be as close to 1 as possible. -Common segmented volume (CSV): CSV ¼ (A \ B)/A, which should be as close to 100% as possible.
-Additional segmented volume (ASV): ASV ¼ (B À A)/ B, which should be as close to 0% as possible. These data were calculated for each patient's brain stem, lenses, eyes, optic chiasm, and optic nerves. We chose the CSV as our main judging criterion to reflect the accuracy of the automated segmentation of the structure. Data were analyzed using open-source R Â64 3. 3. 1. Mean values were calculated for each relevant index alongside standard deviations (SDs) with the open-source prettyR package. We conducted a paired, nonparametric statistical test, that is, a Wilcoxon signed rank paired test, for each comparison. This test is more conservative than the t test and allows comparisons of matched smaller samples when one cannot assume that the samples are normally distributed, such as in small samples.
Dice-Sørensen Coefficients and ORs As reported in Table  4 , iPlan performed better for the right lens, while Smart Segmentation performed better for the optical nerves for both indices. However, only the biggest structures such as the eyes and the brain stem reached over 0. 7. Agreement in smaller structures was much poorer and not acceptable for inverse planning. a Reported here are average percentages by organ of voxels belonging to each organ at risk on the automated segmented computed tomography (CT) scan, which were mistakenly included in the organ-at-risk region of interest by the automated segmentation software.
Likewise, a voxel can only be classified as either correct or incorrect with no in-between. A change in the size of the voxels in the matrix will almost always automatically move a slight part of the volume from inside to outside of the structure, from correct to incorrect or vice versa. As the automated segmentation was performed on 2 different software, the volume of each structure from the DICOM RT STRUCT may vary in part simply because of the matrix and not because of an actual change in size. As such, the various data reported here are the closest possible approximations of the truth using each of the software's voxel matrixes. This imprecision is however often minute compared to the size of the entire organ as it only affects the voxels on the border of the structure (long narrow organs such as the optic nerves may be the most affected ones).
It is also unavoidable as each planning image itself is acquired as a voxel matrix. The exploratory analyses seemed to show that the software performed in the same way regardless of the size of the lesions and the removal of the lenses, as all stayed nonstatistically significant but one, which may simply be linked to sample fluctuation. A limitation of this study is that the Eclipse Smart Segmentation software used only 1 atlas from a patient, which can explain why it did not perform as well as iPlan's automated segmentation software in some regards. A way to improve the segmentation would probably have been the use of multiple atlases. Van de Velde et al evaluated the number of atlases necessary for automated segmentation using the simultaneous truth and performance level estimation (STAPLE) and found that 4 were necessary and as many as 9 were best.
17 hile there have been studies in patients with large metastases  8, 9  and in other body regions such as head and neck and pelvis,  7, [18] [19] [20] [21] [22]  to our knowledge, this study is the first to compare multiple commercial segmentation platforms in the context of intracranial stereotactic hypofractionated radiation therapy, using both an user-generated and a commercial software. In this context, tumors are often smaller and as such induce less displacement, making it easier to use an automated segmentation software. This is evidenced in our study by the fact that Smart Segmentation and iPlan both performed the most poorly in the patient with the biggest lesions. Despite the predictable reproducibility of this situation, as intracranial structures are subject to less internal movement and interpersonal variations than other parts of the human body, automated segmentation was a rather ineffective tool.
Manual editing, often even manual segmentation altogether, remained mandatory. This warrants further studies and cooperation between vendors and radiation oncologists to improve those platforms before they can be used in routine practice. One could also view automated segmentation as a first step, followed by manual editing of the structures, which could allow a time gain while retaining human control. Deeley et al showed that both automated software and experts were challenged for tubular structure segmentation in the brain (ie, chiasm and optic nerves), as compared to a STAPLE-generated ground truth. 8 sing atlas-based registration to segment the eyes and the brain stem and the atlas-navigated optimal medial axis and deformable model algorithm for other structures followed by manual editing, they then showed in another study that it could reduce interobserver variation, improve accuracy, and reduce segmentation time by about 60%.
| Study area and design The study area (5,327 km 2 ) in NE Iceland and survey methods used have been extensively detailed elsewhere  (Nielsen, 1999 (Nielsen, , 2011 ) so we will remain brief. The study area is centered on Lake Mývatn (N65°60′, W17°00′) and is constituted of hilly terrain up to 600-800 m above sea level. The gyrfalcon population is censused annually by visiting all known territories within the study area to determine predator occupancy (n = 83 territories). The number of territorial rock ptarmigan males is surveyed every spring (mostly in May) on six plots (total area 26. 8 km 2 ) within the general study area. The study started in 1981, and we used data for the period 1981-2014.
Several model fitting techniques have been considered in preliminary explorations (MCMC using JAGS within R, least squares for vector autoregressive models in R package vars, simple independent linear autoregressive models using lm() in R). Maximumlikelihood estimation using the MARSS package  (Holmes, Ward, & Wills, 2012)  and the EM algorithm was finally chosen because it allowed to easily perform model selection for contrasted interaction matrices (i. e. , setting some interactions to zero). All algorithms gave, however, similar model estimates (see Supporting Information Appendix S1). We then considered more complex MAR(2) models that are able to allow for both populations to cycle independently, because each univariate AR(2) component can model long cycles (≈7-10-year cycles, like those observed in the field).
Selection of the optimal time lag p in MAR(p) model using a variety of model information theoretic criteria (see code in https://github. com/fbarraquand/ GyrfalconPtarmigan_MAR) suggested an optimal lag order of 2 (BIC, HQ) or 3 (AIC, FPE). Because 2 time lags are enough to model independently cycling populations of period up to 10 years and more  (Royama, 1992) , and MAR(  2 ) models are already parameter-rich, we considered a maximum of 2 time lags in MAR models. The MAR  (2)  model can be written as where the two B (1) and B (2) matrices correspond to the two time lags. The independent cycling model has diagonal matrices B (1) and B (2). The full model has interaction matrices B (1) = b (1) 11 b (1) 12 b (1) 21 b (1) 22 and B (2) = b (2)   11 b (2)   12 b (2)   21 b (2).
22. The model was named "bottom-up", in order to designate a predator dynamics driven by that of its cyclic prey. When including time series x in a time series model for y improves the in-sample prediction of y (i. e. , reduces the residual variance), x is said to Granger-causes y  (Detto et al. , 2012; Granger, 1969). The concept is therefore interwoven with nonzero interaction coefficients between system components (here, predator and prey). Granger causality testing was done with grangertest() using a Wald test, in the R package lmtest.
, 2004). The effect of rain in May t+1 on ptarmigan population growth was negative and relatively strong, but not statistically significant at 95% (point estimate -0. 1596, 95% CI: F I G U R E 1 [-0. 3735; 0. 0541]). It is therefore possible that such a negative effect is present, but it does not appear clearly with the current dataset. We also fitted models where winter weather affects ptarmigan growth (Supporting Information Appendix S2), to test the idea that the survival of first-year chicks might be lower in harsher winters, but again we did not find consistent effects of weather on ptarmigan dynamics.
Models from rows 3 to 7 considered May temperature for ptarmigan, log(precipitation), or both. The models of rows 8 and 9 considered instead July and June temperatures as environmental variables for ptarmigan We therefore simulated 100 datasets using the fitted models (Figure  3 ) and examined their cross-correlations. These show that MAR(1) and MAR(2) models with reciprocal predator-prey feedback (full interaction matrices) outperform both the bottom-up model (medium reproduction of the cross-correlation pattern) and the null model (no reproduction of the cross-correlation pattern).
155 in  Lütkepohl, 2005). Using BIC, he found even 80% misclassified as MAR(1). Different model selection criteria gave different answers, and the BIC tended to be most conservative, but the baseline was that for n = 30, selection according to information criteria only gave inconsistent answers, while in most cases, the right model order was found for n = 100. Results were overall better with simulated bivariate MAR(1) models (p. 156 in  Lütkepohl, 2005) , where all model selection criteria were able to pinpoint the correct lag order at 90%. Such results, however, are likely to be model-structure and model-parameter specific (they depend on MAR coefficient values); therefore, we performed again such analysis for the models that we fitted (Section ). With the current length of our dataset n = 34, the important message from our simulations is that based on AIC or BIC, we are much more likely to mistake a fully interacting predator-prey system for a bottom-up system than the reverse.
research was funded by the Icelandic Institute of Natural History. FB was supported by Labex COTE (ANR-10-LABX-45). The Lake Mývatn Research Station provided field facilities. Field assistance was provided by J. Ó. Hilmarsson, G. Þráinsson, I. Petersen, H. Bárðarson, Ó. Einarsson, E. Ó. Þorleifsson, Ó. H. Nielsen, A. Ö. Snaeþórsson, Þ. Þ. Björnsson, and S. Nielsen. Many more people have helped in various ways with the field studies. These studies started out as ÓKN, Ph. D. , at Cornell University under the supervision of T. J. Cade. FB thanks J-A Henden for discussions on ptarmigan dynamics. AUTH O R CO NTR I B UTI O N SFB and ÓKN conceived the ideas; ÓKN collected the biological data and identified relevant abiotic variables; FB designed the models and analyzed the data; FB led the writing of the manuscript with inputs from ÓKN.
The siRNA sequences used for knockdown are (5'-3'): SET8 (GUACGGAGCGCCAUGAAGU) and MCM7 (UAGCCUACCUCUACAAUGA). Flow cytometry. Cells were fixed in 70% ethanol and stained with the indicated antibodies for 1 h followed by 1 h incubation with the secondary antibodies. DNA was stained using 0. 1 mg/ml propidium iodide containing RNase for 30 min at 37 °C. Flow cytometric analysis was performed on FACSCalibur using the CellQuest Pro software (BD). Data were analyzed using the FlowJo software (v7. 2. 2; Tree Star). The details of all primary and secondary antibodies used in this study are provided as Supplementary Tables  2  and 5. Cellular fractionation and chromatin isolation. To obtain soluble and chromatinenriched cellular fractions, cell fractionation was performed. In brief, cells were lysed for 10 min in a small volume of CSK buffer (0.
Images were recorded using the systems software and processed using Adobe Photoshop. Quantification of chromatin density was performed using ImageJ. Briefly, on each image 15 points/areas were randomly selected and pixel density was measured. Pixel density of the background was measured at five random image points outside the cell. Background density was subtracted from the measured chromatin density and values were normalized relative to chromatin density in control siRNA-treated cells set as 1 (Fig. 1h ). To avoid negative relative values, values in Supplementary Fig. 6b  were normalized to the lowest value (siSET8), which was defined as 1. FLIM-FRET measurements and analysis. FLIM-FRET experiments were performed in U2OS cells stably expressing H2B-GFP alone (U2OS H2B-GFP ) or with mCherry-tagged histone H2B (U2OS H2B-2FPs ).
TCSPC measures the time elapsed between laser pulses and the fluorescence photons. Enhanced green fluorescent protein (EGFP) and mCherry fluorophores were used as a FRET pair. The optimal two-photon excitation wavelength to excite the donor (EGFP) was 1 × 10 5 -5 × 10 5 photons/s. Fluorescence lifetime measurements were acquired over 60 s and fluorescence lifetimes were calculated for all pixels in the field of view (256 × 256 pixel). The analysis of the FLIM measurements was performed by using the SPCImage software (Becker & Hickl, GmbH). Because FRET interactions cause a decrease in the fluorescence lifetime of the donor molecules (EGFP), the FRET efficiency was calculated by comparing the FLIM values obtained for the EGFP donor fluorophores in the presence (U2OS H2B-2FPs ) and absence (U2OS H2B-GFP ) of the mCherry acceptor fluorophores.
FRET efficiency (E FRET) was derived by applying the following equation: E FRET ¼ 1 À τDA=τD ð Þ where τDA is the mean fluorescence lifetime of the donor (H2B-EGFP) in the presence of the acceptor mCherry-H2B in U2OS H2B-2FPs cells and τD is the mean fluorescence lifetime of H2B-EGFP (in the absence of acceptor) in U2OSH2BGFP cells that are present in the same field of view. FRET efficiency values were calculated from 20 to 30 cells and then normalized. Graphical representation was done using the GraphPad Prism software. Assay of transposase accessible chromatin sequencing. ATAC-seq was performed as originally described by Buenrostro et al. 15. Briefly, nuclei were prepared by spinning 50,000 cells at 500 × g for 5 min, followed by washing with ice-cold 1× PBS followed by centrifugation at 500 × g for 5 min.
The filtered bam files were converted to bed format using bedtools bamtobed (v2. 26. 0-92-g88cd6c5), and read start and stop coordinates were finally adjusted by +5 bp and -4 bp, respectively, to adjust for Tn5-binding properties as previously described  15. ATAC-seq peaks were identified individually for each set of data using macs2 (v2. 1. 1. 20160309)  60 callpeak broad -f BAMPE -t $f -g hs -q 0. 05, intersected using bedtools 61 multiinter -I, and regions positive in at least two sets were merged within 1 kbp of each other using bedtools merge -i 1000. Subsequent handling and visualization was done using EaSeq (v1. 05)  62. Values in scatter plots were quantified within a 1-kbp window surrounding the center of each region using the Quantify-tool, quantile normalized using the Normalize-tool, and averaged for all replicates in Microsoft Excel.