dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of BRAF fusions was confirmed by western blot analysis using antibodies directed against the C-terminal part of BRAF and against p-BRAF S445. Aberrant bands, uniquely present in the 6 cell lines with BRAF fusions, were observed at the molecular weight matching the fusions products predicted from the RNA-seq data (Figure  2b , S2c Table  S5  and S7 ). Transfection of the cell lines harboring BRAF fusions with a pool of siRNA targeting the C-terminal-encoding portion of the BRAF gene led to marked decrease in cell viability, indicating 'oncogene addiction' to the fusions (Figure  S2c ). All cell lines with BRAF fusions showed activation levels of the MAP-kinase signaling pathway comparable to melanoma cell lines with BRAF V600E mutations, which -in the absence of other mutations in the MAP-kinase pathway -indicates that BRAF fusion kinases are functional.
The target genes were curated to comprise common cancer genes with particular relevance to melanoma. Captured libraries were sequenced as paired-end 100-bp reads on a HiSeq 2000 or HiSeq 2500 instrument (Illumina, San Diego, CA, USA). Sequence reads were mapped to the reference human genome (hg19) by use of the Burrows -Wheeler aligner (BWA)  (Li and Durbin, 2010). Recalibration of reads and variant calling were performed with the Genome Analysis Toolkit  (McKenna et al. , 2010). Coverage and sequencing statistics were determined with Picard CalculateHsMetrics and Picard CollectInsertSizeMetrics (http:// broadinstitute. github. io/picard) (See Table  S7 ). Variant annotation was performed with Annovar  (Wang et al. , 2010). Copy number variations were inferred with the use of CNVkit version 0.
8. 5  (Talevich et al. , 2016)  (https://github. com/etal/cnvkit). Allelic imbalance of single-nucleotide polymorphisms was used to call loss of heterozygosity as previously described  (Shain et al. , 2015). For fusion detection, read pairs with one or more reads unaligned, with insert sizes of >1000 bp or with soft clipping of at least one read were extracted and realigned by the use of BWA-SW  (Li and Durbin, 2010)  and used as input to CREST  (Wang et al. , 2011). Structural variants predicted by CREST were reviewed by visual inspection in the Integrative Genomics Viewer  (Thorvaldsdóttir et al. , 2013). We predicted the resulting fusion transcripts by joining the exon directly upstream from the genomic breakpoint with the exon directly downstream. Protein sequences were then determined from the predicted transcripts and molecular weight was estimated by the Compute pl/Mw tool from ExPASy.
In the absence of matching normal, the number of mutation per megabase was estimated by filtering out common SNPs listed on the 1000 Genomes Project, the Exome Sequencing Project 6500 and a list of sequencing artifacts recurrently detected in normal samples run on our platform from the list of point mutations detected by Annovar on targeted DNA-seq and dividing this number by the footprint of the sequencing library. UV signature was inferred by quantifying the percentage of C>T and G>A transitions in mutated genes as previously filtered. RNA sequencing-Cell line RNA was extracted using the RNeasy kit (Qiagen, Germantown, MD, USA) according to the manufacturer's protocol. RNA sequencing of mRNA transcripts from C022 and C037 was performed by the commercial service center Macrogen (Seoul, Korea) as previously described  (Ju et al.
, 2011). RNA sequencing of mRNA transcripts from C0902 was performed by Genewiz, Inc (South Plainfield, NJ, USA). For other lines, double-stranded cDNA was synthesized with the NEBNext mRNA Library Prep Reagent Set for Illumina (New England Biolabs, Ipswich, MA) with up to 250ng of input total RNA. Library preparation was performed with the TruSeq RNA Access Library Prep kit (Illumina, San Diego, CA, USA) according to the manufacturer's protocol. Sequencing was performed on the HiSeq 2500 instrument (Illumina) with paired-end 100-bp reads. Sequence reads were mapped to the reference human genome (hg19) and transcriptome with BOWTIE2  (Langmead and Salzberg, 2012) , and fusions were identified by FusionCatcher  (Nicorici et al. , 2014)  or TopHat-Fusion  (Kim and Salzberg, 2011)  and reviewed by visual inspection in the Integrative Genomics Viewer.
Predicted protein sequences were then determined from the detected transcripts. NanoString Assay for AGK-BRAF fusions-Detection of AGK-BRAF fusion transcripts using NanoString nCounter technology (NanoString Technologies, Inc. , Seattle, WA) was performed following manufacturer's instructions. In brief, after incubation of total RNA with nCounter probe sets, samples were processed in an automated nCounter Sample Prep Station (NanoString Technologies, Inc. , Seattle, WA). nCounter Cartridges (NanoString Technologies, Inc. ) containing immobilized and aligned reporter complexes were subsequently imaged on an nCounter Digital Analyzer (NanoString Technologies, Inc. ). Reporter counts were collected using NanoString's nSolver analysis software. Western blotting-Cell lysates were prepared in RIPA buffer supplemented with Halt protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific, Waltham, MA, USA).
, 2006; Tucker, 2009). Hierarchical analysis-In Figure  3 , hierarchical analysis of the results was obtained by complete linkage clustering based on Spearman rank correlation of all data points using Cluster 3. 0. Dendrogram was generated with Java TreeView 1. 1. 6r4. Western blot quantification-Western blot quantification was performed using Studio Lite 5. 2 (LI-COR Biosciences, Lincoln, Nebraska, USA). Isobologram and synergy scores-In Figure  7e , S7e and S7g, isobologram analysis and synergy scores were obtained using Chalice Analyzer Online http:// chalice. horizondiscovery. com/analyzer-server/cwr/analyze. jsp (Horizon Discovery, Cambridge, UK).
DATA AND CODE AVAILABILITY The RNA and DNA sequence data have been deposited in the GenBank sequence read archive (SRA) under ID code SRP118152. a) The bar graph shows varying levels of RAS-GTP in serum-starved cells with BRAF fusions (red labels), BRAF V600E (A375, SK-MEL-28) or KRAS G12A mutation (M418). RAS-GTP levels shown are the mean ± SEM of three independent experiments normalized to 293FT cells. Western blots from one representative experiment are shown below. b) Primary HRAS -/-;NRAS -/-;KRAS lox/lox MEFs stably transduced with the NUDCD3-BRAF fusion that contains a dimerization domain were propagated in the presence of 600 nM of 4OHT or vehicle for 3 weeks. Western blot analysis was performed on lysates from serum-starved cells treated for 1 hour with 0, 1 or 3 μM vemurafenib. c) Subcellular fractionation of serum starved C037 cells treated with 1 μM of vemurafenib or 100 nM of LY3009120 show no change of cellular location of the BRAF fusion protein under conditions inducing paradoxical activation.
Acknowledgements This research was supported in part by the  National Institutes of Health  Grant  P01 CA025874  and the  Melanoma Research Alliance Established Investigator Award. The cell line establishment program from the  Wistar Institute  was supported by  The Dr. Miriam and Sheldon G. Adelson Medical Research Foundation. T. B. is a recipient of the  Franco-American Exchange Prize  from  Philippe Foundation Inc  and of a grant from the  Fondation ARC pour la recherche sur le Cancer, France.
Predictive model All modeling and statistical analysis were conducted in (MatLab v. 9. 0, Natick, MA, USA). Our modeling approach involved two steps that are also detailed in Appendix S1. First, we developed a process model that produced an annual fishery recruitment index by summing the fractional contributions of several settlement year-classes as they enter the fishery some 5-10 yr after settlement. This involved the novel approach of scaling the natural mortality term (Fig. 2b ) and logistic growth function (Fig. 2c, d ) to area-specific conditions. The process model also employed a Monte Carlo method to estimate uncertainty in the annual recruitment index using observed variability in settlement index and growth rates for each study area. Next, we employed a validation step that statistically compared the predicted recruitment time series to observed landings.
C ommunication Maine study areas portend a downturn in future landings, and continued low settlement in southern New England gives little sign of recovery in the near future. Our out-of-sample skill assessment helps test the performance of the model in forecasting future change. Excluding the last four years of data in our outof-sample skill assessment did not dramatically affect model performance for most study areas, except for northern Massachusetts, where the model underpredicted landings in the last 3 yr (Fig. 3 ). For example, using the model with settlement data up to 2013 we underpredicted landings for Beaver Harbour, New Brunswick, and overpredicted landings for Jonesport, Maine, but including the more recent settlement data has modified that prediction to align with observed landings.
DISCUSSION This study describes an enhanced model developed on the premise that year-class strength measured at larval settlement, and key environmental indicators influencing demographic rates, can provide useful predictions of subsequent fishery recruitment several years later. To tune the model locally, we used temperature variability and shell disease prevalence to parameterize growth and mortality rates, respectively. Of 10 study areas spanning New England's steep latitudinal temperature gradient, our hindcast validations produced model output for nine areas that significantly correlated with observed landings. We suspect the absence of a significant recruitment index-to-landings relationship for York, Maine, is related to the short length of the settlement time series and the minimal contrast in the landings data (Appendix S1: Fig.
ACKNOWLEDGMENTS The authors acknowledge  NSF   Coastal   SEES OCE1325484 ,  NSF   CBET1360415  and # 11A-1355457 ,  NOAA-FATE ,  Rhode Island Department of Environmental Management, Massachusetts Division of Marine Fisheries, Maine Department of Marine Resources, DFO Canada , and the  Atlantic Coastal Cooperative Statistics Program. The manuscript benefitted from helpful discussion and editing by  M. Baskett ,  Y. Chen ,  C. Guenther ,  L. Jacobson ,  B. Shank ,  R. Steneck , and two anonymous reviewers.
Mail survey design and sampling frame We used survey data responses collected by the 2012 Maine Landowner Survey, a mail survey of landowners from the 30 study area towns. We developed the sampling frame for our two focal metropolitan areas using data provided by local municipalities. We defined the frame as all towns with available digital property attribute and boundary data. Within these towns, we drew our sample from land parcels that were greater than or equal to 1. 2 ha (3 acres) and less than or equal to 404. 7 ha (1000 acres) and were held by individual or family landowners. We excluded parcels owned by limited liability companies, commercial and industrial owners, land developers, and timber companies. Notably, our sampling frame includes agricultural, residential, and small woodlot landowners, reflecting the broad mix of owners that is representative of the human population within these urbanizing forested landscapes.
70  (VanderLugt, 2013; Nunally and Bernstein, 1994). Using the final set of factors from the factor analysis, we conducted a k-means non-hierarchical cluster analysis using the SAS FASTCLUS procedure. The FASTCLUS procedure minimizes the sum of squared Euclidean distances from the cluster means and sorts each observation into only one cluster  (SAS Institute, 2015). We explored 2-, 3-, 4-, 5-, 6-, and 7-cluster solutions, choosing the solution that produced the most distinct clusters with maximized within-cluster homogeneity. We compared descriptive statistics of landowner demographics, parcel characteristics, and past land management behaviors of each cluster to better understand the differences among the clusters.
g. , financial gains; other nonmarket returns related to personal beliefs, values, and objectives; costs), and assigned greater intentions to actions associated with higher net returns  (Greene, 1992). Following previous research, we explained differences in stated intentions using (i) binary-dummy variables (1/0) representing membership in our landowner classes, (ii) a set of variables describing parcel characteristics, and (iii) an additional set of variables describing landowner demographics not included in the landowner classification process (Table  S2 ). We derived parcel characteristic variables using municipal property information and ArcMap 10. 2 GIS software. We combined a geodatabase coverage of land parcels owned by survey participants with spatial data layers from the Maine Office of GIS and the National Land Cover Database (NLCD).
We controlled for the 30 communities using 29 binary dummy variables or fixed effects; these variables control for unobserved community factors that may influence landowner behaviors, such as zoning requirements, comprehensive plans, and other town regulations. We estimated the ordered logit models using maximum likelihood estimation methods (SAS 9. 4. logistic procedure) and, for the purposes of consistency within this study, use the same set of explanatory variables (Table  S2 ) in all seven models.
Many of these measures cannot be easily extracted from GIS data layers or Census datasets. As knowledge of the dynamics of coupled natural and human systems, environmental change, and effectiveness of conservation programs advances, the data needs of practitioners are changing. Landowners are complex, and they, together with biophysical processes, greatly influence landscape and conservation outcomes. Continued data advances focused on the human systems of urbanizing forested landscapes  (Butler et al. , 2007)  have great potential to strengthen the representation of coupled systems and ultimately better support conservation researchers and practitioners. While we are encouraged by our findings, we do recognize several limitations of our study. Sample selection bias could be influencing our classification results.
Acknowledgements This work is supported by  National Science Foundation  (grant number  1313627 ) at the  University of Maine  and by the  Maine Agricultural and Forest Experiment Station  No. 3192. We thank  Judy Colby-George ,  Blair Vanderlugt , and  Michael Quartuch  for their work on the Maine Landowner Survey. We also thank  Erik J. Nelson ,  Aram J. K. Calhoun , and  Malcolm Hunter, Jr. for reviewing and improving this manuscript.
In HEK293T cells transfected with nuclease, we observed a significant 10% increase in cells displaying a 2 O/3 G profile compared to non-transfected (NT) cells (17% of cells transfected with nuclease with a 2 O/3 G profile compared to 7% of NT cells with a 2 O/3 G profile, p < 0. 05) (Fig. 3a ). A similar result was observed in HEK293T cells transfected with nuclease and ssODN (18% of cells with a 2 O/3 G profile). These results    The data are from independent experiments. Statistical significance is inferred on raw data using two-tailed unpaired t test for UROS-specific activity. ***p < 0. 001. For a, c, source data are provided as a Source data file could be consistent with a terminal deletion of Chr10 downstream of the UROS locus induced by nuclease-mediated DSB. To confirm this hypothesis, we next hybridized the cells with one Chr10q sub-telomeric orange probe (Supplementary Figure  3a ).
We confirmed the complex pattern of Chr10 in HEK293T as revealed by a large analysis of a higher percentage of cells with only two Chr10q probes compared to nontransfected cells (9% in NT versus 22. 6% and 20. 1% in cells transfected with nuclease only or nuclease with ssODN, respectively, Supplementary Figure  4a ). As previously, we then focused on HEK293T cells apparently trisomic for Chr10. To do so, we used another couple of probes: sub-telomeric probes significance is inferred on raw data using two-tailed unpaired t-test for UROS-specific activity. ns, not significant. For (a, c), source data are provided as a Source data file at Chr10p-arm (labeled in green) and another one at Chr10q-arm (labeled in orange) (Fig. 3b ) and focused on cells with a FISH profile with three green signals (3 G).
These data highlight the high genomic instability at the DNA break point with megabase-scale deletion or gain. Altogether, these results strongly suggest that the editing strategy using a single DSB in one chromosome may induce megabase-scale chromosomal damage. To explore whether a similar effect occurs in other cell types, we transfected the K-562 hematopoietic cell line with nuclease and analysed outcomes using UROS-framing probes. Similarly, we found that nuclease transfection induced a significant increase in cells with only one signal of the probe distal to UROS (+ 11%), suggesting that chromosomal terminal deletion induced by nuclease-mediated DSB is not cell type-dependent (Supplementary Figure  4b ). HEK293T and K562 cell lines have a very complex and unstable karyotype and are deficient in p53 activity.
An Illumina MiSeq instrument (Illumina®) was used for high-throughput sequencing. The average depth of each genome analysis was 10,000. Quality of paired-end reads was checked with FastQC (Galaxy, https://usegalaxy. org/). Then, data were analyzed using the CRISPR Data Analysis and Visualization (CRISPR-DAV) pipeline  70. Cytogenetic examination of chromosome 10. FISH was performed on interphase nuclei of HEK cells or K-562 cell line, with probes targeting the following regions on chromosome 10: centromeric region (XCE 10 probe, labeled in orange) (MetaSystems Probes, Altlussheim, Germany), locus 10q26. 11 (BAC RP11-79M19 probe, labeled in green) (Empire Genomics, Buffalo, NY, USA), locus 10q26. 2 (BAC RP11-31M22 probe, labeled in orange) (Empire Genomics, Buffalo, NY, USA), sub-telomeric regions (p-arm and q-arm sub-telomere probes, respectively.
Acknowledgements We thank  Dr Feng Zhang  for sharing plasmids through  Addgene (Cambridge, MA, USA)  and  Vincent Pitard  from the cytometry platform at  Bordeaux University. This work was supported by the  Association Française contre les Myopathies  Grant #  20957 , and the  Agence de Biomédecine. We thank  Florence Lichou  ( Inserm U1218 ) and  Nicolas Sevenet  ( Institut Bergonié, Bordeaux, France ) for NGS analysis,  Julie Bouron  for array-CGH analysis (Génétique médicale,  CHU Bordeaux, France ). Katia Obama  and  Isabelle Lamy  are thanked for administrative assistance and financial management.
Data Availability All next-generation sequencing data sets have been deposited in the NCBI database under BioProject accession no. PRJNA521053 with associated BioSample no. SAMN10877893 for HEK293T NT and BioSample no. SAMN10877894, no. SAMN10877895 and no. SAMN10877896 for HEK293T respectively transfected with Nuclease + 181nt-ssODN (Fig. 2b ), Nickase + 181nt-ssODN (Fig. 4b ) and Nickase + 80nt-ssODN-A647 (Fig. 4d ). Reads are available at Sequence Read Archive database (accession number no. SRR8534354, no. SRR8534355, no. SRR8534352 and no. SRR8534353 for HEK293T NT and HEK293T respectively transfected with Nuclease + 181nt-ssODN, Nickase + 181nt-ssODN and Nickase + 80nt-ssODN-A647). The data that support the findings of this study are available from the corresponding author upon reasonable request.
After each trial the arena and objects were cleaned using 70% ethanol. The preference score (%) for OR the task quantifies the degree of preference the mouse shows for the moved object and was calculated as ([exploration time of the moved object]/[exploration time of both objects]) × 100 from the data in trial 2. The Y-Maze task was used to measure working memory by quantifying spontaneous alternation between arms of the maze. The Y-maze consisted of three arms at 120° and was made of light grey plastic. Each arm was 6-cm wide and 36-cm long and had 12. 5-cm high walls. The maze was cleaned with 70% ethanol after each mouse. A mouse was placed in the Y-maze and allowed to explore for 6 min. A mouse was considered to have entered an arm if the whole body (except for the tail) entered the arm and to have exited if the whole body (except for the tail) exited the arm.
If an animal consecutively entered three different arms, it was counted as an alternating trial. Because the maximum number of such alternating triads is the total number of arm entries minus 2, the spontaneous alternation score (%) was calculated as (the number of alternating triads)/(the total number of arm entries -2) × 100. Behavioral data for the OR and Y-maze tasks was quantified in two ways. First, the position of the nose and body of the mice was traced using Viewer III software (Biobserve, Bonn, Germany), and these positions were used together with the criteria described above to automatically score behavior. In addition, the videos were manually scored, again using the criteria described above, by a blinded experimenter. Automated tracking and manual scoring yielded similar results across all groups, so we report the automated tracking results.
Statistical Analysis Behavioral data and measurements of changes in vessel diameter and blood flow were plotted using Tukey boxplots, where the box contains the middle two quartiles of the data, the whiskers extend 1. 5 times the difference between the 75th and 25th percentiles, the black line defines the median, and the red line defines the mean, which is calculated excluding any outliers that sit outside the whiskers. Normality was tested with the D'Agostino-Pearson test and the data for each measurement of each group was found to be normally distributed. The statistical significance of differences between groups was evaluated by one-way analysis of variance (ANOVA) followed by pairwise comparisons using the multiple-comparison corrected Holm-Šídák test. To compare baseline blood flow (Fig.
The measurements presented in this paper use proton-proton (pp) collision data samples collected by the LHCb experiment, corresponding to an integrated luminosity of 3. 0 fb -1 , of which 1. 0 fb -1 was recorded at a center-of-mass energy of 7 TeV and 2. 0 fb -1 at 8 TeV. The LHCb detector  [22, 23]  is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The tracking system provides a measurement of the momentum, p, of charged particles with a relative uncertainty that varies from 0. 5% at low momentum to 1. 0% at 200 GeV/c. The minimum distance of a track to a primary vertex (PV), the impact parameter (IP), is measured with a resolution of σ IP = (15 + 29/p T ) µm  [24] , where p T is the component of the momentum transverse to the beam, in GeV/c.
Thus, the free parameters in the fit are r Hc , as shown in Eq. (  1 ), and τ D + fit. In the Ξ 0 c decay-time fit, β 0 is scaled by 4/3 since the effect scales with the number of charged final-state particles in the H c decay  [38]. The procedure for determining the optimal values of τ Hc sim to use in forming the templates f (t rec ; τ Hc sim ) is first developed and validated using simulation. A series of templates, f i (t rec ; τ Hc sim ), spanning a wide range of τ Hc sim values is produced for each charm baryon. From one template with true lifetime τ Hc,true sim , a pseudo-dataset set of decay times is formed that has comparable yield to that of the data. The decay-time fit is then performed using each template f i (t rec ; τ Hc sim ) to this pseudo-dataset, with each fit yielding a value of τ fit and a χ 2 of the fit.