dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimated parameter (EP): The initial conditions are obtained with a data assimilation experiment that includes an augmented state so that both the state and parameters are estimated. Each forecast ensemble member uses parameter values taken from the corresponding estimated parameter ensemble. In this way, the ensemble represents the uncertainty in the initial conditions as well as the uncertainty in the optimal value for the parameters. The time-mean RMSE is used as a measure of the forecast skill. The evolution of the RMSE as a function of forecast lead time for the different experiments is shown in Fig. 5a. As expected, the best results are achieved by the PM experiment and the worst results Vol. 91, No. 2 Jounal of the Meteorological Society of Japan 92 by the IM experiment. The reduction in the error growth rate at the end of the 15-day period in the IM experiment suggests nonlinear saturation of errors.
Experiments using a method for parameter estimation based on the LETKF in a simple GCM were presented. Three parameters associated with the convective scheme of the GCM were estimated simultaneously with the state variables. Although the response of the model to perturbations in these parameters showed some nonlinearities, the LETKF could estimate the true value of the parameters in the absence of other model error sources. More experiments should be performed to investigate the performance of Kalman filter-based methods under stronger nonlinear responses to changes in the parameters and also their relative skill compared with other methods such as particle filter-based methods that are designed to account for nonlinearities. One important issue regarding parameter estimation using ensemble-based data assimilation is how to represent the uncertainty in the optimal parameters, which is not known a priori.
Our model provides a general framework to estimate costs and benefits and plan consequently. Uncertainty of future benefits may be one of the larger barriers to reserve formation. The simulation model, paired with local biological and economic data could reduce uncertainty regarding long-run financial benefits of a potential marine reserve. The tourism literature is rich with methodologies to estimate the priceelasticity of demand. The model parameter a 1 could be estimated in some cases. When decisions must be made quickly and data are lacking, literature estimates from similar locations may still provide useful information on a 1. In either case, information on divers price sensitivity paired with the simulation model can give critical information on potential revenues from reserve user-fees.
Acknowledgments We are grateful to  S. Case ,  J. De Bourbon Parme ,  M. Fiorese ,  J. Garrett-Cox ,  J. Jacquet ,  Y. Jarrar ,  B. Jenks ,  M. Jorge ,  S. Lindblad ,  L. Linden ,  A. Loesekrug-Pietri ,  P. McWhinney ,  O. Oullier ,  P. Patil ,  J. Plunkett ,  K. Rechberger ,  L. Schlesinger ,  N. Schwab ,  D. Shapiro , and the  Young Global Leaders' Fish Banks Task Force  for discussions and suggestions that greatly improved previous versions of this manuscript.
The Senescence Entry Point Marks the Sharp Transition to a Slowly Replicating State Early studies used standard microdissection techniques to investigate the RLS of individual yeast cells  (Egilmez and Jazwinski, 1989; Kennedy et al. , 1994; Bitterman et al. , 2003)  and showed that the average division time increased with generation number. This key observation suggested that mother cells undergo a progressive age-associated decline in cellular function and eventually reach a state of replicative senescence. To investigate the dynamics of cell division in aging yeast cells, we measured the division times of individual CLiC cells (using custom MATLAB software phyloCell; Supplemental Information). Analysis of division frequency as a function of generation number unexpectedly revealed that most cells underwent a sharp transition from a fast to a rapidly declining division rate (Figure  2A ).
We used our custom software phyloCell to delineate the contours of mitochondrial structures using the Tom70-GFP signal (Figures  3A  and 3B ) and then quantified the mean mitochondrial fluorescence levels of Tom70-GFP and preCox4-mCherry in individual cells (Figures  3C  and 3D ). Strikingly, we observed that, in some cells, the preCox4-mCherry fluorescence within the mitochondria eventually vanished (Figures  3A  and 3C ) but in other cells was maintained at consistently high levels throughout their lifespan (Figures  3B  and 3D ; see also Figure  S6  and Movie S3). The loss of mitochondrial preCox4-mCherry fluorescence was consistent with experiments in which cells were treated with ethidium bromide to induce mtDNA mutation (Figure  S7 ), as described previously  (Veatch et al. , 2009). We used a piecewise linear model  (Figure 3C)  to quantify the decline in mitochondrial fluorescence, which clearly distinguished between the subpopulations of cells that did and did not undergo a loss of DJ over their lifespan (referred to as DJÀ and DJ+, respectively).
We assumed that the probability of cell death per generation for a cell of replicative age n, referred to as q(n), increased quadratically with n: q(n) = q 0 3 n 2 (where q 0 is a constant; Figure  4A ), as proposed previously  (Gillespie et al. , 2004). Because the RLS of DJ+ and DJÀ cells were identical (Figure  S8 ), we used the same value of q 0 for both cell types. We implemented a stochastic simulation of a lifespan assay according to this set of rules in MATLAB (Supplemental Experimental Procedures) and found that the quadratic model for cell mortality fitted the experimental RLS curve very accurately, yielding a best fit for q 0 of 3 3 10 À4 (Figure  4B ). For the age-independent model, we assumed that the onset of loss of DJ occurred randomly according to a constant probability per generation p.
Image Analysis Raw images were processed using custom software, termed phyloCell, using MATLAB and the image-processing toolbox. This software is freely available upon request. See the Supplemental Experimental Procedures for a detailed description of the image processing and quantification pipeline. Petite Colony-Plating Assay Cells were grown overnight in liquid YPD media and the next morning were plated on yeast extract peptone ethanol containing 2% glycerol and 0. 1% glucose to monitor the formation of petite colonies, according to standard techniques. After 2 days of growth, the fraction of petites was scored by counting 200 cells from a triplicate experiment. The probability of petite formation per generation was calculated as described in the Supplemental Experimental Procedures.
ACKNOWLEDGMENTS We are very grateful to  Dan Gottschling  for providing mitochondrial markers used throughout this study. We acknowledge fruitful discussions regarding microfabrication with AndreEstevez-Torres,  Christian Rick , and  Hicham Majjad. We are grateful to  Daniel Riveline  for giving us the opportunity to use the  Institut de Sciences et d'Ingenierie Supramoleculaire cleanroom. We also thank  David Stillmann  and  Joseph Schacherer  for providing yeast strains and  Bertrand Se  ´raphin for sharing his tetrad dissection microscope. We thank  Zhou Xu ,  Teresa Teixeira ,  Michael Knop ,  Michel Toledano , and  Poonam Bheda  for insightful discussion and for a critical reading of the manuscript. We thank  Marc Koch  for imaging the microfabricated master wafer. This work was supported by the  French Agence Nationale Pour la Recherche  ( ANR PCVI-2008-003 ) and the  ATIP-AVENIR program.
aCGH data processing and analysis Resulting raw signals and log2 (ratio) profiles were normalized and centered according to their dye composition (Cy5/Cy3) and local GC content. These profiles were segmented with the Circular Binary Segmentation algorithm  [7]  through its implementation in the DNAcopy package for R v2. 8. 1 using default parameters. DNA copy number imbalances were detected considering a minimum of 3 consecutive probes and a minimal absolute amplitude threshold that was specific for each profile, accordingly with its internal noise. This specific internal noise was computed as one-fourth of the median of the absolute log2 (ratio) distances across consecutive probes on the genome. Of the 128 aCGH hybridizations performed, 17 were discarded: 7 due to their clinical annotations, 2 due to anomalies in their normal reference, and 8 due to the bad quality of their profile, resulting in 111 usable profiles.
Gene mutations analysis Sequencing was performed at IGR and at the Royal Institute of Technology (Stockholm, Sweden). DNA was extracted with QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). After PCR amplification of target exons, sequencing reactions were carried out using the BigDye® Terminator Cycle Sequencing Kit (Applied Biosystems, Forster City, CA). The primer sequences are available on request. Sequencing reactions were run on a 48-capillary 3730 DNA Analyzer®. Sequence analysis and alignment was performed with SeqScape® software (Applied Biosystems). All detected mutations were confirmed in at least one independent PCR reaction. In all 123 samples, full coding sequences of exons including oncogenic mutational hotspots were analyzed corresponding to: TP53 (NM_000546. 4) exons 5-8; KRAS (NM_004448.
Gene-expression data processing and normalisation All processing methods used for gene expression analysis were performed on the median signal from Agilent Feature Extraction raw data files using functions and packages collected in the R Bioconductor project  [9]  as well as custom written routines. For gene expression data, dye-swap arrays were first combined (by taking the average of intensities) to obtained only one array per condition. This combination has the result of centering the M values (log2ratios) on zero. Then, flagged spots as well as control spots were removed. Normalization was then performed using the normalize-WithinArrays function from R package LIMMA  [10]. For miRNA data, control spots were systematically removed, and flagged spots (gIsFeatNonUnifOL and gIsSaturated columns from raw files) were considered as missing values ("NA").
We then apply the cnvpack to identify recurrent CNA regions  [14] , defined as alterations found in at least 10% of the subjects in whom at least one abnormal region is found by MPSS. Genes / miRNAs in recurrent CNAs are chosen as candidate drivers. Step 2: Set up initial modules. First identify candidate drivers from CNA regions from Step 1, and compute pair-wise correlation between candidate driver and target genes. Each target gene is grouped with the highest significantly correlated candidate driver gene, using P < 0. 001. From this initial pairing, we know that genes within each module are all highly correlated with at least one candidate driver. Step 3: Find final modules. It is reasonable to assume that these target genes are also possibly correlated with other candidate drivers. In this step, we identify the correlation between several candidate drivers and the target genes in every module using the canonical correlation analysis (CCA).
Validation of expression data analyses For mRNA expression we used previously published NSCLC expression dataset GSE3141  [18]  from the Gene Expression Omnibus website in order to validate some of the results based on mRNA expression. The dataset comprised of 58 AC and 53 SCC samples, and mRNA was extracted from frozen tissue of primary lung tumors. The gene expression was measured on Affymetrix Human Genome U133 Plus 2. 0 Array, which we subsequently normalized using global normalization at the log-expression level. If a gene is represented by several Affymetrix probes, we take the average of the log-expression value to represent the gene. For miRNA expression we validated our results using Ming You et al's data GSE29135  [19].
Validation of candidate driver genes For the candidate driver genes we also attempted to verify the CNA status and the correlation of CNA and expression in an independent dataset previously published by Chitale et al. [20]. Frequency of CNAs was validated in 199 AC cases hybridized to Agilent 44K CGH arrays, where probes were less dense than the 244K arrays used in our sudy. Of the 199 samples, 102 were hybridized to the HG-U133A 2. 0 Affymetrix oligonucleotide arrays. The robust multichip average method, which outputs log2-transformed expression values, was used to summarize probe measurements into one measurement for each probe. We assessed copy number status of the 500 kb region surrounding each driver gene. On average, 20 probes were targeted at the driver gene region. A region was considered as amplified if its regional averaged probe intensity was significantly higher than zero, which was the global mean after standardization of the LogRatio intensities within samples.
We validated this result using Bild et al's NSCLC data GSE3141  [18] , where the  15  Candidate targets expression: rest of genes/miRNAs + Input: Figure  1  Flowchart of DGS algorithm. Driver and targets are identified in a three step process, as shown. Candidate drivers are firstly selected from genes/miRNAs that reside in copy-number altered (CNA) regions and filtered by various procedures, for example based on fold-change and consistency between expression level and copy number status. The rests of genes/miRNAs are candidate targets, which are grouped based on correlation with the candidate drivers. Correlation between all drivers and targets in each module is highlighted using sparse canonical correlation analysis (SCCA). an AUC of 92%. We performed a Monte-Carlo test by taking 1000 random selections of 15 genes and considered each as a signature.
As shown in these figures, the corresponding distributions of these markers in Bild et al's NSCLC data GSE3141  [18]  were largely similar to those in our data. The list of the genes and the probes from both platforms are given in Additional file 1: Table  S4. SPP1, CTHRC1 and GREM1 are candidate biomarkers to identify the cancer. Additionally, based on the AUC values in Table  2 , SPINK1 and BMP7 would help to distinguish AC from SCC. The non-secreted IHC biomarkers can be used for annotation of small biopsies in order to identify the cancer and, except for GCNT3, they are informative for distinguishing AC from SCC.
We then compared the mRNA expression in samples with vs those without altered genes, and selected four candidate driver genes with fold-change > 4 and P < 0. 001: MRPS22, RNF7, NDRG1, FAM49B; see Additional file 1: Figure  S5. Next we assessed their predictive value in predicting tumor histology. The individual AUCs using of these four genes were 0. 81 (Pvalue = 0. 02), 0. 75 (0. 05), 0. 73 (0. 07), 0. 40 (0. 76), respectively; see Table  3. The Pvalues were obtained from a Monte-Carlo test by computing the AUCs of 1000 random genes. We validated this result using Bild et al's data GSE3141, where the candidate driver genes achieved AUCs of 0. 74 (Pvalue = 0. 04), 0. 81 (0. 01), 0. 72 (0. 05), 0. 40 (0. 77). Thus MRPS22, RNF7 and NDRG1 were significant predictors of histology, but FAM49B does not appear to be specific to any histological type.
MRPS22 was over-expressed in 78% and amplified in 16% of the SCC tumors, but showed little amplification or over-expression in AC (Additional file 1: Table  S5 ). RNF7 and NDRG1 showed more amplification and mRNA over-expression in SCC compared to AC. Next we used an independent data set published by Chitale et al's data  [20]  to get a bioinformatics validation of the potential driver genes. To assess the copy-number alteration profiles, Chitale et al. used Agilent 44K CGH arrays, which are much less dense than the 244K arrays in our study. Because the sensitivity of CNV detection algorithms is limited by the resolution of the array, we decided to validate the frequency of copy number gains for the candidate driver genes directly, as well as their properties, including the number of correlated genes and relationship between the copy number status and gene expression.
We kept only those miRNAs that (1) exhibited up-regulated expression in samples with amplifications compared to non-mutated samples, based on P < 0. 01 using one-sided Welch t test; vice versa for samples with deletions, and (2) showed over-expression on average in tumors with amplifications, and under-expression in tumors with deletions. Using these requirements, three miRNAs hsa-miR-16-2*, hsa-miR-570 and hsa-miR-944 with recurrent copy number gains were found, and one miRNA hsa-miR-31* with recurrent copy number losses was identified as a final list of candidate driver miRNAs. Next we assessed the predictive ability of the four potential driver miRNAs in classifying tumor histology; Table  3. Only hsa-miR-944 showed a significant AUC of 88% (Pvalue = 0. 001 from the Monte-Carlo test). We validated this result using Ming You et al's data GSE29135  [19] , where hsa-miR-944 achieved a median AUC of 78% (Pvalue = 0.
13 For the HADH sequencing, 100% percent of the targeted 93,929 bases were covered with at least 30 reads and had an average coverage of 1,2073. For the ABCC8 sequencing, 99. 6% percent of the targeted 116,738 bases were covered with at least 30 reads and had an average coverage of 9693. GATK was used for calling SNVs and indels. 14 e identified 123 homozygous variants in the proband with reduced HADH activity and homozygosity across HADH (RefSeq accession number NM_005374. 4). We then removed SNPs present in dbSNP132 and the 1000 Genomes Project (November 2011 version), indels that occurred in homopolymer tracts, and variants that were homozygous in the unaffected sibling (Table  S1 , available online). This left two intronic HADH variants and an intergenic change within the chromosome 4 region, and none of these occurred at highly conserved bases (the genomic evolutionary rate profiling [GERP] scores were <0).
Sequencing analysis of an unaffected individual homozygous for the wild-type allele demonstrated normal splicing of exons 5 and 6. (B) Sequencing analysis of cDNA from a parent with the heterozygous c. 1333-1013A>G ABCC8 mutation identified a 76 bp pseudoexon between exons 8 and 9 of ABCC8. This pseudoexon results in the addition of 25 amino acids, a shift in the reading frame of exon 9, and a premature termination codon after residue 4 of exon 10. that analyzing noncoding regions of known genes by targeted NGS might prove useful before starting to search for mutations in unknown genes. The contribution of deep intronic mutations to the overall mutation burden has yet to be determined given that the current estimate of 0. 9% (Human Gene Mutation Database) is based on partial ascertainment.
Acknowledgments Bayarchimeg Mashbat provided technical assistance, and  Hana Lango Allen  provided bioinformatic support. We are grateful to  Lorna Harries ,  Jonathan Locke , and  Alice Holley  for their assistance with the RNA studies. We also thank  Konrad Paszkiewicz  and colleagues at the  University of Exeter Sequencing Service. The research leading to these results was funded by the  Medical Research Council and Diabetes UK. Professor Peter T. Clayton  is funded by  Great Ormond Street Hospital Children's Charity.
These fluxes are then sent back to the atmosphere with the addition of radiative terms like surface temperature, surface direct and diffuse albedo, and surface emissivity. The above information transferred to the atmosphere from the land surface provides the lower boundary conditions for the radiation and turbulent schemes in an atmospheric model coupled to SURFEX or forced by SURFEX output. In SURFEX, each grid box is made up of four adjacent surfaces: one for nature, one for urban areas, one for sea or ocean and one for lake, identified by the global ECOCLIMAP land database. The SURFEX fluxes are the average of the fluxes computed over nature, town, sea/ocean or lake, weighted by their respective fraction. The assimilation system at NILU is illustrated in Fig. 4  with reference to the EnKF.
In situ snow data assimilation is performed operationally (see Sect. 4. 7 below), usually with simple assimilation techniques. An example where both the snow state and parameters were estimated using an EnKF in a 1-D setting is given by  Su et al. (2011). A number of point-or single-grid-scale studies have tried to relate brightness temperature data to snowpack characteristics  (Durand et al. 2008; Andreadis et al. 2008) , in preparation for T b assimilation. Many of these studies highlight the large sensitivity of snowpack estimates to model parameters  (Davenport et al. 2012) , which makes both forward simulation and inversion of T b observations for SWE estimation a difficult task.
2004). Operational land data assimilation has initially focused on ingesting precipitation observations (e. g. ,  Saha et al. 2010; Reichle et al. 2011 ), but improved snow and soil moisture state updates are now emerging, as documented, for example, for the ECMWF Integrated Forecasting System by de  Rosnay et al. (2012a). An unprecedented operational land data assimilation product will be provided by the Global Modeling and Assimilation Office (NASA GMAO) in the form of a level 4 satellitebased soil moisture product  (Reichle et al. 2012; De Lannoy et al. 2013). The assimilation of SMAP brightness temperatures into the Goddard Earth Observing System land surface model will yield a global root zone soil moisture product.
Land Surface Models • There is a need to use advanced methods to describe physical processes (this limits structural uncertainty) and couple land surface models with models describing more specialized processes such as run-off routing, dynamic vegetation or snow  (Pauwels et al. 2006 ); • There is a need for consistent global parameter datasets to limit predictive uncertainty due to parameter uncertainty; • There is a need for high-quality forcing data (this limits input uncertainty), mainly for precipitation  (Maggioni et al. 2011; Reichle et al. 2011).
) a land surface model (SURFEX model developed at Meteo-France, Le Moigne 2009); (3) observations; (4) the observation operator; and (5) error characteristics for the model and the observations. The SURFEX model used at NILU (and at Meteo-France) can be run in uncoupled or coupled mode. It includes the following elements: • A soil and vegetation scheme: ISBA and ISBA-A-gs; • A water surface scheme: COARE/ECUME (Coupled Ocean-Atmosphere Response Experiment/Exchange Coefficients from Unified Multi-campaign Estimates) for the sea; FLAKE for inland water; • Urban and artificial areas: Town Energy Balance-TEB model; • A surface boundary layer (SBL) scheme; • Chemistry and aerosols; • A land use database: ECOCLIMAP.
Health economics While remote monitoring (RM) may be able to reduce the time spent on device follow up it is not clear whether this relates to an overall reduction in costs. RM has its own costs including the cost of the transmitter, the setup and maintenance of the central server and database, patient and clinic staff education and staff time to read, interpret, import information into electronic medical records and act on transmission events/problems. The frequency of in office visits and the frequency of RM transmissions, proximity of the patient to the clinic and many other factors will affect the economic modeling as to the potential cost savings associated with RM. Beyond assessing the simple economic modeling is the assessment of cost effectiveness which also needs to consider the improvement in patient outcomes as well as the costs involved for each form of follow up.