dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation of fixed effects and variance parameters by maximum likelihood is in widespread use in mixed model software packages, such as the R package ''lme4''  [51]. Mathematical notation. We denote fixed effects by Greek letters (a,b,…), with s being reserved for standard deviations; roman letters (u,v,…) are used for the random effects. We describe a general three level model with individuals (indexed by i) being nested within groups (index by j), which again are nested within population. For computational reasons we only allow random effects to occur at the individual level, although in principle a full hierarchy of effects is possible. There exist several general guidelines about which parameters in a hierarchical model should be taken as random effects  [14, 52] ; our considerations are mostly pragmatic, aimed at both obtaining parameter estimates instrumental for understanding the system investigated and facilitating rapid and stable estimation of parameters.
The latter (used in ADMB-RE) is obtained by maximizing the integrand f(data ij ,u ij ;h) in Eq. 2 with respect to u ij , with h fixed at its maximum likelihood estimate. The ''borrowing strength'' aspect of EB is that the standard normal prior placed on u ij ''pulls'' its estimate toward zero (the population value) to an extent that depends on s u , which is estimated from the full dataset.
7). All fish in a cohort experience the same population density in the first year of life, thus we can intuitively think of the cohort effect as including other factors beyond early density affecting growth that are largely shared by the cohort, such as temperature at emergence/first stages of life (although the cohort effect is categorical, while the density effect is continuous). We treated predictors as fixed effects for two reasons. First, introducing predictors as random effect is computationally more demanding in ADMB than using fixed effects, in the sense that run times for parameter estimation are substantially longer. This drawback is greatest when thousands of individuals are included in the dataset, as in the case of the marble trout population of Gacnik. Second, treating a factor with just a few levels as random factors may generate imprecise estimates of the associated standard deviation  [52].
We then investigated correlation between the EB estimates of L ? and k at the individual level and of cohort-specific mean L ? and k when cohort was a predictor of both L ? and k (e. g. following Eq. 7, k (j) ~exp(a 0 za 1 (j) )). We used simulated data to test whether a significant correlation may emerge as an artifact of the algorithm for parameter estimation. Specifically, we simulated growth data with a randomly drawn correlation r between L ? and k at the individual or cohort level and we then tested whether the empirical correlation between estimates of L ? and k at the individual or cohort level obtained using the model fitting procedure in ADMB-RE was equal (or very close) to r. We also tested whether there were noticeable differences in vBGF cohort-specific models when estimating parameters separately for each cohort using a standard non-linear regression routine with no random effects (nls function in R  [77] ) or using ADMB-RE.
We carried out this analysis in order to determine whether the fitting of a random-effects model is recommended even when only mean growth trajectories at the group level are needed, thus in the case when the fitting of a standard non-linear regression model may represent a theoretically viable procedure. Predicting missing data. We tested the predictive ability of the best vBGF model (after AIC selection) as follows. For each population, we: (i) identified fish that were sampled more than 3 times; (ii) randomly sampled one third of them (validation sample); (iii) deleted from the data set all observations except the first one from each individual fish in the validation sample; (iv) estimated the parameters of the vBGF for each individual including those in the validation sample; and (v) predicted the missing observations.
Supplementary information and code In Supporting Information we provide (i) tests of correlation between individual and mean cohort-specific L ? and k using simulated datasets (fig. S1  and table  S3 ), (ii) the mean estimate and confidence intervals for parameters of the best models (table  S2  and table  S3 ), (iii) cohort-specific growth trajectories (fig. S2 ), (iv) derivation of the correlation between parameters of the vBGF under size-dependent mortality and description of potential processes leading to a negative correlation between L ? and k (text S1), (v) confidence bands estimated using a MonteCarlo algorithm (fig. S3 ), (vi) a comparison with JAGS and the nlme function in R (text S2), (vii) results of a repeatability analysis of body size throughout the lifetime  [78, 79]  (text S3), and (viii) details of the Empirical Bayes algorithm (text S4).
Estimates of parameters For each vBGF model we tested, we obtained convergence of the algorithm for parameter estimation in ADMB, and the data used for the estimation of the parameters were well predicted by the models (for the model with no predictors except individual random effects: Zakojska, R 2 = 0. 97, MAE = 9. 58 mm; Gacnik, R 2 = 0. 98, MAE = 6. 82 mm). We obtained consistent parameter estimates when starting ADMB-RE from different initial parameter values. For each model, the standard deviation of the probability distribution of random effects was larger than 0. In the vBGF model with no predictors for both L ? and k, the two parameters at the individual level were strongly and positively correlated (Zakojska; r = 0. 79, p,0. 01; Gacnik, r = 0. 85, p,0. 01) (fig. 3 ). However, the correlation was inflated by the almost perfect correlation of k and L ? for fish that were sampled just once (Zakojska; r = 0.
In text S1, we discuss the processes that may lead to a negative correlation between L ? and k and here focus on the positive correlation. At the population or group level, the correlation between L ? and k obtained from the Hessian estimated at maximum likelihood estimates of the parameters is usually negative. This correlation does not offer any biological insights, since it occurs because different combinations of L ? and k can basically provide the same fit to the data, in particular when the range of ages is limited  [58, 82, 83]. In other words, by slightly increasing or decreasing L ? and k in opposite directions, the same likelihood is obtained. Although it is possible to estimate the correlation between random effects within ADMB-RE, this may lead to computational instabilities and possibly to ambiguous interpretation of the correlation parameter when other predictors are taken into account (we provide the code in the online repository).
This may be in part related to the estimation of L ? being particularly sensitive to the presence in the dataset of older individuals  [101]. In Zakojska, the dramatic reduction in population size after the flood of 2007 accompanied by the natural thinning of cohorts over time reduced the number of older individuals in the dataset, and this may lead to less accurate predicted mean size of cohorts at older ages. However, we observed the same strong negative   S2 ), although the diversity of cohort-specific growth pattern was noticeably smaller and comparable to the diversity observed in Gacnik. Fast-growing cohorts can play a key role in the persistence of small fish populations. Since sexual maturity and egg production in fish are generally size dependent  [102] , a higher proportion of fish can reach sexual maturity at younger ages in a fast-growing cohorts than in slow-growing ones.
In addition, when using the vBGF model with both L ? and k function of cohort and individual random effects, the estimation of cohort effects should be robust to the deletion from the dataset of one-third of the individuals that have been sampled more than 3 times, since the presence of only a few old individuals in the dataset is likely to bias the estimation of L ?  [101]. Our results indicate that when strength is borrowed from other individuals, parameters estimated on a single measurement can be used to summarize the growth trajectory of marble trout living in Zakojska and Gacnik and to impute missing observation for the estimation of size-dependent survival. The best vBGF model provided predictions of future growth trajectories in both populations that were consistently (i. e. for all validation samples) better than simply using the mean length-at-age of the fish cohort.
Acknowledgments We thank  Dusan Jesensek  and the  Tolmin Angling Association  for fieldwork and  Giulio A. De Leo  for comments and suggestions that helped us greatly improve the manuscript. This work was mainly completed when  Hans Skaug  was a sabbatical visitor to the  Center for Stock Assessment Research (CSTAR) , a partnership between  UCSC  and  NOAA Fisheries ,  Santa Cruz.
SV is supported by an  IOF   Marie Curie Fellowship   FP7-PEOPLE-2011-IOF for the project ''RAPIDEVO' ' on rapid evolutionary responses to climate change in natural populations and by the  Center for Stock Assessment Research. MM was supported by  NSF  grant  EF-0924195. Funding for Open Access  provided by the  University of California, Santa Cruz, Open Access Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Abstract Phytoplankton observation in the ocean can be a challenge in oceanography. Accurate estimations of their biomass and dynamics will help to understand ocean ecosystems and refine global climate models. This requires relevant datasets of phytoplankton at a functional level and on a daily and sub meso scale. In order to achieve this, an automated, high frequency, dedicated scanning flow cytometer (SFC, Cytobuoy, NL), has been developed to cover the entire size range of phytoplankton cells whilst simultaneously taking pictures of the largest of them. This cytometer was directly connected to the water inlet of a pocket Ferry Box during a cruise in the North Sea, 8-12 May 2011 (DYMAPHY project, INTERREG IV A "2 Seas"), in order to identify the phytoplankton community structure of near surface waters (6 m) with a high resolution spacial basis (2.
The instrument enables single cell analysis of phytoplankton from 1 to 800 µm and several mm in length for chain forming cells and automated sampling allows large space and time domains to be covered at a high resolution  (Sosik et al. , 2003; Thyssen et al. , 2008b Thyssen et al. , , 2009;; Ribalet et al. , 2010). Based on this approach, a high frequency study of the phytoplankton community structure in the North Sea was conducted. The in situ observations have been used to label PHYSAT anomalies, showing the potential of such in situ and remote sensed data coupling. Thus, the available dataset makes it possible to distinguish between different water-leaving radiance anomaly signatures in which significant distinct phytoplankton community structures can be described, rather than just the dominant communities, as it is the case of previous studies.
5 µm silica beads (Polyscience, Silica microspheres)  (Foladori et al. , 2008). The phytoplankton community was described using several two-dimensional cytograms built with the Cytoclus ® software. For each autofluorescing phytoplankton cell analysed, the integrated value of FLR pulse shape (Total red fluorescence TFLR, a. u. ) was calculated. For each phytoplankton cluster, the amount of TFLR is reported per unit volume (TFLR cm -3 , a. u. cm -3 ). The TFLR cm -3 of each resolved phytoplankton cluster was summed (Total TFLR cm -3 ) and was used as a proxy for chlorophyll a concentration (µg dm by using the low sensitivity PMTs that behaved linearly below this value with the high sensitivity PMT. Discrete samples were collected during the cruise and analyzed using a second Cytosense SFC equipped with the Image in Flow system.
Temperature and salinity The PFB (4H-JENA©) was fixed on the wet laboratory bench, close to the Cytosense, in order to share the same water inlet. This instrument recorded temperature and conductivity (from which salinity was computed) from the clean water supplied by the ships seawater pumping system at a frequency of one sample every minute. Within the PFB dataset, only data related to automated SFC analyses were selected for plotting temperature-salinity diagrams.
Mixed layer depth Daily water column temperature mapping was obtained from the Forecasting Ocean Assimilation Model 7 km Atlantic Margin model (FOAM AMM7), available at MyOcean data base (http://www. myocean. eu. org/). Model output temperature depths were as follows: 0,  3, 10, 15, 20, 30, 50, 75, 100, 125, 150  m. Average mixed layer depth (MLD) on the 5 sampling days was calculated from daily temperature datasets. MLD was defined as the absolute temperature difference of more than 0. 2 • C from one depth to the surface (defined at 10 m,  de Boyer Montégut et al. , 2004).
Statistics Statistics were run under R software (CRAN, http://cran. r-project. org/). Before running correlation and comparison tests on the different in situ sensors (for chl a and Total TFLR), the Shapiro normality test was run. When Normality was not applied, a Wilcox signed rank test was applied. Correlations between data were defined using Spearman's rank correlation coefficient. As the PHYSAT approach is based on the link between specific Ra spectra (in terms of shapes and amplitudes) and specific phytoplankton composition, the set of remote sensed data was separated into distinct groups with similar Ra. The PHYSAT Ra found over the studied area and matching the in situ SFC samples was differentiated by applying a k-means clustering partitioning method (tested either around means  (Everitt and Hothorn, 2006)  or around menoids  (Kaufman and Rousseeuw, 1990) ).
The SFC Total TFLR (a. u. cm -3 ) summing up the TFLR of all the phytoplankton groups was used for comparison with other chl a determinations. Correlations with the AOA fluorometer, the HPLC and the Turner fluorometer results were all significant as shown in  Considering our database of coincident SFC in situ and MODIS remote sensed observations, a total of 56 matching points were identified, from which only 38 points corresponded to non-turbid classified waters. Matching points between in situ sampling and remote sensing pixels for the purpose of the PHYSAT empirical calibration were selected in the daytime period 06:00-18:00 UTC, the limit of the correlation significance between MODIS chl a and the AOA fluorometer chl a within the SFC dataset (r = 0. 49, p = 0. 06, n = 15, Spearman rank test), leaving 15 SFC matching points (Figs.
The remote sensing synoptical extrapolation concerning phytoplankton community structure remains to be established and in spite of a theoretical validation  (Alvain et al. , 2012) , still depends on important in situ data point collection in order to build robust empirical relationships. In this study, the combination of phytoplankton high frequency analysis from an automated SFC with the PHYSAT method proved to be an excellent calibration by giving an unprecedented amount of matching points for only two significant sampling days (number of analyzed samples for non-turbid waters matching MODIS pixels: 38, number of used samples between 06:00 and 18:00 UTC: 15, corresponding to 39. 5 % profitability), compared to the 14 % matching points from the GeP&CO dataset  (Alvain et al. , 2005)  The coupling of SFC and PHYSAT has shown that a first set of specific anomalies (N1) can be associated to NanoRED1, NanoORG and MicroORG, which contributed more to the Total TFLR cm -3 (a proxy of chl a, Fig.
Acknowledgements. This study was funded by the  DYMPAHY  (Development of a DYnamic observation system for the assessment of MArine water quality, based on PHYtoplankton analysis) INTERREG IVA "2 Mers Seas Zeeën" European cross-border project, co-funded by the  European Regional Development Fund (ERDF)  and  French  ( ULCO-CNRS-UL1 ), English (Cefas) and Dutch (RWS) partners. We thank the captain and crew of the  RV Cefas Endeavour , as well as  Anne-Hélène Rêve  for chlorophyll a bench top analysis. We also thank  Rodney Forster  for his invitation onboard the ship during the  EU   FP7 PROTOOL  (Automated Tools to Measure Primary Productivity in European Seas) cruise. We are also grateful to our funding sources, the  CNRS , the  CNES-TOSCA / PHYTOCOT  project. The authors thank  NASA/GSFC/DAAC  for providing access to daily L3 MODIS binned products.
We report acquisition of diagonal-compensated protein structural restraints from four-dimensional solid-state NMR spectra on extensively deuterated and 1 H backexchanged proteins. To achieve this, we use homonuclear 1 H-1 H correlations with diagonal suppression and nonuniform sampling (NUS). Suppression of the diagonal allows the accurate identification of cross-peaks which are otherwise obscured by the strong autocorrelation or whose intensity is biased due to partial overlap with the diagonal. The approach results in unambiguous spectral interpretation and relatively few but reliable restraints for structure calculation. In addition, the diagonal suppression produces a spectrum with low dynamic range for which ultrasparse NUS data sets can be readily reconstructed, allowing straightforward application of NUS with only 2% sampling density with the advantage of more heavily sampling time-domain regions of high signal intensity.
Hydrophobin spectra were recorded as nontime-shared versions in a 1. 3 mm rotor at 1 GHz Larmor frequency and 60 kHz MAS at approximately 20 °C. Indirect spectral widths were set to 4. 6 and 28. 6 ppm for 1 H and 15 N, respectively. Carriers were set to the center of the amide bulk in both cases. Mixing was achieved using 6. 7 ms RFDR with 180°pulses of 2. 5 μs (ν RF = 200 kHz). Four identical data sets with RFDR mixing were recorded, and one data set was recorded in the absence of mixing. Each data set was shifted independently to compensate for drift of the B 0 field before further processing. Figure  1  depicts a 4D experimental scheme using dipolar transfers between protons and heteronuclei and time-shared  68  indirect heteronuclear evolution periods. The pulse program used (for Bruker software) can be found on the Linser group webpage (mpibpc.
44, 68 Phases of the first proton πι/2 pulse and the CP pulses marked by asterisks were cycled for phase-sensitive incrementation. sampling for 4D experiments was implemented via a variable counter (vc) list with a predefined 2% schedule. To avoid aliasing artifacts, sampling gaps should be kept small but as random as possible, which is achieved by Poisson distribution centered at zero value. 63 We recorded eight successive scans according to States-TPPI phase-sensitive incrementation of the three indirect dimensions for each set of indirect evolution increments. The 2D reference spectrum plotted under the 4D planes in Figure  2A ,B and Supporting Information, Figures  1  and 6  was recorded using the same (truncated) scheme without F1 incrementation and without mixing. 2D spectra were processed using Topspin, and 4D data was processed using NMRpipe.
69 For the 4D spectra, Fourier transformation of the direct dimension was followed by the hmsIST routine  64  with 200 iterations and subsequent reshuffling of transients for phase-sensitive recording and time increments in order to allow for standard FFT of the indirect dimensions. Apodization was performed using a sine bell shifted by π/2 for all dimensions and 2048 × 64 × 64 × 64 points for the direct and indirect ones. The nmrPipe processing scripts are provided on the Linser group webpage. The hmsIST routine  64  as well as a sampling schedule generator can be obtained on the Wagner lab webpage (gwagner. med. harvard. edu/intranet/ hmsIST/). Spectra were converted to ucsf format with the routine included in the Sparky program  70  and used for analysis in CcpNmr. 71 ssigned 4D HN-HN cross-peaks were converted into distance restraints with a uniform calibration procedure to convert peak intensity into distance.
■ ACKNOWLEDGMENTS We thank Drs. J. Hook, D. Thomas, D. Lawes, and A. Rawal from the NMR facility at University of New South Wales for spectrometer access and assistance and Dr. Jim Sun, Harvard Medical School, for initial help with NMRpipe. We are particularly grateful for Prof. Dr. Bernd Reif, TU Munchen, for generously providing SH3 protein for study in Australia and to Dr. Anthony Duff and Karyn Wilde for assistance with hydrophobin production. The project was funded by the Australian Research Council (LP0776672 and DP0879121) and ANSTO Bragg Institute (NDF 1668), the Agilent Thought.
Toxin exposure Regarding cytotoxicity data of the literature mentioned in the introduction, we chose for the cytotoxicity assay a concentration range of CYN that frame the sub-toxic concentration which will be used for the later experiments. In this way, differentiated Caco-2 cells were exposed for 24 hrs to a range of 9 concentrations (from 0. 2 to 50 mM CYN) in fresh FCS-free medium for the cytotoxicity assay, or to 1. 6 mM of CYN for the microarray and immunolabelling experiments. A vehicle control (sterile water) was also included for each experiment. For the cytotoxicity assay, three independent experiments (biological replicates) were performed, each including three technical replicates (wells) per treatment condition. For the microarray and immunolabelling assays, six independent experiments were performed; for the immunolabelling assay, three technical replicates (wells) per treatment condition were included for each independent experiment.
Total RNA extraction Following cell treatments, total RNA was isolated using the NucleoSpin RNA II kit according to the manufacturer's instructions with a final elution volume of 10 mL of RNase free water (Macherey-Nagel, Hoerd, France). RNA was quantified with the BioSpec-Nano (Shimadzu, Marne la Vallee, France), and RNA integrity was assessed with the ''Experion RNA StdSens analysis'' kit using the Experion automated electrophoresis system (Bio-Rad, Marnes-la-Coquette, France). Only RNA with an RNA quality indicator $ 9 was used for further experiments (Experion software 3. 0; Bio-Rad). A negative extraction control of lysis buffer RA1 was included for contamination assessment.
Data analysis Raw data, corresponding to the median pixel intensity for each probe, were extracted with Mapix software, version 5. 0. The quality of the extracted raw data was checked for signal homogeneity using the Pearson correlation. A Lowess normalisation was performed on the raw data with R software using the Linear Model for Microarray Data Bioconductor package  [32]. A background signal was calculated from the mean of the one hundred lowest values of each sample. Following this, the normalised data were filtered on threshold intensity (3 times the background signal): for each probe, values were selected if the median value for at least one experimental condition was higher than the threshold intensity. This dataset, including the 28 616 post-filtering probes, was labelled ''filtered data''.
These data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE55723. From the ''filtered data'', the differentially expressed genes were selected at P,0. 05 (Student t-test) and with a fold change (FC) greater than 2 (for ''up-regulated genes''), or less than 0. 5 (for ''down-regulated genes'') leading to two distinct clusters. In order to identify over-represented gene categories, analysis was performed on these clusters using the GoMiner application (http:// www. discover. nci. nih. gov/gominer/index. jsp) which is based on the gene ontology (GO) annotation system. GO terms with a false discovery rate (FDR) score lower than 0. 05 and an enrichment score greater than 1. 5 was declared to be significant. Significance analysis of microarrays (SAM) method was also performed on the ''filtered data'' with R software and the samr plug-in.